Index of vignette topics
emmeans package, Version 1.10.3
Source:vignettes/vignette-topics.Rmd
vignette-topics.Rmd# {##}
- a probability scale, and whatever link was used (say, probit) has already been
- accounted for, so is not “remembered” for possible later back-transformation. In
add_grouping()addl.vars-
adjust - Adjusted means
- Adjusted R-squared
afex_aovobjects- Alias matrix
- Analysis of subsets of data
- Analysis of variance
aovListobjectsappx-satterthwaitemethod- Arguments
-
as.mcmc() - ASA Statement on P values
- Asymptotic tests
- ATOM
averagingmodels
[ {#[}
< {#<}
- Causal inference
cld()clmmodels- coda package
coef()- Cohen’s d
- Compact letter displays
- Comparison arrows
- Comparisons
- Comparisons result in
(nothing) - Confidence intervals
confint()- Confounded effects
- Confounding
conseccontrasts- Constrained marginal means
- Consultants
- Containment d.f.
-
contrast() - Contrasts
- Controlled experiments
- Count regression
- Counterfactuals
cov.reduce- Covariates
- cross-group comparisons
cross.adjust
B
effcontrastseff_size()- Effect size
-
emm_basis() emm_listobjectemm_options().emmcfunctions- emmeans package
-
emmeans() -
emmGridobjects -
emmip() - EMMs
-
emtrends() estHook- Estimability
- Estimability issues
- Estimable functions
- Estimated marginal means
- estimating marginal means will be averages of the probabilities in the reference
- Examples
auto.noise- Bayesian model
-
cbpp ChickWeightcowsfeedlot-
fiber framing- Gamma regression
InsectSpraysInsurance- Insurance claims (SAS)
- Logistic regression
lqsobjectsMOats-
mtcars - Multivariate
- Nested fixed effects
-
neuralgia -
nutrition Oats-
oranges - Ordinal model
-
pigs rlmobjects- Robust regression
- Split-plot experiment
- Unbalanced data
-
warpbreaks - Welch’s t comparisons
wine
- Expected marginal means
- Experimental versus observational data
- Exporting output
- Extending emmeans
C
D
- G-Computation
gammodelsgamlssmodels- GEE models
- Generalized additive models
- Generalized linear models
- Geometric means
- Get the model right first
get_emm_option()- ggplot2 package
- GIGO (garbage in, garbage out)
glmxxx modelsglsmodels- Graphical displays
- grid
- Grouping factors
- Grouping into separate sets
I
- Labels
- Large models
- Latin squares
- Least-squares means
- Levels
- Linear functions
- Link functions
lmemodels-
lmerModmodels - Logistic-like regression
- Logistic regression
- LSD
J
make.tran()mcmcobjects- Means
- Mediating covariates
- Memory usage
miramodelsmiscattribute and argument- Missing cells
mlmmodelsmmermodels-
modeargument - Model
- Model averaging
- Modeling
- Modelling
- Models
- models for ordinal data allow
for a
"prob"mode that produces estimates of - Multi-factor studies
- Multinomial models
- Multiple imputation
- Multiplicity adjustments
- Multivariate contrasts
- Multivariate models
- Multivariate t
(
"mvt") adjustment mvcontrast()- mvtnorm package
X
L
- Observational data
- Observational versus experimental data
- Odds ratios
- Offsets
- Only one mean
opt.digitsoption- Options
- Ordinal models
M
- P values
pairs()-
pairwise ~ factors - Pairwise comparisons
pairwisecontrasts- Pairwise P-value plots
params- Percentage differences
-
plot() plot.emmGrid()- Plots
+operator- Poisson regression
polregmodels- Polynomial regression
- Pooled t
postGridHook- Practices, recommended
- Precision
- Predictions
- preempting any timing choices you might otherwise have made about handling the
print.summary_emm()- probabilities for each ordinal level. The reference grid comprises estimates on
- Probit regression
pwpm()pwpp()
O
- R-squared
- Random predictors
- Random slopes
- Rank deficiency
- Ratios
rbind()- Re-gridding
- Re-labeling
- Recommended practices
-
recover_data() -
recover_data.call() -
ref_grid() - Reference grid
- Reference grids
- Region of practical equivalence
- Registering
recover_dataandemm_basismethods regridargument-
regrid() regrid()that takes place at the time the reference grid is constructed,- Residual plots
- Response scale
- Response transformations
revpairwisecontrastsrg.limitoption- Risk ratios
- RMarkdown
- ROPE
- rsm package
rstanarm
P
- Sample size, displaying
- Sandwich estimators
- Satterthwaite d.f.
"scale"typescale()- Selecting results
- Sidak adjustment
- Significance
- Similar things happen with certain options with multinomial models,
simple = "each"- Simple comparisons
- Simpson’s paradox
- so they will be different than what you would have obtained by keeping
- Some model classes provide
special argument(s) (typically
mode) that may cause -
specs - Standardized response
stanregobjects- * gazing (star gazing)
- Startup options
- Statistical consultants
- Statistics is hard
str()-
submodel - Subsets of data
-
summary() -
summary_emmobject
Q
- t tests vs. z tests
-
test() - Tests
- that sense, when we use
mode = "prob", it is sort of like an implied call to - the link scale.
- things on the link scale and then computing the probabilities after averaging on
- Too few means
- transformation. If there are one or more factors that are averaged over in
- Transformations
- transformations or links to be handled early. For example, cumulative link
- Transformations|Implied re-gridding
- Transformations|Special modes
- Trends
trt.vs.ctrlcontrasts- Tukey adjustment
typetype = "response"type = "scale"- Type II analysis
- Type III tests
S
- Variables that are not predictors
vcov.vcovHook- Vignettes
W
- z tests
- zero-inflated, or hurdle models. Those special modes are a great convenience
zeroinflmodels
Index generated by the vigindex package.