lessR (version 2.4.2)

corProp: Proportionality Coefficients from Correlations

Description

Abbreviation: prop

Each graph is based on a default color theme. The original default is "blue", but corProp allows other color palettes to be corProp as default as well.

Usage

corProp(x=mycor, 
          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)
prop(...)

Arguments

x

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

Proportionality coefficients indicate the extent of proportionality between two variables. Perfect proportionality of two variables is consistent with both variables being indicators of the same latent variable or factor and indicators of no other factor.

Define the constituent variables, the items, of each factor 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.

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)

# proportionality coefficients of correlation matrix mycor
# indicators of the same factor have proportional correlations
corProp()

# abbreviated form
prop()

# calculate and store proportionality coefficients in myprop
# order the proportionality coefficients to help identify factors
myprop <- corProp()
corReorder(myprop)