rcorr Computes a matrix of Pearson's r or Spearman's
rho rank correlation coefficients for all possible pairs of
columns of a matrix. Missing values are deleted in pairs rather than
deleting all rows of x having any missing variables. Ranks are
computed using efficient algorithms (see reference 2), using midranks
for ties.spearman2 computes the square of Spearman's rho rank correlation
and a generalization of it in which x can relate
non-monotonically to y. This is done by computing the Spearman
multiple rho-squared between (rank(x), rank(x)^2) and y.
When x is categorical, a different kind of Spearman correlation
used in the Kruskal-Wallis test is computed (and spearman2 can do
the Kruskal-Wallis test). This is done by computing the ordinary
multiple R^2 between k-1 dummy variables and
rank(y), where x has k categories. x can
also be a formula, in which case each predictor is correlated separately
with y, using non-missing observations for that predictor.
print and plot methods allow one to easily print or plot
the results of spearman2(formula). The adjusted rho^2 is
also computed, using the same formula used for the ordinary adjusted
R^2. The F test uses the unadjusted R2. For plot,
a dot chart is drawn which by default shows, in sorted order, the
adjusted rho^2.
spearman computes Spearman's rho on non-missing values of two
variables. spearman.test is a simple version of spearman2.default.
rcorr(x, y, type=c("pearson","spearman"))## S3 method for class 'rcorr':
print(x, ...)
spearman2(x, ...)
## S3 method for class 'default':
spearman2(x, y, p=1, minlev=0, exclude.imputed=TRUE, ...)
## S3 method for class 'formula':
spearman2(x, p=1,
data, subset, na.action, minlev=0, exclude.imputed=TRUE, ...)
## S3 method for class 'spearman2.formula':
print(x, ...)
## S3 method for class 'spearman2.formula':
plot(x, what=c('Adjusted rho2','rho2','P'),
sort.=TRUE, main, xlab, ...)
spearman(x, y)
spearman.test(x, y, p=1)
y is absent). For spearman2, the first argument may be a vector
of any type, including character or factor. The first argument may also be a
formula, in which casx. If
y is omitted for rcorr, x must be a matrix.rho^2 to
use. The default is p=1 to compute the ordinary rho^2. Use p=2
to compute the quadratic rank generalization to allow
non-monotonicityna.action is to retain
all values, NA or not, so that NAs can be deleted in only a pairwise
fashion.combine.levels) in spearman2. The default, minlev=0 causes no pooling.FALSE to include imputed values (created by impute) in the calculations.sort.=FALSE to suppress sorting variables by the statistic being plottedwhat.dotchart2rcorr returns a list with elements r, the
matrix of correlations, n the
matrix of number of observations used in analyzing each pair of variables,
and P, the asymptotic P-values.
Pairs with fewer than 2 non-missing values have the r values set to NA.
The diagonals of n are the number of non-NAs for the single variable
corresponding to that row and column. spearman2.default (the
function that is called for a single x, i.e., when there is no
formula) returns a vector of statistics for the variable.
spearman2.formula returns a matrix with rows corresponding to
predictors.t distribution.Press WH, Flannery BP, Teukolsky SA, Vetterling, WT (1988): Numerical Recipes in C. Cambridge: Cambridge University Press.
hoeffd, cor, combine.levels, varclus, dotchart2, imputex <- c(-2, -1, 0, 1, 2)
y <- c(4, 1, 0, 1, 4)
z <- c(1, 2, 3, 4, NA)
v <- c(1, 2, 3, 4, 5)
rcorr(cbind(x,y,z,v))
spearman2(x, y)
plot(spearman2(z ~ x + y + v, p=2))Run the code above in your browser using DataLab