rMMSN.contour: Pairwise Scatter Plots and Histograms for Finite Mixture of... In CensMFM: Finite Mixture of Multivariate Censored/Missing Data

Description

It plots the scatter plots with density contours for different multivariate distributions. Possible options are the Skew-normal (`family == "SN"`), Normal (`family == "Normal"`) and Student-t (`family == "t"`) distribution. Different colors are used by groups. Histograms are shown in the diagonal.

Usage

 ```1 2 3 4 5``` ```rMMSN.contour(model = NULL, y = NULL, mu = NULL, Sigma = NULL, shape = NULL, nu = NULL, pii = NULL, Zij = NULL, contour = FALSE, hist.Bin = 30, contour.Bin = 10, slice = 100, col.names = NULL, length.x = c(0.5, 0.5), length.y = c(0.5, 0.5), family = "SN") ```

Arguments

 `model` is an object resultant from the codefit.FMMSNC function. `y` the response matrix with dimension nxp. `mu` a list with g entries, where each entry represents location parameter per group, being a vector of dimension p. `Sigma` a list with g entries, where each entry represents a scale parameter per group, a matrix with dimension pxp. `shape` a list with g entries, where each entry represents a skewness parameter, being a vector of dimension p. `nu` the degrees of freedom for the Student-t distribution case, being a vector with dimension g. `pii` a vector of weights for the mixture of dimension g, the number of clusters. It must sum to one! `Zij` a matrix of dimension nxp indicating the group for each observation. `contour` If `contour == TRUE` the density contour will be shown, if `contour == FALSE` the density contour must be not returned. `hist.Bin` number of bins in the histograms. Default is 30. `contour.Bin` creates evenly spaced contours in the range of the data. Default is 10. `slice` desired length of the sequence for the variables grid. This grid is build for the contours. `col.names` names passed to the data matrix y of dimension p. `length.x` a vector of dimension 2 with the value to be subtracted and added from the minimum and maximum observation in the x-axis respectively. Default is `c(0.5,0.5)`. `length.y` a vector of dimension 2 with the value to be subtracted and added from the minimum and maximum observation in the y-axis respectively. Default is `c(0.5,0.5)`. `family` distribution family to be used. Available distributions are the Skew-normal ("SN"), normal ("Normal") or Student-t ("t") distribution.

Details

If the `model` object is used, the user still has the option to choose the `family`. If the `model` object is not used, the user must input all other parameters. User may use the `rMMSN` function to generate data.

Note

This functions works well for any length of g and p, but contour densities are only shown for p = 2.

Author(s)

Francisco H. C. de Alencar hildemardealencar@gmail.com, Christian E. Galarza cgalarza88@gmail.com, Victor Hugo Lachos hlachos@uconn.edu and Larissa A. Matos larissam@ime.unicamp.br

Maintainer: Francisco H. C. de Alencar hildemardealencar@gmail.com

References

Cabral, C. R. B., Lachos, V. H., & Prates, M. O. (2012). Multivariate mixture modeling using skew-normal independent distributions. Computational Statistics & Data Analysis, 56(1), 126-142.

Prates, M. O., Lachos, V. H., & Cabral, C. (2013). mixsmsn: Fitting finite mixture of scale mixture of skew-normal distributions. Journal of Statistical Software, 54(12), 1-20.

C.E. Galarza, L.A. Matos, D.K. Dey & V.H. Lachos. (2019) On Moments of Folded and Truncated Multivariate Extended Skew-Normal Distributions. Technical report. ID 19-14. University of Connecticut.

F.H.C. de Alencar, C.E. Galarza, L.A. Matos & V.H. Lachos. (2019) Finite Mixture Modeling of Censored and Missing Data Using the Multivariate Skew-Normal Distribution. echnical report. ID 19-31. University of Connecticut.

`fit.FMMSNC`, `rMMSN` and `fit.FMMSNC`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```mu <- Sigma <- shape <- list() mu[[1]] <- c(-3,-4) mu[[2]] <- c(2,2) Sigma[[1]] <- matrix(c(3,1,1,4.5), 2,2) Sigma[[2]] <- matrix(c(2,1,1,3.5), 2,2) shape[[1]] <- c(-2,2) shape[[2]] <- c(-3,4) nu <- 0 pii <- c(0.6,0.4) percent <- c(0.1,0.2) n <- 100 seed <- 654678 set.seed(seed) test = rMMSN(n = n, pii = pii,mu = mu,Sigma = Sigma,shape = shape, percent = percent, each = TRUE, family = "SN") ## SN ## SN.contour = rMMSN.contour(model = NULL, y = test\$y, Zij = test\$G ,mu = mu, Sigma = Sigma, shape = shape, pii = pii, family = "SN") #Plotting contours may take some time... ## SN ## SN.contour = rMMSN.contour(model = NULL, y = test\$y, Zij = test\$G ,mu = mu, Sigma = Sigma, shape = shape, pii = pii, contour = TRUE, family = "SN") ## Normal ## N.contour = rMMSN.contour(model = NULL,y = test\$y, Zij = test\$G ,mu = mu, Sigma = Sigma, shape = shape, pii = pii, contour = TRUE, family = "Normal") ## t ## t.contour = rMMSN.contour(model = NULL,y = test\$y, Zij = test\$G ,mu = mu, Sigma = Sigma, shape = shape, pii = pii, nu = c(4,3), contour = TRUE, family = "t") ```