Package {R4VN}

R4VN: Publication-Ready Statistical Tables for Health Research

Description

R4VN provides short, consistent commands for data management, descriptive statistics, epidemiological analysis, regression models, publication-ready tables, and graphs. Commands can use an explicit data frame or the active data selected by usedf().

Details

Main workflows include:

• usedf(): select active data and optionally link it to a visible data-frame object;

• tab1(), sum1(), and describe(): inspect variables quickly in the console, including nested stratification with by = c(...);

• vars(), tab(), and tabmulti(): create descriptive, comparative, and regression tables;

• ttest(), ranksum(), signrank(), kwallis(), friedman(), and swilk(): common parametric and nonparametric analyses;

• tabexport(): export one or more results.

Author(s)

Maintainer: Thai Thanh Truc thaithanhtruc@gmail.com

Authors:

• Thai Thanh Truc thaithanhtruc@gmail.com

Resolve an R4VN Variable Specification Against Data

Description

Internal R4VN helper that expands ., wildcard selectors, and exclusions after the analysis function has obtained its data frame.

Usage

.r4vn_resolve_vars(x, data, default_type = "auto", strict = TRUE)

Arguments

Value

A fully expanded r4vn_vars object containing concrete variable names in the order requested in vars(). Variables expanded from the same wildcard or all-variable selector retain their original order in data.

R4VN Example Cookbook

Description

A central index of practical examples. The detailed examples are also merged into the individual help pages, so use ?labvar, ?tab, ?ttest, ?ranksum, and the corresponding command name while working.

Data workflow

usedf() sets active data. opendata() and savedata() import and export. appenddata() stacks observations; mergedata() joins by keys. keepvar(), dropvar(), ordervar(), renvar(), genvar(), replacevar(), and labvar() edit active or explicit data.

Statistical workflow

tab1() and sum1() provide quick console checks, including nested by stratification. describe() shows active-data structure. tab() and tabmulti() produce publication tables. ttest() includes one-sample, independent, and paired t tests. ranksum(), signrank(), kwallis(), and friedman() provide the principal nonparametric tests. swilk() assesses normality. corr(), regress(), logistic(), and poisson() provide correlation and regression models. tabsurv() and tabmeta() provide complete one-command survival and meta-analysis reports; their help pages contain scenario-based cookbooks.

Graph workflow

gbar(), ghist(), gbox(), gscatter(), gline(), gdensity(), and gpie(), gforest(), and groc() use base graphics and return reusable r4vn_graph objects.

Examples

# Discover all examples attached to a command
example(labvar)
example(ttest)
example(ranksum)
example(tab1)
example(sum1)
example(describe)
example(signrank)
example(kwallis)
example(friedman)
example(swilk)
example(tabmeta)

# Open the full help pages
help("labvar", package = "R4VN")
help("tabmeta", package = "R4VN")
help("R4VN_examples", package = "R4VN")

Ask an AI service to interpret summarized statistical results

Description

Automatically discovers compact statistical result components inside an object, sends only the most useful results to the selected AI service, and attaches the returned interpretation. Lists receive an ai element; other objects receive an "ai" attribute.

Usage

aiask(x, ai = TRUE, prompt = NULL)

Arguments

Details

The extractor is generic rather than command-specific. It recursively searches for result-like tables, matrices, estimates, tests, diagnostics, and short statistical context while ignoring raw data, HTML/CSS, formatting objects, plots, model internals, residuals, and other high-volume noise. The same aiask() can therefore be used for R4VN descriptive, multivariable, longitudinal, survival, diagnostic, scale, regression, and future result objects without a separate prepare function for every command.

A conservative input budget is applied before the API call. If the provider reports that the request is too large, aiask() automatically compacts the results further and retries up to two times.

Value

Invisibly returns x with an attached AI result. Successful results contain status, name, model, comment, input, optional usage, and created. API failures are attached with status = "error" and do not destroy x.

Examples

## Not run: 
aisetup(
  name = "gpt",
  provider = "openai",
  model = "gpt-5.6",
  tokenenv = "OPENAI_API_KEY",
  default = TRUE
)

result <- list(
  analysis = "Logistic regression",
  outcome = "Hypertension",
  estimates = data.frame(
    variable = c("Age", "Smoking"),
    OR = c(1.05, 1.82),
    lower = c(1.02, 1.15),
    upper = c(1.08, 2.88),
    p = c(0.001, 0.011)
  )
)

result <- aiask(result)
result$ai$comment

result <- aiask(
  result,
  ai = "gpt",
  prompt = "Focus on modifiable risk factors."
)

## End(Not run)

Configure AI services for R4VN

Description

Adds, updates, lists, selects, or removes named AI configurations. Only the configuration is saved by R4VN. A token supplied through tokenenv remains in an environment variable. A token supplied directly is stored in the system keyring when package keyring is available; otherwise it is kept only for the current R session.

Usage

aisetup(
  name = NULL,
  provider = "openai",
  model = NULL,
  url = NULL,
  token = NULL,
  tokenenv = NULL,
  api = NULL,
  language = "vi",
  prompt = NULL,
  default = FALSE,
  use = NULL,
  list = FALSE,
  clear = FALSE,
  save = TRUE,
  timeout = 120,
  temperature = NULL,
  maxtokens = 1200,
  inputtokens = 3000,
  overwrite = TRUE
)

Arguments

Value

Invisibly returns the saved configuration, the configuration table, or TRUE after a management action.

Examples

## Not run: 
# Recommended: keep the token in .Renviron
# OPENAI_API_KEY=your-token
aisetup(
  name = "gpt",
  provider = "openai",
  model = "gpt-5.6",
  tokenenv = "OPENAI_API_KEY",
  default = TRUE
)

# Direct token: saved securely when keyring is installed
aisetup(
  name = "gpt",
  provider = "openai",
  model = "gpt-5.6",
  token = "your-token",
  default = TRUE
)

# OpenAI-compatible local or third-party service
aisetup(
  name = "local",
  provider = "compatible",
  model = "local-model",
  url = "http://localhost:11434/v1",
  default = TRUE
)

aisetup(list = TRUE)
aisetup(use = "gpt")
aisetup(clear = "local")

## End(Not run)

One-way ANOVA with Bartlett Test

Description

anovai() reconstructs one-way ANOVA from c(n, mean, sd) summaries. anova() analyzes a numeric variable by a grouping variable. Bartlett's test of equal variances is included by default. When the first argument is a fitted model, the call is delegated to stats::anova(). For one-way ANOVA, posthoc can request Tukey, Games-Howell, Scheffe, Bonferroni, or other multiplicity-adjusted pairwise comparisons. Eta-squared and omega-squared remain reported by the ANOVA engine. Hierarchical by = vars(...) is supported for data-based ANOVA.

Usage

anovai(
  ...,
  group.names = NULL,
  bartlett = TRUE,
  level = 0.95,
  posthoc = c("none", "tukey", "games-howell", "scheffe", "bonferroni", "pairwise"),
  adjust = "holm",
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

anovai(
  ..., group.names = NULL, bartlett = TRUE, level = 0.95,
  posthoc = c("none", "tukey", "games-howell", "scheffe", "bonferroni", "pairwise"),
  adjust = "holm", digits = 3, p_digits = 3, show = TRUE, console = FALSE
)

anova(
  object, ..., by = NULL, data = NULL, bartlett = TRUE, level = 0.95,
  posthoc = c("none", "tukey", "games-howell", "scheffe", "bonferroni", "pairwise"),
  adjust = "holm", digits = 3, p_digits = 3, show = TRUE, console = FALSE
)

anova(
  object,
  ...,
  by = NULL,
  data = NULL,
  bartlett = TRUE,
  level = 0.95,
  posthoc = c("none", "tukey", "games-howell", "scheffe", "bonferroni", "pairwise"),
  adjust = "holm",
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

A r4vn_stat object for one-way ANOVA, or the ordinary result from stats::anova() for fitted models.

Examples

anovai(c(n = 20, mean = 10, sd = 2),
       c(n = 25, mean = 15, sd = 4),
       c(n = 18, mean = 13, sd = 3), posthoc = "bonferroni")

d <- data.frame(score = c(10,12,11,18,17,20,25,24,27),
                treatment = factor(rep(c("A","B","C"), each = 3)))
anova(score, by = treatment, data = d, posthoc = "games-howell")

Append data frames by observations

Description

Appends data frames or files below a master data frame. Active data is used as master when available.

Usage

appenddata(
  ...,
  data = NULL,
  source = NULL,
  force = FALSE,
  active = TRUE,
  quiet = FALSE,
  labels = c("factor", "labelled", "numeric")
)

Arguments

Details

Columns are aligned by name. Missing columns are filled with NA. If no active data and no explicit data are supplied, the first object in ... becomes master.

Value

The appended data frame invisibly.

Examples

before <- data.frame(id = 1:2, age = c(20, 30))
after <- data.frame(id = 3:4, age = c(40, 50), sex = c("M", "F"))
usedf(before, quiet = TRUE)
appenddata(after, source = TRUE, quiet = TRUE)
combined <- usedf(quiet = TRUE)
combined2 <- appenddata(before, after, active = FALSE, quiet = TRUE)

Convert a tabmachine result to a data frame

Description

Convert a tabmachine result to a data frame

Usage

## S3 method for class 'r4vn_machine'
as.data.frame(x, ...)

Arguments

Value

A data frame containing the final evaluation performance stored in x$performance. Each row represents a model/metric combination and reports the evaluation data set, method, metric, estimate, confidence limits when available, and the confidence-interval method.

Convert an R4VN meta-analysis to a data frame

Description

Convert an R4VN meta-analysis to a data frame

Usage

## S3 method for class 'r4vn_meta'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

The main publication table.

Specify predictor values for margins

Description

Creates predictor settings used by margins().

Usage

at(...)

Arguments

Value

An R4VN result object, invisibly.

See Also

margins

Examples

at(age = 40)
at(age = seq(30, 60, 5), hypertension = c(0, 1))

Confidence Intervals from Summaries or Variables

Description

Calculates confidence intervals for means, proportions, and variances.

Usage

cii(
  n,
  mean = NULL,
  sd = NULL,
  events = NULL,
  variance = NULL,
  type = c("auto", "mean", "proportion", "variance"),
  method = c("exact", "wilson", "wald"),
  level = 0.95,
  digits = 3,
  show = TRUE,
  console = FALSE
)

ci(
  x,
  data = NULL,
  type = c("auto", "mean", "proportion", "variance"),
  event = NULL,
  method = c("exact", "wilson", "wald"),
  level = 0.95,
  digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

cii(50, mean = 10, sd = 2)
cii(100, events = 45, type = "proportion", method = "wilson")

Correlation matrix

Description

Computes Pearson, Spearman, or Kendall correlations from variables in a data frame. With data = NULL, the active data set is used.

Usage

corr(
  ...,
  data = NULL,
  method = c("pearson", "spearman", "kendall"),
  missing = c("pairwise", "listwise"),
  sig = FALSE,
  obs = FALSE,
  ci = FALSE,
  star = FALSE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

An object of class r4vn_stat, returned invisibly. Its sections component contains the formatted correlation matrix and any requested p-value, pairwise sample-size, or confidence-interval tables. Its raw component contains the numeric correlation (correlation), p-value (p.value), and pairwise sample-size (n) matrices plus the selected correlation method; call records the matched function call.

Examples

# Extended usage examples
d <- data.frame(age = c(20, 25, 30, 35, 40, 45),
                bmi = c(20, 22, 24, 23, 26, 28),
                score = c(60, 65, 68, 72, 75, 80))

corr(age, bmi, score, data = d, show = FALSE)
corr(age, bmi, score, data = d, method = "spearman", show = FALSE)
corr(age, bmi, score, data = d, sig = TRUE, obs = TRUE, ci = TRUE, show = FALSE)
corr(age, bmi, score, data = d, star = TRUE, show = FALSE)

Direct Cox Proportional Hazards Model

Description

A compact R4VN wrapper around tabsurv() for a final multivariable Cox model.

Usage

cox(
  time,
  event,
  vars,
  data = NULL,
  failure = NULL,
  id = NULL,
  start = NULL,
  strata = NULL,
  cluster = NULL,
  frailty = NULL,
  ph = FALSE,
  ci = 0.95,
  ties = c("efron", "breslow", "exact"),
  diagnosis = FALSE,
  show = TRUE,
  console = FALSE
)

Arguments

Value

An object of class r4vn_surv.

Examples

if (requireNamespace("survival", quietly = TRUE)) {
  d <- data.frame(
    time = c(5, 8, 10, 12, 15, 18, 20, 22, 25, 30),
    event = c(1, 0, 1, 1, 0, 1, 0, 1, 1, 0),
    age = c(40, 45, 50, 55, 60, 48, 52, 63, 58, 67),
    sex = factor(rep(c("Female", "Male"), 5))
  )
  cox(time, event, vars = vars(c.age, i.sex), data = d, show = FALSE)
  cox(time, event, vars = vars(c.age, i.sex), data = d,
      diagnosis = TRUE, show = FALSE)
  cox(time, event, vars = vars(c.age), data = d, ph = TRUE, show = FALSE)
}

Example controlled interrupted time-series data

Description

Synthetic long-format controlled ITS dataset with a Control and an Intervention series observed monthly over the same 96-month period.

Usage

data(dengue_its_control)

Format

A data frame with 192 rows and 7 variables:

month

Monthly date.

group

Control or Intervention series.

cases

Synthetic monthly case count.

rainfall

Synthetic monthly rainfall in mm.

temperature

Synthetic mean monthly temperature in degrees C.

population

Synthetic population denominator.

intervention

Indicator used for teaching; intervention applies to the Intervention group from January 2023 onward.

Source

Synthetic data generated for R4VN examples.

Example monthly dengue time series

Description

A reproducible synthetic monthly time-series dataset for demonstrating tabts(). It contains 96 monthly observations from January 2018 through December 2025. The data were simulated for teaching and software testing; they do not represent a real surveillance system.

Usage

data(dengue_ts)

Format

A data frame with 96 rows and 6 variables:

month

Monthly date (first day of month).

cases

Synthetic monthly dengue case count.

rainfall

Synthetic monthly rainfall in mm.

temperature

Synthetic mean monthly temperature in degrees C.

population

Synthetic population denominator.

intervention

0 before January 2023 and 1 thereafter.

Source

Synthetic data generated for R4VN examples.

Describe variables in the active data

Description

Displays a compact description of variable names, types, missing values, distinct values, labels, and factor levels. This is intended for quickly checking the structure of active data without opening an HTML file.

Usage

describe(..., data = NULL, show = TRUE, console = FALSE)

Arguments

Value

Invisibly returns a data frame.

Examples

d <- data.frame(
  id = 1:4,
  sex = factor(c("Female", "Male", "Female", "Male")),
  age = c(25, 40, NA, 52)
)
attr(d$sex, "label") <- "Sex"
usedf(d, quiet = TRUE)
describe()
describe(sex, age)
usedf(clear = TRUE, quiet = TRUE)

# Extended usage examples

patient <- data.frame(
  id = 1:4,
  sex = factor(c("Female", "Male", "Female", "Male")),
  age = c(25, 40, NA, 52),
  outcome = c(FALSE, TRUE, FALSE, TRUE)
)
attr(patient$sex, "label") <- "Sex"
usedf(patient)

# Describe every variable or selected variables
describe()
describe(sex)
describe(sex, age, outcome)

# Explicit data and invisible returned metadata
describe(patient)
info <- describe(age, sex, data = patient, show = FALSE)
info

R4VN Study Design Studio

Description

Launch an interactive Shiny Studio for study-design guidance, sample-size planning, probability sampling, and random allocation. The working areas are independent: users may open any area directly and may optionally reuse or save results in one R4VN design project.

Usage

design(
  data = NULL,
  project_name = "Untitled Study",
  launch.browser = interactive(),
  port = NULL,
  host = "127.0.0.1"
)

Arguments

Details

design() contains four independent working areas:

Study Design

A guided cascade that recommends a design and explains why.

Sample Size

A searchable registry of formula-, precision-, power-, and model-based sample-size methods. Results include a formula or method rule, numerical substitution when available, calculation steps, adjustments, sensitivity analyses, references, and a Methods statement.

Sampling

Simple, systematic, stratified, PPS, cluster, and multistage sampling from an uploaded frame or, for methods that do not require frame variables, directly from generated IDs 1...N.

Randomization

Simple, complete, permuted-block, and stratified-block random allocation from a participant list or from generated participant numbers, with reproducibility metadata and printable allocation lists.

Word export uses rmarkdown/Pandoc so mathematical formulas are exported as formatted Word equations. PDF export prefers pagedown with Chrome and uses a LaTeX fallback when necessary. Excel templates/workbooks use openxlsx.

Advanced sample-size methods use method-specific packages when available, including presize, WebPower, statpsych, TrialSize, and pmsampsize. The Studio reports the method/reference used rather than silently replacing an unavailable advanced method with an unrelated approximation.

Value

Invisibly returns the Shiny app result when the app exits.

Examples

if (interactive()) {
  design()
}

Create an HTML data dictionary

Description

Creates a dictionary from an explicit data frame or active R4VN data.

Usage

dict(
  ...,
  data = NULL,
  describe = TRUE,
  file = NULL,
  title = "Data dictionary",
  open = TRUE
)

Arguments

Value

The HTML path invisibly.

Examples

d <- data.frame(sex = c(1, 2), age = c(20, NA))
usedf(d, quiet = TRUE)
path <- dict(file = tempfile(fileext = ".html"), open = FALSE)
file.exists(path)

# Extended usage examples

d <- data.frame(sex = c(1, 2, 1), age = c(20, 30, NA), bmi = c(21, 24, 26))
labvar(d, sex, label = "Sex", values = c("1" = "Male", "2" = "Female"))
labvar(d, age, label = "Age in years")

# Dictionary for every variable
f1 <- dict(d, file = tempfile(fileext = ".html"), open = FALSE)

# Dictionary for selected variables
f2 <- dict(d, sex, age, file = tempfile(fileext = ".html"), open = FALSE)

# Omit descriptive summaries
f3 <- dict(d, describe = FALSE, file = tempfile(fileext = ".html"), open = FALSE)

# Active-data syntax
usedf(d, quiet = TRUE)
f4 <- dict(file = tempfile(fileext = ".html"), open = FALSE)

Explore a probability distribution and calculate probabilities

Description

distdata() is the command-line probability-distribution calculator for R4VN. It combines distribution properties, point probabilities/densities, tail probabilities, interval probabilities, quantiles, simulation, and a Viewer-ready plot. distlearn() provides the interactive Shiny companion.

Usage

distdata(
  distribution,
  ...,
  x = NULL,
  lower = NULL,
  upper = NULL,
  probs = NULL,
  nsim = 0L,
  seed = NULL,
  plot = TRUE,
  digits = 6L,
  show = TRUE,
  console = FALSE
)

Arguments

Details

A particularly useful teaching call is distdata("binomial", n = 10, x = 3, p = .2). With only these inputs R4VN reports P(X=3), P(X<3), P(X<=3), P(X>3), and P(X>=3), together with the distribution's mean, variance, standard deviation, parameters, support, and plot. Supplying lower and upper adds the probability inside and outside an interval; supplying probs adds quantiles.

The beta-binomial accepts either shape1/shape2 or the more interpretable pair p/rho. The negative binomial accepts size with either p or mu. Gamma accepts rate or scale.

Value

Invisibly returns an object of classes r4vn_distdata and r4vn_stat. Its raw component contains the distribution specification, parameters, point/range probabilities, quantiles, simulation, and plot grid.

Examples

distdata("binomial", n = 10, x = 3, p = .2, show = FALSE)
distdata("binomial", n = 20, p = .35, lower = 5, upper = 10, show = FALSE)
distdata("normal", mean = 100, sd = 15, x = 130, show = FALSE)
distdata("normal", mean = 100, sd = 15, probs = c(.025, .5, .975), show = FALSE)
distdata("poisson", lambda = 2.5, x = 0:4, show = FALSE)
distdata("nbinom", size = 2, mu = 5, x = 0:4, show = FALSE)
distdata("betabinom", n = 20, p = .3, rho = .1, x = 0:5, show = FALSE)

R4VN Distribution Learning Studio

Description

Launches an interactive Shiny application for learning probability distributions. The interface is inspired by the idea of a distribution explorer, but is designed around the R4VN teaching workflow: change parameters, immediately see the distribution, calculate commonly used probability statements, read historical and practical context, simulate data, and copy an equivalent distdata() command.

Usage

distlearn(
  distribution = "normal",
  launch.browser = interactive(),
  port = NULL,
  host = "127.0.0.1"
)

Arguments

Details

The Studio currently includes the continuous distributions Normal, Log-normal, Uniform, Exponential, Gamma, Beta, Chi-square, Student's t, F, Weibull, Logistic, Cauchy, Laplace, Gumbel, Pareto, Rayleigh, Triangular, Kumaraswamy, Log-logistic, and Half-normal; and the discrete distributions Bernoulli, Binomial, Poisson, Geometric, Negative binomial, Hypergeometric, Discrete uniform, Beta-binomial, Zero-inflated Poisson, finite Zipf, and Logarithmic series.

Each distribution has an interactive probability/density plot and CDF, numerical characteristics, parameter explanations, a historical note, typical applications, cautions, probability/quantile calculation, and simulation. Parameter values may be controlled with sliders or typed directly using numeric inputs.

Value

Invisibly returns the result of shiny::runApp() when the app exits.

Examples

if (interactive()) {
  distlearn("binomial")
}

Drop variables and/or observations

Description

Drops variables or observations from an explicit data frame or active data.

Usage

dropvar(..., data = NULL, obs = NULL)

Arguments

Value

The edited data frame invisibly.

Examples

d <- data.frame(id = 1:4, age = c(10, 20, NA, 40), note_kt = letters[1:4])
usedf(d, quiet = TRUE)
dropvar("*_kt")
dropvar(obs = age < 18)

# Extended usage examples

d <- data.frame(id = 1:5, age = c(10, 20, NA, 40, 50),
                temp_a = 1:5, temp_b = 6:10, note = letters[1:5])

# Drop one or more variables
d1 <- d; dropvar(d1, note)
d2 <- d; dropvar(d2, temp_a, temp_b)

# Drop variables with a wildcard
d3 <- d; dropvar(d3, "temp_*")

# Drop observations satisfying a condition
d4 <- d; dropvar(d4, obs = age < 18)
d5 <- d; dropvar(d5, obs = missing(age))

# Drop variables and observations together
d6 <- d; dropvar(d6, note, obs = age < 18)

# Active-data syntax
usedf(d, quiet = TRUE); dropvar("temp_*"); dropvar(obs = missing(age))

Extended variable generation

Description

Creates row-wise, group-wise, ranking, standardization, and identifier variables using a compact syntax inspired by Stata's egen, while following the active-data conventions of genvar().

Usage

egenvar(..., data = NULL, by = NULL, label = NULL, values = NULL)

Arguments

Details

Row-wise functions accept individual variables, variable ranges, vars() selections, and wildcard selectors: rowmin(), rowmax(), rowmean(), rowsum(), rowmedian(), rowsd(), rowmiss(), rownonmiss(), rowfirst(), and rowlast(). Thus rowmean(q1:q10) is valid R4VN syntax.

Group/overall functions are mean(), sd(), min(), max(), median(), total(), count(), n(), seq(), z(), pctile(), and rank(). Without by, they operate over the complete data set. With by, they operate separately within groups. Missing values are ignored by summary functions; count() counts non-missing values.

Special identifier functions are group() and tag(). They use one or more variables supplied inside the function and do not depend on the by argument. group(site, sex) gives consecutive integer IDs for observed combinations; tag(id) marks the first occurrence of each distinct value.

Value

The edited data frame invisibly. If active data are linked to a visible object through usedf(), that object is updated as well.

Examples

d <- data.frame(
  id = c(1, 1, 2, 3),
  sex = c("F", "F", "M", "M"),
  q1 = c(2, 4, 3, NA), q2 = c(3, 5, 2, 4), q3 = c(4, NA, 1, 5),
  bmi = c(20, 22, 25, 27)
)
egenvar(d, min_score = rowmin(q1:q3),
        max_score = rowmax(q1:q3),
        mean_score = rowmean(q1:q3))

egenvar(d, mean_bmi = mean(bmi), z_bmi = z(bmi), by = sex)
egenvar(d, p75_bmi = pctile(bmi, p = 75), rank_bmi = rank(bmi), by = sex)
egenvar(d, n_group = n(), sequence = seq(), by = sex)
egenvar(d, person_group = group(id, sex), first_id = tag(id))

Epidemiological 2 by 2 Analysis

Description

epii() analyzes typed 2 by 2 counts. epi() analyzes binary outcome and exposure variables. With by, stratum-specific estimates, a Mantel-Haenszel common odds ratio, a pooled risk ratio, homogeneity, and interaction tests are displayed. The stratified output also reports the crude-versus-Mantel-Haenszel OR difference as both 100*(ORcrude-ORMH)/ORMH and 100*(ORcrude-ORMH)/ORcrude. cci()/cc() and csi()/cs() are familiar aliases.

Usage

epi(
  outcome,
  exposure,
  by = NULL,
  data = NULL,
  event = NULL,
  exposed = NULL,
  level = 0.95,
  correction = 0.5,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

cc(
  outcome,
  exposure,
  by = NULL,
  data = NULL,
  event = NULL,
  exposed = NULL,
  level = 0.95,
  correction = 0.5,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

cs(
  outcome,
  exposure,
  by = NULL,
  data = NULL,
  event = NULL,
  exposed = NULL,
  level = 0.95,
  correction = 0.5,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

epii(
  a = NULL,
  b = NULL,
  c = NULL,
  d = NULL,
  by = NULL,
  level = 0.95,
  correction = 0.5,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

cci(a, b, c, d, ...)

csi(a, b, c, d, ...)

epi(
  outcome,
  exposure,
  by = NULL,
  data = NULL,
  event = NULL,
  exposed = NULL,
  level = 0.95,
  correction = 0.5,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Details

The 2 by 2 layout is exposure by outcome: a exposed cases, b exposed noncases, c unexposed cases, and d unexposed noncases.

Value

Invisibly returns an object of class r4vn_stat.

Examples

epii(40, 10, 20, 30)
epii(by = list(
  Female = c(12, 18, 8, 32),
  Male = c(28, 12, 12, 18)
))

R4VN EpiTool Studio

Description

Opens an interactive field epidemiology workspace for FETP-style work. EpiTool combines epidemiologic calculators, outbreak workflows, surveillance, universal file templates and validation, and optional spatial epidemiology.

Usage

epitool(
  data = NULL,
  mode = c("field", "teaching"),
  level = c("frontline", "advanced"),
  launch.browser = interactive(),
  host = "127.0.0.1",
  port = NULL
)

Arguments

Details

File-first usability

Every EpiTool workflow that needs external tabular data is paired with a downloadable Excel template containing DATA, DICTIONARY and INSTRUCTIONS sheets. Exact column names are not mandatory because EpiTool includes a column mapper and validation layer.

Spatial epidemiology

Spatial functions are optional. When sf and leaflet are installed, EpiTool supports case maps, source buffers, nearest-source distance, density grids, DBSCAN point clusters, point-to-area spatial joins, population-based area rates, Local Moran's I and Getis-Ord G* analysis.

Density, rate and hotspot outputs are deliberately separated because they answer different epidemiologic questions. A density concentration or statistical hotspot should not be interpreted automatically as a causal source or confirmed outbreak.

Privacy

Presentation mode can jitter displayed case coordinates. Analysis should continue to use the original coordinates.

Value

Invisibly returns the Shiny app after it exits.

Examples

if (interactive()) {
epitool()

outbreak <- data.frame(
  case_id = 1:20,
  onset_date = as.Date("2026-08-01") + sample(0:7,20,TRUE),
  latitude = 10.76 + rnorm(20,0,.005),
  longitude = 106.66 + rnorm(20,0,.005)
)
usedf(outbreak, quiet = TRUE)
epitool(mode = "teaching")
}

Standardized Mean Differences

Description

Calculates Cohen's d, Hedges' g, and Glass's delta.

Usage

esizei(
  n1,
  mean1,
  sd1,
  n2,
  mean2,
  sd2,
  level = 0.95,
  digits = 3,
  show = TRUE,
  console = FALSE
)

esize(
  x,
  by,
  data = NULL,
  level = 0.95,
  digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

esizei(40, 12, 3, 35, 14, 4)

Friedman Test for Repeated or Matched Measurements

Description

Performs the Friedman rank-sum test for three or more repeated or matched measurements. Data may be supplied in wide form as several numeric variables, or in long form as one outcome, one occasion/treatment variable, and one subject/block identifier.

Usage

friedman(
  ...,
  by = NULL,
  id = NULL,
  data = NULL,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Details

Wide form uses only rows complete across every repeated measurement. Long form must contain one observation for every subject-by-occasion combination; incomplete blocks are rejected by the underlying Friedman test.

Value

Invisibly returns an object of class r4vn_stat.

Examples

wide <- data.frame(
  baseline = c(22, 20, 19, 25, 23, 21),
  month1   = c(20, 18, 18, 22, 21, 20),
  month3   = c(18, 17, 16, 20, 19, 18)
)
friedman(baseline, month1, month3, data = wide)

long <- data.frame(
  id = rep(1:6, each = 3),
  time = factor(rep(c("Baseline", "Month 1", "Month 3"), 6),
                levels = c("Baseline", "Month 1", "Month 3")),
  score = as.vector(t(as.matrix(wide)))
)
friedman(score, by = time, id = id, data = long)

# Extended usage examples

wide <- data.frame(
  baseline = c(22, 20, 19, 25, 23, 21),
  month1 = c(20, 18, 18, 22, 21, 20),
  month3 = c(18, 17, 16, 20, 19, 18)
)

# Wide form: each row is a subject and each variable is an occasion
friedman(baseline, month1, month3, data = wide)

# Long form: outcome, occasion, and subject identifier
long <- data.frame(
  id = rep(1:6, each = 3),
  time = factor(rep(c("Baseline", "Month 1", "Month 3"), 6),
                levels = c("Baseline", "Month 1", "Month 3")),
  score = as.vector(t(as.matrix(wide)))
)
friedman(score, by = time, id = id, data = long)

usedf(wide)
friedman(baseline, month1, month3)

Bar chart

Description

Draws counts, percentages, means, or medians by a categorical variable. An optional by variable creates grouped or stacked bars. The function uses base R graphics and accepts unquoted variable names. vars() can request several x variables. With by = vars(province, sex, outcome), province and sex are nested strata and outcome is the innermost in-graph grouping.

Usage

gbar(
  data = NULL, x = NULL, vars = NULL, y = NULL, by = NULL,
  stat = c("mean", "median"), percent = FALSE, position = c("dodge", "stack"),
  ci = FALSE, label = FALSE, digits = 1, xlab = NULL, ylab = NULL,
  xtitle = NULL, ytitle = NULL, ybreaks = NULL, title = NULL, subtitle = NULL,
  note = NULL, color = NULL, palette = "default", alpha = 1,
  border_color = NA, border_lwd = 1, legend = TRUE,
  legend_position = "topright", missing = FALSE, missing_label = "Missing",
  xline = NULL, yline = NULL, ref_color = "gray40", ref_lty = 2, ref_lwd = 1,
  theme = "journal", size = 11, combine = FALSE, ncol = NULL, file = NULL,
  width = 7, height = 5, dpi = 300, show = TRUE, bg = "white", vline = NULL,
  hline = NULL
)

Arguments

Value

An object of class r4vn_graph. Its data component contains the plotted summary.

Examples

d <- data.frame(
  sex = factor(c("Male", "Female", "Female", "Male", "Female")),
  hypertension = factor(c("No", "Yes", "No", "Yes", "No")),
  age = c(32, 45, 37, 51, 29)
)
gbar(d, x = sex)
gbar(d, x = sex, by = hypertension, percent = "x")
gbar(d, x = sex, y = age, ci = TRUE, ytitle = "Mean age")

# Extended usage examples

d <- data.frame(sex = factor(c("Male", "Female", "Female", "Male", "Female")),
                outcome = factor(c("No", "Yes", "No", "Yes", "No")),
                age = c(32, 45, 37, 51, 29))
gbar(d, x = sex, show = FALSE)
gbar(d, x = sex, percent = TRUE, label = TRUE, show = FALSE)
gbar(d, x = sex, by = outcome, percent = "x", position = "dodge", show = FALSE)
gbar(d, x = sex, by = outcome, percent = "total", position = "stack", show = FALSE)
gbar(d, x = sex, y = age, stat = "mean", ci = TRUE, show = FALSE)
gbar(d, x = sex, y = age, stat = "median", show = FALSE)
gbar(d, x = sex, title = "Participants by sex", palette = "journal", show = FALSE)
f <- tempfile(fileext = ".png")
gbar(d, x = sex, file = f, show = FALSE)

Box plot

Description

Draws one or several numeric distributions overall or across categories. vars() can request several numeric outcomes; hierarchical by is supported.

Usage

gbox(
  data = NULL, x = NULL, y = NULL, vars = NULL, by = NULL, horizontal = FALSE,
  points = FALSE, outliers = TRUE, xlab = NULL, ylab = NULL, xtitle = NULL,
  ytitle = NULL, xbreaks = NULL, ybreaks = NULL, title = NULL,
  subtitle = NULL, note = NULL, color = NULL, palette = "default",
  alpha = 0.7, border_color = "gray30", point_color = "black",
  point_alpha = 0.45, point_size = 0.55, point_pch = 16, xline = NULL,
  yline = NULL, ref_color = "gray40", ref_lty = 2, ref_lwd = 1,
  theme = "journal", size = 11, combine = FALSE, ncol = NULL, file = NULL,
  width = 7, height = 5, dpi = 300, show = TRUE, bg = "white", vline = NULL,
  hline = NULL
)

Arguments

Value

An object of class r4vn_graph; its data component contains group sample sizes and five-number summaries.

Examples

d <- data.frame(sex = factor(c("Male", "Female", "Female", "Male")), bmi = c(22, 24, 27, 21))
gbox(d, y = bmi)
gbox(d, x = sex, y = bmi, points = TRUE)

# Extended usage examples

d <- data.frame(sex = factor(c("Male", "Female", "Female", "Male")),
                bmi = c(22, 24, 27, 21))
gbox(d, y = bmi, show = FALSE)
gbox(d, x = sex, y = bmi, points = TRUE, show = FALSE)
gbox(d, x = sex, y = bmi, horizontal = TRUE, outliers = FALSE, show = FALSE)

Density plot

Description

Draws a kernel density estimate overall or by a categorical variable.

Usage

gdensity(
  data = NULL, x = NULL, vars = NULL, by = NULL, adjust = 1, fill = FALSE,
  line_width = 2, line_type = 1, xlab = NULL, ylab = NULL, xtitle = NULL,
  ytitle = "Density", xbreaks = NULL, ybreaks = NULL, title = NULL,
  subtitle = NULL, note = NULL, color = NULL, palette = "default",
  alpha = 0.35, legend = TRUE, legend_position = "topright", xline = NULL,
  yline = NULL, ref_color = "gray40", ref_lty = 2, ref_lwd = 1,
  theme = "journal", size = 11, combine = FALSE, ncol = NULL, file = NULL,
  width = 7, height = 5, dpi = 300, show = TRUE, bg = "white", vline = NULL,
  hline = NULL
)

Arguments

Details

Draws densities for one or several numeric variables with optional hierarchical grouping.

Value

An object of class r4vn_graph; its data component contains density coordinates.

Examples

d <- data.frame(age = c(18, 21, 25, 27, 31, 34, 38, 45, 51, 62), sex = rep(c("M", "F"), 5))
gdensity(d, x = age)
gdensity(d, x = age, by = sex, fill = TRUE)

# Extended usage examples

d <- data.frame(age = c(18, 21, 25, 27, 31, 34, 38, 45, 51, 62),
                sex = rep(c("M", "F"), 5))
gdensity(d, x = age, show = FALSE)
gdensity(d, x = age, by = sex, show = FALSE)
gdensity(d, x = age, by = sex, fill = TRUE, adjust = 1.2, show = FALSE)

Generate one or more variables

Description

Creates variables in an explicit data frame or the active R4VN data frame. Logical expressions are stored as 1 and 0. If no active data exists, genvar() can initialize a temporary active data frame from entered vectors. A later vector may contain more observations than the current data; R4VN expands the data frame and pads existing/shorter columns with missing values.

Usage

genvar(
  ..., data = NULL, label = NULL, values = NULL, recode = NULL, ref = NULL,
  ordered = FALSE, times = NULL, each = NULL, fill = NA
)

Arguments

Value

The edited data frame invisibly.

Examples

d <- data.frame(sbp = c(120, 150), dbp = c(75, 95))
usedf(d, quiet = TRUE)
genvar(hypertension = sbp >= 140 | dbp >= 90,
       label = "Hypertension",
       values = c("0" = "No", "1" = "Yes"))

# Extended usage examples

d <- data.frame(id = 1:4, age = c(17, 25, 40, 70),
                sbp = c(118, 145, 132, 160),
                dbp = c(75, 92, 80, 95), bmi = c(18, 22, 25, 30))
usedf(d, quiet = TRUE)

# Arithmetic expression
genvar(age_decade = age / 10, label = "Age in decades")

# Logical expression is stored as 0/1
genvar(hypertension = sbp >= 140 | dbp >= 90,
       label = "Hypertension",
       values = c("0" = "No", "1" = "Yes"), ref = "No")

# Generate several variables in one call
genvar(adult = age >= 18, overweight = bmi >= 23,
       label = c("Adult", "Overweight or obesity"),
       values = c("0" = "No", "1" = "Yes"))

# Generate and recode a grouped variable
genvar(age_group = age,
       recode = c("min:17" = 1, "18:59" = 2, "60:max" = 3),
       label = "Age group",
       values = c("1" = "<18", "2" = "18-59", "3" = "60+"),
       ordered = TRUE)

# Explicit-data syntax remains available
genvar(d, pulse_pressure = sbp - dbp, label = "Pulse pressure")

# Quick vector entry without creating a data frame first
genvar(weight = c(29, 26, 13, 23, 23, 25, 17, 22))
ghist(x = weight)

# A later variable may be longer; existing columns are padded with NA
genvar(age = c(81, 65, 89, 70, 87, 61, 94, 98, 81, 70))

# Compact repeated values
genvar(smoking = c(1, 0), times = c(10, 10),
       values = c("0" = "No", "1" = "Yes"))

Forest plot

Description

Draws estimates and confidence intervals on a linear or logarithmic scale. It is suitable for odds ratios, risk ratios, prevalence ratios, hazard ratios, and regression coefficients.

Usage

gforest(
  data = NULL,
  estimate,
  lower,
  upper,
  label,
  reference = 1,
  log = TRUE,
  sort = FALSE,
  digits = 2,
  xtitle = NULL,
  title = NULL,
  subtitle = NULL,
  note = NULL,
  color = NULL,
  palette = "journal",
  point_size = 1.1,
  line_width = 2,
  reference_color = "gray50",
  reference_lty = 2,
  reference_lwd = 1,
  theme = "journal",
  size = 11,
  file = NULL,
  width = 7,
  height = 5,
  dpi = 300,
  show = TRUE,
  bg = "white"
)

Arguments

Value

An object of class r4vn_graph; its data component contains plotted rows.

Examples

d <- data.frame(
  term = c("Smoking", "Obesity", "Male"),
  or = c(1.8, 2.4, 1.2),
  lower = c(1.2, 1.5, 0.8),
  upper = c(2.7, 3.8, 1.8)
)
gforest(d, estimate = or, lower = lower, upper = upper, label = term)

# Extended usage examples

# Ratio measures use reference = 1 and a logarithmic axis
d <- data.frame(
  term = c("Smoking", "Obesity", "Male"),
  estimate = c(1.80, 2.40, 1.20),
  lower = c(1.20, 1.50, 0.80),
  upper = c(2.70, 3.80, 1.80)
)
gforest(d, estimate, lower, upper, term, show = FALSE)
gforest(d, estimate, lower, upper, term,
        sort = "descending", title = "Adjusted odds ratios", show = FALSE)

# Regression coefficients use reference = 0 and a linear axis
beta <- data.frame(term = c("Age", "BMI", "Male"),
                   estimate = c(0.12, 0.34, -0.18),
                   lower = c(0.04, 0.10, -0.45),
                   upper = c(0.20, 0.58, 0.09))
gforest(beta, estimate, lower, upper, term,
        reference = 0, log = FALSE, xtitle = "Regression coefficient",
        show = FALSE)

# Export directly to a graphics file
gforest(d, estimate, lower, upper, term,
        file = tempfile(fileext = ".png"), show = FALSE)

Histogram

Description

Draws histograms for one or several numeric variables using base R graphics. by may be a single grouping variable or hierarchical vars(...); histogram panels are created for observed group/stratum combinations.

Usage

ghist(
  data = NULL, x = NULL, vars = NULL, by = NULL, bins = "Sturges",
  density = FALSE, normal = FALSE, xlab = NULL, ylab = NULL, xtitle = NULL,
  ytitle = NULL, xbreaks = NULL, ybreaks = NULL, title = NULL,
  subtitle = NULL, note = NULL, color = NULL, palette = "default",
  alpha = 0.85, border_color = "white", normal_color = "black",
  normal_lty = 1, normal_lwd = 2, xline = NULL, yline = NULL,
  ref_color = "gray40", ref_lty = 2, ref_lwd = 1, theme = "journal",
  size = 11, combine = FALSE, ncol = NULL, file = NULL, width = 7, height = 5,
  dpi = 300, show = TRUE, bg = "white", vline = NULL, hline = NULL
)

Arguments

Value

An object of class r4vn_graph; its data component contains histogram breaks, counts, density, and midpoints.

Examples

d <- data.frame(age = c(18, 21, 25, 27, 31, 34, 38, 45, 51, 62))
ghist(d, x = age)
ghist(d, x = age, bins = 5, normal = TRUE, color = "steelblue")

d$sex <- rep(c("Female", "Male"), length.out = nrow(d))
ghist(d, x = age, by = sex, normal = TRUE, xline = 40, ref_lty = 2)
ghist(d, vars = vars(age), by = vars(sex), normal = TRUE, combine = TRUE)


# Extended usage examples

d <- data.frame(age = c(18, 21, 25, 27, 31, 34, 38, 45, 51, 62))
ghist(d, x = age, show = FALSE)
ghist(d, x = age, bins = 5, show = FALSE)
ghist(d, x = age, density = TRUE, normal = TRUE, show = FALSE)

Line chart

Description

Draws an ordered trend for one numeric outcome, optionally with separate lines by group.

Usage

gline(
  data = NULL, x, y = NULL, vars = NULL, by = NULL,
  stat = c("identity", "mean", "median"), points = TRUE, line_width = 2,
  line_type = 1, sort = TRUE, pch = 16, point_size = 0.9, xlab = NULL,
  ylab = NULL, xtitle = NULL, ytitle = NULL, xbreaks = NULL, ybreaks = NULL,
  title = NULL, subtitle = NULL, note = NULL, color = NULL,
  palette = "default", alpha = 1, legend = TRUE, legend_position = "topright",
  xline = NULL, yline = NULL, ref_color = "gray40", ref_lty = 2, ref_lwd = 1,
  theme = "journal", size = 11, combine = FALSE, ncol = NULL, file = NULL,
  width = 7, height = 5, dpi = 300, show = TRUE, bg = "white", vline = NULL,
  hline = NULL
)

Arguments

Details

Draws one x variable against one or several y variables with optional hierarchical grouping.

Value

An object of class r4vn_graph; its data component contains the plotted or aggregated values.

Examples

d <- data.frame(
  year = rep(2022:2024, 2),
  rate = c(12, 15, 18, 10, 13, 17),
  sex = rep(c("Male", "Female"), each = 3)
)
gline(d, x = year, y = rate)
gline(d, x = year, y = rate, by = sex, points = TRUE)

# Extended usage examples

d <- data.frame(year = rep(2022:2024, 2),
                rate = c(12, 15, 18, 10, 13, 17),
                sex = rep(c("Male", "Female"), each = 3))
gline(d, x = year, y = rate, show = FALSE)
gline(d, x = year, y = rate, by = sex, points = TRUE, show = FALSE)

# Collapse repeated x values to means or medians
d2 <- rbind(d, transform(d, rate = rate + 2))
gline(d2, x = year, y = rate, by = sex, stat = "mean", show = FALSE)

Pie or donut chart

Description

Draws the distribution of a categorical variable as a pie or donut chart. Bar charts are usually preferable when there are many categories.

Usage

gpie(
  data = NULL, x = NULL, vars = NULL, by = NULL, donut = FALSE, label = TRUE,
  percent = TRUE, digits = 1, xlab = NULL, title = NULL, subtitle = NULL,
  note = NULL, color = NULL, palette = "default", alpha = 1,
  border_color = "white", border_lwd = 1, clockwise = TRUE, missing = FALSE,
  missing_label = "Missing", theme = "journal", size = 11, combine = FALSE,
  ncol = NULL, file = NULL, width = 7, height = 5, dpi = 300, show = TRUE,
  bg = "white"
)

Arguments

Details

Draws one or several categorical variables; by creates separate charts for group/stratum combinations.

Value

An object of class r4vn_graph; its data component contains counts and percentages.

Examples

d <- data.frame(group = c("A", "A", "B", "B", "B", "C"))
gpie(d, x = group)
gpie(d, x = group, donut = TRUE, palette = "journal")

# Extended usage examples

d <- data.frame(group = c("A", "A", "B", "B", "B", "C"))
gpie(d, x = group, show = FALSE)
gpie(d, x = group, donut = TRUE, palette = "journal", show = FALSE)
gpie(d, x = group, label = TRUE, percent = FALSE, show = FALSE)

ROC curve

Description

Calculates and draws a receiver operating characteristic curve from a binary outcome and numeric predicted probabilities or scores. No external package is required.

Usage

groc(
  data = NULL, outcome, pred = NULL, vars = NULL, by = NULL, event = NULL,
  diagonal = TRUE, auc = TRUE, digits = 3, curve_lty = 1, curve_lwd = 2.5,
  diagonal_color = "gray60", diagonal_lty = 2, diagonal_lwd = 1, xlab = NULL,
  ylab = NULL, xtitle = "1 - Specificity", ytitle = "Sensitivity",
  title = NULL, subtitle = NULL, note = NULL, color = NULL,
  palette = "journal", xline = NULL, yline = NULL, ref_color = "gray40",
  ref_lty = 2, ref_lwd = 1, theme = "journal", size = 11, combine = FALSE,
  ncol = NULL, file = NULL, width = 6, height = 6, dpi = 300, show = TRUE,
  bg = "white", vline = NULL, hline = NULL
)

Arguments

Details

Draws ROC curves for one or several predictor/score variables. by can create ROC analyses within hierarchical strata.

Value

An object of class r4vn_graph; its data component contains thresholds, sensitivity, and specificity, and its auc attribute contains the AUC.

Examples

d <- data.frame(y = c(0, 0, 1, 1, 1), p = c(.10, .35, .40, .75, .90))
groc(d, outcome = y, pred = p, event = 1)

# Extended usage examples

d <- data.frame(
  outcome = factor(c("No", "No", "No", "Yes", "Yes", "Yes", "Yes")),
  probability = c(.05, .20, .35, .40, .65, .80, .95)
)

# ROC curve with explicit event
roc1 <- groc(d, outcome, probability, event = "Yes", show = FALSE)
attr(roc1$data, "auc")

# Hide the diagonal or AUC annotation and customize labels
groc(d, outcome, probability, event = "Yes",
     diagonal = FALSE, auc = FALSE,
     xtitle = "False-positive rate", ytitle = "True-positive rate",
     show = FALSE)

# Numeric binary outcome; the larger value is the default event
d2 <- data.frame(y = c(0, 0, 1, 1, 1), score = c(.10, .35, .40, .75, .90))
groc(d2, y, score, show = FALSE)

# Export the ROC curve
groc(d, outcome, probability, event = "Yes",
     file = tempfile(fileext = ".pdf"), show = FALSE)

Scatter plot

Description

Draws the relationship between two numeric variables, optionally colored by a group and with fitted lines.

Usage

gscatter(
  data = NULL, x, y = NULL, vars = NULL, by = NULL, fit = FALSE,
  fit_color = NULL, fit_lty = 1, fit_lwd = 2, cor = FALSE, pch = 16,
  point_size = 1, xlab = NULL, ylab = NULL, xtitle = NULL, ytitle = NULL,
  xbreaks = NULL, ybreaks = NULL, title = NULL, subtitle = NULL, note = NULL,
  color = NULL, palette = "default", alpha = 0.75, legend = TRUE,
  legend_position = "topright", xline = NULL, yline = NULL,
  ref_color = "gray40", ref_lty = 2, ref_lwd = 1, theme = "journal",
  size = 11, combine = FALSE, ncol = NULL, file = NULL, width = 7, height = 5,
  dpi = 300, show = TRUE, bg = "white", vline = NULL, hline = NULL
)

Arguments

Details

Draws one x variable against one or several y variables. Hierarchical by uses all but the last variable as strata and the last variable as the plotted grouping variable.

Value

An object of class r4vn_graph; its data component contains complete plotted observations.

Examples

d <- data.frame(
  age = c(22, 28, 35, 41, 55),
  bmi = c(20, 23, 25, 27, 29),
  sex = c("M", "F", "F", "M", "F")
)
gscatter(d, x = age, y = bmi)
gscatter(d, x = age, y = bmi, by = sex, fit = "linear", cor = TRUE)

# Extended usage examples

d <- data.frame(age = c(22, 28, 35, 41, 55),
                bmi = c(20, 23, 25, 27, 29),
                sex = c("M", "F", "F", "M", "F"))
gscatter(d, x = age, y = bmi, show = FALSE)
gscatter(d, x = age, y = bmi, fit = "linear", cor = TRUE, show = FALSE)
gscatter(d, x = age, y = bmi, by = sex, fit = "linear", show = FALSE)
gscatter(d, x = age, y = bmi, fit = "loess", show = FALSE)

Survival and Cumulative Incidence Curves

Description

Draws Kaplan-Meier survival, cumulative risk, or competing-risk cumulative incidence curves from a tabsurv() result. Uses the existing R4VN base-graphics engine and can add confidence intervals, censor marks, log-rank p-values, median lines, and number-at-risk tables. Examples below are self-contained.

Usage

gsurv(
  x,
  type = NULL,
  xlab = NULL,
  ylab = NULL,
  xtitle = NULL,
  ytitle = NULL,
  xlim = NULL,
  ylim = NULL,
  breaks = NULL,
  percent = TRUE,
  ci = FALSE,
  censor = TRUE,
  color = NULL,
  palette = "default",
  linetype = NULL,
  line_width = 1.5,
  ci_color = NULL,
  ci_alpha = 0.45,
  ci_linetype = 3,
  ci_line_width = NULL,
  censor_color = NULL,
  censor_pch = 3,
  censor_size = 0.7,
  median_color = "gray50",
  median_lty = 2,
  median_lwd = 1,
  ref_color = "gray55",
  ref_lty = 3,
  ref_lwd = 1,
  size = 11,
  labels = NULL,
  legend = TRUE,
  legend_position = "topright",
  pvalue = FALSE,
  median = FALSE,
  risk_table = FALSE,
  risk_at = NULL,
  xline = NULL,
  yline = NULL,
  title = NULL,
  subtitle = NULL,
  note = NULL,
  theme = "journal",
  file = NULL,
  width = 7,
  height = NULL,
  dpi = 300,
  show = TRUE,
  bg = "white",
  vline = NULL,
  hline = NULL
)

Arguments

Value

An r4vn_graph object.

Examples

if (requireNamespace("survival", quietly = TRUE)) {
  d <- data.frame(
    time = c(5, 8, 10, 12, 15, 18, 20, 22),
    event = c(1, 0, 1, 1, 0, 1, 0, 1),
    group = factor(rep(c("A", "B"), 4))
  )
  s <- tabsurv(time, event, by = group, data = d, km = TRUE, show = FALSE)
  gsurv(s, show = FALSE)
  gsurv(s, type = "risk", ci = TRUE, pvalue = TRUE, show = FALSE)
  gsurv(s, risk_table = TRUE, risk_at = c(0, 10, 20), show = FALSE)
}

Incidence-rate Comparison

Description

Compares incidence rates from typed cases and person-time or from variables.

Usage

ir(
  cases,
  exposure,
  time,
  data = NULL,
  exposed = NULL,
  digits = 4,
  p_digits = 3,
  level = 0.95,
  show = TRUE,
  console = FALSE
)

iri(
  cases.exposed,
  cases.unexposed,
  time.exposed,
  time.unexposed,
  level = 0.95,
  digits = 4,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

iri(41, 15, 28010, 19017)

Keep variables and/or observations

Description

Keeps variables or observations in an explicit data frame or active data.

Usage

keepvar(..., data = NULL, obs = NULL)

Arguments

Value

The edited data frame invisibly.

Examples

d <- data.frame(
  id = 1:5,
  age = c(10, 20, NA, 40, 50),
  sex = c("M", "F", "F", "M", "F"),
  score_a = 1:5,
  score_b = 6:10
)

# Each explicit-data example uses its own copy because keepvar()
# intentionally edits the supplied object.
d1 <- d
keepvar(d1, id, age, sex)

d2 <- d
keepvar(d2, id:sex)

d3 <- d
keepvar(d3, id, "score_*")

d4 <- d
keepvar(d4, obs = age >= 18)

d5 <- d
keepvar(d5, id, age, obs = !missing(age))

d6 <- d
keepvar(d6, obs = c(1, 3, 5))

# Active-data syntax
active_d <- d
usedf(active_d, quiet = TRUE)
keepvar(id, age, obs = age >= 18)
usedf(clear = TRUE, quiet = TRUE)

Kruskal-Wallis Test

Description

Performs the Kruskal-Wallis rank-sum test for a numeric outcome across two or more independent groups. Optional Dunn or pairwise Wilcoxon post-hoc tests and rank-based effect sizes can be requested. With by = vars(region, sex, treatment), region and sex are nested strata and treatment is the innermost Kruskal-Wallis factor.

Usage

kwallis(
  x, by, data = NULL, posthoc = c("none", "dunn", "wilcoxon"),
  adjust = "holm", effect = FALSE, digits = 3, p_digits = 3, show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

d <- data.frame(
  score = c(10, 12, 11, 18, 17, 20, 25, 24, 27),
  group = factor(rep(c("A", "B", "C"), each = 3))
)
kwallis(score, by = group, data = d, posthoc = "dunn", effect = TRUE)

# Extended usage examples

d <- data.frame(
  score = c(10, 12, 11, 18, 17, 20, 25, 24, 27),
  treatment = factor(rep(c("A", "B", "C"), each = 3))
)
kwallis(score, by = treatment, data = d)
usedf(d)
result <- kwallis(score, by = treatment, show = FALSE)
result$raw$test

Label and recode existing variables

Description

Adds variable labels, value labels, recodes values, and sets reference categories. The function can edit an explicit data frame or the active R4VN data frame.

Usage

labvar(
  ...,
  data = NULL,
  label = NULL,
  values = NULL,
  recode = NULL,
  ref = NULL,
  ordered = FALSE
)

Arguments

Details

Explicit-data syntax remains valid:

labvar(data, sex, label = "Sex", values = c("1" = "Male", "2" = "Female"))

After usedf(data), active-data syntax is:

labvar(sex, label = "Sex", values = c("1" = "Male", "2" = "Female"))

When active data were selected with usedf(patient), edits update both the active data and the linked patient object. For an unlinked active copy, retrieve the result with data <- usedf().

Value

The edited data frame invisibly.

Examples

d <- data.frame(sex = c(1, 2, 1), age = c(8, 15, 30))
usedf(d, quiet = TRUE)
labvar(sex, label = "Sex",
       values = c("1" = "Male", "2" = "Female"))
labvar(age,
       recode = c("min:12" = 1, "13:17" = 2, "18:max" = 3),
       label = "Age group",
       values = c("1" = "0-12", "2" = "13-17", "3" = "18+"))

# Extended usage examples

# ------------------------------------------------------------------
# 1. Add only a variable label; numeric values remain numeric
d1 <- data.frame(age = c(18, 25, 40))
labvar(d1, age, label = "Age in years")
attr(d1$age, "label")

# 2. Add value labels; the variable becomes a factor
d2 <- data.frame(sex = c(1, 2, 2, 1))
labvar(d2, sex, label = "Sex",
       values = c("1" = "Male", "2" = "Female"))
levels(d2$sex)

# 3. Set the reference category by stored code
d3 <- data.frame(smoke = c(0, 1, 1, 0))
labvar(d3, smoke, label = "Current smoking",
       values = c("0" = "No", "1" = "Yes"), ref = 0)
levels(d3$smoke)

# 4. Set the reference category by displayed label
d4 <- data.frame(treatment = c(1, 2, 3, 1))
labvar(d4, treatment,
       values = c("1" = "Standard", "2" = "Drug A", "3" = "Drug B"),
       ref = "Standard")

# 5. Create an ordered factor
d5 <- data.frame(severity = c(1, 3, 2, 1))
labvar(d5, severity, label = "Disease severity",
       values = c("1" = "Mild", "2" = "Moderate", "3" = "Severe"),
       ordered = TRUE)
is.ordered(d5$severity)

# 6. Recode inclusive numeric ranges and then label the new categories
d6 <- data.frame(age = c(8, 12, 13, 17, 18, 65))
labvar(d6, age,
       recode = c("min:12" = 1, "13:17" = 2, "18:max" = 3),
       label = "Age group",
       values = c("1" = "0-12", "2" = "13-17", "3" = "18+"))

# 7. Collapse several exact values into one category
d7 <- data.frame(answer = c(1, 2, 3, 2, 1))
labvar(d7, answer,
       recode = c("1" = 1, "2 3" = 0),
       values = c("0" = "No/uncertain", "1" = "Yes"))

# 8. Recode without value labels; the result remains numeric
d8 <- data.frame(score = c(2, 6, 9, 15))
labvar(d8, score,
       recode = c("min:4" = 1, "5:9" = 2, "10:max" = 3),
       label = "Score category code")
is.numeric(d8$score)

# 9. Apply common value labels to several binary variables
d9 <- data.frame(smoke = c(0, 1), alcohol = c(1, 0), exercise = c(1, 1))
labvar(d9, smoke, alcohol, exercise,
       label = c("Smoking", "Alcohol use", "Regular exercise"),
       values = c("0" = "No", "1" = "Yes"))

# 10. Supply labels as a named vector
d10 <- data.frame(sbp = c(120, 130), dbp = c(75, 85))
labvar(d10, sbp, dbp,
       label = c(sbp = "Systolic blood pressure",
                 dbp = "Diastolic blood pressure"))

# 11. Select a contiguous range of variables
d11 <- data.frame(q1 = c(0, 1), q2 = c(1, 0), q3 = c(1, 1), age = c(20, 30))
labvar(d11, q1:q3, values = c("0" = "No", "1" = "Yes"))

# 12. Select variables with a wildcard
d12 <- data.frame(symptom_a = c(0, 1), symptom_b = c(1, 1), age = c(20, 30))
labvar(d12, "symptom_*", values = c("0" = "Absent", "1" = "Present"))

# 13. Use explicit-data syntax
d13 <- data.frame(outcome = c(0, 1, 0))
labvar(d13, outcome, label = "Outcome",
       values = c("0" = "No", "1" = "Yes"), ref = "No")

# 14. Use active-data syntax
d14 <- data.frame(outcome = c(0, 1, 0))
usedf(d14, quiet = TRUE)
labvar(outcome, label = "Outcome",
       values = c("0" = "No", "1" = "Yes"), ref = "No")
d14_active <- usedf(quiet = TRUE)

# 15. Use separate calls when variables need different value-label systems
d15 <- data.frame(sex = c(1, 2), outcome = c(0, 1))
labvar(d15, sex, values = c("1" = "Male", "2" = "Female"))
labvar(d15, outcome, values = c("0" = "No", "1" = "Yes"))

Linear combinations of fitted-model coefficients

Description

Calculates estimates, standard errors, confidence intervals and Wald tests for linear combinations of coefficients from the active or supplied model.

Usage

lincom(...,
  model = NULL,
  rhs = 0,
  exp = FALSE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE)

Arguments

Value

An R4VN result object, invisibly.

See Also

margins, predict

Examples

d <- data.frame(
  y = c(50, 54, 57, 61, 65, 68, 72, 76),
  age = c(20, 25, 30, 35, 40, 45, 50, 55),
  bmi = c(20, 22, 21, 24, 25, 27, 26, 29)
)
regress(y, c.age, c.bmi, data = d, show = FALSE)
lincom(age + 2 * bmi, show = FALSE)
lincom("age - bmi", show = FALSE)

Binary logistic regression

Description

Fits binary logistic regression using formula syntax or compact R4VN syntax. Compact syntax avoids the need to type ~ and +.

Usage

logistic(
  y,
  ...,
  vars = NULL,
  data = NULL,
  event = NULL,
  or = FALSE,
  exp = FALSE,
  noconstant = FALSE,
  vce = c("model", "robust", "cluster"),
  cluster = NULL,
  weights = NULL,
  subset = NULL,
  ref = NULL,
  gof = FALSE,
  groups = 10,
  classification = FALSE,
  cutoff = 0.5,
  vif = FALSE,
  diagnosis = FALSE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Details

Compact model syntax:

• x: use the variable as stored in the data.

• c.x: force x to be continuous.

• i.x: force x to be categorical.

• b2.x, b3.x, ...: categorical with the corresponding factor-level position as reference.

• ib0.x, ib1.x, ib2.x, ...: categorical with the requested value/level as reference. If that literal level is unavailable, a positive integer can fall back to the corresponding factor-level position.

• i.a*i.b: main effects for a and b plus their interaction.

• i.a:i.b: interaction only.

• c.x*i.a: continuous and categorical main effects plus interaction.

Thus logistic(y, ib2.occupation*i.treatment, c.age) fits occupation, treatment, occupation-by-treatment interaction, and age without requiring formula operators ~ or +.

The fitted glm object is stored in result$raw$model, so nested models can be compared directly with lrtest().

Value

An object of class r4vn_stat, returned invisibly. Its sections component contains the formatted model summary, coefficient and/or odds- ratio tables, and any requested goodness-of-fit, classification, or VIF tables. In raw, model is the fitted binomial glm object, vcov is the covariance matrix, coefficients contains coefficient-level estimates and tests, logLik and null.logLik are model log likelihoods, pseudo.r2 is McFadden-style pseudo-R-squared, event records the modeled outcome level, and vce and model.terms record the covariance estimator and fitted terms.

See Also

lrtest(), poisson()

Examples

set.seed(2026)
d <- data.frame(
  outcome = factor(rbinom(200, 1, .35), levels = 0:1,
                   labels = c("No", "Yes")),
  age = rnorm(200, 45, 12),
  occupation = factor(sample(c("Office", "Worker", "Other"), 200, TRUE)),
  treatment = factor(sample(c("No", "Yes"), 200, TRUE))
)

m1 <- logistic(
  outcome,
  c.age,
  i.occupation,
  i.treatment,
  data = d,
  event = "Yes",
  show = FALSE
)

m2 <- logistic(
  outcome,
  c.age,
  ib2.occupation*i.treatment,
  data = d,
  event = "Yes",
  show = FALSE
)

m3 <- logistic(
  outcome,
  vars = vars(c.age, ib2.occupation*i.treatment),
  data = d,
  event = "Yes",
  show = FALSE
)

lrtest(m1, m2, show = FALSE)

# Request a complete diagnostic panel
logistic(outcome, c.age, i.occupation, data = d, event = "Yes",
         diagnosis = TRUE, show = FALSE)

Likelihood-ratio test for nested regression models

Description

Compares two or more nested likelihood-based regression models. Objects returned by R4VN logistic() and poisson() can be supplied directly.

Usage

lrtest(..., digits = 3, p_digits = 3, show = TRUE, console = FALSE)

Arguments

Details

When more than two models are supplied, comparisons are sequential: M1 versus M2, then M2 versus M3, and so on.

The models must use the same outcome, analytic observations, weights, offsets/exposure definition, and likelihood family/link, and each larger model must contain the smaller model.

Supported fits include ordinary likelihood-based glm models such as logistic and Poisson regression, MASS::glm.nb() negative-binomial models, survival::coxph() Cox models, and survival::survreg() parametric survival models.

Quasi-likelihood models are not supported. R4VN models fitted with vce = "robust" or vce = "cluster" are also rejected because the classical likelihood-ratio chi-square test is model-likelihood inference, not robust covariance inference.

For ordinary linear regression use the nested-model F test rather than lrtest().

Value

An object of class r4vn_stat. The unformatted comparison table is stored in result$raw$table; the backward-compatible result$raw$comparison table is also retained.

See Also

logistic(), poisson()

Examples

set.seed(2026)
d <- data.frame(
  y = factor(rbinom(250, 1, .35), levels = 0:1,
             labels = c("No", "Yes")),
  age = rnorm(250, 45, 12),
  sex = factor(sample(c("Female", "Male"), 250, TRUE)),
  treatment = factor(sample(c("No", "Yes"), 250, TRUE))
)

m1 <- logistic(y, c.age, i.sex, i.treatment,
               data = d, event = "Yes", show = FALSE)
m2 <- logistic(y, c.age, i.sex*i.treatment,
               data = d, event = "Yes", show = FALSE)

lrtest(m1, m2, show = FALSE)

Predictive margins after an R4VN model

Description

Calculates average adjusted predictions and confidence intervals from the active or supplied model.

Usage

margins(model = NULL,
  at = NULL,
  over = NULL,
  type = c("response",
    "link"),
  atmeans = FALSE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE)

Arguments

Details

Unless atmeans = TRUE, variables not specified in at() retain their observed values and predictions are averaged over the estimation sample. Standard errors use the fitted covariance matrix and a delta-method gradient when the model provides the information required.

Value

An R4VN result object, invisibly.

See Also

at, marginsplot, predict, lincom

Examples

d <- data.frame(
  outcome = factor(c(0, 0, 0, 1, 0, 1, 1, 1), levels = 0:1,
                   labels = c("No", "Yes")),
  age = c(20, 25, 30, 35, 40, 45, 50, 55),
  sex = factor(rep(c("Female", "Male"), 4))
)
m <- logistic(outcome, c.age, i.sex, data = d, event = "Yes", show = FALSE)
margins(m, at = at(age = 40), show = FALSE)
margins(m, over = sex, show = FALSE)

Plot predictive margins

Description

Plots estimates and confidence intervals from margins().

Usage

marginsplot(
  result = NULL,
  x = NULL,
  by = NULL,
  ci = TRUE,
  line = TRUE,
  points = TRUE,
  line_width = 2,
  line_type = 1,
  point_size = 1,
  point_pch = 16,
  ci_color = NULL,
  ci_alpha = 1,
  ci_lwd = 1,
  ci_lty = 1,
  xline = NULL,
  yline = NULL,
  ref_color = "gray40",
  ref_lty = 2,
  ref_lwd = 1,
  xlab = NULL,
  ylab = NULL,
  xtitle = NULL,
  ytitle = NULL,
  title = NULL,
  subtitle = NULL,
  note = NULL,
  color = NULL,
  palette = "journal",
  alpha = 1,
  legend = TRUE,
  legend_position = "topright",
  theme = "journal",
  size = 11,
  file = NULL,
  width = 7,
  height = 5,
  dpi = 300,
  show = TRUE,
  bg = "white",
  vline = NULL,
  hline = NULL
  )

Arguments

Value

An R4VN result object, invisibly.

See Also

margins

Examples

d <- data.frame(
  outcome = factor(c(0, 0, 0, 1, 0, 1, 1, 1), levels = 0:1,
                   labels = c("No", "Yes")),
  age = c(20, 25, 30, 35, 40, 45, 50, 55)
)
m <- logistic(outcome, c.age, data = d, event = "Yes", show = FALSE)
mg <- margins(m, at = at(age = seq(30, 50, 10)), show = FALSE)
marginsplot(mg, show = FALSE)

Matched Case-control Analysis

Description

Calculates the matched odds ratio from the discordant pairs and an exact conditional confidence interval and p-value.

Usage

mcc(
  case,
  control,
  data = NULL,
  exposed = NULL,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

mcci(
  a,
  b,
  c,
  d,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

mcci(20, 14, 5, 31)

Merge data frames by key variables

Description

Merges a using data frame or file into a master data frame. Active data is used as master by default. Cardinality can be checked like Stata merges.

Usage

mergedata(
  using,
  by,
  data = NULL,
  type = c("1:1", "1:m", "m:1", "m:m"),
  join = c("full", "left", "right", "inner"),
  suffix = c("_master", "_using"),
  generate = "_merge",
  keep = NULL,
  update = FALSE,
  replace = FALSE,
  active = TRUE,
  quiet = FALSE,
  labels = c("factor", "labelled", "numeric")
)

Arguments

Value

The merged data frame invisibly.

Examples

master <- data.frame(id = 1:3, age = c(20, 30, 40))
using <- data.frame(id = 2:4, sex = c("F", "M", "F"))
usedf(master, quiet = TRUE)
mergedata(using, by = id, type = "1:1", quiet = TRUE)
result <- usedf(quiet = TRUE)
result2 <- mergedata(using, data = master, by = id, type = "1:1",
                     active = FALSE, quiet = TRUE)

Flexible nonlinear-shape regression

Description

Fits flexible nonlinear predictor shapes without requiring users to construct spline bases manually.

Usage

nlregress(y,
  x,
  covariates = NULL,
  data = NULL,
  spline = c("natural",
    "bspline",
    "polynomial",
    "linear"),
  df = 4,
  degree = 3,
  knots = NULL,
  boundary_knots = NULL,
  family = c("gaussian",
    "binomial",
    "poisson"),
  event = NULL,
  robust = FALSE,
  diagnosis = FALSE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE)

Arguments

Details

The fitted model is stored as the active model and can be used immediately by margins(), predict() and lincom().

Value

An R4VN result object, invisibly.

See Also

qregress, margins

Examples

d <- data.frame(
  age = seq(20, 75, by = 5),
  bmi = c(20, 21, 22, 24, 23, 25, 26, 27, 29, 28, 30, 31),
  sex = factor(rep(c("Female", "Male"), 6)),
  y = c(48, 52, 55, 61, 60, 66, 69, 73, 78, 80, 85, 89),
  outcome = c(0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1)
)
nlregress(y, age, data = d, spline = "natural", df = 3, show = FALSE)
nlregress(y, age, covariates = vars(sex, bmi), data = d,
          spline = "natural", knots = c(35, 50), show = FALSE)
nlregress(outcome, age, data = d, family = "binomial", event = 1,
          show = FALSE)
nlregress(y, age, data = d, spline = "bspline", df = 4, diagnosis = TRUE, show = FALSE)
nlregress(y, age, data = d, spline = "polynomial", degree = 2, show = FALSE)

Normality tests for one or more variables

Description

Runs normality/distribution tests consistently for one or many variables, overall or within hierarchical groups.

Usage

normtest(x = NULL,
  vars = NULL,
  by = NULL,
  data = NULL,
  method = c("all",
    "shapiro",
    "ks",
    "lilliefors",
    "anderson",
    "cramer.von.mises",
    "shapiro.francia",
    "pearson",
    "jarque.bera"),
  digits = 4,
  p_digits = 3,
  show = TRUE,
  console = FALSE)

Arguments

Details

Different normality tests have different sensitivities and sample-size behavior. The ordinary fitted-normal Kolmogorov-Smirnov p-value is approximate because mean and SD are estimated from the same sample; use the Lilliefors option when available. A nonsignificant test does not prove normality, so graphical inspection remains important.

Value

An R4VN result object, invisibly.

See Also

swilk, varform

Examples

d <- data.frame(
  x = rnorm(80),
  y = rexp(80),
  province = rep(c("A", "B"), each = 40),
  sex = rep(rep(c("F", "M"), each = 20), 2)
)
normtest(x, data = d)
normtest(vars = vars(x, y), data = d, method = c("shapiro", "jarque.bera"))
normtest(vars = vars(x, y), by = vars(province, sex), data = d)

Nonparametric trend across ordered groups

Description

Tests for monotonic trend across ordered groups, including quantitative and binary outcomes and hierarchical strata.

Usage

nptrend(x,
  by,
  data = NULL,
  method = c("auto",
    "cuzick",
    "cochran-armitage",
    "spearman",
    "linear"),
  event = NULL,
  scores = NULL,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE)

Arguments

Value

An R4VN result object, invisibly.

See Also

kwallis

Examples

d <- data.frame(y = c(2,3,4,4,5,7,8,9,10), dose = ordered(rep(1:3, each=3)))
nptrend(y, by = dose, data = d)

Open data from files, research platforms, online forms, and databases

Description

Reads local/remote files and connects to supported research data sources. Existing file and REDCap behavior is retained. Additional connectors support KoboToolbox, Google Forms, Google Sheets, Microsoft Forms/Excel, and MySQL/MariaDB.

Usage

opendata(
  file = NULL,
  api = NULL,
  url = NULL,
  token = NULL,
  vars = NULL,
  obs = NULL,
  active = FALSE,
  labels = c("factor", "labelled", "numeric"),
  header = TRUE,
  sep = NULL,
  sheet = 1,
  range = NULL,
  skip = 0,
  na = c("", "NA"),
  encoding = "UTF-8",
  check.names = FALSE,
  records = NULL,
  fields = NULL,
  forms = NULL,
  events = NULL,
  quiet = FALSE,
  uid = NULL,
  server = NULL,
  form = NULL,
  access_token = NULL,
  question_names = c("id", "title"),
  sheet_id = NULL,
  drive_id = NULL,
  item_id = NULL,
  table = NULL,
  page_size = 1000L,
  host = "localhost",
  port = 3306L,
  dbname = NULL,
  database = NULL,
  user = NULL,
  password = NULL,
  query = NULL,
  ssl_ca = NULL,
  ssl_cert = NULL,
  ssl_key = NULL,
  db_timeout = 10,
  bigint = "integer64",
  ...
)

Arguments

Details

Existing local-file and REDCap behavior is unchanged. When file is a bare file name that does not exist in the current working directory, opendata() also looks in the package's bundled extdata directory. This makes the teaching dataset available simply as opendata("ivf_v3_vi.dta") after R4VN is installed.

KoboToolbox uses API v2 and Token authentication. Google Forms direct access uses the official Forms API and OAuth. Google Sheets public share links can be read without OAuth when the sheet is accessible to anyone with the link; private sheets can be read with an OAuth access token.

Google Forms and Microsoft Forms response files exported to CSV/XLSX can be opened directly with opendata(file). api = "googleform" or api = "msforms" may also be supplied together with file as a friendly alias; in that case the normal file reader is used.

Microsoft Forms direct response access is handled through its linked Excel workbook via Microsoft Graph. MySQL/MariaDB uses DBI + RMariaDB and closes the database connection automatically before returning.

Value

A data frame.

Examples

# Bundled R4VN teaching dataset (Stata format).
if (requireNamespace("haven", quietly = TRUE) ||
    requireNamespace("readstata13", quietly = TRUE)) {
  ivf <- opendata("ivf_v3_vi.dta", quiet = TRUE)
  head(ivf)
}

## Not run: 
# Existing REDCap behavior
redcap <- opendata(
  api = "redcap",
  url = "https://example.org/api/",
  token = "REDCAP_TOKEN",
  active = TRUE
)

# KoboToolbox
kobo <- opendata(
  api = "kobo",
  uid = "aBcDeFg123",
  token = "KOBO_TOKEN",
  active = TRUE
)

# Public Google Sheet using only a share link
gsheet <- opendata(
  api = "gsheet",
  url = "https://docs.google.com/spreadsheets/d/SPREADSHEET_ID/edit?gid=0",
  active = TRUE
)

# Named tab and range from a public Google Sheet
gsheet2 <- opendata(
  api = "gsheet",
  url = "https://docs.google.com/spreadsheets/d/SPREADSHEET_ID/edit",
  sheet = "Responses",
  range = "A1:H500"
)

# Google Forms / Microsoft Forms: easiest route after exporting responses
gform_export <- opendata("google-form-responses.xlsx")
msform_export <- opendata("microsoft-form-responses.xlsx")

# Direct Google Forms API (OAuth token required)
gform <- opendata(
  api = "googleform",
  form = "FORM_ID",
  access_token = "GOOGLE_OAUTH_ACCESS_TOKEN"
)

# Microsoft Forms linked Excel workbook via Microsoft Graph
msform <- opendata(
  api = "msforms",
  item_id = "WORKBOOK_ITEM_ID",
  access_token = "MS_GRAPH_ACCESS_TOKEN"
)

# MySQL table
mysql_data <- opendata(
  api = "mysql",
  host = "db.example.org",
  dbname = "research",
  user = "reader",
  password = "PASSWORD",
  table = "participants"
)

# MySQL read-only query
mysql_subset <- opendata(
  api = "mysql",
  host = "db.example.org",
  database = "research",
  user = "reader",
  password = "PASSWORD",
  query = "SELECT id, age, sex FROM participants WHERE age >= 18"
)

## End(Not run)

Reorder variables

Description

Reorders variables in an explicit data frame or the active R4VN data frame.

Usage

ordervar(..., data = NULL, before = NULL, after = NULL, last = FALSE)

Arguments

Value

The edited data frame invisibly.

Examples

d <- data.frame(age = 1, sex = 2, id = 3, bmi = 4)
usedf(d, quiet = TRUE)
ordervar(id, sex)
ordervar(bmi, after = age)

# Extended usage examples

d <- data.frame(age = 1, sex = 2, id = 3, bmi = 4, outcome = 5)

# Move variables to the beginning
d1 <- d; ordervar(d1, id, outcome)

# Move before or after an anchor
d2 <- d; ordervar(d2, outcome, before = age)
d3 <- d; ordervar(d3, bmi, after = age)

# Move to the end
d4 <- d; ordervar(d4, id, last = TRUE)

# Use a range or wildcard
d5 <- data.frame(id = 1, q1 = 2, q2 = 3, q3 = 4, age = 5)
ordervar(d5, q1:q3, last = TRUE)

# Active-data syntax
usedf(d, quiet = TRUE); ordervar(id, outcome)

Plot an R4VN diagnostic analysis

Description

Draw ROC curves and/or sensitivity-specificity curves over empirical thresholds from an object returned by tabdiag().

Usage

## S3 method for class 'r4vn_diag'
plot(
  x,
  what = c("roc", "cutoff", "both"),
  color = NULL,
  lty = 1,
  line_width = 2,
  legend = TRUE,
  legend_position = "bottomright",
  auc = TRUE,
  diagonal = TRUE,
  diagonal_lty = 2,
  grid = FALSE,
  xlim = c(0, 1),
  ylim = c(0, 1),
  xlab = "1 - Specificity",
  ylab = "Sensitivity",
  main = NULL,
  cutoff_mark = TRUE,
  cutoff_legend = TRUE,
  cutoff_xlab = "Threshold",
  cutoff_ylab = "Probability",
  cutoff_main = NULL,
  file = NULL,
  width = 7,
  height = 7,
  res = 300,
  bg = "white",
  font_family = NULL,
  cex_axis = 1,
  cex_lab = 1,
  cex_main = 1,
  bty = "l",
  ...
)

Arguments

Value

Invisibly returns x.

Plot an R4VN probability-distribution result

Description

Plot an R4VN probability-distribution result

Usage

## S3 method for class 'r4vn_distdata'
plot(x, type = c("density", "cdf"), main = NULL, ...)

Arguments

Value

The input object, invisibly.

Plot an R4VN graph result

Description

Plot an R4VN graph result

Usage

## S3 method for class 'r4vn_graph'
plot(x, ...)

## S3 method for class 'r4vn_graph_set'
plot(x, ncol = NULL, ...)

Arguments

Value

x, invisibly.

Plot a tabmachine result

Description

Plot a tabmachine result

Usage

## S3 method for class 'r4vn_machine'
plot(x, type = NULL, title = NULL, font_family = "sans", ...)

Arguments

Value

The input r4vn_machine object, invisibly. The requested figure is drawn on the current graphics device as a side effect; the fitted machine- learning result itself is not modified.

Plot an R4VN meta-analysis

Description

Draw forest, funnel, trim-and-fill, influence, cumulative, diagnostic, and meta-regression figures. Every figure can be drawn in the R/RStudio Plot pane or saved independently in a publication format.

Usage

## S3 method for class 'r4vn_meta'
plot(
  x,
  type = "forest",
  moderator = NULL,
  contour = FALSE,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  xlab = NULL,
  ref = NULL,
  xlim = NULL,
  ticks = NULL,
  color = NULL,
  font_family = "sans",
  text_size = 0.82,
  axis_size = 0.9,
  title_size = 1.05,
  title_color = "black",
  subtitle_size = 0.9,
  subtitle_color = "gray30",
  caption_size = 0.75,
  caption_color = "gray40",
  margins = NULL,
  background = "white",
  point_color = NULL,
  point_bg = "white",
  ci_color = NULL,
  summary_color = NULL,
  summary_border = NULL,
  point_shape = NULL,
  point_size = NULL,
  line_type = 1,
  line_width = 1,
  ref_color = "gray40",
  ref_type = 2,
  ref_width = 1,
  show_weights = TRUE,
  show_prediction = FALSE,
  weight_title = "Weight",
  estimate_title = NULL,
  show_abcd = FALSE,
  abcd_titles = c("a", "b", "c", "d"),
  prediction_style = "bar",
  row_shade = "zebra",
  shade_color = "gray95",
  digits = NULL,
  study_order = NULL,
  header = NULL,
  annotate = TRUE,
  yaxis = "sei",
  ylab = NULL,
  contour_levels = c(90, 95, 99),
  contour_colors = c("#FEE2E2", "#FEF3C7", "#E5E7EB"),
  funnel_label = FALSE,
  funnel_legend = FALSE,
  bubble_ci = TRUE,
  bubble_pi = FALSE,
  bubble_shade = c("#DBEAFE", "#E5E7EB"),
  grid = FALSE,
  engine_args = list(),
  file = NULL,
  width = 1800,
  height = 1400,
  res = 180,
  ...
)

Arguments

Value

The meta-analysis object invisibly.

Examples

if (requireNamespace("metafor", quietly = TRUE)) {
  dat <- read.csv(system.file("extdata", "meta_example.csv", package = "R4VN"))
  m <- tabmeta(
    dat, study, effect = OR, lower = LCI, upper = UCI, or = TRUE,
    show = FALSE
  )

  # Journal-style forest plot in the Plot pane.
  plot(
    m, type = "forest", font_family = "sans",
    subtitle = "Random-effects model with 95% confidence intervals",
    caption = "Square size reflects study weight; diamond is pooled effect.",
    margins = c(5.5, 4.2, 5.0, 2.0),
    point_color = "#1F4E79", ci_color = "#5B9BD5",
    summary_color = "#C00000", summary_border = "#7F0000",
    row_shade = "zebra", shade_color = "#F5F7FA",
    show_weights = TRUE, weight_title = "Weight",
    estimate_title = "OR (95% CI)", show_prediction = FALSE,
    xlim = c(0.2, 2), ticks = c(0.25, 0.5, 1, 1.5, 2)
  )

  # Contour-enhanced funnel and trim-and-fill plots.
  plot(
    m, type = "funnel", contour = TRUE,
    point_shape = 21, point_color = "#1F4E79", point_bg = "#D9EAF7",
    contour_levels = c(90, 95, 99),
    contour_colors = c("#FFF2CC", "#FCE4D6", "#E2F0D9"),
    funnel_label = "out", funnel_legend = "topright"
  )
  if (!is.null(m$models$trimfill)) {
    plot(m, type = "trimfill", contour = TRUE)
  }

  # Save a 300-dpi TIFF independently.
  forest_file <- tempfile(fileext = ".tiff")
  plot(
    m, type = "forest", file = forest_file,
    width = 2400, height = 1800, res = 300,
    font_family = "sans", point_color = "#1F4E79",
    ci_color = "#5B9BD5", summary_color = "#C00000"
  )
  unlink(forest_file)

  # Advanced metafor controls.
  plot(m, type = "forest",
       engine_args = list(efac = c(1, 1.2), plim = c(0.6, 1.8)))
}

Plot an R4VN regression forest object

Description

Re-draws a tabforest() object without refitting models. All graphical settings may be overridden here, which is useful for trying different journal layouts after the analysis is finalized.

Usage

## S3 method for class 'r4vn_tabforest'
plot(x, ..., file = NULL, width = NULL, height = NULL, dpi = NULL)

Arguments

Value

x, invisibly.

Plot an R4VN Longitudinal Analysis

Description

Recreates the observed longitudinal profile stored by tablong(). This is useful when the original analysis used plot = FALSE or when different plot labels/sizing are wanted without refitting the statistical model.

Usage

## S3 method for class 'r4vn_tablong'
plot(x, ci = TRUE, ...)

Arguments

Value

An R4VN plot specification, invisibly; the plot is drawn with base R graphics.

Plot diagnostics from a scale analysis

Description

Displays every available tabscale graphic in the R graphics device. In RStudio, use the back/forward arrows in the Plots pane to review them.

Usage

## S3 method for class 'r4vn_tabscale'
plot(x, which = NULL, ...)

Arguments

Value

The input object, invisibly.

Plot an R4VN scorecard

Description

Draw one or all publication-oriented scorecard graphics using base R only. With which="all" (the default), every available graph is drawn in sequence; in RStudio the back/forward arrows in the Plots pane can be used to review the complete plot history. The same available graphics are embedded automatically in the HTML Viewer when the original tabscore() call used plot=TRUE.

Usage

## S3 method for class 'r4vn_tabscore'
plot(
  x,
  which = c("all", "risk", "roc", "calibration", "decision", "distribution"),
  title = NULL,
  font_family = "sans",
  ...
)

Arguments

Value

For one plot, invisibly returns its plotted data. With which="all", invisibly returns a named list containing the data for every graph drawn.

Examples

set.seed(23)
d <- data.frame(
  age = rnorm(160, 50, 11),
  smoke = factor(rbinom(160, 1, .30), 0:1, c("No", "Yes"))
)
d$event <- rbinom(160, 1, plogis(-3.5 + .045*d$age + .7*(d$smoke == "Yes")))
z <- tabscore(event, c(age, smoke), data=d,
              validate="none", plot=TRUE, show=FALSE)
plot(z, which="risk")
plot(z, which="roc")

plot(z) # all available plots, one after another

Plot an R4VN tabts result

Description

Plot an R4VN tabts result

Usage

## S3 method for class 'r4vn_tabts'
plot(
  x,
  which = c("series", "forecast", "its", "counterfactual", "decomposition", "acf",
    "pacf", "residual"),
  type = NULL,
  file = NULL,
  width = NULL,
  height = NULL,
  dpi = NULL,
  ...
)

Arguments

Value

Invisibly returns the plot object that is displayed or saved.

Examples

data(dengue_ts)
x <- tabts(cases, time = month, data = dengue_ts,
           model = "arima", order = c(1, 0, 1), forecast = 6,
           show = FALSE)
plot(x, "series")
plot(x, type = "forecast")
plot(x, "forecast", file = file.path(tempdir(), "forecast.png"),
     width = 8, height = 5, dpi = 300)

Poisson regression or Poisson family

Description

Fits Poisson regression using formula syntax or compact R4VN syntax. When called without a model, for example poisson() or poisson(link = "identity"), returns the ordinary stats::poisson() family. A binary outcome is also supported; event explicitly identifies the event category and rr = TRUE requests a risk-ratio display. Robust or cluster-robust VCE is generally appropriate for modified-Poisson binary models.

Usage

poisson(
  y, ..., vars = NULL, data = NULL, exposure = NULL, offset = NULL,
  event = NULL, irr = FALSE, rr = FALSE, exp = FALSE, link = "log",
  noconstant = FALSE, vce = c("model", "robust", "cluster"), cluster = NULL,
  weights = NULL, subset = NULL, ref = NULL, vif = FALSE, diagnosis = FALSE,
  level = 0.95, digits = 3, p_digits = 3, show = TRUE, console = FALSE
)

Arguments

Details

The same compact syntax used by logistic() is supported: c.x, i.x, b2.x, ib2.x, * for main effects plus interaction, and : for interaction only.

Standard Poisson models fitted with model-based VCE can be compared using lrtest(). Quasi-Poisson models do not have an ordinary likelihood and are not supported by lrtest().

Value

If y is omitted, a base-R family object for the Poisson distribution with the requested link is returned for compatibility with modeling functions. Otherwise an object of class r4vn_stat is returned invisibly. Its sections component contains the formatted model summary, coefficient and/or exponentiated-effect tables, goodness-of-fit results, and any requested VIF table. In raw, model is the fitted Poisson glm object, vcov is the covariance matrix, coefficients contains coefficient-level estimates and tests, logLik and null.logLik are model log likelihoods, pearson is the Pearson chi-square statistic, offset stores the offset used by the fitted model, and event, binary, vce, and model.terms describe binary-event handling, covariance estimation, and fitted terms.

See Also

logistic(), lrtest()

Examples

set.seed(2026)
d <- data.frame(
  cases = rpois(200, 2),
  time = runif(200, .5, 4),
  age = rnorm(200, 45, 12),
  sex = factor(sample(c("Female", "Male"), 200, TRUE)),
  treatment = factor(sample(c("No", "Yes"), 200, TRUE))
)

p1 <- poisson(
  cases,
  c.age,
  i.sex,
  i.treatment,
  data = d,
  exposure = time,
  show = FALSE
)

p2 <- poisson(
  cases,
  c.age,
  i.sex*i.treatment,
  data = d,
  exposure = time,
  show = FALSE
)

lrtest(p1, p2, show = FALSE)

# Modified Poisson for a binary outcome
d$event01 <- as.integer(d$cases > 1)
poisson(event01, c.age, i.sex, data = d, event = 1, rr = TRUE, vce = "robust")

# Dispersion, residual, influence, and collinearity diagnostics
poisson(cases, c.age, i.sex, data = d, exposure = time, diagnosis = TRUE, show = FALSE)

Prediction and postestimation diagnostics

Description

predict() provides one consistent R4VN postestimation interface. With an explicit fitted model and no newvar, ordinary prediction types continue to delegate to the model's stats::predict() method. R4VN also adds common residual and influence statistics. In variable-generation mode, provide newvar and R4VN writes the selected statistic back to data or the active data frame while preserving omitted estimation rows as NA.

Usage

predict(
  object = NULL, ..., newvar = NULL, type = "auto", term = NULL,
  data = NULL, replace = FALSE, show = TRUE
)

Arguments

Details

Standardized residuals are computed with stats::rstandard() and studentized residuals with stats::rstudent() when those methods are available. These are different from raw residuals. For linear regression, leverage is obtained with hatvalues(), Cook's distance with cooks.distance(), DFFITS with dffits(), and COVRATIO with covratio().

Influence statistics and residuals are defined for the estimation sample. When they are written to the original active data, observations omitted from model fitting because of missing values are filled with NA.

When R4VN compact syntax declared a predictor categorical (for example i.htn) but the original data store it as numeric 0/1, prediction data are automatically reconstructed with the factor levels retained by the fitted model. Unknown new levels remain an error rather than being silently recoded.

Value

Without newvar, returns the requested prediction, residual, or diagnostic statistic. With newvar, invisibly returns the updated data frame after writing the generated variable.

Examples

# Linear regression: fitted values and regression diagnostics
d <- data.frame(
  y = c(12, 15, 17, 20, 21, 25, 28, 31, 35, 38),
  age = seq(20, 65, by = 5),
  bmi = c(19, 21, 20, 23, 25, 24, 27, 28, 30, 29)
)
usedf(d)
m1 <- regress(y, c.age, c.bmi, show = FALSE)
predict(m1, type = "response")
predict(m1, type = "standardized")
predict(m1, type = "studentized")
predict(m1, type = "leverage")
predict(m1, type = "cooksd")
predict(m1, type = "dffits")
predict(m1, type = "covratio")

# Store diagnostics in the active data frame
predict(newvar = fitted_y, type = "fitted", show = FALSE)
predict(newvar = residual_y, type = "residual", show = FALSE)
predict(newvar = stdres, type = "standardized", show = FALSE)
predict(newvar = studres, type = "studentized", show = FALSE)
predict(newvar = leverage, type = "leverage", show = FALSE)
predict(newvar = cooksd, type = "cooksd", show = FALSE)

# DFBETA/DFBETAS are coefficient-specific; select a term when generating
names(stats::coef(m1$raw$model))
predict(m1, type = "dfbetas", term = "age")
predict(m1, newvar = dfb_age, type = "dfbetas", term = "age", show = FALSE)

# Linear-model prediction standard error and confidence limits
predict(m1, type = "se.fit")
predict(m1, type = "lower", level = 0.95)
predict(m1, type = "upper", level = 0.95)

# Logistic regression: probability and residual diagnostics
g <- data.frame(
  outcome = factor(c(0,0,0,0,1,0,1,1,1,1,1,1), levels = 0:1,
                   labels = c("No", "Yes")),
  age = seq(25, 80, by = 5),
  bmi = c(20,21,22,24,23,26,25,28,29,30,31,33)
)
usedf(g)
m2 <- logistic(outcome, c.age, c.bmi, event = "Yes", show = FALSE)
predict(m2, type = "probability")
predict(m2, type = "pearson")
predict(m2, type = "deviance")
predict(m2, type = "standardized")
predict(m2, type = "leverage")
predict(m2, type = "cooksd")

Predict from a tabmachine model

Description

Predict from a tabmachine model

Usage

## S3 method for class 'r4vn_machine'
predict(
  object,
  newdata,
  type = c("response", "prob", "class"),
  threshold = NULL,
  ...
)

Arguments

Value

Predictions whose structure depends on the task and type. For a regression task, a numeric vector of predicted outcomes is returned. For a binary task, type = "response" or "prob" returns a numeric vector of probabilities for the modeled event, while type = "class" returns a factor of predicted classes. For a multiclass task, type = "response" or "prob" returns a numeric matrix of class probabilities and type = "class" returns a factor of predicted classes.

Predict scores and risk from an R4VN scorecard

Description

Predict scores and risk from an R4VN scorecard

Usage

## S3 method for class 'r4vn_tabscore'
predict(
  object,
  newdata,
  type = c("all", "score", "risk", "group", "model", "model_lp", "model_score"),
  times = NULL,
  ...
)

Arguments

Value

A numeric vector, matrix, or data frame depending on type.

Print an R4VN Export Result

Description

Lists exported file paths, or prints returned table data when no files were created.

Usage

## S3 method for class 'r4vn_export'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print an R4VN graph result

Description

Returns an R4VN graph object invisibly without writing status text to the console.

Usage

## S3 method for class 'r4vn_graph'
print(x, ...)

Arguments

Value

x, invisibly.

Return an R4VN graph collection silently

Description

Returns the collection invisibly without writing graph status or row counts to the Console. Use plot() to redraw the collection.

Usage

## S3 method for class 'r4vn_graph_set'
print(x,
  ...)

Arguments

Value

The graph collection, invisibly.

Print a tabmachine result

Description

Print a tabmachine result

Usage

## S3 method for class 'r4vn_machine'
print(x, ...)

Arguments

Value

The input r4vn_machine object, invisibly, after printing a compact summary of the analysis and the final-model performance table.

Print or reopen an R4VN meta-analysis

Description

Print or reopen an R4VN meta-analysis

Usage

## S3 method for class 'r4vn_meta'
print(x, ...)

Arguments

Value

The input object invisibly.

Print a quick R4VN console summary

Description

Print a quick R4VN console summary

Usage

## S3 method for class 'r4vn_quick'
print(x, ...)

Arguments

Value

x, invisibly.

Print an R4VN statistical result

Description

Print an R4VN statistical result

Usage

## S3 method for class 'r4vn_stat'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print or Reopen an R4VN Table

Description

Opens the HTML file stored in an r4vn_tab object.

Usage

## S3 method for class 'r4vn_tab'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print an R4VN Longitudinal Table

Description

Print an R4VN Longitudinal Table

Usage

## S3 method for class 'r4vn_tablong'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print or Reopen an R4VN Model-Comparison Table

Description

Opens the HTML file stored in an r4vn_tabmulti object.

Usage

## S3 method for class 'r4vn_tabmulti'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print or reopen a scale analysis

Description

Print or reopen a scale analysis

Usage

## S3 method for class 'r4vn_tabscale'
print(x, ...)

Arguments

Value

The input object, invisibly.

Proportions and Tests of Proportions

Description

Immediate and data-based commands for confidence intervals, exact binomial tests, and one- or two-sample z tests of proportions. prtest() and prtesti() support a one-sample null proportion through p0; prtest() also supports hierarchical grouping.

Usage

propi(
  events,
  total,
  p0 = NULL,
  method = c("all", "exact", "wilson", "wald"),
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

bitesti(
  total,
  events,
  p = 0.5,
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

prtesti(
  events1,
  n1,
  events2 = NULL,
  n2 = NULL,
  p0 = NULL,
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

propi(
  events, total, p0 = NULL, method = c("all", "exact", "wilson", "wald"),
  alternative = c("two.sided", "less", "greater"), level = 0.95, digits = 3,
  p_digits = 3, show = TRUE, console = FALSE
)

bitesti(
  total, events, p = 0.5, alternative = c("two.sided", "less", "greater"),
  level = 0.95, digits = 3, p_digits = 3, show = TRUE, console = FALSE
)

prtesti(
  events1, n1, events2 = NULL, n2 = NULL, p0 = NULL,
  alternative = c("two.sided", "less", "greater"), level = 0.95, digits = 3,
  p_digits = 3, show = TRUE, console = FALSE
)

prop(
  x, data = NULL, event = NULL, p0 = NULL,
  method = c("all", "exact", "wilson", "wald"),
  alternative = c("two.sided", "less", "greater"), level = 0.95, digits = 3,
  p_digits = 3, show = TRUE, console = FALSE
)

bitest(
  x, p = 0.5, data = NULL, event = NULL,
  alternative = c("two.sided", "less", "greater"), level = 0.95, digits = 3,
  p_digits = 3, show = TRUE, console = FALSE
)

prtest(
  x, by = NULL, data = NULL, event = NULL, p0 = NULL,
  alternative = c("two.sided", "less", "greater"), level = 0.95, digits = 3,
  p_digits = 3, show = TRUE, console = FALSE
)

prop(
  x,
  data = NULL,
  event = NULL,
  p0 = NULL,
  method = c("all", "exact", "wilson", "wald"),
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

bitest(
  x,
  p = 0.5,
  data = NULL,
  event = NULL,
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

prtest(
  x,
  by = NULL,
  data = NULL,
  event = NULL,
  p0 = NULL,
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

propi(32, 100, p0 = .25)
bitesti(40, 23, p = 0.5)
prtesti(30, 100, p0 = .20)
prtesti(30, 100, 20, 80)

d <- data.frame(case = c(rep(1,30), rep(0,70), rep(1,20), rep(0,60)),
                group = rep(c("A","B"), c(100,80)))
prtest(case, data = d, p0 = .25, event = 1)
prtest(case, by = group, data = d, event = 1)

Quantile regression

Description

Fits one or several conditional quantile regression models using the optional quantreg package and R4VN model syntax.

Usage

qregress(y,
  ...,
  vars = NULL,
  data = NULL,
  tau = 0.5,
  method = "br",
  se = "nid",
  weights = NULL,
  subset = NULL,
  ref = NULL,
  noconstant = FALSE,
  diagnosis = FALSE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE)

Arguments

Details

The model nearest tau = 0.5 is stored as the primary active model; all requested quantile fits are retained in raw$models.

Value

An R4VN result object, invisibly.

See Also

regress, nlregress

Examples

if (requireNamespace("quantreg", quietly = TRUE)) {
  # Use a reasonably sized, full-rank data set so the example is stable
  # across quantreg and R versions.
  d <- datasets::mtcars
  d$am <- factor(d$am, levels = c(0, 1),
                 labels = c("Automatic", "Manual"))

  # Median regression with one continuous and one categorical predictor.
  qregress(mpg, c.wt, i.am, data = d, tau = .5,
           se = "iid", show = FALSE)

  # Fit several conditional quantiles in one call.
  qregress(mpg, c.wt, i.am, data = d,
           tau = c(.25, .5, .75), se = "iid", show = FALSE)

  # Request the R4VN quantile-regression diagnostic section.
  qregress(mpg, c.wt, i.am, data = d, tau = .5,
           se = "iid", diagnosis = TRUE, show = FALSE)
}

Launch R4VN Playground

Description

Starts a lightweight collection of short games, puzzles, and relaxation activities bundled with R4VN. The interface can be switched between English and Vietnamese. The function is intentionally independent of active data and statistical workflows.

Usage

r4fun(
  language = c("en", "vi"),
  launch.browser = TRUE,
  host = "127.0.0.1",
  port = NULL,
  ...
)

Arguments

Details

The Playground uses only the optional package shiny. Vietnamese interface strings are stored in Unicode escape form so the R source file remains ASCII-only and portable across locales. Games include a collapsible Trick section with a practical strategy or shortcut for easier play.

Value

Invisibly returns the value from shiny::runApp().

Examples

if (interactive() && requireNamespace("shiny", quietly = TRUE)) {
  r4fun()
}

Wilcoxon Rank-sum Test

Description

Performs the Wilcoxon rank-sum test, also known as the Mann-Whitney U test, for two independent samples. Samples may be supplied as two numeric variables or as one numeric outcome and a two-level grouping variable.

Usage

ranksum(
  x,
  y = NULL,
  by = NULL,
  data = NULL,
  alternative = c("two.sided", "less", "greater"),
  exact = NULL,
  correct = TRUE,
  conf.int = TRUE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Details

With by, the first observed factor level is sample 1 and the second level is sample 2. Use factor() or labvar(..., ref = ...) to control level order. Missing values are removed independently when x and y are supplied, and complete cases are used when by is supplied.

Value

Invisibly returns an object of class r4vn_stat.

Examples

d <- data.frame(
  score = c(12, 15, 11, 19, 18, 21, 14, 17),
  group = factor(rep(c("Control", "Intervention"), each = 4)),
  score2 = c(10, 13, 12, 14, 19, 20, 18, 22)
)
ranksum(score, by = group, data = d)
ranksum(score, score2, data = d)
ranksum(score, by = group, data = d, alternative = "less")

# Extended usage examples

d <- data.frame(
  score = c(10, 11, 12, 13, 18, 19, 20, 21),
  score2 = c(9, 10, 12, 11, 17, 18, 19, 22),
  group = factor(rep(c("Control", "Intervention"), each = 4))
)

# Two independent variables or one outcome by a two-level group
ranksum(score, score2, data = d)
ranksum(score, by = group, data = d)

# One-sided alternatives, approximation controls, and confidence interval
ranksum(score, by = group, data = d, alternative = "less")
ranksum(score, by = group, data = d, exact = FALSE, correct = FALSE)
ranksum(score, by = group, data = d, conf.int = FALSE)

# Active data and hidden console output
usedf(d)
result <- ranksum(score, by = group, show = FALSE)
result$raw$test

Linear regression

Description

Fits an ordinary least-squares model. R4VN compact syntax allows models such as regress(y, c.age, i.sex, i.sex*i.treatment) without ~ or +.

Usage

regress(
  y,
  ...,
  vars = NULL,
  data = NULL,
  noconstant = FALSE,
  vce = c("model", "robust", "cluster"),
  cluster = NULL,
  weights = NULL,
  subset = NULL,
  ref = NULL,
  standardized = FALSE,
  vif = FALSE,
  diagnosis = FALSE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Details

Compact model prefixes are c.x for continuous, i.x for categorical, b2.x for the second factor level as reference, and ib2.x for value/level 2 as reference. Use * for main effects plus interaction and : for interaction only.

Ordinary linear regression models should be compared with the usual nested F test rather than lrtest().

Value

An object of class r4vn_stat, returned invisibly. Its sections component contains the formatted model summary, ANOVA, coefficient table, and any requested standardized-coefficient or VIF tables. In raw, model is the fitted lm object, vcov is the covariance matrix, coefficients contains coefficient-level estimates and tests, overall contains the overall model test, and vce and model.terms record the covariance estimator and fitted terms.

Examples

d <- data.frame(score = c(60, 65, 68, 72, 75, 80, 77, 70),
                age = c(20, 25, 30, 35, 40, 45, 50, 55),
                bmi = c(20, 22, 24, 23, 26, 28, 27, 25),
                sex = factor(rep(c("Female", "Male"), 4)))

regress(score, age, bmi, i.sex, data = d, show = FALSE)
regress(score, vars = vars(c.age, c.bmi, i.sex), data = d, show = FALSE)
# Full model diagnostics
m <- regress(score, c.age, c.bmi, i.sex, data = d, diagnosis = TRUE, show = FALSE)
m$sections$`Model diagnosis`
m$sections$`Influence diagnostics`
# Postestimation diagnostics can also be generated as variables
usedf(d)
regress(score, c.age, c.bmi, i.sex, diagnosis = FALSE, show = FALSE)
predict(newvar = stdres, type = "standardized", show = FALSE)
predict(newvar = cooksd, type = "cooksd", show = FALSE)

Rename variables

Description

Renames variables in an explicit data frame or the active R4VN data frame.

Usage

renvar(..., data = NULL)

Arguments

Value

The edited data frame invisibly.

Examples

d <- data.frame(sex = 1:2, age_kt = 3:4)
usedf(d, quiet = TRUE)
renvar(sex, gender)
renvar("*_kt", "*")

# Extended usage examples

d <- data.frame(sex = 1:2, age = 3:4, score_pre = 5:6, bmi_pre = 7:8)

# Rename one variable
d1 <- d; renvar(d1, sex, gender)

# Rename parallel groups
d2 <- d; renvar(d2, vars(sex, age), vars(gender, age_year))

# Space-separated names are also accepted
d3 <- d; renvar(d3, "sex age", "gender age_year")

# Wildcard: remove or replace a common suffix
d4 <- d; renvar(d4, "*_pre", "*")
d5 <- d; renvar(d5, "*_pre", "baseline_*")

# Active-data syntax
usedf(d, quiet = TRUE); renvar(sex, gender)

Replace values under one or more conditions

Description

Replaces values in an explicit data frame or the active R4VN data frame.

Usage

replacevar(..., data = NULL)

Arguments

Details

missing(x) may be used inside conditions. Use . as a missing replacement.

Value

The edited data frame invisibly.

Examples

d <- data.frame(age = c(8, 15, 200, NA_real_))
usedf(d, quiet = TRUE)
replacevar(age, ., age > 120)
replacevar(age, 99, missing(age))

# Extended usage examples

d <- data.frame(age = c(8, 15, 200, NA_real_),
                sex = factor(c("M", "F", "M", "F")))
usedf(d, quiet = TRUE)

# Replace an impossible value by missing
replacevar(age, ., age > 120)

# Replace missing values
replacevar(age, 99, missing(age))

# Replace a factor value under a condition
replacevar(sex, "Female", sex == "F")

# Explicit-data syntax
replacevar(d, age, ., age > 120)

Save data according to file extension

Description

Saves active data or an explicitly supplied data frame. Variables and observations can be selected without changing the source data.

Usage

savedata(
  data = NULL,
  file = NULL,
  vars = NULL,
  obs = NULL,
  sheet = "Data",
  header = TRUE,
  sep = NULL,
  na = "",
  encoding = "UTF-8",
  replace = FALSE,
  quiet = FALSE,
  ...
)

Arguments

Details

Use savedata("file.csv") for active data, or savedata(data, "file.csv") for an explicit data frame.

Value

The normalized saved path invisibly.

Examples

d <- datasets::iris
usedf(d, quiet = TRUE)

# Base-R formats: short examples run during R CMD check.
f_csv <- tempfile(fileext = ".csv")
f_sub <- tempfile(fileext = ".csv")
f_rds <- tempfile(fileext = ".rds")

savedata(f_csv, quiet = TRUE)
savedata(
  f_sub,
  vars = vars(Sepal.Length, Species),
  obs = Species == "setosa",
  quiet = TRUE
)
savedata(d, f_rds, quiet = TRUE)
unlink(c(f_csv, f_sub, f_rds))
usedf(clear = TRUE, quiet = TRUE)

# Optional formats are guarded because their writers are in Suggests.
if (requireNamespace("writexl", quietly = TRUE) ||
    requireNamespace("openxlsx", quietly = TRUE)) {
  f <- tempfile(fileext = ".xlsx")
  savedata(d, f, sheet = "Data", quiet = TRUE)
  unlink(f)
}

if (requireNamespace("haven", quietly = TRUE)) {
  # Stata/SPSS impose variable-name rules. Use portable names here.
  d_haven <- data.frame(
    sepal_length = d$Sepal.Length,
    sepal_width = d$Sepal.Width,
    petal_length = d$Petal.Length,
    petal_width = d$Petal.Width,
    species = as.character(d$Species),
    stringsAsFactors = FALSE
  )
  f_dta <- tempfile(fileext = ".dta")
  f_sav <- tempfile(fileext = ".sav")
  savedata(d_haven, f_dta, quiet = TRUE)
  savedata(d_haven, f_sav, quiet = TRUE)
  unlink(c(f_dta, f_sav))
}

if (requireNamespace("jsonlite", quietly = TRUE)) {
  f <- tempfile(fileext = ".json")
  savedata(d, f, quiet = TRUE)
  unlink(f)
}


if (requireNamespace("arrow", quietly = TRUE)) {
  f <- tempfile(fileext = ".parquet")
  savedata(d, f, quiet = TRUE)
  unlink(f)
}

Tests of Standard Deviations and Variances

Description

Performs a one-sample chi-squared variance test or a two-sample F test.

Usage

sdtesti(
  n1,
  sd1,
  n2 = NULL,
  sd2 = NULL,
  sd0 = NULL,
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

sdtest(
  x,
  y = NULL,
  by = NULL,
  data = NULL,
  sd0 = NULL,
  alternative = c("two.sided", "less", "greater"),
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

sdtesti(40, 3, sd0 = 2.5)
sdtesti(40, 3, 35, 4)

Reshape Data Between Wide and Long Formats

Description

Converts repeated-measures data from wide to long format or from long to wide format using base R only. The active R4VN data frame is used when data is omitted.

Usage

shapevar(
  to = NULL,
  long = FALSE,
  wide = FALSE,
  id,
  vars,
  time = time,
  value = value,
  times = NULL,
  sep = "_",
  data = NULL,
  quiet = FALSE
)

Arguments

Details

Wide to long example:

⁠shapevar(long = TRUE, id = id, vars = vars(bp1, bp2, bp3),⁠ ⁠ time = visit, value = bp)⁠

Long to wide example:

shapevar(wide = TRUE, id = id, time = visit, vars = vars(bp))

For long-to-wide conversion, each id by time combination must be unique. Variables not included in vars are preserved when they are constant within each ID. A changing non-reshaped variable triggers an error rather than being silently discarded.

Value

The reshaped data frame invisibly.

Examples

wide <- data.frame(id = 1:2, sex = c("F", "M"),
                   bp1 = c(120, 130), bp2 = c(118, 128), bp3 = c(116, 125))
usedf(wide, quiet = TRUE)
shapevar(long = TRUE, id = id, vars = vars(bp1, bp2, bp3),
         time = visit, value = bp, quiet = TRUE)
long <- usedf(quiet = TRUE)
shapevar(wide = TRUE, id = id, time = visit, vars = vars(bp), quiet = TRUE)

Wilcoxon Signed-rank Test

Description

Performs a one-sample or paired Wilcoxon signed-rank test. With two variables, complete pairs are analyzed and the tested difference is x - y.

Usage

signrank(
  x,
  y = NULL,
  data = NULL,
  mu = 0,
  alternative = c("two.sided", "less", "greater"),
  exact = NULL,
  correct = TRUE,
  conf.int = TRUE,
  level = 0.95,
  digits = 3,
  p_digits = 3,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_stat.

Examples

d <- data.frame(
  before = c(18, 20, 15, 22, 17, 19, 24, 16),
  after  = c(15, 18, 14, 19, 16, 17, 20, 15)
)
signrank(before, data = d, mu = 18)
signrank(before, after, data = d)
signrank(before, after, data = d, alternative = "greater")

# Extended usage examples

d <- data.frame(
  before = c(18, 20, 15, 22, 17, 19, 24, 16),
  after = c(15, 18, 14, 19, 16, 17, 20, 15)
)

# One-sample signed-rank test against a specified median
signrank(before, data = d, mu = 18)

# Paired signed-rank test; the analyzed difference is before - after
signrank(before, after, data = d)
signrank(before, after, data = d, alternative = "greater")

# Approximation and confidence-interval controls
signrank(before, after, data = d, exact = FALSE, correct = FALSE)
signrank(before, after, data = d, conf.int = FALSE)

usedf(d)
signrank(before, after)

Quick numeric descriptive statistics

Description

Displays console descriptive statistics for one or more numeric variables. With nested grouping such as by = c(sex, agegroup), results are first separated by sex and then summarized for each age group within sex.

Usage

sum1(
  ...,
  by = NULL,
  data = NULL,
  detail = FALSE,
  digits = 2,
  missing = c("ifany", "no", "always"),
  overall = TRUE,
  show = TRUE,
  console = FALSE
)

Arguments

Value

Invisibly returns an object of class r4vn_quick.

Examples

d <- data.frame(
  sex = factor(rep(c("Female", "Male"), each = 4)),
  agegroup = factor(rep(c("<40", "40+"), 4)),
  age = c(25, 42, 31, 55, 29, 48, 36, 61),
  bmi = c(20.1, 23.5, 21.7, 25.2, 22.4, 26.1, NA, 24.8)
)
usedf(d, quiet = TRUE)

sum1(age)
sum1(age, bmi)
sum1(age, by = sex)
sum1(age, bmi, by = c(sex, agegroup))
sum1(age, detail = TRUE)

usedf(clear = TRUE, quiet = TRUE)

# Extended usage examples

d <- data.frame(
  sex = factor(rep(c("Female", "Male"), each = 6)),
  agegroup = factor(rep(c("<40", "40+"), 6)),
  province = factor(rep(c("HCMC", "Other"), each = 3, times = 2)),
  age = c(25, 42, 31, 55, 29, 48, 36, 61, 33, 47, 52, 40),
  bmi = c(20.1, 23.5, 21.7, 25.2, 22.4, 26.1, NA, 24.8, 23.0, 27.1, 22.8, 24.2),
  sbp = c(110, 128, 118, 145, 121, 138, 125, 151, 130, 142, 136, 129)
)
usedf(d)

# One or several numeric variables
sum1(age)
sum1(age, bmi, sbp)

# One and several nested grouping variables
sum1(age, by = sex)
sum1(age, bmi, by = c(sex, agegroup))
sum1(age, bmi, by = c(province, sex, agegroup))

# Detailed statistics
sum1(age, bmi, detail = TRUE)

# Missing grouping strata, decimal places, and overall display
sum1(age, by = sex, missing = "no", digits = 1)
sum1(age, by = sex, missing = "ifany", overall = FALSE)

# Retain results without printing and use explicit data
result <- sum1(age, bmi, by = sex, show = FALSE)
result$variables[[1]]$overall
sum1(d, age, bmi, by = c(sex, agegroup))
sum1(age, bmi, data = d)

Summarize an R4VN Survey Design

Description

Summarize an R4VN Survey Design

Usage

## S3 method for class 'r4vn_survey'
summary(object, ...)

Arguments

Value

A data frame describing the survey design.

Summarize a tabsurvey Result

Description

Summarize a tabsurvey Result

Usage

## S3 method for class 'r4vn_tabsurvey'
summary(object, ...)

Arguments

Value

A list containing the publication table, design summary, tests, effects, diagnostics, optional interpretation, and notes.

Extract Tables from an R4VN Survival Result

Description

Converts every non-empty report component of a tabsurv() result to a named list of data frames, including descriptive, life-table, and optional interpretation tables. For a hierarchical result, tables are retained separately for every outer stratum. This keeps survival export compatible with the existing tabexport() data-frame workflow without changing tabexport() itself. Examples are self-contained.

Usage

surv_tables(x)

Arguments

Value

A named list of data frames.

Examples

if (requireNamespace("survival", quietly = TRUE)) {
  d <- data.frame(time = c(5, 8, 10, 12, 15, 18),
                  event = c(1, 0, 1, 1, 0, 1),
                  group = factor(rep(c("A", "B"), 3)))
  s <- tabsurv(time, event, by = group, data = d, show = FALSE)
  z <- surv_tables(s)
  names(z)
}

Export an R4VN Survival Analysis

Description

Passes all non-empty tables from tabsurv() to the existing tabexport() function. It is also called internally when tabsurv(export=, file=) is used, so a complete Word/Excel/HTML report can be requested in one command.

Usage

survexport(
  x,
  export = NULL,
  file = NULL,
  open = FALSE,
  title = NULL,
  sheet = NULL,
  overwrite = TRUE,
  quiet = FALSE
)

Arguments

Value

The result returned by tabexport().

Examples

if (requireNamespace("survival", quietly = TRUE)) {
  d <- data.frame(time = c(5, 8, 10, 12, 15, 18),
                  event = c(1, 0, 1, 1, 0, 1),
                  group = factor(rep(c("A", "B"), 3)))
  s <- tabsurv(time, event, by = group, data = d, show = FALSE)
  # Export when a file is wanted, for example:
  # survexport(s, export = "xlsx", file = tempfile(fileext = ".xlsx"))
}

Define a Complex Survey Design for R4VN

Description

Creates a reusable complex-survey design for tabsurvey() and future survey-aware R4VN analyses. Designs may include sampling weights, strata, one or more clustering stages, finite-population corrections, or replicate weights. More than one named survey design can be stored in the same R session, which is useful when one data set provides different weights for interviews, examinations, laboratory subsamples, household analyses, and other analytic components.

Usage

surveyset(
  data = NULL,
  name = "survey",
  weight = NULL,
  strata = NULL,
  cluster = NULL,
  fpc = NULL,
  repweights = NULL,
  rep_type = NULL,
  weightscale = c("relative", "population"),
  nest = TRUE,
  pps = FALSE,
  variance = NULL,
  combined.weights = TRUE,
  rho = NULL,
  mse = getOption("survey.replicates.mse"),
  lonely = c("adjust", "fail", "average", "certainty", "remove"),
  active = TRUE
)

Arguments

Details

Weight meaning is explicit. R4VN deliberately does not assume that sum(weight) is a population size. Many public-use surveys provide normalized or relative weights. Set weightscale = "population" only when documentation for the survey confirms that the weight has an expansion/population interpretation.

Multiple named designs. A single survey file may contain different weights for different analytic subsamples. Define each one separately, for example "interview" and "fasting", and select it in tabsurvey(design = "fasting").

Survey weight versus other weights. The weight argument is intended for sampling/design/final survey weights. Propensity-score IPTW, frequency weights, analytic weights, and arbitrary regression weights are different concepts and should not be silently treated as survey sampling weights.

After changing the data. A survey design stores the data and design information that existed when surveyset() was called. If rows or variables are changed afterward, recreate the survey design so the design and analytic data remain aligned.

Value

An object of class r4vn_survey. The design is stored internally under name; when active = TRUE it also becomes the active survey design.

See Also

tabsurvey, vars, usedf

Other R4VN survey: tabsurvey()

Examples

set.seed(2026)
n <- 600
d <- data.frame(
  psu = sample(1:60, n, TRUE),
  strata = sample(1:8, n, TRUE),
  wt = runif(n, 0.5, 2.5),
  age = rnorm(n, 45, 14),
  sex = factor(sample(c("Female", "Male"), n, TRUE)),
  hypertension = factor(sample(c("No", "Yes"), n, TRUE,
                               prob = c(.72, .28)))
)

usedf(d)
surveyset(weight = wt, strata = strata, cluster = psu)

# A second named design for a hypothetical laboratory subsample
d$labwt <- d$wt * runif(n, .8, 1.2)
surveyset(d, name = "lab", weight = labwt,
          strata = strata, cluster = psu, active = FALSE)

# Inspect the active design
summary(surveyset(d, weight = wt, strata = strata, cluster = psu))

Shapiro-Wilk normality test

Description

Runs Shapiro-Wilk tests overall, for many variables, and/or within hierarchical groups. Each tested subgroup must have enough complete observations for the Shapiro-Wilk procedure.

Usage

swilk(x = NULL,
  vars = NULL,
  by = NULL,
  data = NULL,
  digits = 4,
  p_digits = 3,
  show = TRUE,
  console = FALSE)

Arguments