normalization.diagnostic {MVR}

Function for Plotting Summary Normalization Diagnostic Plots

Description

Plot comparative Box-Whisker and Heatmap plots of variables across samples check the effectiveness of normalization before and after Mean-Variance Regularization.

Usage

    normalization.diagnostic(obj, 
                             pal,
                             title = "Normalization Diagnostic Plots",
                             device = NULL, 
                             file = "Normalization Diagnostic Plots",
                             path = getwd(),
                             horizontal = FALSE, 
                             width = 7, 
                             height = 8, ...)

Arguments

obj

Object of class "mvr" returned by mvr.

title

Title of the plot. Defaults to "Normalization Diagnostic Plots".

pal

Color palette.

device

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

File name for output graphic. Defaults to "Normalization Diagnostic Plots".

path

Absolute path (without final (back)slash separator). Defaults to working directory path.

horizontal

Logical scalar. Orientation of the printed image. Defaults to FALSE, that is potrait orientation.

width

Numeric scalar. Width of the graphics region in inches. Defaults to 7.

height

Numeric scalar. Height of the graphics region in inches. Defaults to 8.

...

Generic arguments passed to other plotting functions.

Details

Option file is used only if device is specified (i.e. non NULL). The argument pal can be any color palette, e.g. as provided by R package RColorBrewer.

Value

None. Displays the plots on the chosen device.

Acknowledgments

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).

Note

End-user function.

Author(s)

• "Jean-Eudes Dazard, Ph.D." [e-mail]

• "Hua Xu, Ph.D." [e-mail]

• "Alberto Santana, MBA." [e-mail]

Maintainer: "Jean-Eudes Dazard, Ph.D." [e-mail]

References

• 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.

See Also

justvsn (R package vsn) Variance stabilization and calibration for microarray data. loess (R package stats) Fit a polynomial surface determined by one or more numerical predictors, using local fitting.

Examples

## Not run: 
    #===================================================
    # Loading the library and its dependencies
    #===================================================
    library("MVR")
    library("RColorBrewer")

    #===================================================
    # 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 Normalization Diagnostic Plots (Real dataset)
    # Multi-Group Assumption
    # Assuming unequal variance between groups
    #===================================================
    normalization.diagnostic(obj = mvr.obj, 
                             title = "Normalization Diagnostic Plots 
                             (Real - Multi-Group Assumption)",
                             pal = brewer.pal(n=11, name="RdYlGn"),
                             device = NULL,
                             horizontal = FALSE, 
                             width = 7, 
                             height = 8)

    
## End(Not run)