View source: R/xtab_statistics.R
cramer | R Documentation |
This function calculates various measure of association for contingency tables and returns the statistic and p-value. Supported measures are Cramer's V, Phi, Spearman's rho, Kendall's tau and Pearson's r.
cramer(tab, ...) ## S3 method for class 'formula' cramer( formula, data, ci.lvl = NULL, n = 1000, method = c("dist", "quantile"), ... ) phi(tab, ...) crosstable_statistics( data, x1 = NULL, x2 = NULL, statistics = c("auto", "cramer", "phi", "spearman", "kendall", "pearson", "fisher"), weights = NULL, ... ) xtab_statistics( data, x1 = NULL, x2 = NULL, statistics = c("auto", "cramer", "phi", "spearman", "kendall", "pearson", "fisher"), weights = NULL, ... )
tab |
A |
... |
Other arguments, passed down to the statistic functions
|
formula |
A formula of the form |
data |
A data frame or a table object. If a table object, |
ci.lvl |
Scalar between 0 and 1. If not |
n |
Number of bootstraps to be generated. |
method |
Character vector, indicating if confidence intervals should be
based on bootstrap standard error, multiplied by the value of the
quantile function of the t-distribution (default), or on sample
quantiles of the bootstrapped values. See 'Details' in |
x1 |
Name of first variable that should be used to compute the
contingency table. If |
x2 |
Name of second variable that should be used to compute the
contingency table. If |
statistics |
Name of measure of association that should be computed. May
be one of |
weights |
Name of variable in |
The p-value for Cramer's V and the Phi coefficient are based
on chisq.test()
. If any expected value of a table cell is
smaller than 5, or smaller than 10 and the df is 1, then fisher.test()
is used to compute the p-value, unless statistics = "fisher"
; in
this case, the use of fisher.test()
is forced to compute the
p-value. The test statistic is calculated with cramer()
resp.
phi()
.
Both test statistic and p-value for Spearman's rho, Kendall's tau
and Pearson's r are calculated with cor.test()
.
When statistics = "auto"
, only Cramer's V or Phi are calculated,
based on the dimension of the table (i.e. if the table has more than
two rows or columns, Cramer's V is calculated, else Phi).
For phi()
, the table's Phi value. For cramer()
, the
table's Cramer's V.
For crosstable_statistics()
, a list with following components:
estimate
the value of the estimated measure of association.
p.value
the p-value for the test.
statistic
the value of the test statistic.
stat.name
the name of the test statistic.
stat.html
if applicable, the name of the test statistic, in HTML-format.
df
the degrees of freedom for the contingency table.
method
character string indicating the name of the measure of association.
method.html
if applicable, the name of the measure of association, in HTML-format.
method.short
the short form of association measure, equals the statistics
-argument.
fisher
logical, if Fisher's exact test was used to calculate the p-value.
# Phi coefficient for 2x2 tables tab <- table(sample(1:2, 30, TRUE), sample(1:2, 30, TRUE)) phi(tab) # Cramer's V for nominal variables with more than 2 categories tab <- table(sample(1:2, 30, TRUE), sample(1:3, 30, TRUE)) cramer(tab) # formula notation data(efc) cramer(e16sex ~ c161sex, data = efc) # bootstrapped confidence intervals cramer(e16sex ~ c161sex, data = efc, ci.lvl = .95, n = 100) # 2x2 table, compute Phi automatically crosstable_statistics(efc, e16sex, c161sex) # more dimensions than 2x2, compute Cramer's V automatically crosstable_statistics(efc, c172code, c161sex) # ordinal data, use Kendall's tau crosstable_statistics(efc, e42dep, quol_5, statistics = "kendall") # calcilate Spearman's rho, with continuity correction crosstable_statistics(efc, e42dep, quol_5, statistics = "spearman", exact = FALSE, continuity = TRUE )
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