# Contour plot for Manly mixture model

### Description

Provides a contour plot for the fitted data with Manly mixture model.

### Usage

1 2 | ```
Manly.contour(X, var1 = 1, var2 = 2, model = NULL, x.slice = 100,
y.slice = 100, x.mar = 1, y.mar = 1, col = "lightgrey", ...)
``` |

### Arguments

`X ` |
dataset matrix (n x p) |

`var1 ` |
x-axis variable |

`var2 ` |
y-axis variable |

`model ` |
fitted Manly mixture model |

`x.slice ` |
number of slices in the first variable sequence in the contour |

`y.slice ` |
number of slices in the second variable sequence in the contour |

`x.mar ` |
value to be subtracted/added to the smallest/largest observation in the x-axis |

`y.mar ` |
value to be subtracted/added to the smallest/largest observation in the y-axis |

`col ` |
color of the contour lines |

`...` |
further arguments related to |

### Details

Provides the contour plot for the fitted data by Manly mixture model. This plot is designed for two-dimensional data.

### See Also

Manly.EM

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
set.seed(123)
K <- 2; p <- 2
X <- as.matrix(faithful)
# Obtain initial memberships based on the K-means algorithm
id.km <- kmeans(X, K)$cluster
# Run the EM algorithm for a Manly mixture model based on K-means solution
la <- matrix(0.1, K, p)
B <- Manly.EM(X, id.km, la)
Manly.contour(X, model = B, x.mar = 1, y.mar = 2,
xaxs="i", yaxs="i", xaxt="n", yaxt="n", xlab="",
ylab = "", nlevels = 10, drawlabels = FALSE,
lwd = 3.2, col = "lightgrey", pch = 19)
``` |

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