Generates a plot of a 2-way interaction.
Usage
plot2WayInteraction(
predictor,
outcome,
moderator,
predictorLabel = "predictor",
outcomeLabel = "outcome",
moderatorLabel = "moderator",
varList,
varTypes,
values = NA,
interaction = "normal",
legendLabels = NA,
legendLocation = "topright",
ylim = NA,
pvalues = TRUE,
data
)Arguments
- predictor
character name of predictor variable (variable on x-axis).
- outcome
character name of outcome variable (variable on y-axis).
- moderator
character name of moderator variable (variable on z-axis).
- predictorLabel
label on x-axis of plot
- outcomeLabel
label on y-axis of plot
- moderatorLabel
label on z-axis of plot
- varList
names of predictor variables in model
- varTypes
types of predictor variables in model; one of:
"mean"= plots at mean of variable – should be used for ALL covariates (apart from main predictor and moderator)"sd"= plots at +/- 1 sd of variable (for most continuous predictors and moderators)"binary"= plots at values of 0,1 (for binary predictors and moderators)"full"= plots full range of variable (for variables like age when on x-axis)"values"= allows plotting moderator at specific values (e.g.,0,1,2)"factor"= plots moderator at different categories (e.g.,TRUE,FALSE)
- values
specifies values at which to plot moderator (must specify varType =
"values")- interaction
one of:
"normal"= standard interaction"meancenter"= calculates the interaction from a mean-centered predictor and moderator (subtracting each individual's value from the variable mean to set the mean of the variable to zero)"orthogonalize"= makes the interaction orthogonal to the predictor and moderator by regressing the interaction on the predictor and outcome and saving the residual
- legendLabels
vector of 2 labels for the two levels of the moderator; leave as
NAto see the actual levels of the moderator- legendLocation
one of:
"topleft","topright","bottomleft", or"bottomright"- ylim
vector of min and max values on y-axis (e,g.,
0,10)- pvalues
whether to include p-values of each slope in plot (
TRUEorFALSE)- data
name of data object
Details
Generates a plot of a 2-way interaction: the association between a predictor and an outcome at two levels of the moderator.
See also
Other plot:
addText(),
ppPlot(),
semPlotInteraction(),
vwReg()
Other multipleRegression:
lmCombine(),
ppPlot(),
semPlotInteraction(),
update_nested()
Examples
# Prepare Data
predictor <- rnorm(1000, 10, 3)
moderator <- rnorm(1000, 50, 10)
outcome <- (1.7 * predictor) + (1.3 * moderator) +
(1.5 * predictor * moderator) + rnorm(1000, sd = 3)
covariate <- rnorm(1000)
df <- data.frame(predictor, moderator, outcome, covariate)
# Linear Regression
lmModel <- lm(outcome ~ predictor + moderator + predictor:moderator,
data = df, na.action = "na.exclude")
summary(lmModel)
#>
#> Call:
#> lm(formula = outcome ~ predictor + moderator + predictor:moderator,
#> data = df, na.action = "na.exclude")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.0946 -1.9719 -0.0184 2.1292 8.7541
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.202401 1.687107 -0.12 0.905
#> predictor 1.746065 0.162076 10.77 <2e-16 ***
#> moderator 1.313694 0.033490 39.23 <2e-16 ***
#> predictor:moderator 1.498472 0.003229 464.12 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.011 on 996 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.917e+06 on 3 and 996 DF, p-value: < 2.2e-16
#>
# 1. Plot 2-Way Interaction
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
data = df)
#>
#> Call:
#> lm(formula = modelFormula, data = modelData, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.148179 1.685545 -0.088 0.9300
#> predictor 1.741212 0.161923 10.753 <2e-16 ***
#> moderator 1.312322 0.033463 39.218 <2e-16 ***
#> covariate -0.177163 0.099470 -1.781 0.0752 .
#> predictor:moderator 1.498591 0.003226 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
# 2. Specify y-axis Range
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
ylim = c(10,50), #new
data = df)
#>
#> Call:
#> lm(formula = modelFormula, data = modelData, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.148179 1.685545 -0.088 0.9300
#> predictor 1.741212 0.161923 10.753 <2e-16 ***
#> moderator 1.312322 0.033463 39.218 <2e-16 ***
#> covariate -0.177163 0.099470 -1.781 0.0752 .
#> predictor:moderator 1.498591 0.003226 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
# 3. Add Variable Labels
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
ylim = c(10,50),
predictorLabel = "Stress", #new
outcomeLabel = "Aggression", #new
moderatorLabel = "Gender", #new
data = df)
#>
#> Call:
#> lm(formula = modelFormula, data = modelData, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.148179 1.685545 -0.088 0.9300
#> predictor 1.741212 0.161923 10.753 <2e-16 ***
#> moderator 1.312322 0.033463 39.218 <2e-16 ***
#> covariate -0.177163 0.099470 -1.781 0.0752 .
#> predictor:moderator 1.498591 0.003226 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
# 4. Change Legend Labels
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
ylim = c(10,50),
predictorLabel = "Stress",
outcomeLabel = "Aggression",
moderatorLabel = "Gender",
legendLabels = c("Boys","Girls"), #new
data = df)
#>
#> Call:
#> lm(formula = modelFormula, data = modelData, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.148179 1.685545 -0.088 0.9300
#> predictor 1.741212 0.161923 10.753 <2e-16 ***
#> moderator 1.312322 0.033463 39.218 <2e-16 ***
#> covariate -0.177163 0.099470 -1.781 0.0752 .
#> predictor:moderator 1.498591 0.003226 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
# 5. Move Legend Location
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
ylim = c(10,50),
predictorLabel = "Stress",
outcomeLabel = "Aggression",
moderatorLabel = "Gender",
legendLabels = c("Boys","Girls"),
legendLocation = "topleft", #new
data = df)
#>
#> Call:
#> lm(formula = modelFormula, data = modelData, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.148179 1.685545 -0.088 0.9300
#> predictor 1.741212 0.161923 10.753 <2e-16 ***
#> moderator 1.312322 0.033463 39.218 <2e-16 ***
#> covariate -0.177163 0.099470 -1.781 0.0752 .
#> predictor:moderator 1.498591 0.003226 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
#6. Turn Off p-Values
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
ylim = c(10,50),
predictorLabel = "Stress",
outcomeLabel = "Aggression",
moderatorLabel = "Gender",
legendLabels = c("Boys","Girls"),
legendLocation = "topleft",
pvalues = FALSE, #new
data = df)
#>
#> Call:
#> lm(formula = modelFormula, data = modelData, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.148179 1.685545 -0.088 0.9300
#> predictor 1.741212 0.161923 10.753 <2e-16 ***
#> moderator 1.312322 0.033463 39.218 <2e-16 ***
#> covariate -0.177163 0.099470 -1.781 0.0752 .
#> predictor:moderator 1.498591 0.003226 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
#7. Get Regression Output from Mean-Centered Predictor and Moderator
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
ylim = c(10,50),
predictorLabel = "Stress",
outcomeLabel = "Aggression",
moderatorLabel = "Gender",
legendLabels = c("Boys","Girls"),
legendLocation = "topleft",
interaction = "meancenter", #new
data = df)
#>
#> Call:
#> lm(formula = modelFormula, data = modelDataMC, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 824.418640 0.095168 8662.737 <2e-16 ***
#> predictor 76.574370 0.031503 2430.722 <2e-16 ***
#> moderator 16.166946 0.009438 1713.016 <2e-16 ***
#> covariate -0.177163 0.099470 -1.781 0.0752 .
#> predictor:moderator 1.498591 0.003226 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
#8. Get Regression Output from Orthogonalized Interaction Term
plot2WayInteraction(predictor = "predictor",
outcome = "outcome",
moderator = "moderator",
varList = c("predictor","moderator","covariate"),
varTypes = c("sd","binary","mean"),
ylim = c(10,50),
predictorLabel = "Stress",
outcomeLabel = "Aggression",
moderatorLabel = "Gender",
legendLabels = c("Boys","Girls"),
legendLocation = "topleft",
interaction = "orthogonalize", #new
data = df)
#>
#> Call:
#> lm(formula = modelFormulaOrtho, data = modelDataOrtho, na.action = "na.omit")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -10.1626 -1.9447 -0.0294 2.1367 9.2048
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -7.356e+02 5.786e-01 -1271.510 <2e-16 ***
#> predictor 7.554e+01 3.142e-02 2403.950 <2e-16 ***
#> moderator 1.623e+01 9.437e-03 1719.400 <2e-16 ***
#> covariate -1.772e-01 9.947e-02 -1.781 0.0752 .
#> predictorXmoderator 1.499e+00 3.226e-03 464.558 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.008 on 995 degrees of freedom
#> Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
#> F-statistic: 2.193e+06 on 4 and 995 DF, p-value: < 2.2e-16
#>
