mclust1Dplot.Rd
Plot onedimensional 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.
if (FALSE) { 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") }