spBwplot | R Documentation |
Produce box-and-whisker plots of continuous or semi-continuous variables, possibly broken down according to conditioning variables and taking into account sample weights.
spBwplot( inp, x, cond = NULL, horizontal = TRUE, coef = 1.5, zeros = TRUE, minRatio = NULL, do.out = FALSE, ... ) panelSpBwplot(x, y, coef = 1.5, zeros = TRUE, ratio, outliers, subscripts, ...) getBwplotStats(x, weights = NULL, cond = NULL, data, ..., name = "") prepBwplotStats(x, w, ..., name = "") ## S3 method for class 'data.frame' prepBwplotStats(x, w, ..., name = "") ## Default S3 method: prepBwplotStats(x, w, ..., name = "")
inp |
an object of class |
x |
a character vector specifying the columns of data available in the sample and the population (specified in input object 'inp') to be plotted. |
cond |
an optional character vector (of length 1, if used) specifying the conditioning variable. |
horizontal |
a logical indicating whether the boxes should be horizontal or vertical. |
coef |
a numeric value that determines the extension of the whiskers. |
zeros |
a logical indicating whether the variables specified by
|
minRatio |
a numeric value in (0,1]; if |
do.out |
a logical indicating whether data points that lie beyond the
extremes of the whiskers should be plotted. Note that this is |
... |
further arguments to be passed to
|
Missing values are ignored for producing box plots and weights are directly
extracted from the input object inp
.
An object of class "trellis"
, as returned by
bwplot
.
Andreas Alfons and Bernhard Meindl
A. Alfons, M. Templ (2011) Simulation of close-to-reality population data for household surveys with application to EU-SILC. Statistical Methods & Applications, 20 (3), 383–407. doi: 10.1007/s10260-011-0163-2
spBwplotStats
, bwplot
## these take some time and are not run automatically ## copy & paste to the R command line set.seed(1234) # for reproducibility data(eusilcS) # load sample data ## approx. 20 seconds computation time inp <- specifyInput(data=eusilcS, hhid="db030", hhsize="hsize", strata="db040", weight="db090") simPop <- simStructure(data=inp, method="direct", basicHHvars=c("age", "rb090", "hsize", "pl030", "pb220a")) # multinomial model with random draws eusilcM <- simContinuous(simPop, additional="netIncome", regModel = ~rb090+hsize+pl030+pb220a+hsize, upper=200000, equidist=FALSE, nr_cpus=1) # plot results spBwplot(eusilcM, x="netIncome", cond=NULL) spBwplot(eusilcM, x="netIncome", cond="rb090", layout=c(1,2))
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