`mclust1Dplot.Rd`

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:`pro`

Mixing proportions for the components of the mixture. There should one more mixing proportion than the number of Gaussian components if the mixture model includes a Poisson noise term.

`mean`

The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the

*k*th component of the mixture model.`variance`

A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for

`mclustVariance`

for details.

- z
A matrix in which the

`[i,k]`

th entry gives the probability of observation*i*belonging to the*k*th class. Used to compute`classification`

and`uncertainty`

if those arguments aren't available.- classification
A numeric or character vector representing a classification of observations (rows) of

`data`

. If present argument`z`

will be ignored.- truth
A numeric or character vector giving a known classification of each data point. If

`classification`

or`z`

is also present, this is used for displaying classification errors.- uncertainty
A numeric vector of values in

*(0,1)*giving the uncertainty of each data point. If present argument`z`

will be ignored.- what
Choose from one of the following options:

`"classification"`

(default),`"density"`

,`"error"`

,`"uncertainty"`

.- symbols
Either an integer or character vector assigning a plotting symbol to each unique class

`classification`

. Elements in`symbols`

correspond to classes in`classification`

in order of appearance in the observations (the order used by the function`unique`

). The default is to use a single plotting symbol*|*. Classes are delineated by showing them in separate lines above the whole of the data.- colors
Either an integer or character vector assigning a color to each unique class

`classification`

. Elements in`colors`

correspond to classes in order of appearance in the observations (the order used by the function`unique`

). The default is given is`mclust.options("classPlotColors")`

.- 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

`NULL`

indicating whether or not to add a title to the plot identifying the dimensions used.- ...
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.

```
# \donttest{
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")
# }
```