rockchalk (version 1.8.111)

plotSlopes: Generic function for plotting regressions and interaction effects

Description

This is a generic function for plotting regression objects. So far, there is an implementation for lm() objects. This allows interaction effects, but not nonlinearities like log(x1). For that, please see plotCurves.

This is a "simple slope" plotter for linear regression objects that are created by lm(). The function plotCurves() can handle nonlinear predictive equations and generalized linear models. The term "simple slopes" was coined by psychologists (Aiken and West, 1991; Cohen, et al 2002) for analysis of interaction effects for particular values of a moderating variable. The moderating variable may be continuous or categorical, lines will be plotted for focal values of that variable.

Usage

plotSlopes(model, plotx, ...)
# S3 method for lm
plotSlopes(model, plotx, modx, n = 3, modxVals = NULL,
  plotxRange = NULL, interval = c("none", "confidence", "prediction"),
  plotPoints = TRUE, plotLegend = TRUE, legendTitle = NULL,
  legendPct = TRUE, col = NULL, llwd = 2, opacity = 100, ...)

Arguments

model

Required. A fitted Regression

plotx

Required. Name of one predictor from the fitted model to be plotted on horizontal axis

...

Additional arguments passed to methods. Often includes arguments that are passed to plot. Any arguments that customize plot output, such as lwd, cex, and so forth, may be supplied. These arguments intended for the predict method will be used: c("type", "se.fit", "interval", "level", "dispersion", "terms", "na.action")

modx

Optional. String for moderator variable name. May be either numeric or factor. If omitted, a single predicted value line will be drawn.

n

Optional. Number of focal values of modx, used by algorithms specified by modxVals; will be ignored if modxVals supplies a vector of focal values.

modxVals

Optional. Focal values of modx for which lines are desired. May be a vector of values or the name of an algorithm, "quantile", "std.dev.", or "table".

plotxRange

Optional. If not specified, the observed range of plotx will be used to determine the axis range.

interval

Optional. Intervals provided by the predict.lm may be supplied, either "confidence" (confidence interval for the estimated conditional mean) or "prediction" (interval for observed values of y given the rest of the model). The level can be specified as an argument (which goes into ... and then to the predict method)

plotPoints

Optional. TRUE or FALSE: Should the plot include the scatterplot points along with the lines.

plotLegend

Optional. TRUE or FALSE: Include a default

legendTitle

Optional. You'll get an automatically generated title, such as "Moderator: modx", but if you don't like that, specify your own string here. legend. Set to FALSE if user wants to customize a legend after the plot has been drawn.

legendPct

Default = TRUE. Variable labels print with sample percentages.

col

Optional. A color vector for predicted value lines (and intervals if requested). If not specified, the R's builtin palette() will be used. User may supply a vector of valid color names, either explicitly c("pink","black", "gray70") or implicitly, rainbow(10) or gray.colors(5). Color names will be recycled if there are more focal values of modx than colors provided.

llwd

Optional, default = 2. Line widths for predicted values. Can be single value or a vector, which will be recycled as necessary.

opacity

Optional, default = 100. A number between 1 and 255. 1 means "transparent" or invisible, 255 means very dark. Determines the darkness of confidence interval regions

Value

Creates a plot and an output object that summarizes it.

The return object includes the "newdata" object that was used to create the plot, along with the "modxVals" vector, the values of the moderator for which lines were drawn, and the color vector. It also includes the call that generated the plot.

Details

This function works well with lm models in which the predictor formula includes interactions, but it does not work well with nonlinear predictors (log(x) and poly(x)). For that, please use plotCurves. plotSlopes is needed only when one wants to create an output object that can be used as input for testSlopes().

The argument plotx is the name of the horizontal plotting variable; it must be numeric. The argument modx is the moderator variable. It may be either a numeric or a factor variable. As of version 1.7, the modx argument may be omitted. A single predicted value line will be drawn. That version also introduced the arguments interval and n.

There are many ways to specify focal values using the arguments modxVals and n. This changed in rockchalk-1.7.0. If modxVals is omitted, a default algorithm will be used, selecting n values for plotting. modxVals may be a vector of values (for a numeric moderator) or levels (for a factor). If modxVals is a vector of values, then the argument n is ignored. However, if modxVals is one of the name of one of the algorithms, "table", "quantile", or "std.dev.", then the argument n sets number of focal values to be selected. For numeric modx, n defaults to 3, but for factors modx will be the number of observed values of modx. If modxVals is omitted, the defaults will be used ("table" for factors, "quantile" for numeric variables).

For the predictors besides modx and plotx (the ones that are not explicitly included in the plot), predicted values are calculated with variables set to the mean and mode, for numeric or factor variables (respectively). Those values can be reviewed in the newdata object that is created as a part of the output from this function

References

Aiken, L. S. and West, S.G. (1991). Multiple Regression: Testing and Interpreting Interactions. Newbury Park, Calif: Sage Publications.

Cohen, J., Cohen, P., West, S. G., and Aiken, L. S. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (Third.). Routledge Academic.

See Also

testSlopes plotCurves

Examples

# NOT RUN {
## Manufacture some predictors
set.seed(12345)

dat <- genCorrelatedData2 (N = 100, means = rep(0,4), sds = 1, rho = 0.2,
                           beta = c(0.3, 0.5, -0.45, 0.5, -0.1, 0, 0.6),
                           stde = 2)

dat$xcat1 <- gl(2, 50, labels = c("M", "F"))
dat$xcat2 <- cut(rnorm(100), breaks = c(-Inf, 0, 0.4, 0.9, 1, Inf),
                 labels = c("R", "M", "D", "P", "G"))
## incorporate effect of categorical predictors
dat$y <- dat$y + 1.9 * dat$x1 * contrasts(dat$xcat1)[dat$xcat1] +
           contrasts(dat$xcat2)[dat$xcat2 , ] %*% c(0.1, -0.1, 0, 0.2)

m1 <- lm(y ~ x1 * x2 + x3 + x4 + xcat1 + xcat2, data = dat)
summary(m1)

## New in rockchalk 1.7.x. No modx required:
plotSlopes(m1, plotx = "x1")

## Confidence interval, anybody?
plotSlopes(m1, plotx = "x1", interval = "conf")

## Prediction interval.
plotSlopes(m1, plotx = "x1", interval = "pred")

## Now experiment with a moderator variable
## let default quantile algorithm do its job
plotSlopes(m1, plotx = "x1", modx = "x2")
## previous uses default equivalent to
## plotSlopes(m1, plotx = "x1", modx = "x2", modxVals = "quantile")
## Want more focal values?
plotSlopes(m1, plotx = "x1", modx = "x2", n = 5)
## Pick focal values yourself?
plotSlopes(m1, plotx = "x1", modx = "x2", modxVals = c(-2, 0, 0.5))
## Alternative algorithm?
plotSlopes(m1, plotx = "x1", modx = "x2", modxVals = "std.dev.",
           main = "Uses \"std.dev.\" Divider for the Moderator",
           xlab = "My Predictor", ylab = "Write Anything You Want for ylab")

## Will catch output object from this one
m1ps <- plotSlopes(m1, plotx = "x1", modx = "x2", modxVals = "std.dev.", n = 5,
                 main = "Setting n = 5 Selects More Focal Values for Plotting")

m1ts <- testSlopes(m1ps)

plot(m1ts)

### Examples with categorical Moderator variable

m3 <- lm (y ~ x1 + xcat1, data = dat)
summary(m3)
plotSlopes(m3, modx = "xcat1", plotx = "x1")
plotSlopes(m3, modx = "xcat1", plotx = "x1", interval = "predict")


m4 <- lm (y ~ x1 * xcat1, data = dat)
summary(m4)
plotSlopes(m4, modx = "xcat1", plotx = "x1")
plotSlopes(m4, modx = "xcat1", plotx = "x1", interval = "conf")


m5 <- lm (y ~ x1 + x2 + x1 * xcat2, data = dat)
summary(m5)
plotSlopes(m5, modx = "xcat2", plotx = "x1")
m5ps <- plotSlopes(m5, modx = "xcat2", plotx = "x1", interval = "conf")

testSlopes(m5ps)



## Now examples with real data. How about Chilean voters?
library(car)
m6 <- lm(statusquo ~ income * sex, data = Chile)
summary(m6)
plotSlopes(m6, modx = "sex", plotx = "income")
plotSlopes(m6, modx = "sex", plotx = "income", col = c("yellow", "blue"))


m7 <- lm(statusquo ~ region * income, data= Chile)
summary(m7)
plotSlopes(m7, plotx = "income", modx = "region")

plotSlopes(m7, plotx = "income", modx = "region", plotPoints = FALSE)
plotSlopes(m7, plotx = "income", modx = "region", plotPoints = FALSE,
           interval = "conf")
plotSlopes(m7, plotx = "income", modx = "region", modxVals = c("SA","S", "C"),
           plotPoints = FALSE, interval = "conf")
## Same, choosing 3 most frequent values
plotSlopes(m7, plotx = "income", modx = "region", n = 3, plotPoints = FALSE,
           interval = "conf")


m8 <- lm(statusquo ~ region * income + sex + age, data= Chile)
summary(m8)
plotSlopes(m8, modx = "region", plotx = "income")


m9 <- lm(statusquo ~ income * age + education + sex + age, data = Chile)
summary(m9)
plotSlopes(m9, modx = "income", plotx = "age")

plotSlopes(m9, modx = "income", plotx = "age", plotPoints = FALSE)


## Convert education to numeric, for fun
Chile$educationn <- as.numeric(Chile$education)
m10 <- lm(statusquo ~ income * educationn + sex + age, data = Chile)
summary(m10)
plotSlopes(m10, plotx = "educationn",  modx = "income")

## Now, the occupational prestige data. Please note careful attention
## to consistency of colors selected
data(Prestige)
m11 <- lm(prestige ~ education * type, data = Prestige)

plotSlopes(m11, plotx = "education", modx = "type", interval = "conf")
dev.new()
plotSlopes(m11, plotx = "education", modx = "type",
           modxVals = c("prof"), interval = "conf")
dev.new()
plotSlopes(m11, plotx = "education", modx = "type",
           modxVals = c("bc"), interval = "conf")
dev.new()
plotSlopes(m11, plotx = "education", modx = "type",
           modxVals = c("bc", "wc"), interval = "conf")
# }