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scale_mod (previously known as scale_lm) takes fitted regression models
and scales all
predictors by dividing each by 1 or 2 standard deviations (as chosen by the
user).
scale_mod(model, ...)# S3 method for default
scale_mod(
model,
binary.inputs = "0/1",
n.sd = 1,
center = TRUE,
scale.response = FALSE,
center.only = FALSE,
data = NULL,
vars = NULL,
apply.weighted.contrasts = getOption("jtools-weighted.contrasts", FALSE),
...
)
A regression model of type lm, glm,
svyglm, or lme4::merMod. Other model types
may work as well but are not tested.
Ignored.
Options for binary variables. Default is "0/1";
"0/1" keeps original scale; "-0.5,0.5" rescales 0 as -0.5
and
1 as 0.5; center subtracts the mean; and full treats them
like other continuous variables.
How many standard deviations should you divide by for standardization? Default is 1, though some prefer 2.
Default is TRUE. If TRUE, the predictors are
also
mean-centered. For binary predictors, the binary.inputs argument
supersedes this one.
Should the response variable also be rescaled? Default
is FALSE.
Rather than actually scale predictors, just mean-center them.
If you provide the data used to fit the model here, that data
frame is used to re-fit the model instead of the stats::model.frame()
of the model. This is particularly useful if you have variable
transformations or polynomial terms specified in the formula.
A character vector of variable names that you want to be scaled. If NULL, the default, it is all predictors.
Factor variables cannot be scaled, but you
can set the contrasts such that the intercept in a regression model will
reflect the true mean (assuming all other variables are centered). If set
to TRUE, the argument will apply weighted effects coding to all factors.
This is similar to the R default effects coding, but weights according to
how many observations are at each level. An adapted version of
wec::contr.wec() from the wec package is used to do this. See
that package's documentation and/or Grotenhuis et al. (2016) for more
info.
The functions returns a re-fitted model object, inheriting from whichever class was supplied.
This function will scale all continuous variables in a regression model for ease of interpretation, especially for those models that have interaction terms. It can also mean-center all of them as well, if requested.
The scaling happens on the input data, not the terms themselves. That means interaction terms are still properly calculated because they are the product of standardized predictors, not a standardized product of predictors.
This function re-estimates the model, so for large models one should expect a runtime equal to the first run.
Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analyses for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
sim_slopes performs a simple slopes analysis.
interact_plot creates attractive, user-configurable plots of
interaction models.
Other standardization:
center_mod(),
center(),
gscale(),
standardize()
# NOT RUN {
fit <- lm(formula = Murder ~ Income * Illiteracy,
data = as.data.frame(state.x77))
fit_scale <- scale_mod(fit)
fit_scale <- scale_mod(fit, center = TRUE)
# With weights
fitw <- lm(formula = Murder ~ Income * Illiteracy,
data = as.data.frame(state.x77),
weights = Population)
fitw_scale <- scale_mod(fitw)
fitw_scale <- scale_mod(fitw, center = TRUE, binary.input = "0/1")
# With svyglm
if (requireNamespace("survey")) {
library(survey)
data(api)
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
regmodel <- svyglm(api00~ell*meals,design=dstrat)
regmodel_scale <- scale_mod(regmodel)
regmodel_scale <- scale_mod(regmodel, binary.input = "0/1")
}
# }
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