biVar is a generic function that accepts a formula and usual
data, subset, and na.action parameters plus a
list statinfo that specifies a function of two variables to
compute along with information about labeling results for printing and
plotting. The function is called separately with each right hand side
variable and the same left hand variable. The result is a matrix of
bivariate statistics and the statinfo list that drives printing
and plotting. The plot method draws a dot plot with x-axis values by
default sorted in order of one of the statistics computed by the function.
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.
biVar is used to do the looping and bookkeeping. By default the
plot shows the adjusted rho^2, using the same formula used for
the ordinary adjusted R^2. The F test uses the unadjusted
R2.
spearman computes Spearman's rho on non-missing values of two
variables. spearman.test is a simple version of
spearman2.default.
chiSquare is set up like spearman2 except it is intended
for a categorical response variable. Separate Pearson chi-square tests
are done for each predictor, with optional collapsing of infrequent
categories. Numeric predictors having more than g levels are
categorized into g quantile groups. chiSquare uses
biVar.
biVar(formula, statinfo, data=NULL, subset=NULL,
na.action=na.retain, exclude.imputed=TRUE, ...)# S3 method for biVar
print(x, ...)
# S3 method for biVar
plot(x, what=info$defaultwhat,
sort.=TRUE, main, xlab,
vnames=c('names','labels'), ...)
spearman2(x, ...)
# S3 method for default
spearman2(x, y, p=1, minlev=0, na.rm=TRUE, exclude.imputed=na.rm, ...)
# S3 method for formula
spearman2(formula, data=NULL,
subset, na.action=na.retain, exclude.imputed=TRUE, ...)
spearman(x, y)
spearman.test(x, y, p=1)
chiSquare(formula, data=NULL, subset=NULL, na.action=na.retain,
exclude.imputed=TRUE, ...)
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.
biVar, spearman2.formula, and chiSquare return a
matrix with rows corresponding to predictors.
Uses midranks in case of ties, as described by Hollander and Wolfe.
P-values for Spearman, Wilcoxon, or Kruskal-Wallis tests are
approximated by using the t or F distributions.
Hollander M. and Wolfe D.A. (1973). Nonparametric Statistical Methods. New York: Wiley.
Press WH, Flannery BP, Teukolsky SA, Vetterling, WT (1988): Numerical Recipes in C. Cambridge: Cambridge University Press.
combine.levels,
varclus, dotchart3, impute,
chisq.test, cut2.
x <- 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)
spearman2(x, y)
plot(spearman2(z ~ x + y + v, p=2))
f <- chiSquare(z ~ x + y + v)
f
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