
Plot similarity statistic profiles and the optimal joint clustering configuration for the means and the variances by group.
Plot quantile profiles of means and standard deviations by group and for each clustering configuration, to check that the distributions of first and second moments of the MVR-transformed data approach their respective null distributions under the optimal configuration found, assuming independence and normality of all the variables.
cluster.diagnostic(obj,
span = 0.75,
degree = 2,
family = "gaussian",
title = "Cluster Diagnostic Plots",
device = NULL,
file = "Cluster Diagnostic Plots",
path = getwd(),
horizontal = FALSE,
width = 8.5,
height = 11, ...)
Object of class "mvr
" returned by mvr
.
Title of the plot. Defaults to "Cluster Diagnostic Plots".
Span parameter of the loess()
function (R package stats), which controls the degree of smoothing.
Defaults to 0.75.
Degree parameter of the loess()
function (R package stats), which controls the degree of the polynomials to be used.
Defaults to 2. (Normally 1 or 2. Degree 0 is also allowed, but see the "Note" in loess stats package.)
Family distribution in "gaussian", "symmetric" of the loess()
function (R package stats), used for local fitting .
If "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function.
Graphic display device in {NULL, "PS", "PDF"}. Defaults to NULL (standard output screen). Currently implemented graphic display devices are "PS" (Postscript) or "PDF" (Portable Document Format).
File name for output graphic. Defaults to "Cluster Diagnostic Plots".
Absolute path (without final (back)slash separator). Defaults to working directory path.
Logical
scalar. Orientation of the printed image. Defaults to FALSE
, that is potrait orientation.
Numeric
scalar. Width of the graphics region in inches. Defaults to 8.5.
Numeric
scalar. Height of the graphics region in inches. Defaults to 11.
Generic arguments passed to other plotting functions.
None. Displays the plots on the chosen device
.
This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. This project was partially funded by the National Institutes of Health (P30-CA043703).
In a plot of a similarity statistic profile, one checks the goodness of fit of the transformed data relative to the hypothesized underlying reference
distribution with mean-0 and standard deviation-1 (e.g. nc.max
parameter in the mvr
as well as
in mvrt.test
functions until the minimum of the similarity statistic profile is reached.
Option file
is used only if device is specified (i.e. non NULL
).
Dazard J-E. and J. S. Rao (2010). "Regularized Variance Estimation and Variance Stabilization of High-Dimensional Data." In JSM Proceedings, Section for High-Dimensional Data Analysis and Variable Selection. Vancouver, BC, Canada: American Statistical Association IMS - JSM, 5295-5309.
Dazard J-E., Hua Xu and J. S. Rao (2011). "R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization." In JSM Proceedings, Section for Statistical Programmers and Analysts. Miami Beach, FL, USA: American Statistical Association IMS - JSM, 3849-3863.
Dazard J-E. and J. S. Rao (2012). "Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data." Comput. Statist. Data Anal. 56(7):2317-2333.
loess
(R package stats) Fit a polynomial surface determined by one or more numerical predictors, using local fitting.
# NOT RUN {
#===================================================
# Loading the library and its dependencies
#===================================================
library("MVR")
#===================================================
# MVR package news
#===================================================
MVR.news()
#================================================
# MVR package citation
#================================================
citation("MVR")
#===================================================
# Loading of the Synthetic and Real datasets
# (see description of datasets)
#===================================================
data("Synthetic", "Real", package="MVR")
?Synthetic
?Real
#===================================================
# Mean-Variance Regularization (Real dataset)
# Multi-Group Assumption
# Assuming unequal variance between groups
# Without cluster usage
===================================================
nc.min <- 1
nc.max <- 30
probs <- seq(0, 1, 0.01)
n <- 6
GF <- factor(gl(n = 2, k = n/2, length = n),
ordered = FALSE,
labels = c("M", "S"))
mvr.obj <- mvr(data = Real,
block = GF,
log = FALSE,
nc.min = nc.min,
nc.max = nc.max,
probs = probs,
B = 100,
parallel = FALSE,
conf = NULL,
verbose = TRUE,
seed = 1234)
#===================================================
# Summary Cluster Diagnostic Plots (Real dataset)
# Multi-Group Assumption
# Assuming unequal variance between groups
#===================================================
cluster.diagnostic(obj = mvr.obj,
title = "Cluster Diagnostic Plots
(Real - Multi-Group Assumption)",
span = 0.75,
degree = 2,
family = "gaussian",
device = NULL,
horizontal = FALSE,
width = 8.5,
height = 11)
# }
Run the code above in your browser using DataLab