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,
transform = "none",
method = "pairwise",
...
)
model_emcontrasts(
model,
contrast = NULL,
by = NULL,
transform = "none",
method = "pairwise",
...
)
get_emmeans(model, by = "auto", transform = "response", ...)
model_emmeans(model, by = "auto", transform = "response", ...)
get_emtrends(model, trend = NULL, by = NULL, ...)
model_emtrends(model, trend = NULL, by = 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).
- 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.
- ...
Other arguments passed for instance to
insight::get_datagrid().- trend
A character indicating the name of the variable for which to compute the slopes.
Examples
# Basic usage
model <- lm(Sepal.Width ~ Species, data = iris)
get_emcontrasts(model)
#> No variable was specified for contrast estimation. Selecting `contrast =
#> "Species"`.
#> contrast estimate SE df t.ratio p.value
#> setosa - versicolor 0.658 0.0679 147 9.685 <.0001
#> setosa - virginica 0.454 0.0679 147 6.683 <.0001
#> versicolor - virginica -0.204 0.0679 147 -3.003 0.0088
#>
#> P value adjustment: tukey method for comparing a family of 3 estimates
# Dealing with interactions
model <- lm(Sepal.Width ~ Species * Petal.Width, data = iris)
# By default: selects first factor
get_emcontrasts(model)
#> No variable was specified for contrast estimation. Selecting `contrast =
#> "Species"`.
#> NOTE: Results may be misleading due to involvement in interactions
#> contrast estimate SE df t.ratio p.value
#> setosa - versicolor 1.590 0.394 144 4.039 0.0003
#> setosa - virginica 1.774 0.413 144 4.293 0.0001
#> versicolor - virginica 0.184 0.145 144 1.272 0.4131
#>
#> P value adjustment: tukey method for comparing a family of 3 estimates
# Can also run contrasts between points of numeric
get_emcontrasts(model, contrast = "Petal.Width", length = 3)
#> NOTE: Results may be misleading due to involvement in interactions
#> contrast estimate SE df t.ratio p.value
#> Petal.Width0.1 - Petal.Width1.3 -1.01 0.195 144 -5.180 <.0001
#> Petal.Width0.1 - Petal.Width2.5 -2.02 0.390 144 -5.180 <.0001
#> Petal.Width1.3 - Petal.Width2.5 -1.01 0.195 144 -5.180 <.0001
#>
#> Results are averaged over the levels of: Species
#> P value adjustment: tukey method for comparing a family of 3 estimates
# Or both
get_emcontrasts(model, contrast = c("Species", "Petal.Width"), length = 2)
#> contrast estimate SE df
#> setosa Petal.Width0.1 - versicolor Petal.Width0.1 1.8275 0.279 144
#> setosa Petal.Width0.1 - virginica Petal.Width0.1 1.5479 0.312 144
#> setosa Petal.Width0.1 - setosa Petal.Width2.5 -2.0093 0.977 144
#> setosa Petal.Width0.1 - versicolor Petal.Width2.5 -0.7012 0.268 144
#> setosa Petal.Width0.1 - virginica Petal.Width2.5 0.0325 0.112 144
#> versicolor Petal.Width0.1 - virginica Petal.Width0.1 -0.2797 0.406 144
#> versicolor Petal.Width0.1 - setosa Petal.Width2.5 -3.8368 0.957 144
#> versicolor Petal.Width0.1 - versicolor Petal.Width2.5 -2.5288 0.521 144
#> versicolor Petal.Width0.1 - virginica Petal.Width2.5 -1.7951 0.282 144
#> virginica Petal.Width0.1 - setosa Petal.Width2.5 -3.5571 0.967 144
#> virginica Petal.Width0.1 - versicolor Petal.Width2.5 -2.2491 0.399 144
#> virginica Petal.Width0.1 - virginica Petal.Width2.5 -1.5154 0.375 144
#> setosa Petal.Width2.5 - versicolor Petal.Width2.5 1.3080 0.954 144
#> setosa Petal.Width2.5 - virginica Petal.Width2.5 2.0417 0.922 144
#> versicolor Petal.Width2.5 - virginica Petal.Width2.5 0.7337 0.272 144
#> t.ratio p.value
#> 6.550 <.0001
#> 4.955 <.0001
#> -2.057 0.3158
#> -2.614 0.1005
#> 0.289 0.9997
#> -0.689 0.9829
#> -4.009 0.0013
#> -4.858 <.0001
#> -6.355 <.0001
#> -3.678 0.0044
#> -5.642 <.0001
#> -4.043 0.0012
#> 1.371 0.7441
#> 2.214 0.2379
#> 2.699 0.0817
#>
#> P value adjustment: tukey method for comparing a family of 6 estimates
# Or with custom specifications
estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)"))
#> Marginal Contrasts Analysis
#>
#> Level1 | Level2 | Difference | 95% CI
#> -------------------------------------------------------------------------------
#> setosa Petal.Width1 | setosa Petal.Width2 | -0.84 | [-2.05, 0.38]
#> setosa Petal.Width1 | versicolor Petal.Width1 | 1.63 | [ 0.68, 2.59]
#> setosa Petal.Width1 | versicolor Petal.Width2 | 0.58 | [-0.45, 1.61]
#> setosa Petal.Width1 | virginica Petal.Width1 | 1.73 | [ 0.68, 2.78]
#> setosa Petal.Width1 | virginica Petal.Width2 | 1.10 | [ 0.17, 2.04]
#> setosa Petal.Width2 | versicolor Petal.Width2 | 1.42 | [-0.77, 3.60]
#> setosa Petal.Width2 | virginica Petal.Width2 | 1.94 | [-0.20, 4.08]
#> versicolor Petal.Width1 | setosa Petal.Width2 | -2.47 | [-4.62, -0.32]
#> versicolor Petal.Width1 | versicolor Petal.Width2 | -1.05 | [-1.70, -0.41]
#> versicolor Petal.Width1 | virginica Petal.Width1 | 0.10 | [-0.45, 0.65]
#> versicolor Petal.Width1 | virginica Petal.Width2 | -0.53 | [-0.81, -0.25]
#> versicolor Petal.Width2 | virginica Petal.Width2 | 0.52 | [ 0.05, 0.99]
#> virginica Petal.Width1 | setosa Petal.Width2 | -2.57 | [-4.76, -0.38]
#> virginica Petal.Width1 | versicolor Petal.Width2 | -1.15 | [-1.83, -0.48]
#> virginica Petal.Width1 | virginica Petal.Width2 | -0.63 | [-1.10, -0.17]
#>
#> Level1 | SE | t(144) | p
#> ------------------------------------------------
#> setosa Petal.Width1 | 0.41 | -2.06 | 0.166
#> setosa Petal.Width1 | 0.32 | 5.09 | < .001
#> setosa Petal.Width1 | 0.35 | 1.68 | 0.191
#> setosa Petal.Width1 | 0.35 | 4.93 | < .001
#> setosa Petal.Width1 | 0.31 | 3.52 | 0.005
#> setosa Petal.Width2 | 0.73 | 1.94 | 0.166
#> setosa Petal.Width2 | 0.72 | 2.71 | 0.038
#> versicolor Petal.Width1 | 0.72 | -3.43 | 0.005
#> versicolor Petal.Width1 | 0.22 | -4.86 | < .001
#> versicolor Petal.Width1 | 0.19 | 0.54 | 0.589
#> versicolor Petal.Width1 | 0.09 | -5.72 | < .001
#> versicolor Petal.Width2 | 0.16 | 3.31 | 0.007
#> virginica Petal.Width1 | 0.73 | -3.50 | 0.005
#> virginica Petal.Width1 | 0.23 | -5.13 | < .001
#> virginica Petal.Width1 | 0.16 | -4.04 | < .001
#>
#> Marginal contrasts estimated at Species, Petal.Width
#> p-value adjustment method: Holm (1979)
# Or modulate it
get_emcontrasts(model, by = "Petal.Width", length = 4)
#> 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)
}
#> Welcome to emmeans.
#> Caution: You lose important information if you filter this package's results.
#> See '? untidy'
#> 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