Easy 'emmeans' and 'emtrends'
Source:R/get_emcontrasts.R, R/get_emmeans.R, R/get_emtrends.R
get_emmeans.RdThe get_emmeans() function is a wrapper to facilitate the usage of
emmeans::emmeans() and emmeans::emtrends(), providing a somewhat simpler
and intuitive API to find the specifications and variables of interest. It is
meanly made to for the developers to facilitate the organization and
debugging, and end-users should rather use the estimate_*() series of
functions.
Usage
get_emcontrasts(
model,
contrast = NULL,
by = NULL,
fixed = NULL,
transform = "none",
method = "pairwise",
at = NULL,
...
)
model_emcontrasts(
model,
contrast = NULL,
by = NULL,
fixed = NULL,
transform = "none",
method = "pairwise",
at = NULL,
...
)
get_emmeans(
model,
by = "auto",
fixed = NULL,
transform = "response",
levels = NULL,
modulate = NULL,
at = NULL,
...
)
model_emmeans(
model,
by = "auto",
fixed = NULL,
transform = "response",
levels = NULL,
modulate = NULL,
at = NULL,
...
)
get_emtrends(
model,
trend = NULL,
by = NULL,
fixed = NULL,
levels = NULL,
modulate = NULL,
at = NULL,
...
)
model_emtrends(
model,
trend = NULL,
by = NULL,
fixed = NULL,
levels = NULL,
modulate = NULL,
at = NULL,
...
)Arguments
- model
A statistical model.
- contrast
A character vector indicating the name of the variable(s) for which to compute the contrasts.
- by
The predictor variable(s) at which to evaluate the desired effect / mean / contrasts. Other predictors of the model that are not included here will be collapsed and "averaged" over (the effect will be estimated across them).
- fixed
A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.
- transform
Is passed to the
typeargument inemmeans::emmeans(). See this vignette. Can be"none"(default for contrasts),"response"(default for means),"mu","unlink","log"."none"will leave the values on scale of the linear predictors."response"will transform them on scale of the response variable. Thus for a logistic model,"none"will give estimations expressed in log-odds (probabilities on logit scale) and"response"in terms of probabilities.- method
Contrast method. See same argument in emmeans::contrast.
- at
Deprecated, use
byinstead.- ...
Other arguments passed for instance to
insight::get_datagrid().- levels, modulate
Deprecated, use
byinstead.- trend
A character indicating the name of the variable for which to compute the slopes.
Examples
if (require("emmeans", quietly = TRUE)) {
# Basic usage
model <- lm(Sepal.Width ~ Species, data = iris)
get_emcontrasts(model)
# Dealing with interactions
model <- lm(Sepal.Width ~ Species * Petal.Width, data = iris)
# By default: selects first factor
get_emcontrasts(model)
# Can also run contrasts between points of numeric
get_emcontrasts(model, contrast = "Petal.Width", length = 3)
# Or both
get_emcontrasts(model, contrast = c("Species", "Petal.Width"), length = 2)
# Or with custom specifications
estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)"))
# Can fixate the numeric at a specific value
get_emcontrasts(model, fixed = "Petal.Width")
# Or modulate it
get_emcontrasts(model, by = "Petal.Width", length = 4)
}
#> No variable was specified for contrast estimation. Selecting `contrast =
#> "
#> Species
#> "`.
#> No variable was specified for contrast estimation. Selecting `contrast =
#> "
#> Species
#> "`.
#> NOTE: Results may be misleading due to involvement in interactions
#> NOTE: Results may be misleading due to involvement in interactions
#> No variable was specified for contrast estimation. Selecting `contrast =
#> "
#> Species
#> "`.
#> No variable was specified for contrast estimation. Selecting `contrast =
#> "
#> Species
#> "`.
#> Petal.Width = 0.1:
#> contrast estimate SE df t.ratio p.value
#> setosa - versicolor 1.8275 0.279 144 6.550 <.0001
#> setosa - virginica 1.5479 0.312 144 4.955 <.0001
#> versicolor - virginica -0.2797 0.406 144 -0.689 0.7703
#>
#> Petal.Width = 0.9:
#> contrast estimate SE df t.ratio p.value
#> setosa - versicolor 1.6544 0.288 144 5.743 <.0001
#> setosa - virginica 1.7125 0.325 144 5.276 <.0001
#> versicolor - virginica 0.0581 0.208 144 0.280 0.9577
#>
#> Petal.Width = 1.7:
#> contrast estimate SE df t.ratio p.value
#> setosa - versicolor 1.4812 0.600 144 2.467 0.0390
#> setosa - virginica 1.8771 0.597 144 3.144 0.0057
#> versicolor - virginica 0.3959 0.113 144 3.502 0.0018
#>
#> Petal.Width = 2.5:
#> contrast estimate SE df t.ratio p.value
#> setosa - versicolor 1.3080 0.954 144 1.371 0.3587
#> setosa - virginica 2.0417 0.922 144 2.214 0.0722
#> versicolor - virginica 0.7337 0.272 144 2.699 0.0212
#>
#> P value adjustment: tukey method for comparing a family of 3 estimates
model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris)
if (require("emmeans", quietly = TRUE)) {
# By default, 'by' is set to "Species"
get_emmeans(model)
# Overall mean (close to 'mean(iris$Sepal.Length)')
get_emmeans(model, by = NULL)
# One can estimate marginal means at several values of a 'modulate' variable
get_emmeans(model, by = "Petal.Width", length = 3)
# Interactions
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
get_emmeans(model)
get_emmeans(model, by = c("Species", "Petal.Length"), length = 2)
get_emmeans(model, by = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2)
}
#> We selected `by = c("Species")`.
#> We selected `by = c("Species")`.
#> NOTE: Results may be misleading due to involvement in interactions
#> Species Petal.Length emmean SE df lower.CL upper.CL
#> setosa 1 3.25 0.1280 144 2.995 3.50
#> versicolor 1 1.55 0.3170 144 0.924 2.18
#> virginica 1 1.91 0.3750 144 1.165 2.65
#> setosa 3 4.02 0.4030 144 3.229 4.82
#> versicolor 3 2.30 0.1290 144 2.043 2.55
#> virginica 3 2.38 0.2140 144 1.954 2.80
#> setosa 5 4.80 0.9220 144 2.979 6.62
#> versicolor 5 3.05 0.0840 144 2.881 3.21
#> virginica 5 2.84 0.0636 144 2.719 2.97
#>
#> Confidence level used: 0.95
if (require("emmeans")) {
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
get_emtrends(model)
get_emtrends(model, by = "Species")
get_emtrends(model, by = "Petal.Length")
get_emtrends(model, by = c("Species", "Petal.Length"))
model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris)
get_emtrends(model)
get_emtrends(model, by = "Sepal.Width")
}
#> No numeric variable was specified for slope estimation. Selecting `trend
#> = "
#> Petal.Length
#> "`.
#> No numeric variable was specified for slope estimation. Selecting `trend
#> = "
#> Petal.Length
#> "`.
#> No numeric variable was specified for slope estimation. Selecting `trend
#> = "
#> Petal.Length
#> "`.
#> NOTE: Results may be misleading due to involvement in interactions
#> No numeric variable was specified for slope estimation. Selecting `trend
#> = "
#> Petal.Length
#> "`.
#> No numeric variable was specified for slope estimation. Selecting `trend
#> = "
#> Sepal.Width
#> "`.
#> No numeric variable was specified for slope estimation. Selecting `trend
#> = "
#> Sepal.Width
#> "`.
#> Sepal.Width Sepal.Width.trend SE df lower.CL upper.CL
#> 2.00 7.484 5.420 145 -3.225 18.192
#> 2.27 3.779 2.090 145 -0.358 7.916
#> 2.53 0.831 0.765 145 -0.681 2.342
#> 2.80 -1.337 0.706 145 -2.732 0.058
#> 3.07 -2.699 0.543 145 -3.772 -1.626
#> 3.33 -3.231 0.607 145 -4.430 -2.032
#> 3.60 -2.909 0.838 145 -4.564 -1.254
#> 3.87 -1.708 1.010 145 -3.702 0.287
#> 4.13 0.398 2.390 145 -4.330 5.125
#> 4.40 3.431 5.800 145 -8.028 14.890
#>
#> Confidence level used: 0.95