corReflect: Reflect Specified Variables in a Correlation Matrix

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Reflect Specified Variables in a Correlation Matrix

Description

Abbreviation: reflect

Reflects the specified variables by multiplying each correlation of the variable by -1. Usually a prelude to a factor analysis, such as provided by corCFA.

Usage

corReflect(R=mycor, vars,
          main=NULL, heat_map=TRUE, bottom=NULL,right=NULL, 
          pdf_file=NULL, width=5, height=5, ...)

reflect(...)

Arguments

R	Correlation matrix.
vars	List of the re-ordered variables, each variable listed by its ordinal position in the input correlation matrix.
main	Graph title. Set to main="" to turn off.
heat_map	If TRUE, display a heat map of the item correlations with item communalities in the diagonal.
bottom	Number of lines of bottom margin.
right	Number of lines of right margin.
pdf_file	Name of the pdf file to which graphics are redirected.
width	Width of the pdf file in inches.
height	Height of the pdf file in inches.
...	Parameter values_

Details

Reflects the specified variables by multiplying each correlation of the variable by -1. The original data from which the correlations are computed is unmodified unless the output of the function is written into the input correlation matrix, by default mycor.

Define the constituent variables, the items, with a listing of each variable by its name in the correlation matrix. If the specified variables are in consecutive order in the input correlation matrix, the list can be specified by listing the first variable, a colon, and then the last variable. To specify multiple variables, a single variable or a list, separate each by a comma, then invoke the R combine or c function. For example, if the list of variables in the input correlation matrix is from m02 through m05, and the variable Anxiety, then define the list in the corReflect function call according to vars=c(m02:m05,Anxiety).

Author(s)

David W. Gerbing (Portland State University; [e-mail])

See Also

Correlation, recode.

Examples

# input correlation matrix of perfect two-factor model
# Factor Pattern for each Factor: 0.8, 0.6, 0.4
# Factor-Factor correlation: 0.3
mycor <- matrix(nrow=6, ncol=6, byrow=TRUE,
c(1.000,0.480,0.320,0.192,0.144,0.096,
  0.480,1.000,0.240,0.144,0.108,0.072,
  0.320,0.240,1.000,0.096,0.072,0.048,
  0.192,0.144,0.096,1.000,0.480,0.320,
  0.144,0.108,0.072,0.480,1.000,0.240,
  0.096,0.072,0.048,0.320,0.240,1.000))
colnames(mycor) <- c("V1", "V2", "V3", "V4", "V5", "V6")
rownames(mycor) <- colnames(mycor) 

# reflect all 3 indicators of the second factor
mynewcor <- corReflect(vars=c(V4,V5,V6))

# abbreviated form
# replace original mycor
mycor <- reflect(vars=c(V4,V5,V6))

lessR documentation built on Nov. 12, 2023, 1:08 a.m.