either a factor vector giving the values,
or (if data is not NULL) a character string, an integer or a
logical vector specifying the corresponding column of data.
weights
optional; either a numeric vector giving the personal sample
weights, or (if data is not NULL) a character string, an
integer or a logical vector specifying the corresponding column of
data.
sort
optional; either a numeric vector giving the personal IDs to be
used as tie-breakers for sorting, or (if data is not NULL) a
character string, an integer or a logical vector specifying the corresponding
column of data.
years
optional; either a numeric vector giving the different years of
the survey, or (if data is not NULL) a character string, an
integer or a logical vector specifying the corresponding column of
data. If supplied, values are computed for each year.
breakdown
optional; either a numeric vector giving different domains,
or (if data is not NULL) a character string, an integer or a
logical vector specifying the corresponding column of data. If
supplied, the values for each domain are computed in addition to the overall
value.
design
optional and only used if var is not NULL; either
an integer vector or factor giving different domains for stratified sampling
designs, or (if data is not NULL) a character string, an
integer or a logical vector specifying the corresponding column of
data.
cluster
optional and only used if var is not NULL;
either an integer vector or factor giving different clusters for cluster
sampling designs, or (if data is not NULL) a character string,
an integer or a logical vector specifying the corresponding column of
data.
data
an optional data.frame.
var
a character string specifying the type of variance estimation to
be used, or NULL to omit variance estimation. See
variance for possible values.
alpha
numeric; if var is not NULL, this gives the
significance level to be used for computing the confidence interval (i.e.,
the confidence level is 1 - alpha).
na.rm
a logical indicating whether missing values should be removed.
...
if var is not NULL, additional arguments to be
passed to variance.
Details
If weights are provided, the weighted proportion is estimated.
Value
A list of class "prop" (which inherits from the class
"indicator") with the following components:
value
a numeric vector containing the overall value(s).
valueByStratum
a data.frame containing the values by
domain, or NULL.
varMethod
a character string specifying the type of variance
estimation used, or NULL if variance estimation was omitted.
var
a numeric vector containing the variance estimate(s), or
NULL.
varByStratum
a data.frame containing the variance
estimates by domain, or NULL.
ci
a numeric vector or matrix containing the lower and upper
endpoints of the confidence interval(s), or NULL.
ciByStratum
a data.frame containing the lower and upper
endpoints of the confidence intervals by domain, or NULL.
alpha
a numeric value giving the significance level used for
computing the confidence interval(s) (i.e., the confidence level is 1 -
alpha), or NULL.
years
a numeric vector containing the different years of the
survey.
strata
a character vector containing the different domains of the
breakdown.
Author(s)
Matthias Templ, using code for breaking down
estimation by Andreas Alfons
References
A. Alfons and M. Templ (2013) Estimation of Social Exclusion Indicators
from Complex Surveys: The R Package laeken. Journal of
Statistical Software, 54(15), 1–25. URL
http://www.jstatsoft.org/v54/i15/
Working group on Statistics on Income and Living Conditions (2004)
Common cross-sectional EU indicators based on EU-SILC; the gender
pay gap. EU-SILC 131-rev/04, Eurostat, Luxembourg.
See Also
variance
Examples
data(eusilc)
# overall value
prop("rb090", weights = "rb050", data = eusilc)
# values by region
prop("rb090", weights = "rb050",
breakdown = "db040", data = eusilc)