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Index of vignette topics

emmeans package, Version 1.10.5

Jump to: A B C D E F G H I J K L M N O P Q R S T U V W X Z

C

Causal inference
cld()
clm models
coda package
coef()
Cohen’s d
Compact letter displays
Comparison arrows
- Derivation
Comparisons
- Back-transforming
- Displaying as groups
- Displaying P values
- Graphical
  - How arrows are determined
- with logs
- Obtaining differences rather than ratios
- with overlapping CIs
Comparisons result in (nothing)
Confidence intervals
- Overlapping
confint()
Confounded effects
Confounding
consec contrasts
Constrained marginal means
Consultants
Containment d.f.
contrast()
- adjust
- Changing defaults
- combine
- interaction
- Linear functions
- simple
  - in confidence-intervals: simple
  - in interactions: simple
Contrasts
- of other contrasts
- Custom
- Formula
- Multivariate
- Pairwise
- Polynomial
- Tests of
  - with transformations
Controlled experiments
Count regression
Counterfactuals
cov.reduce
Covariates
- Adjusted
- cov.keep
- cov.reduce
- Derived
- emmeans() doesn’t work
- Interacting with factors
- Mediating
cross-group comparisons
cross.adjust

D

E

eff contrasts
eff_size()
Effect size
emm_basis()
- Arguments and returned value
- Communicating with recover_data()
- Dispatching
- Hook functions
- for lqs objects
- Re-gridded
- for rsm objects
emm_list object
emm_options()
.emmc functions
emmeans package
- Exporting extensions to
- Quick-start guide
emmeans()
- And the underlying model
- Arguments passed to ref_grid()
- Changing defaults
- Fastest way to get wrong answers
- Surprising results from
- weights
  - in basics: weights
  - in messy-data: weights
- With transformations
emmGrid objects
- Accessing data
- Combining and subsetting
- Modifying
- Setting defaults for
emmip()
- in basics: emmip
- in basics: plots
- in interactions: factors
- nested factors
EMMs
- Projecting to a submodel
- What are they?
emtrends()
- in interactions: covariates
- in interactions: oranges
estHook
Estimability
Estimability issues
Estimable functions
Estimated marginal means
- Definition of
- Demonstrated
- Manually computed
- Reference grid
- vs. ordinary marginal means
Examples
- auto.noise
- Bayesian model
- cbpp
  - in sophisticated: mcmc
  - in transformations: cbpp
- ChickWeight
- cows
- feedlot
- fiber
  - in interactions: covariates
  - in transformations: stdize
- framing
- Gamma regression
- InsectSprays
- Insurance
- Insurance claims (SAS)
- Logistic regression
- lqs objects
- MOats
- mtcars
  - in basics: depcovs
  - in messy-data: nuis.example
- Multivariate
- Nested fixed effects
- neuralgia
  - counterfactuals
  - in transformations: links
  - in transformations: trangraph
- nutrition
  - submodel
  - weights
- Oats
- oranges
  - in comparisons: formulas
  - in interactions: oranges
- Ordinal model
- pigs
  - in basics: pigs
  - in confidence-intervals: summary
  - in transformations: altscale
  - in transformations: overview
  - in transformations: pigs-biasadj
- rlm objects
- Robust regression
- Split-plot experiment
- Unbalanced data
- warpbreaks
  - in confidence-intervals: cross-adjust
  - in transformations: tranlink
  - in utilities: relevel
- Welch’s t comparisons
- wine
Expected marginal means
- Foundations of
- Reliance on a model
Experimental versus observational data
Exporting output
Extending emmeans
- Exports useful to developers
- Restrictions

F

F test
- vs. pairwise comparisons
- Role in post hoc tests
Factors
- Mediating
Formatting results
Foundations of EMMs
Frequently asked questions

G

H

I

J

joint
joint_tests()

K

L

Labels
- Changing
Large models
Latin squares
Least-squares means
Levels
- Changing
Linear functions
Link functions
lme models
lmerMod models
Logistic-like regression
- Non-logit links
Logistic regression
LSD
- protected

M

make.tran()
mcmc objects
Means
- Generalized
- Weighted
Mediating covariates
Memory usage
- Limiting
mira models
misc attribute and argument
misc$regrid.flag
Missing cells
mlm models
mmer models
mode argument
- implied re-gridding
Model
- Get it right first
- Importance of
  - in basics: models
  - in FAQs: fastest
- Importance of getting it right
Model averaging
Modeling
- pigs example
- Response transformation
Modelling
Models
- Constrained
- Large
- Quick reference
- Unsupported
Multi-factor studies
Multinomial models
Multiple imputation
Multiplicity adjustments
Multivariate contrasts
Multivariate models
- in basics: multiv
- in interactions: oranges
- in models: M
- with submodel
Multivariate t ("mvt") adjustment
mvcontrast()
mvtnorm package

N

O

Observational data
- in basics: weights
- in messy-data: issues
Observational versus experimental data
Odds ratios
- but I wanted differences
Offsets
- Models with offsets
- offset() term vs. offset argument in model
Only one mean
opt.digits option
Options
- Startup
Ordinal models

P

P values
- Adjusted
- Adjustment is ignored
- Interpreting
pairs()
pairwise ~ factors
- Why you should avoid this
Pairwise comparisons
- Matrix displays
pairwise contrasts
Pairwise P-value plots
params
Percentage differences
plot()
- nested factors
plot.emmGrid()
Plots
- of confidence intervals
- of EMMs
- Enhancing with ggplot2 functions
- Interaction-style
+ operator
Poisson regression
- Surprising results
polreg models
Polynomial regression
Pooled t
- Instead of Welch’s t
postGridHook
Practices, recommended
Precision
- Displaying results with more digits
Predictions
- Bayesian models
  - in predictions: bayes
  - in sophisticated: predict-mcmc
- Error SD
- graphics
- on Particular strata
- Posterior predictive distribution
- Reference grid
- Total SD
print.summary_emm()
Probit regression
- Odds or risk ratios
pwpm()
pwpp()

Q

R

R-squared
Random predictors
- Accessing levels
Random slopes
- Bias adjustment
Rank deficiency
Ratios
- but I wanted differences
rbind()
Re-gridding
- Implied
Re-labeling
Recommended practices
recover_data()
- Communicating with emm_basis()
- data and params arguments
- Dispatching
- Error handling
- for lqs objects
- for rsm objects
recover_data.call()
- frame argument
ref_grid()
- at
- cov.reduce
  - in basics: plot.emmGrid
  - in basics: RG
- mult.name
- nesting
- offset
- regrid argument
Reference grid
- Covariates
- Defined
- Indicator variables
- Predictions on
Reference grids
- Prediction on
Region of practical equivalence
Registering recover_data and emm_basis methods
regrid argument
regrid()
- probit or other lon-logit models
- regrid vs. type
- to obtain risk ratios
- transform = "log"
regrid.flag
Residual plots
Response scale
Response transformations
revpairwise contrasts
rg.limit option
Risk ratios
RMarkdown
ROPE
rsm package
rstanarm

S

Sample size, displaying
Sandwich estimators
Satterthwaite d.f.
- in models: K
- in models: L
"scale" type
scale()
Selecting results
Sidak adjustment
Significance
- Assessing
simple = "each"
Simple comparisons
- in confidence-intervals: simple
- in FAQs: interactions
- in interactions: simple
Simpson’s paradox
specs
- Formula
Standardized response
stanreg objects
* gazing (star gazing)
Startup options
Statistical consultants
Statistics is hard
str()
submodel
- "minimal" and "type2"
- in a multivariate model
- in messy-data: submodels
- in xplanations: submodels
Subsets of data
summary()
- adjust
- in basics: emmobj
- Bayesian models
  - in confidence-intervals: summary
  - in models: S
- Calculated columns
- in confidence-intervals: summary
- HPD intervals
- hpd.summary()
- infer
  - in comparisons: pairwise
  - in confidence-intervals: summary
- Show sample size
- type = "unlink"
summary_emm object
- As a data frame

U

V

W

X

xtable method

Z

Index generated by the vigindex package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.