lessR (version 2.4.2)

corReorder: Reorder Variables in a Correlation Matrix

Description

Abbreviation: reorder

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(x=mycor, vars=NULL, first=0,
          heat.map=TRUE, main=NULL, bottom=3,right=3,
          colors=c("blue", "gray", "rose", "green", "gold", "red"),
          pdf.file=NULL, pdf.width=5, pdf.height=5)
reord(...)

Arguments

x

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.

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

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

Explicitly specify the ordering of the specified variables, the items, 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 re-ordered correlation matrix consists of the second, fifth and twenty-first variables in the input correlation matrix, then in the corReorder function call specify vars=c(2,5,21).

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

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)

# leave only the 3 indicators of the second factor
#  in reverse order
corReorder(vars=c(6,5,4))

# reorder the variables according to the ordering algorithm
corReorder()

# abbreviated form
# retain only the first three variables
reord(vars=c(1:3))

# reorder the variables according to the ordering algorithm
#  with the 4th variable listed first
corReorder(first=4)