rockchalk (version 1.4)

plotSlopes: Assists creation of predicted value lines for values of a moderator variable.

Description

This is a "simple slope" plotter, especially for regressions that include interactive variables. This version works with factor or numeric moderator. It insists that the plotx variable must be numeric. It only prints out the t tests for "simple slopes" if user gives a numeric moderator. Otherwise, user can simply read the t tests from the regression output itself. I may re-think that (stubborn) stance. Here is the wrinkle. How to calculate predicted values in regressions that include categorical variables? Obviously, numeric variables can be set at observed means. This version sets categorical (factor) variables at mode value (most frequently observed value).

Usage

plotSlopes(model = NULL, plotx = NULL, modx = NULL,
    modxVals = NULL, plotPoints = TRUE, ...)

Arguments

model

Fitted regression object. Must have a predict method

plotx

String with name of IV to be plotted on x axis

modx

String for moderator variable name. May be either numeric or factor.

modxVals

A vector of numeric values for which plotted lines are sought. This is intended for numeric variables referred to by "modx". These are user specified "for instance" values. If omitted, quantiles will be used for numeric variables. If "modx" is a

plotPoints

Should the plot include the scatterplot points along with the lines.

...

further arguments that are passed to plot

Value

• If modx is the name of a numeric variable, will return a table of estimates and hypothesis tests for the simple slopes.

Examples

set.seed(12345)
x1 <- rnorm(100)
x2 <- rnorm(100)
x3 <- rnorm(100)
x4 <- rnorm(100)
y <- rnorm(100)
y2 <- 0.03 + 0.1*x1 + 0.1*x2 + 0.25*x1*x2 + 0.4*x3 -0.1*x4 + 1*rnorm(100)
dat <- data.frame(x1,x2,x3,x4,y, y2)
rm(x1, x2, x3, x4, y, y2)

##ordinary regression
m1 <- lm(y ~ x1 + x2 +x3 + x4, data=dat)
## must specify depvar parameter
plotSlopes(m1, plotx="x1", modx="x2", modxVals=c(-0.5,0,0.5))
plotSlopes(m1, plotx="x1", modx="x2")
plotSlopes(m1, plotx="x4", modx="x1")

m2 <- lm(y2 ~ x1*x2 + x3 +x4, data=dat)
summary(m2)
plotSlopes(m2, plotx="x1", modx="x2")

plotSlopes(m2, plotx="x1", modx="x2", modxVals=c( -2, -1, 0, 1, 2))

plotSlopes(m2, plotx="x3", modx="x2")

m3 <- lm(y2 ~ x1 * x2 + x3, data=dat)
plotSlopes(m3, plotx="x3", modx="x2")
plotSlopes(m3, plotx="x1", modx="x2")
plotSlopes(m3, plotx="x2", modx="x3")


### Examples with categorical Moderator variable

xcontinuous <- rnorm(100)
xcategorical <- gl(2,50, labels=c("Gigantic","Humongous"))
stde <- 8
y <- 3 + 0.5*xcontinuous + 1.2 * (as.numeric(xcategorical)-1) +
  -0.8* (as.numeric(xcategorical)-1) * xcontinuous +  stde * rnorm(100)

m1 <- lm (y ~ xcontinuous + xcategorical)
summary(m1)

plotSlopes(m1, modx = "xcategorical", plotx = "xcontinuous")

m2 <- lm (y ~ xcontinuous * xcategorical)
summary(m2)
plotSlopes(m2, modx = "xcategorical", plotx = "xcontinuous")


library(car)
m3 <- lm(statusquo ~ income * sex, data = Chile)
summary(m3)
plotSlopes(m3, modx = "sex", plotx = "income")


m4 <- lm(statusquo ~ region * income, data= Chile)
summary(m4)
plotSlopes(m4, modx = "region", plotx = "income")

plotSlopes(m4, modx = "region", plotx = "income", plotPoints=FALSE)


m5 <- lm(statusquo ~ region * income + sex + age, data= Chile)
summary(m5)
plotSlopes(m5, modx = "region", plotx = "income")

m6 <- lm(statusquo ~ income * age + education + sex + age, data=Chile)
summary(m6)
plotSlopes(m6, modx = "income", plotx = "age")

plotSlopes(m6, modx = "income", plotx = "age", plotPoints=FALSE)


## Should cause error because education is not numeric
## m7 <- lm(statusquo ~ income * age + education + sex + age, data=Chile)
## summary(m7)
## plotSlopes(m7, modx = "income", plotx = "education")

## Should cause error because "as.numeric(education") not same as
## plotx="education"
## m8 <- lm(statusquo ~ income * age + as.numeric(education) + sex + age, data=Chile)
## summary(m8)
## plotSlopes(m8, modx = "income", plotx = "education")

## Still fails.
## plotSlopes(m8, modx = "income", plotx = "as.numeric(education)")

## Must recode variable first so that variable name is coherent
Chile$educationn <- as.numeric(Chile$education)
m9 <- lm(statusquo ~ income * age + educationn + sex + age, data=Chile)
summary(m9)
plotSlopes(m9, modx = "income", plotx = "educationn")