lessR (version 2.4.2)

corReflect: 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(x=mycor, vars,
          main=NULL, heat.map=TRUE, bottom=3,right=3, 
          colors=c("blue", "gray", "rose", "green", "gold", "red"),
          pdf.file=NULL, pdf.width=5, pdf.height=5)
reflect(...)

Arguments

x

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.

colors

Sets the color palette.

pdf.file

Name of the pdf file to which graphics are redirected.

pdf.width

Width of the pdf file in inches.

pdf.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.

Specify which variables, the items, are to be reflected with a listing of each variable in terms of the ordinal position in the correlation matrix to be analyzed. For convenience, a list of this ordering is obtained from corListVars. For the listing of the variables, commas separate consecutive variables and the entire list is specified with the R combine or c function, preceded by vars and an equals sign. For example, if the variables to be reflected consist of the second, fifth and twenty-first variables in the input correlation matrix, then in the corReflect function call specify vars=c(2,5,21).

See Also

cr.

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
corReflect(vars=c(4,5,6))

# abbreviated form
reflect(vars=c(4,5,6))