| weighted_estimators | R Documentation |
Compute mean, variance, covariance matrix and correlation matrix, taking into account sample weights.
meanWt: a simple wrapper that calls mean(x, na.rm=na.rm) if
weights is missing and weighted.mean(x, w=weights,
na.rm=na.rm) otherwise. Implemented methods for this generic are:
meanWt.default(x, weights, na.rm=TRUE, ...)
meanWt.dataObj(x, vars, na.rm=TRUE, ...)
varWt: calls var(x, na.rm=na.rm) if weights is missing.
Implemented methods for this generic are:
varWt.default(x, weights, na.rm=TRUE, ...)
varWt.dataObj(x, vars, na.rm=TRUE, ...)
covWt and covWt: always remove missing values pairwise and call
cov and cor, respectively, if weights is missing.
Implemented methods for these generics are:
covWt.default(x, y, weights, ...)
covWt.matrix(x, weights, ...)
covWt.data.frame(x, weights, ...)
covWt.dataObj(x, vars, ...)
corWt.default(x, y, weights, ...)
corWt.matrix(x, weights, ...)
corWt.data.frame(x, weights, ...)
corWt.dataObj(x, vars, ...)
The additional parameters are now described:
y: a numeric vector. If missing, this defaults to x.
vars: a character vector of variable names that should be used for the calculation.
na.rm: a logical indicating whether any NA or NaN values
should be removed from x before computation. Note that the default
is TRUE.
weights: an optional numeric vector containing sample weights.
meanWt(x, ...)
varWt(x, ...)
covWt(x, ...)
corWt(x, ...)
x |
for |
... |
for the generic functions |
For meanWt, the (weighted) mean.
For varWt, the (weighted) variance.
For covWt, the (weighted) covariance matrix or, for the default
method, the (weighted) covariance.
For corWt, the (weighted) correlation matrix or, for the default
method, the (weighted) correlation coefficient.
meanWt, varWt, covWt and corWt all make use of
slot weights of the input object if the dataObj-method is
used.
Stefan Kraft and Andreas Alfons
mean, weighted.mean,
var, cov,
cor
data(eusilcS)
meanWt(eusilcS$netIncome, weights=eusilcS$rb050)
sqrt(varWt(eusilcS$netIncome, weights=eusilcS$rb050))
# dataObj-methods
inp <- specifyInput(data=eusilcS, hhid="db030", hhsize="hsize", strata="db040", weight="db090")
meanWt(inp, vars="netIncome")
sqrt(varWt(inp, vars="netIncome"))
corWt(inp, vars=c("age", "netIncome"))
covWt(inp, vars=c("age", "netIncome"))
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