• Gettingstarted
• Simple moderation:nice_mod
  • Open (or save) table toWord
• Simple slopes:nice_slopes
• Special cases
  • Covariates
  • Three-way interaction
• Complex models:nice_lm
  • nice_lm
  • nice_lm_slopes
  • Thanks for checking in

Getting started

Let’s first load the demo data. This data set comes with baseR (meaning you have it too and can directly type thiscommand into yourR console).

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Load therempsyc package:

library(rempsyc)

Note: If you haven’t installed this packageyet, you will need to install it via the following command:install.packages("rempsyc"). Furthermore, you may be askedto install the following packages if you haven’t installed them already(you may decide to install them all now to avoid interrupting yourworkflow if you wish to follow this tutorial from beginning to end):

pkgs<-c("effectsize","flextable","interactions")install_if_not_installed(pkgs)

For moderations and simple slopes, we usually want to standardize (orat least center) our variables.

mtcars2<-lapply(mtcars, scale)|>as.data.frame()

Simple moderation:nice_mod

moderations<-nice_mod(data = mtcars2,response ="mpg",predictor ="gear",moderator ="wt")moderations

##   Dependent Variable Predictor df           B          t            p## 1                mpg      gear 28 -0.08718042 -0.7982999 4.314156e-01## 2                mpg        wt 28 -0.94959988 -8.6037724 2.383144e-09## 3                mpg   gear:wt 28 -0.23559962 -2.1551077 3.989970e-02##           sr2     CI_lower   CI_upper## 1 0.004805465 0.0000000000 0.02702141## 2 0.558188818 0.3142326391 0.80214500## 3 0.035022025 0.0003502202 0.09723370

If we want it to look nice

(my_table<-nice_table(moderations,highlight =TRUE))

Note: The sr2 (semi-partial correlationsquared,alsoknown as delta R-square) allows us to quantify the uniquecontribution (proportion of variance explained) of an independentvariable on the dependent variable, over and above the other variablesin the model. sr2 is often considered a better indicator of thepractical relevance of a variable.

# Open in Wordprint(my_table,preview ="docx")# Save in Wordflextable::save_as_docx(my_table,path ="moderations.docx")

Simple slopes:nice_slopes

You might have heard about “simple slopes” before. But what does thatmean? Essentially, this means looking at the strength (regressioncoefficient) and significance of the slope, when subsetting forobservations that are high, low, or average on a variable, typically themoderating variable. A bit further down, this will get clearer bylooking at the plot of the interaction, which shows one slope forobservations that are high on the wt (moderating) variable, a secondslope for those that are low, and a third slope for those that areaverage.

Let’s extract the simple slopes now, including the sr2.

slopes<-nice_slopes(data = mtcars2,response ="mpg",predictor ="gear",moderator ="wt")slopes

##   Dependent Variable Predictor (+/-1 SD) df           B          t          p## 1                mpg       gear (LOW-wt) 28  0.14841920  1.0767040 0.29080233## 2                mpg      gear (MEAN-wt) 28 -0.08718042 -0.7982999 0.43141565## 3                mpg      gear (HIGH-wt) 28 -0.32278004 -1.9035367 0.06729622##           sr2 CI_lower   CI_upper## 1 0.008741702        0 0.03886052## 2 0.004805465        0 0.02702141## 3 0.027322839        0 0.08179662

nice_table(slopes,highlight =TRUE)

In this specific case, the interaction is significant but none of thesimple slopes. This means that although the two slopes are significantlydifferent from each other, taken individually, the slopes aren’tsignificantly different from a straight line.

The neat thing is that you can add as many dependent variables atonce as you want.

# Moderationsnice_mod(data = mtcars2,response =c("mpg","disp","hp"),predictor ="gear",moderator ="wt")|>nice_table(highlight =TRUE)

# Simple slopesnice_slopes(data = mtcars2,response =c("mpg","disp","hp"),predictor ="gear",moderator ="wt")|>nice_table(highlight =TRUE)

Pro tip: Both thenice_mod() andnice_slopes() functions take the same argument, so you canjust copy-paste the first and change the function call to save time!

Special cases

nice_mod(data = mtcars2,response ="mpg",predictor ="gear",moderator ="wt",covariates =c("am","vs"))|>nice_table(highlight =TRUE)

nice_slopes(data = mtcars2,response ="mpg",predictor ="gear",moderator ="wt",covariates =c("am","vs"))|>nice_table(highlight =TRUE)

# First need to define model for plot functionmod<-lm(mpg~ gear* wt+ am+ vs,data = mtcars2)# Plot the modellibrary(interactions)interact_plot(mod,pred ="gear",modx ="wt",interval =TRUE)

mtcars2$am<- mtcars$am

nice_mod(response ="mpg",predictor ="gear",moderator ="disp",moderator2 ="am",data = mtcars2)|>nice_table(highlight =TRUE)

nice_slopes(data = mtcars2,response ="mpg",predictor ="gear",moderator ="disp",moderator2 ="am")|>nice_table(highlight =TRUE)

Complex models:nice_lm

For more complicated models not supported bynice_mod,one can define the model in the traditional way and feed it tonice_lm andnice_lm_slopes instead. Theysupport multiplelm models as well.

model1<-lm(mpg~ cyl+ wt* hp, mtcars2)model2<-lm(qsec~ disp+ drat* carb, mtcars2)my.models<-list(model1, model2)nice_lm(my.models)|>nice_table(highlight =TRUE)

model1<-lm(mpg~ gear* wt, mtcars2)model2<-lm(disp~ gear* wt, mtcars2)my.models<-list(model1, model2)nice_lm_slopes(my.models,predictor ="gear",moderator ="wt")|>nice_table(highlight =TRUE)