lessR (version 2.8)

Correlation: Correlation Analysis

Description

Abbreviation: cr, cr.brief

For two variables the correlation coefficient with hypothesis test and confidence interval, or for a data frame, or list of variables from a data frame, the correlation matrix. The default 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. For a correlation matrix a statistical summary of the missing data across all cells is provided.

Usage

Correlation(x, y, data=mydata,
         miss=c("pairwise", "listwise", "everything"),
         show.n=NULL, brief=FALSE, n.cat=getOption("n.cat"),
         digits.d=NULL, graphics=FALSE,
         main=NULL, bottom=3, right=3,
         pdf=FALSE, pdf.width=5, pdf.height=5, ...)
cr.brief(..., brief=TRUE) 
cr(...)

Arguments

x

First variable, or list of variables for a correlation matrix.

y

Second variable or not specified if the first argument is a list.

data

Optional data frame that contains 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

Peta ins to a single correlation coefficient analysis. 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.

graphics

If TRUE, generate a scatter plot matrix and heat map.

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

If TRUE, generate scatter plot matrix and heat map and write to pdf files.

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, which include method="spearman" and method="kendall" and also alternative="less" and alternative="more".

Value

• If a correlation matrix is computed, the matrix is returned. Usually assign the name of mycor to the output matrix, as in following examples. 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

Details

When two variables are specified, both x and y, the output is the 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, either specified with the name of a data frame, or blank to rely upon the default data frame mydata. Or, x can be a list of variables from the input data frame. In these situations y is missing. Any non-numeric variables in the data frame or specified variable list are automatically deleted from the analysis.

Set graphics=TRUE to generate a heat map and scatter plot matrix to standard graphics windows. Set pdf=TRUE to generate these graphics but have them directed to their respective pdf files.

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 extreme everything option, any missing data values for a variable result in all correlations for that variable reported as missing.

See Also

cor.test, cov.

Examples

# data
n <- 12
f <- sample(c("Group1","Group2"), size=n, replace=TRUE)
x1 <- round(rnorm(n=n, mean=50, sd=10), 2)
x2 <- round(rnorm(n=n, mean=50, sd=10), 2)
x3 <- round(rnorm(n=n, mean=50, sd=10), 2)
x4 <- round(rnorm(n=n, mean=50, sd=10), 2)
mydata <- data.frame(f,x1, x2, x3, x4)
rm(f); rm(x1); rm(x2); rm(x3); rm(x4)

# correlation and covariance
Correlation(x1, x2)
# short name
cr(x1, x2)
# brief form of output
cr.brief(x1, x2)

# Spearman rank correlation, one-sided test
Correlation(x1, x2, method="spearman", alternative="less")

# correlation matrix of the numerical variables in mycor
mycor <- Correlation()

# correlation matrix of Kendall's tau coefficients
mycor <- cr(method="kendall")

# correlation matrix of specified variables in mycor with graphics
mycor <- Correlation(x1:x3, graphics=TRUE)

# analysis with data not from data frame mycor
data(attitude)
mycor <- Correlation(rating, learning, data=attitude)

# analysis of entire data frame that is not mycor
data(attitude)
mycor <- Correlation(attitude)