cr, cr.briefFor two variables yields the correlation coefficient with hypothesis test and confidence interval. For a data frame or list of variables from a data frame, yields the correlation matrix. The default computed coefficient(s) are the standard Pearson's product-moment correlation, with Spearman and Kendall coefficients available. 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.
Versions of this function from lessR 3.3 or earlier returned just a correlation matrix. Now other values are returned as well so that the correlation matrix is now stored as part of a returned list in cors, directly available, for example, as mycor$cors from mycor <- cr(mydata). This revision is automatically adjusted for in the lessR routines that read the subsequent correlation matrix, so all pre-existing code continues to work. That is, the input into any of these routines could be, for example, mycor, mycor$cors or a stand-alone correlation matrix such as in pre-lessR 3.3.
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(...)
mydata.FALSE, then the sample covariance and number of non-missing
and missing observations are displayed.TRUE, generate a scatter plot matrix and heat map.main="" to turn off.TRUE, generate scatter plot matrix and heat map
and write to pdf files.method="spearman" and method="kendall" and also
alternative="less" and alternative="more".lessR of 3.3 and earlier, if a correlation matrix is computed, the matrix is returned. Now more values are returned, so the matrix is embedded in a list of returned elements.READABLE OUTPUT
single coefficient
out_background: Variables in the model, any variable labels
out_describe: Estimated coefficients
out_inference: Hypothesis test and confidence interval estimated coefficient
matrix
out_background: Variables in the model, any variable labels
out_missing: Missing values anlaysis
out_cor: Correlations
STATISTICS
single coefficient
r: Model formula that specifies the model
tvalue: t-statistic of estimated value of null hypothesis of no relationship
df: Degrees of freedom of hypothesis test
pvalue: Number of rows of data submitted for analysis
lb: Lower bound of confidence interval
ub: Upper bound of confidence interval
matrix
cors: Correlations
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
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.
cor.test, cov.# 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)Run the code above in your browser using DataLab