Title: | Graphical Check for Proportional Odds Assumption |
Version: | 0.1.1 |
Description: | Implements the method described at the UCLA Statistical Consulting site https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/ for checking if the proportional odds assumption holds for a cumulative logit model. |
License: | MIT + file LICENSE |
Encoding: | UTF-8 |
LazyData: | true |
RoxygenNote: | 7.1.1 |
Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown |
Config/testthat/edition: | 3 |
Imports: | dplyr, tidyr (≥ 1.0.0), ggplot2, assertthat, stats, rlang, magrittr, stringr |
Depends: | R (≥ 2.10) |
VignetteBuilder: | knitr |
NeedsCompilation: | no |
Packaged: | 2021-06-06 00:08:55 UTC; melissa |
Author: | Melissa Wong |
Maintainer: | Melissa Wong <melissa.wong.stats@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2021-06-06 04:10:02 UTC |
Pipe operator
Description
See magrittr::%>%
for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling rhs(lhs)
.
National Health and Nutrition Examination Survey 2011-2012
Description
A dataset used in the UCLA Statistical Consulting Survey Analysis in R guide https://stats.idre.ucla.edu/r/seminars/survey-data-analysis-with-r/
Usage
nhanes
Format
A data frame with 9756 rows and 16 variables:
- seqn
Respondent sequence number
- ridageyr
Age in years at screening
- riagendr
Gender
- dmdmartl
Marital status
- dmdeduc2
Education level - Adults 20+
- sdmvpsu
Masked variance pseudo-PSU
- sdmvstra
Masked variance pseudo-stratum
- wtint2yr
Full sample 2 year interview weight
- female
Gender
- hsq496
How many days feel anxious
- hsq571
SP donated blood in the past 12 months
- hsd010
General health condition
- pad630
Minutes moderate-intensity work
- pad675
Minutes moderate recreational activities
- paq665
Moderate recreational activities
- pad680
Minutes sedentary activity
Source
https://wwwn.cdc.gov/nchs/nhanes/Search/DataPage.aspx?Component=Demographics&CycleBeginYear=2011
Simulated data for ordinal logistic regression example.
Description
A dataset used in the UCLA Statistical Consulting Ordinal Logistic Regression Example https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/
Usage
ologit
Format
A data frame with 400 rows and 4 variables:
- apply
Likelihood of applying to graduate school
- pared
Indicator for whether at least 1 parent has a graduate degree
- public
Indicator for whether undergraduate institution is public or private
- gpa
Student's grade point average
Source
https://stats.idre.ucla.edu/stat/data/ologit.dta
Graphical check for proportional odds assumption
Description
Generates the plots described in https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/ for checking if the proportional odds assumption holds for a cumulative logit model.
Usage
## S3 method for class 'pomcheck'
plot(x, legend.position = "none", ...)
Arguments
x |
a pomcheck object |
legend.position |
the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector) |
... |
currently unused |
Value
None
See Also
Examples
plot(pomcheck(Species ~ Sepal.Width, iris))
Graphical check for proportional odds assumption
Description
Implements the method described in https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/ for checking if the proportional odds assumption holds for a cumulative logit model.
Usage
pomcheck(object, ...)
## Default S3 method:
pomcheck(object, x, data, ...)
## S3 method for class 'formula'
pomcheck(formula, data, ...)
Arguments
object |
character string for response |
... |
currently unused |
x |
vector of character string(s) for explanatory variable(s) |
data |
data frame containing the variables |
formula |
formula of the form y ~ x1 + x2 + ... |
Value
an object of class 'pomcheck'
Methods (by class)
-
default
: default -
formula
: supports formula specification
See Also
Examples
pomcheck(Species ~ Sepal.Length, iris)
pomcheck(Species ~ Sepal.Length + Sepal.Width, iris)
pomcheck(object="Species", x="Sepal.Length", iris)
pomcheck(object="Species", x=c("Sepal.Length", "Sepal.Width"), iris)