mclust1Dplot {mclust} | R Documentation |
Plot one-dimensional data given parameters of an MVN mixture model for the data.
mclust1Dplot(data, ..., type = c("classification","uncertainty","density","errors"), ask = TRUE, symbols, grid = 100, identify = FALSE, CEX = 1, xlim)
data |
A numeric vector of observations. Categorical variables are not allowed. |
... |
One or more of the following:
|
type |
Any subset of
c("classification","uncertainty","density","errors") .
The function will produce the corresponding plot if
it has been supplied sufficient information to do so. If more than
one plot is possible then users will be asked to choose from a menu
if ask=TRUE .
|
ask |
A logical variable indicating whether or not a menu should be produced
when more than one plot is possible. The default is ask=TRUE .
|
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.
|
grid |
Number of grid points to use. |
identify |
A logical variable indicating whether or not to add a title to the plot identifying the dimensions used. |
CEX |
An argument specifying the size of the plotting symbols. The default value is 1. |
xlim |
An argument specifying bounds of the plot. This may be useful for when comparing plots. |
One or more plots showing location of the mixture components, classification, uncertainty, density and/or classification errors. Points in the different classes are shown in separate lines above the whole of the data.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.
mclust2Dplot
,
clPairs
,
coordProj
,
do.call
n <- 250 ## create artificial data set.seed(0) y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) yclass <- c(rep(1,n), rep(2,n), rep(3,n)) yEMclust <- summary(EMclust(y),y) mclust1Dplot(y, identify = TRUE, truth = yclass, z = yEMclust$z, ask=FALSE, mu = yEMclust$mu, sigma = yEMclust$sigma, pro = yEMclust$pro) do.call("mclust1Dplot", c(list(data = y, identify = TRUE, truth = yclass, ask=FALSE), yEMclust))