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DAMisc (version 1.3)

intQualQuant: Predictions for Factor-Numeric Interactions in Linear Models

Description

This function works on linear models with a single interaction between a continuous (numeric) variable and a factor. The output is a data frame that gives the predicted effect of moving from each category to each other category of the factor over the range of values of the continuous conditioning variable.

Usage

intQualQuant(obj, vars, level = .95, labs=NULL, 
		n=10, onlySig=FALSE, type=c("facs", "slopes"),
		plot=TRUE, vals = NULL, rug=TRUE, ci=TRUE, ...)

Arguments

obj
An object of class lm.
vars
A vector of two variable names giving the two quantitative variables involved in the interaction. These variables must be involved in one, and only one, interaction.
level
Confidence level desired for lower and upper bounds of confidence interval.
labs
An optional vector of labels that will be used to identify the effects, if NULL, the factor levels will be used.
n
Number of values of the conditioning variable to use.
onlySig
Logical indicating whether only contrasts with significant differences should be returned. Significance is determined to exist if the largest lower bound is greater than zero or the smallest upper bound is smaller than zero.
type
String indicating whether the conditional partial effect of the factors is plotted (if facs), or the conditional partial effect of the quantitative variable (if slopes) is produced.
plot
Logical indicating whether graphical results (if TRUE) or numerical results (if FALSE) are produced.
vals
A vector of values at which the continuous variable will be held constant. If NULL, a sequence of length n across the variable's range will be used.
rug
Logical indicating whether rug plots should be plotted in the panels.
ci
Logical indicating whether confidence bounds should be drawn.
...
Other arguments to be passed down to effect if plot.type = slopes.

Value

  • For type = facs and plot = FALSE, a data frame with the following values:
  • fitThe expected difference between the two factor levels at the specified value of the conditioning variable.
  • se.fitThe standard error of the expected differences.
  • xThe value of the continuous conditioning variable
  • contrastA factor giving the two values of the factor being evaluated.
  • lowerThe lower 95% confidence interval for fit
  • upperThe upper 95% confidence interval for fit
  • For type = facs and plot = TRUE, a lattice display is returned For type = slopes and plot = FALSE, A character matrix with the following columns:
  • BThe conditional effect of the quantitative variable for each level of the factor.
  • SE(B)The standard error of the conditional effect.
  • t-statThe t-statistic of the conditional effect.
  • Pr(>|t|)The two-sided p-value.
  • For type = slopes and plot = TRUE, a lattice display is returned

Examples

Run this code
library(car)
data(Prestige)
Prestige$income <- Prestige$income/1000
mod <- lm(prestige ~ income * type + education, data=Prestige)
intQualQuant(mod, c("income", "type"), n=10, 
	plot.type="none")
intQualQuant(mod, c("income", "type"), n=10, 
	plot.type="facs")
intQualQuant(mod, c("income", "type"), n=10, 
	plot.type="slopes")

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