Means {memisc} | R Documentation |
The function Means()
creates a table of group
means, optionally with standard errors, confidence intervals, and
numbers of valid observations.
Means(data, ...) ## S3 method for class 'data.frame' Means(data, by, weights=NULL, subset=NULL, default=NA, se=FALSE, ci=FALSE, ci.level=.95, counts=FALSE, ...) ## S3 method for class 'formula' Means(data, subset, weights, ...) ## S3 method for class 'numeric' Means(data, ...) ## S3 method for class 'means.table' as.data.frame(x, row.names=NULL, optional=TRUE, drop=TRUE, ...) ## S3 method for class 'xmeans.table' as.data.frame(x, row.names=NULL, optional=TRUE, drop=TRUE, ...)
data |
an object usually containing data, or a formula. If If |
by |
a formula, a vector of variable names or a data frame or list of factors. If If If |
weights |
an optional vector of weights, usually a variable in |
subset |
an optional logical vector to select observations,
usually the result of an expression in variables from |
default |
a default value used for empty cells without observations. |
se |
a logical value, indicates whether standard errors should be computed. |
ci |
a logical value, indicates whether limits of confidence intervals should be computed. |
ci.level |
a number, the confidence level of the confidence interval |
counts |
a logical value, indicates whether numbers of valid observations should be reported. |
x |
for |
row.names |
an optional character vector. This argmument presently is
inconsequential and only included for reasons of compatiblity
with the standard methods of |
optional |
an optional logical value. This argmument presently is
inconsequential and only included for reasons of compatiblity
with the standard methods of |
drop |
a logical value, determines whether "empty cells" should be dropped from the resulting data frame. |
... |
other arguments, either ignored or passed on to other methods where applicable. |
An array that inherits classes "means.table" and "table". If
Means
was called with se=TRUE
or ci=TRUE
then the result additionally inherits class "xmeans.table".
# Preparing example data USstates <- as.data.frame(state.x77) USstates <- within(USstates,{ region <- state.region name <- state.name abb <- state.abb division <- state.division }) USstates$w <- sample(runif(n=6),size=nrow(USstates),replace=TRUE) # Using the data frame method Means(USstates[c("Murder","division","region")],by=c("division","region")) Means(USstates[c("Murder","division","region")],by=USstates[c("division","region")]) Means(USstates[c("Murder")],1) Means(USstates[c("Murder","region")],by=c("region")) # Using the formula method # One 'dependent' variable Means(Murder~1, data=USstates) Means(Murder~division, data=USstates) Means(Murder~division, data=USstates,weights=w) Means(Murder~division+region, data=USstates) as.data.frame(Means(Murder~division+region, data=USstates)) # Standard errors and counts Means(Murder~division, data=USstates, se=TRUE, counts=TRUE) drop(Means(Murder~division, data=USstates, se=TRUE, counts=TRUE)) as.data.frame(Means(Murder~division, data=USstates, se=TRUE, counts=TRUE)) # Confidence intervals Means(Murder~division, data=USstates, ci=TRUE) drop(Means(Murder~division, data=USstates, ci=TRUE)) as.data.frame(Means(Murder~division, data=USstates, ci=TRUE)) # More than one dependent variable Means(Murder+Illiteracy~division, data=USstates) as.data.frame(Means(Murder+Illiteracy~division, data=USstates)) # Confidence intervals Means(Murder+Illiteracy~division, data=USstates, ci=TRUE) as.data.frame(Means(Murder+Illiteracy~division, data=USstates, ci=TRUE)) # Some 'non-standard' but still valid usages: with(USstates, Means(Murder~division+region,subset=region!="Northeast")) with(USstates, Means(Murder,by=list(division,region)))