sim_slopes() conducts a simple slopes analysis for the purposes of
understanding two- and three-way interaction effects in linear regression.
sim_slopes(model, pred, modx, mod2 = NULL, modxvals = NULL,
mod2vals = NULL, centered = NULL, standardize = FALSE,
cond.int = FALSE, johnson_neyman = TRUE, jnplot = FALSE,
jnalpha = 0.05, robust = FALSE, robust.type = "HC3", digits = 3,
n.sd = 1)A regression model of type lm or svyglm.
It should contain the interaction of interest.
The predictor variable involved in the interaction.
The moderator variable involved in the interaction.
Optional. The name of the second moderator variable involved in the interaction.
For which values of the moderator should simple slopes analysis
be performed? Default is NULL. If NULL, then the values will be
the customary +/- 1 standard deviation from the mean as well as the mean itself.
There is no specific limit on the number of variables provided. Factor variables
are not particularly suited to simple slopes analysis, but you could have a
numeric moderator with values of 0 and 1 and give c(0,1) to compare the
slopes at the different conditions. Two-level factor variables are coerced
to numeric 0/1 variables, but are not standardized/centered like they could
be if your input data had a numeric 0/1 variable.
For which values of the second moderator should the plot be
facetted by? That is, there will be a separate plot for each level of this
moderator. Defaults are the same as modxvals.
A vector of quoted variable names that are to be mean-centered. If
NULL, all non-focal predictors are centered. If not NULL, only
the user-specified predictors are centered. User can also use "none" or "all"
arguments. The response variable is not centered unless specified directly.
Logical. Would you like to standardize the variables
that are centered? Default is FALSE, but if TRUE it will
standardize variables specified by the centered argument. Note that
non-focal predictors are centered when centered = NULL, its default.
Should conditional intercepts be printed in addition to the
slopes? Default is FALSE.
Should the Johnson-Neyman interval be calculated?
Default is TRUE. This can be performed separately with
johnson_neyman.
Should the Johnson-Neyman interval be plotted as well? Default
is FALSE.
What should the alpha level be for the Johnson-Neyman interval? Default is .05, which corresponds to a 95% confidence interval.
Logical. If TRUE, computes heteroskedasticity-robust
standard errors.
Type of heteroskedasticity-robust standard errors to use
if robust=TRUE. See details of j_summ for more on
options.
How many significant digits after the decimal point should the output contain?
How many standard deviations should be used if standardize
= TRUE? Default is 1, but some prefer 2.
A table of coefficients for the focal predictor at each value of the moderator
A table of coefficients for the intercept at each value of the moderator
The values of the moderator used in the analysis
A list containing each regression model created to estimate the conditional coefficients.
If johnson_neyman = TRUE, a list of `johnson_neyman`
objects from johnson_neyman. These contain the values of the
interval and the plots. If a 2-way interaction, the list will be of length
1. Otherwise, there will be 1 `johnson_neyman` object for each value of the
2nd moderator for 3-way interactions.
This allows the user to perform a simple slopes analysis for the purpose of probing interaction effects in a linear regression. Two- and three-way interactions are supported, though one should be warned that three-way interactions are not easy to interpret in this way.
For more about Johnson-Neyman intervals, see johnson_neyman.
The function accepts a lm object and uses it to recompute models with
the moderating variable set to the levels requested. svyglm
objects are also accepted, though users should be cautioned against using
simple slopes analysis with non-linear models (svyglm also estimates
linear models).
Factor moderators are coerced to a 0/1 numeric variable and are not centered, even when requested in arguments. To avoid this, modify your data to change the factor to a binary numeric variable. Factors with more than 2 levels trigger an error.
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. http://dx.doi.org/10.1207/s15327906mbr4003_5
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.
interact_plot accepts similar syntax and will plot the
results with ggplot.
testSlopes performs a hypothesis test of differences
and provides Johnson-Neyman intervals.
simpleSlope performs a similar analysis and can analyze
a second moderator.
Other interaction tools: interact_plot,
johnson_neyman,
probe_interaction
# NOT RUN {
# Using a fitted model as formula input
fiti <- lm(Income ~ Frost + Murder*Illiteracy,
data=as.data.frame(state.x77))
sim_slopes(model=fiti, pred=Murder, modx=Illiteracy)
# With svyglm
library(survey)
data(api)
dstrat <- svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
regmodel <- svyglm(api00~ell*meals,design=dstrat)
sim_slopes(regmodel, pred = ell, modx = meals)
# 3-way with survey and factor input
regmodel <- svyglm(api00~ell*meals*sch.wide,design=dstrat)
sim_slopes(regmodel, pred = ell, modx = meals, mod2 = sch.wide)
# }
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