mclust1Dplot | R Documentation |
Plot one-dimensional data given parameters of an MVN mixture model for the data.
mclust1Dplot(data, parameters = NULL, z = NULL,
classification = NULL, truth = NULL, uncertainty = NULL,
what = c("classification", "density", "error", "uncertainty"),
symbols = NULL, colors = NULL, ngrid = length(data),
xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL,
cex = 1, main = FALSE, ...)
data |
A numeric vector of observations. Categorical variables are not allowed. |
parameters |
A named list giving the parameters of an MCLUST model, used to produce superimposing ellipses on the plot. The relevant components are as follows:
|
z |
A matrix in which the |
classification |
A numeric or character vector representing a classification of
observations (rows) of |
truth |
A numeric or character vector giving a known
classification of each data point.
If |
uncertainty |
A numeric vector of values in (0,1) giving the
uncertainty of each data point. If present argument |
what |
Choose from one of the following options: |
symbols |
Either an integer or character vector assigning a plotting symbol to
each unique class |
colors |
Either an integer or character vector assigning a color to each
unique class |
ngrid |
Number of grid points to use for density computation over the interval spanned by the data. The default is the length of the data set. |
xlab , ylab |
An argument specifying a label for the axes. |
xlim , ylim |
An argument specifying bounds of the plot. This may be useful for when comparing plots. |
cex |
An argument specifying the size of the plotting symbols. The default value is 1. |
main |
A logical variable or |
... |
Other graphics parameters. |
A plot showing location of the mixture components, classification, uncertainty, density and/or classification errors. Points in the different classes are shown in separated levels above the whole of the data.
mclust2Dplot
,
clPairs
,
coordProj
n <- 250 ## create artificial data
set.seed(1)
y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5))
yclass <- c(rep(1,n), rep(2,n), rep(3,n))
yModel <- Mclust(y)
mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
what = "classification")
mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
what = "error", truth = yclass)
mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
what = "density")
mclust1Dplot(y, z = yModel$z, parameters = yModel$parameters,
what = "uncertainty")
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