Introduction: Adjusted Predictions And Marginal Means At Specific Values
Source:vignettes/introduction_effectsatvalues.Rmd
introduction_effectsatvalues.Rmd
Adjusted predictions and marginal means at specific values or levels
This vignettes shows how to calculate adjusted predictions at specific values or levels for the terms of interest. It is recommended to read the general introduction first, if you haven’t done this yet.
The terms
argument not only defines the model terms
(i.e. focal variables) of interest, but each model term can be limited
to certain “meaningful” (or “representative”) values. This allows to
compute and plot adjusted predictions for (grouping) terms at specific
values only, or to define values for the main effect of interest.
Summary of most important points:

The
terms
argument is not only used to define the focal terms, but also allows to specify meaningful values, at which predictions are calculated. 
terms
can be a character vector, a list, a formula, or a data frame. If a character vector, the values for the focal terms are placed in square brackets directly after the term name.  Although providing a list is probably the most Rnative way to define the focal terms and meaningful values, providing a character vector additionally allows to use predefined "shortcuts". That's why this is the preferred way demonstrated throughout the packagedocumentation.

Nonfocal terms can be fixed at specific values using the
condition
argument.
There are several options to define these meaningful values:
A character vector, specifying the names of the focal terms. This is the preferred and probably most flexible way to specify focal terms.
A list, where each element is a named vector, specifying the focal terms and their values. This is the “classical” R way to specify focal terms.
A formula, e.g.
terms = ~ x + z
, which is internally converted to a character vector. This is probably the least flexible way, as you cannot specify representative values for the focal terms.A data frame representig a “data grid” or “reference grid”. Predictions are then made for all combinations of the variables in the data frame.
When terms
is specified as character vector, values
always should be placed in square brackets directly after the term name
and can vary for each model term. The following examples show how to
specify values for the terms
argument.
 Concrete values are separated by a comma:
terms = "c172code [1,3]"
. For factors, you could also use factor levels, e.g.terms = "Species [setosa,versicolor]"
. Ifterms
is a named list, it would be specified like this:terms = list(c172code = c(1, 3))
orterms = list(c172code = c(1, 3), Species = c("setosa", "versicolor"))
. As a data frame, this would be:
terms < data.frame(
c172code = c(1, 3, 1, 3),
Species = c("setosa", "setosa", "versicolor", "versicolor"),
stringsAsFactors = FALSE
)
terms
#> c172code Species
#> 1 1 setosa
#> 2 3 setosa
#> 3 1 versicolor
#> 4 3 versicolor
Ranges are specified with a colon:
terms = c("c12hour [30:80]", "c172code [1,3]")
. This would plot all values from 30 to 80 for the variable c12hour. By default, the step size is 1, i.e.[1:4]
would create the range1, 2, 3, 4
. You can choose different step sizes withby
, e.g.[1:4 by=.5]
. As named list, this would beterms = list(c12hour = 30:80)
orterms = list(c12hour = seq(1, 4, 0.5))
.Convenient shortcuts to calculate common values like mean +/ 1 SD (
terms = "c12hour [meansd]"
), quartiles (terms = "c12hour [quart]"
) or minumum and maximum values (terms = "c12hour [minmax]"
). Seevalues_at()
for the different options.A function name. The function is then applied to all unique values of the indicated variable, e.g.
terms = "hp [exp]"
. You can also define own functions, and pass the name of it to theterms
values, e.g.terms = "hp [own_function]"
.A variable name. The values of the variable are then used to define the
terms
values, e.g. first, a vector is defined:v = c(1000, 2000, 3000)
and then,terms = "income [v]"
.If the first variable specified in
terms
is a numeric vector, for which no specific values are given, a “pretty range” is calculated (seepretty_range()
), to avoid memory allocation problems for vectors with many unique values. To select all values, use the[all]
tag, e.g.terms = "mpg [all]"
. If a numeric vector is specified as second or third variable interm
(i.e. if this vector represents a grouping structure), representative values (seevalues_at()
) are chosen, which is typically mean +/ SD.To create a pretty range that should be smaller or larger than the default range (i.e. if no specific values would be given), use the
n
tag, e.g.terms = "age [n=5]"
orterms = "age [n = 12]"
. Larger values forn
return a larger range of predicted values.Especially useful for plotting group levels of random effects with many levels, is the
sample
option, e.g.terms = "Subject [sample=9]"
, which will sample nine values from all possible values of the variableSubject
.
Specific values and value range
library(ggeffects)
library(ggplot2)
data(efc, package = "ggeffects")
fit < lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
mydf < predict_response(fit, terms = c("c12hour [30:80]", "c172code [1,3]"))
mydf
#> # Predicted values of Total score BARTHEL INDEX
#>
#> c172code: low level of education
#>
#> c12hour  Predicted  95% CI
#> 
#> 30  67.15  64.03, 70.26
#> 38  65.12  62.05, 68.19
#> 47  62.84  59.80, 65.88
#> 55  60.81  57.77, 63.86
#> 63  58.79  55.72, 61.86
#> 80  54.48  51.28, 57.69
#>
#> c172code: high level of education
#>
#> c12hour  Predicted  95% CI
#> 
#> 30  68.58  65.41, 71.76
#> 38  66.56  63.39, 69.73
#> 47  64.28  61.08, 67.48
#> 55  62.25  59.00, 65.50
#> 63  60.23  56.90, 63.55
#> 80  55.92  52.38, 59.46
#>
#> Adjusted for:
#> * neg_c_7 = 11.84
#> * c161sex = 1.76
ggplot(mydf, aes(x, predicted, colour = group)) + geom_line()
When variables are, for instance, logtransformed, ggeffects
automatically backtransforms predictions to the original scale of the
response and predictors, making the predictions directly interpretable.
However, sometimes it might be useful to define own value ranges. In
such situation, specify the range in the
terms
argument.
data(mtcars)
mpg_model < lm(mpg ~ log(hp), data = mtcars)
# xvalues and predictions based on the full range of the original "hp"values
predict_response(mpg_model, "hp")
#> # Predicted values of mpg
#>
#> hp  Predicted  95% CI
#> 
#> 50  30.53  27.84, 33.22
#> 85  24.82  23.21, 26.42
#> 120  21.11  19.91, 22.30
#> 155  18.35  17.11, 19.59
#> 195  15.88  14.36, 17.41
#> 230  14.10  12.29, 15.92
#> 265  12.58  10.48, 14.68
#> 335  10.06  7.45, 12.66
# xvalues and predictions based on "hp"values ranging from 50 to 150
predict_response(mpg_model, "hp [50:150]")
#> # Predicted values of mpg
#>
#> hp  Predicted  95% CI
#> 
#> 50  30.53  27.84, 33.22
#> 63  28.04  25.86, 30.23
#> 75  26.17  24.33, 28.00
#> 87  24.57  23.00, 26.13
#> 100  23.07  21.71, 24.43
#> 113  21.75  20.52, 22.99
#> 125  20.67  19.49, 21.84
#> 150  18.71  17.49, 19.92
By default, the step size for a range is 1, like
50, 51, 52, ...
. If you need a different step size, use
by=<stepsize>
inside the brackets,
e.g. "hp [50:60 by=.5]"
. This would create a range from 50
to 60, with .5er steps.
# range for xvalues with .5steps
predict_response(mpg_model, "hp [50:60 by=.5]")
#> # Predicted values of mpg
#>
#> hp  Predicted  95% CI
#> 
#> 50.00  30.53  27.84, 33.22
#> 51.50  30.21  27.59, 32.84
#> 52.50  30.01  27.42, 32.59
#> 53.50  29.80  27.26, 32.34
#> 55.00  29.50  27.02, 31.98
#> 56.50  29.22  26.79, 31.64
#> 57.50  29.03  26.64, 31.41
#> 60.00  28.57  26.28, 30.86
Choosing representative values
Especially in situations where we have two continuous variables in interaction terms, or where the “grouping” variable is continuous, it is helpful to select representative values of the grouping variable  else, predictions would be made for too many groups, which is no longer helpful when interpreting adjusted predictions.
You can use

"minmax"
: minimum and maximum values (lower and upper bounds) of the variable are used. 
"meansd"
: uses the mean value as well as one standard deviation below and above mean value. 
"zeromax"
: is similar to the"minmax"
option, however, 0 is always used as minimum value. This may be useful for predictors that don’t have an empirical zerovalue. 
"terciles"
calculates and uses the terciles (lower, middle and upper), including minimum and maximum value. 
"terciles2"
calculates and uses the terciles (lower, middle and upper), excluding minimum and maximum value. 
"quart"
calculates and uses the quartiles (lower, median and upper), including minimum and maximum value. 
"quart2"
calculates and uses the quartiles (lower, median and upper), excluding minimum and maximum value. 
"fivenum"
calculates Tukey’s fivenumbersummary (minimum, lowerhinge, median, upperhinge, maximum). 
"percentile"
(including the percentilevalue) calculates a range of values from the given percentile, e.g."percentile80"
. 
"all"
takes all values of the vector.
data(efc, package = "ggeffects")
# short variable label, for plot
attr(efc$c12hour, "label") < "hours of care"
fit < lm(barthtot ~ c12hour * c161sex + neg_c_7, data = efc)
mydf < predict_response(fit, terms = c("c161sex", "c12hour [meansd]"))
plot(mydf)
mydf < predict_response(fit, terms = c("c161sex", "c12hour [quart]"))
plot(mydf)
Transforming values with functions
The brackets in the terms
argument also accept the name
of a valid function, to (back)transform predicted values. In this
example, we define a custom function to get the original values of the
focal predictor, multiplied by 2.
# xvalues and predictions based on "hp"values, multiplied by 2
hp_double < function(x) 2 * x
predict_response(mpg_model, "hp [hp_double]")
#> # Predicted values of mpg
#>
#> hp  Predicted  95% CI
#> 
#> 104.00  22.65  21.34, 23.96
#> 132.00  20.08  18.91, 21.25
#> 186.00  16.39  14.94, 17.84
#> 210.00  15.08  13.43, 16.73
#> 226.00  14.29  12.51, 16.08
#> 300.00  11.24  8.88, 13.61
#> 410.00  7.88  4.81, 10.95
#> 670.00  2.59  1.63, 6.82
Using a list, the terms
argument in the above example
would look like this:
terms = list(hp = hp_double(seq(100, 700, 7)))
.
Using values from a variable (vector)
val < c(100, 200, 300)
predict_response(mpg_model, "hp [val]")
#> # Predicted values of mpg
#>
#> hp  Predicted  95% CI
#> 
#> 100  23.07  21.71, 24.43
#> 200  15.61  14.04, 17.17
#> 300  11.24  8.88, 13.61
Using a list, the terms
argument in the above example
would look like this: terms = list(hp = val)
.
Pretty value ranges
This section is intended to show some examples how the plotted output
differs, depending on which value range is used. Some transformations,
like polynomial or spline terms, but also quadratic or cubic terms,
result in many predicted values. In such situation, predictions for some
models lead to memory allocation problems. That is why
predict_response()
“prettifies” certain value ranges by
default, at least for some model types (like mixed models).
To see the difference in the “curvilinear” trend, we use a quadratic term on a standardized variable.
library(datawizard)
library(lme4)
data(efc, package = "ggeffects")
efc$c12hour < standardize(efc$c12hour)
efc$e15relat < to_factor(efc$e15relat)
m < lmer(
barthtot ~ c12hour + I(c12hour^2) + neg_c_7 + c160age + c172code + (1  e15relat),
data = efc
)
me < predict_response(m, terms = "c12hour")
plot(me)
Turn off “prettifying”
As said above, predict_response()
“prettifies” the
vector, resulting in a smaller set of unique values. This is less memory
consuming and may be needed especially for more complex models.
You can turn off automatic “prettifying” by adding the
"all"
shortcut to the terms
argument.
me < predict_response(m, terms = "c12hour [all]")
plot(me)
This results in a smooth plot, as all values from the term of interest are taken into account.
Using different ranges for prettifying
To modify the “prettifying”, add the "n"
shortcut to the
terms
argument. This allows you to select a feasible range
of values that is smaller (and hence less memory consuming) them
"terms = ... [all]"
, but still produces smoother plots than
the default prettyfing.
me < predict_response(m, terms = "c12hour [n=2]")
plot(me)
me < predict_response(m, terms = "c12hour [n=10]")
plot(me)
Adjusted predictions conditioned on specific values of the covariates
By default, the typical
argument determines the function
that will be applied to the covariates to hold these terms at constant
values. By default, this is the meanvalue, but other options (like
median or mode) are possible as well.
Use the condition
argument to define other values at
which covariates should be held constant. condition
requires a named vector, with the name indicating the covariate.
data(mtcars)
mpg_model < lm(mpg ~ log(hp) + disp, data = mtcars)
# "disp" is hold constant at its mean
predict_response(mpg_model, "hp")
#> # Predicted values of mpg
#>
#> hp  Predicted  95% CI
#> 
#> 50  25.84  21.86, 29.82
#> 85  22.70  20.67, 24.72
#> 120  20.65  19.55, 21.76
#> 155  19.13  17.91, 20.35
#> 195  17.77  15.91, 19.64
#> 230  16.79  14.36, 19.23
#> 265  15.95  13.00, 18.91
#> 335  14.56  10.73, 18.40
#>
#> Adjusted for:
#> * disp = 230.72
# "disp" is hold constant at value 200
predict_response(mpg_model, "hp", condition = c(disp = 200))
#> # Predicted values of mpg
#>
#> hp  Predicted  95% CI
#> 
#> 50  26.53  22.91, 30.15
#> 85  23.38  21.66, 25.11
#> 120  21.34  20.27, 22.41
#> 155  19.82  18.34, 21.30
#> 195  18.46  16.25, 20.67
#> 230  17.48  14.68, 20.28
#> 265  16.64  13.31, 19.97
#> 335  15.25  11.03, 19.47
Adjusted predictions for each level of random effects
Adjusted predictions can also be calculated for each group level in
mixed models. Simply add the name of the related random effects term to
the terms
argument, and set
type = "random"
.
In the following example, we fit a linear mixed model and first simply plot the marginal effetcs, not conditioned on random effects. Please see also the dedicated vignette for mixed models for further details and examples.
library(lme4)
data(efc, package = "ggeffects")
efc$e15relat < to_factor(efc$e15relat)
m < lmer(neg_c_7 ~ c12hour + c160age + c161sex + (1  e15relat), data = efc)
me < predict_response(m, terms = "c12hour")
plot(me)
Changing the type to type = "random"
still returns
populationlevel predictions by default. The major difference between
type = "fixed"
and type = "random"
is the
uncertainty in the variance parameters. This leads to larger confidence
intervals for adjusted predictions with
type = "random"
.
me < predict_response(m, terms = "c12hour", type = "random")
plot(me)
To compute adjusted predictions for each grouping level, add the
related random term to the terms
argument. In this case,
confidence intervals are not calculated, but adjusted predictions are
conditioned on each group level (unitlevel) of the random effects.
me < predict_response(m, terms = c("c12hour", "e15relat"), type = "random")
plot(me)
Adjusted predictions, conditioned on random effects, can also be
calculated for specific levels only. Add the related values into
brackets after the variable name in the terms
argument.
me < predict_response(m, terms = c("c12hour", "e15relat [child,sibling]"), type = "random")
plot(me)
If the group factor has too many levels, you can also take a random
sample of all possible levels and plot the adjusted predictions for this
subsample of group levels. To do this, use
term = "<groupfactor> [sample=n]"
.
data("sleepstudy")
m < lmer(Reaction ~ Days + (1 + Days  Subject), data = sleepstudy)
me < predict_response(m, terms = c("Days", "Subject [sample=8]"), type = "random")
plot(me)