lessR (version 3.7.6)

corReorder: Reorder Variables in a Correlation Matrix

Description

Abbreviation: reord

Re-arranges the order of the variables in the input correlation matrix. If no variable list is specified then the variables are re-ordered according to first providing the variable with the largest sum of squared correlations of all the variables, then the variable that has the highest correlation with the first variable, and so forth.

Usage

corReorder(R=mycor, vars=NULL, first=0,
          heat.map=TRUE, main=NULL, bottom=3,right=3,
          pdf.file=NULL, width=5, height=5)
reord(…)

Arguments

R

Correlation matrix.

vars

List of the re-ordered variables, each variable listed by its ordinal position in the input correlation matrix.

first

The first variable listed in the ordered 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

Reorder and/or delete variables in the input correlation matrix.

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 corReorder function call according to vars=c(m02:m05,Anxiety).

Or, define the ordering of the variables according to the following algorithm. If no variable list is specified then the variables are re-ordered such that the first variable is that which has the largest sum of squared correlations of all the variables, then the variable that has the highest correlation with the first variable, and so forth.

In the absence of a variable list, the first variable in the re-ordered matrix can be specified with the first option.

See Also

Correlation.

Examples

# NOT RUN {
# 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)

# leave only the 3 indicators of the second factor
#  in reverse order
#replace original mycor
mycor <- corReorder(vars=c(V6,V5,V4))

#  by their sums of squares
mynewcor <- corReorder()

# reorder the variables according to the ordering algorithm
#  with the 4th variable listed first
mynewcor <- corReorder(first=2)
# }