xtabs(formula = ~., data = parent.frame(), subset, sparse = FALSE,
na.action, exclude = c(NA, NaN), drop.unused.levels = FALSE)+) on the right hand side (or an object which
can be coerced to a formula). Interactions are not allowed. On the
left hand side, one may optionally give a vector or a matrix of
counts; in the latter case, the columns are interpreted as
corresponding to the levels of a variable. This is useful if the
data have already been tabulated, see the examples below.model.frame) containing the variables in the
formula formula. By default the variables are taken from
environment(formula).sparseMatrix
Only works for two factors (since there
are no higher-order sparse array classes yet).
NAs.FALSE and
there are unused levels, the table will contain zero marginals, and
a subsequent chi-squared test for independence of the factors will
not work.sparse = FALSE,
a contingency table in array representation of S3 class c("xtabs",
"table"), with a "call" attribute storing the matched call. When sparse = TRUE, a sparse numeric matrix, specifically an
object of S4 class
dgTMatrix from package
https://CRAN.R-project.org/package=Matrix.summary method for contingency table objects created
by table or xtabs(*, sparse = FALSE), which gives basic
information and performs a chi-squared test for independence of
factors (note that the function chisq.test currently
only handles 2-d tables). If a left hand side is given in formula, its entries are simply
summed over the cells corresponding to the right hand side; this also
works if the lhs does not give counts. For variables in formula which are factors, exclude
must be specified explicitly; the default exclusions will not be used.table for traditional cross-tabulation, and
as.data.frame.table which is the inverse operation of
xtabs (see the DF example below). sparseMatrix on sparse
matrices in package https://CRAN.R-project.org/package=Matrix.## 'esoph' has the frequencies of cases and controls for all levels of
## the variables 'agegp', 'alcgp', and 'tobgp'.
xtabs(cbind(ncases, ncontrols) ~ ., data = esoph)
## Output is not really helpful ... flat tables are better:
ftable(xtabs(cbind(ncases, ncontrols) ~ ., data = esoph))
## In particular if we have fewer factors ...
ftable(xtabs(cbind(ncases, ncontrols) ~ agegp, data = esoph))
## This is already a contingency table in array form.
DF <- as.data.frame(UCBAdmissions)
## Now 'DF' is a data frame with a grid of the factors and the counts
## in variable 'Freq'.
DF
## Nice for taking margins ...
xtabs(Freq ~ Gender + Admit, DF)
## And for testing independence ...
summary(xtabs(Freq ~ ., DF))
## Create a nice display for the warp break data.
warpbreaks$replicate <- rep(1:9, len = 54)
ftable(xtabs(breaks ~ wool + tension + replicate, data = warpbreaks))
### ---- Sparse Examples ----
if(require("Matrix")) {
## similar to "nlme"s 'ergoStool' :
d.ergo <- data.frame(Type = paste0("T", rep(1:4, 9*4)),
Subj = gl(9, 4, 36*4))
print(xtabs(~ Type + Subj, data = d.ergo)) # 4 replicates each
set.seed(15) # a subset of cases:
print(xtabs(~ Type + Subj, data = d.ergo[sample(36, 10), ], sparse = TRUE))
## Hypothetical two-level setup:
inner <- factor(sample(letters[1:25], 100, replace = TRUE))
inout <- factor(sample(LETTERS[1:5], 25, replace = TRUE))
fr <- data.frame(inner = inner, outer = inout[as.integer(inner)])
print(xtabs(~ inner + outer, fr, sparse = TRUE))
}<!-- % only if Matrix is available -->
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