lessR (version 2.4.2)

Correlation: Correlation Analysis

Description

Abbreviation: cr, cr.brief

Correlation coefficient with hypothesis test and confidence interval for two variables, or the correlation matrix for a data frame with more than two variables. The computed coefficient(s) are the standard Pearson's product-moment correlation. For the default missing data technique of pairwise deletion, an analysis of missing data for each computed correlation coefficient is provided, and also a statistical summary of the missing data across all cells.

Usage

Correlation(x, y, dframe=mydata,
         miss=c("pairwise", "listwise", "everything"),
         show.n=NULL, brief=FALSE, n.cat=getOption("n.cat"), digits.d=NULL,
         colors=c("blue", "gray", "rose", "green", "gold", "red"),
         heat.map=TRUE, main=NULL, bottom=3, right=3,
         pdf.file=NULL, pdf.width=5, pdf.height=5, ...)
cr.brief(..., brief=TRUE) 
cr(...)

Arguments

x

First variable.

y

Second variable.

dframe

Optional data frame that contains one or both of the variables of interest, default is mydata.

miss

Basis for deleting missing data values.

show.n

For pairwise deletion, show the matrix of sample sizes for each correlation coefficient, regardless of sample size.

brief

If FALSE, then the sample covariance and number of non-missing and missing observations are displayed.

n.cat

When analyzing all the variables in a data frame, specifies the largest number of unique values of variable of a numeric data type for which the variable will be analyzed as a categorical. Set to 0 to turn off.

digits.d

Specifies the number of decimal digits to display in the output.

colors

Sets the color palette for the heat map.

heat.map

If TRUE and a matrix analyzed, displays a heat map of the matrix of correlation coefficients.

main

Graph title of heat map. Set to main="" to turn off.

bottom

Number of lines of bottom margin of heat map.

right

Number of lines of right margin of heat map.

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.

...

Other parameter values for internally called functions.

Details

Correlation coefficient with hypothesis test, for a null hypothesis of 0, and confidence interval. Also displays the sample covariance. Based on R functions cor, cor.test, cov.

In place of two variables x and y, x can be a complete data frame. All the variables in the data frame must be numeric. If not, then an appropriate error message is displayed. Given such a data frame, the correlation matrix of all the variables in the data frame is displayed.

The computed coefficient(s) are the standard Pearson's product-moment correlation. Use the standard R functions cor and cor.test to obtain Spearman and Kendall correlation coefficients.

For treating missing data, the default is pairwise, which means that an observation is deleted only for the computation of a specific correlation coefficient if one or both variables are missing the value for the relevant variable(s). For listwise deletion, the entire observation is deleted from the analysis if any of its data values are missing. For the more radical everything option, any missing data values for a variable result in all correlations for that variable reported as missing.

Text output to the console provides feedback, and the correlation matrix itself is written to a matrix called mycor, stored in the user's workspace. This matrix is ready for input into any of the lessR functions that analyze correlational data, including confirmatory factor analysis by corCFA and also exploratory factor analysis, either the standard R function factanal or the lessR function corEFA.

See Also

cor.test, cov.

Examples

# data
n <- 12
f <- sample(c("Group1","Group2"), size=n, replace=TRUE)
x <- round(rnorm(n=n, mean=50, sd=10), 2)
y <- round(rnorm(n=n, mean=50, sd=10), 2)
z <- round(rnorm(n=n, mean=50, sd=10), 2)
mydata <- data.frame(f,x,y,z)
rm(f); rm(x); rm(y); rm(z)

# correlation and covariance
Correlation(x,y)
# short name
cr(x,y)
# brief form of output
cr.brief(x,y)

# correlation matrix
# first remove the categorical variable f from mydata
mydata <- subset(mydata, select=c(x:z))
# now analyze remaining variables x, y and z
Correlation(mydata)

# analysis with data not from data frame mydata
data(attitude)
Correlation(rating, learning, dframe=attitude)