cor_mat | R Documentation |
Compute correlation matrix with p-values. Numeric columns in the data are detected and automatically selected for the analysis. You can also specify variables of interest to be used in the correlation analysis.
cor_mat( data, ..., vars = NULL, method = "pearson", alternative = "two.sided", conf.level = 0.95 ) cor_pmat( data, ..., vars = NULL, method = "pearson", alternative = "two.sided", conf.level = 0.95 ) cor_get_pval(x)
data |
a data.frame containing the variables. |
... |
One or more unquoted expressions (or variable names) separated by commas. Used to select a variable of interest. |
vars |
a character vector containing the variable names of interest. |
method |
a character string indicating which correlation
coefficient is to be used for the test. One of |
alternative |
indicates the alternative hypothesis and must be
one of |
conf.level |
confidence level for the returned confidence interval. Currently only used for the Pearson product moment correlation coefficient if there are at least 4 complete pairs of observations. |
x |
an object of class |
a data frame
cor_mat()
: compute correlation matrix with p-values. Returns a data
frame containing the matrix of the correlation coefficients. The output has
an attribute named "pvalue", which contains the matrix of the correlation
test p-values.
cor_pmat()
: compute the correlation matrix but returns only the p-values of the tests.
cor_get_pval()
: extract a correlation matrix p-values from an object of
class cor_mat()
. P-values are not adjusted.
cor_test()
, cor_reorder()
,
cor_gather()
, cor_select()
,
cor_as_symbols()
, pull_triangle()
,
replace_triangle()
# Data preparation #::::::::::::::::::::::::::::::::::::::::::: mydata <- mtcars %>% select(mpg, disp, hp, drat, wt, qsec) head(mydata, 3) # Compute correlation matrix #:::::::::::::::::::::::::::::::::::::::::: # Correlation matrix between all variables cor.mat <- mydata %>% cor_mat() cor.mat # Specify some variables of interest mydata %>% cor_mat(mpg, hp, wt) # Or remove some variables in the data # before the analysis mydata %>% cor_mat(-mpg, -hp) # Significance levels #:::::::::::::::::::::::::::::::::::::::::: cor.mat %>% cor_get_pval() # Visualize #:::::::::::::::::::::::::::::::::::::::::: # Insignificant correlations are marked by crosses cor.mat %>% cor_reorder() %>% pull_lower_triangle() %>% cor_plot(label = TRUE) # Gather/collapse correlation matrix into long format #:::::::::::::::::::::::::::::::::::::::::: cor.mat %>% cor_gather()
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