misty (version 0.4.0)

cor.matrix: Correlation Matrix with Statistical Significance Testing

Description

This function computes a correlation matrix and computes significance values (p-values) for testing the hypothesis H0: \(\rho\) = 0 for all possible pairs of variables.

Usage

cor.matrix(x, method = c("pearson", "spearman", "kendall-b", "kendall-c"),
           na.omit = FALSE, group = NULL, print = c("all", "cor", "n", "p"),
           tri = c("both", "lower", "upper"),
           p.adj = c("none", "bonferroni", "holm", "hochberg", "hommel",
                     "BH", "BY", "fdr"), digits = 2, p.digits = 3, as.na = NULL,
           check = TRUE, output = TRUE)

Arguments

x

a matrix or data frame.

method

a character vector indicating which correlation coefficient is to be computed, i.e. "pearson" for Pearson product-moment correlation coefficient (default), "spearman" for Spearman's rank-order correlation coefficient, kendall-b for Kendall's Tau-b correlation coefficient or kendall-c for Kendall-Stuart's Tau-c correlation coefficient.

na.omit

logical: if TRUE, incomplete cases are removed before conducting the analysis (i.e., listwise deletion); if FALSE (default), pairwise deletion is used.

group

a numeric vector, character vector of factor as grouping variable to show results for each group separately, i.e., upper triangular for one group and lower triangular for another group. Note that the grouping variable is limited to two groups.

print

a character string or character vector indicating which additional results to show, i.e. "all", for all additional results: "n" for the sample sizes, and "p" for p-values.

tri

a character string indicating which triangular of the matrix to show on the console, i.e., both for upper and lower triangular, lower (default) for the lower triangular, and upper for the upper triangular.

p.adj

a character string indicating an adjustment method for multiple testing based on p.adjust, i.e., none (default), bonferroni, holm, hochberg, hommel, BH, BY, or fdr.

digits

an integer value indicating the number of decimal places to be used for displaying correlation coefficients.

p.digits

an integer value indicating the number of decimal places to be used for displaying p-values.

as.na

a numeric vector indicating user-defined missing values, i.e. these values are converted to NA before conducting the analysis.

check

logical: if TRUE, argument specification is checked.

output

logical: if TRUE, output is shown on the console.

Value

Returns an object of class misty.object, which is a list with following entries: function call (call), type of analysis type, matrix or data frame specified in x (data), specification of function arguments (args), and list with results (result).

References

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. John Wiley & Sons.

See Also

cohens.d, cor.cont, cor.cramer, multilevel.icc, cor.phi, na.auxiliary, size.cor.

Examples

# NOT RUN {
dat <- data.frame(group = c("a", "a", "a", "a", "a",
                            "b", "b", "b", "b", "b"),
                  x = c(5, NA, 6, 4, 6, 7, 9, 5, 8, 7),
                  y = c(3, 3, 5, 6, 7, 4, 7, NA, NA, 8),
                  z = c(1, 3, 1, NA, 2, 4, 6, 5, 9, 6),
                  stringsAsFactors = TRUE)

# Pearson product-moment correlation coefficient matrix using pairwise deletion
cor.matrix(dat[, c("x", "y", "z")])

# Spearman's rank-order correlation matrix using pairwise deletion
cor.matrix(dat[, c("x", "y", "z")], method = "spearman")

# Kendall's Tau-b correlation matrix using pairwise deletion
cor.matrix(dat[, c("x", "y", "z")], method = "kendall-b")

# Kendall's Tau-c correlation matrix using pairwise deletion
cor.matrix(dat[, c("x", "y", "z")], method = "kendall-c")

# Pearson product-moment correlation coefficient matrix using pairwise deletion,
# print sample size and significance values
cor.matrix(dat[, c("x", "y", "z")], print = "all")

# Pearson product-moment correlation coefficient matrix using listwise deletion,
# print sample size and significance values
cor.matrix(dat[, c("x", "y", "z")], na.omit = TRUE, print = "all")

# Pearson product-moment correlation coefficient matrix using listwise deletion,
# print sample size and significance values with Bonferroni correction
cor.matrix(dat[, c("x", "y", "z")], na.omit = TRUE, print = "all", p.adj = "bonferroni")

# Pearson product-moment correlation coefficient matrix using pairwise deletion,
# results for group "a" and "b" separately
cor.matrix(dat[, c("x", "y", "z")], group = dat$group, print = "all")
# }