Description Usage Arguments Details Value Methods Note Author(s) References See Also Examples
Generic function for producing x-y plots. For simulation results, the average results are plotted against the corresponding contamination levels or missing value rates.
1 2 3 4 5 6 |
x |
the object to be plotted. For plotting simulation results, this
must be an object of class |
true |
a numeric vector giving the true values. If supplied, reference lines are drawn in the corresponding panels. |
epsilon |
a numeric vector specifying contamination levels. If supplied, the values corresponding to these contamination levels are extracted from the simulation results and plotted. |
NArate |
a numeric vector specifying missing value rates. If supplied, the values corresponding to these missing value rates are extracted from the simulation results and plotted. |
select |
a character vector specifying the columns to be plotted. It
must be a subset of the |
cond |
a character string; for simulation results with multiple
contamination levels and multiple missing value rates, this specifies
the column of the simulation results to be used for producing conditional
x-y plots. If |
average |
a character string specifying how the averages should be
computed. Possible values are |
... |
additional arguments to be passed down to methods and eventually
to |
For simulation results with multiple contamination levels and multiple
missing value rates, conditional x-y plots are produced, as specified by
cond
.
An object of class "trellis"
. The
update
method can be used to update
components of the object and the print
method (usually called by default) will plot it on an appropriate plotting
device.
x = "SimResults"
produce x-y plots of simulation results.
Functionality for producing conditional x-y plots (including the argument
cond
) was added in version 0.2. Prior to that, the function gave an
error message if simulation results with multiple contamination levels and
multiple missing value rates were supplied.
The argument average
that specifies how the averages are computed
was added in version 0.1.2. Prior to that, the mean has always been used.
Andreas Alfons
Alfons, A., Templ, M. and Filzmoser, P. (2010) An Object-Oriented Framework for Statistical Simulation: The R Package simFrame. Journal of Statistical Software, 37(3), 1–36. doi: 10.18637/jss.v037.i03.
simBwplot
, simDensityplot
,
xyplot
, "SimResults"
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | #### design-based simulation
set.seed(12345) # for reproducibility
data(eusilcP) # load data
## control objects for sampling and contamination
sc <- SampleControl(size = 500, k = 50)
cc <- DARContControl(target = "eqIncome",
epsilon = seq(0, 0.05, by = 0.01),
fun = function(x) x * 25)
## function for simulation runs
sim <- function(x) {
c(mean = mean(x$eqIncome), trimmed = mean(x$eqIncome, 0.05))
}
## run simulation
results <- runSimulation(eusilcP,
sc, contControl = cc, fun = sim)
## plot results
tv <- mean(eusilcP$eqIncome) # true population mean
simXyplot(results, true = tv)
#### model-based simulation
set.seed(12345) # for reproducibility
## function for generating data
rgnorm <- function(n, means) {
group <- sample(1:2, n, replace=TRUE)
data.frame(group=group, value=rnorm(n) + means[group])
}
## control objects for data generation and contamination
means <- c(0, 0.25)
dc <- DataControl(size = 500, distribution = rgnorm,
dots = list(means = means))
cc <- DCARContControl(target = "value",
epsilon = seq(0, 0.05, by = 0.01),
dots = list(mean = 15))
## function for simulation runs
sim <- function(x) {
c(mean = mean(x$value),
trimmed = mean(x$value, trim = 0.05),
median = median(x$value))
}
## run simulation
results <- runSimulation(dc, nrep = 50,
contControl = cc, design = "group", fun = sim)
## plot results
simXyplot(results, true = means)
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