Extract a reference grid from objects created by {emmeans} and {marginaleffects}
Source: R/get_datagrid.R
get_datagrid.emmGrid.RdExtract a reference grid from objects created by {emmeans} and {marginaleffects}
Usage
# S3 method for class 'emmGrid'
get_datagrid(x, ...)Arguments
- x
An object created by a function such as
emmeans::emmeans(),marginaleffects::slopes(), etc.- ...
Currently not used
Details
Note that for {emmeans} inputs the results is a proper grid (all
combinations of values are represented), except when a nesting structure is
detected. Additionally, when the input is an emm_list object, the function
will rbind() the data-grids of all the elements in the input.
For {marginaleffects} inputs, the output may very well be a non-grid
result. See examples.
Examples
data("mtcars")
mtcars$cyl <- factor(mtcars$cyl)
mod <- glm(am ~ cyl + hp + wt,
family = binomial("logit"),
data = mtcars
)
em1 <- emmeans::emmeans(mod, ~ cyl + hp, at = list(hp = c(100, 150)))
get_datagrid(em1)
#> cyl hp
#> 1 4 100
#> 2 6 100
#> 3 8 100
#> 4 4 150
#> 5 6 150
#> 6 8 150
contr1 <- emmeans::contrast(em1, method = "consec", by = "hp")
get_datagrid(contr1)
#> contrast hp
#> 1 cyl6 - cyl4 100
#> 2 cyl8 - cyl6 100
#> 3 cyl6 - cyl4 150
#> 4 cyl8 - cyl6 150
eml1 <- emmeans::emmeans(mod, pairwise ~ cyl | hp, at = list(hp = c(100, 150)))
get_datagrid(eml1) # not a "true" grid
#> hp cyl contrast
#> 1 100 4 <NA>
#> 2 100 6 <NA>
#> 3 100 8 <NA>
#> 4 150 4 <NA>
#> 5 150 6 <NA>
#> 6 150 8 <NA>
#> 7 100 <NA> cyl4 - cyl6
#> 8 100 <NA> cyl4 - cyl8
#> 9 100 <NA> cyl6 - cyl8
#> 10 150 <NA> cyl4 - cyl6
#> 11 150 <NA> cyl4 - cyl8
#> 12 150 <NA> cyl6 - cyl8
mfx1 <- marginaleffects::slopes(mod, variables = "hp")
get_datagrid(mfx1) # not a "true" grid
#> rowid term contrast am cyl hp wt
#> 1 1 hp dY/dX 1 6 110 2.620
#> 2 2 hp dY/dX 1 6 110 2.875
#> 3 3 hp dY/dX 1 4 93 2.320
#> 4 4 hp dY/dX 0 6 110 3.215
#> 5 5 hp dY/dX 0 8 175 3.440
#> 6 6 hp dY/dX 0 6 105 3.460
#> 7 7 hp dY/dX 0 8 245 3.570
#> 8 8 hp dY/dX 0 4 62 3.190
#> 9 9 hp dY/dX 0 4 95 3.150
#> 10 10 hp dY/dX 0 6 123 3.440
#> 11 11 hp dY/dX 0 6 123 3.440
#> 12 12 hp dY/dX 0 8 180 4.070
#> 13 13 hp dY/dX 0 8 180 3.730
#> 14 14 hp dY/dX 0 8 180 3.780
#> 15 15 hp dY/dX 0 8 205 5.250
#> 16 16 hp dY/dX 0 8 215 5.424
#> 17 17 hp dY/dX 0 8 230 5.345
#> 18 18 hp dY/dX 1 4 66 2.200
#> 19 19 hp dY/dX 1 4 52 1.615
#> 20 20 hp dY/dX 1 4 65 1.835
#> 21 21 hp dY/dX 0 4 97 2.465
#> 22 22 hp dY/dX 0 8 150 3.520
#> 23 23 hp dY/dX 0 8 150 3.435
#> 24 24 hp dY/dX 0 8 245 3.840
#> 25 25 hp dY/dX 0 8 175 3.845
#> 26 26 hp dY/dX 1 4 66 1.935
#> 27 27 hp dY/dX 1 4 91 2.140
#> 28 28 hp dY/dX 1 4 113 1.513
#> 29 29 hp dY/dX 1 8 264 3.170
#> 30 30 hp dY/dX 1 6 175 2.770
#> 31 31 hp dY/dX 1 8 335 3.570
#> 32 32 hp dY/dX 1 4 109 2.780
mfx2 <- marginaleffects::slopes(mod, variables = c("hp", "wt"), by = "am")
get_datagrid(mfx2)
#> term contrast am
#> 1 hp dY/dX 0
#> 2 hp dY/dX 1
#> 3 wt dY/dX 0
#> 4 wt dY/dX 1
contr2 <- marginaleffects::avg_comparisons(mod)
get_datagrid(contr2) # not a "true" grid
#> term contrast
#> 1 cyl 6 - 4
#> 2 cyl 8 - 4
#> 3 hp +1
#> 4 wt +1