mvr: Function for Mean-Variance Regularization and Variance...

In jedazard/MVR: Mean-Variance Regularization

Function for Mean-Variance Regularization and Variance Stabilization

Description

End-user function for Mean-Variance Regularization (MVR) and Variance Stabilization by similarity statistic under sample group homoscedasticity or heteroscedasticity assumptions.

Return an object of class "MVR". Offers the option of parallel computation for improved efficiency.

Usage

    mvr(data,
        block = rep(1,nrow(data)),
        tolog = FALSE,
        nc.min = 1,
        nc.max = 30,
        probs = seq(0, 1, 0.01),
        B = 100,
        parallel = FALSE,
        conf = NULL,
        verbose = TRUE, 
        seed = NULL)

Arguments

data	numeric matrix of untransformed (raw) data, where samples are by rows and variables (to be clustered) are by columns, or an object that can be coerced to a matrix (such as a numeric vector or a data.frame with all numeric columns). Missing values (NA), NotANumber values (NaN) or Infinite values (Inf) are not allowed.
block	character or numeric vector, or factor of group membership indicator variable (grouping/blocking variable) of length the data sample size with as many different values or levels as the number of data sample groups. Defaults to single group situation. See details.
tolog	logical scalar. Is the data to be log2-transformed first? Optional, defaults to FALSE. Note that negative or null values will be changed to 1 before taking log2-transformation.
nc.min	Positive integer scalar of the minimum number of clusters, defaults to 1
nc.max	Positive integer scalar of the maximum number of clusters, defaults to 30
probs	numeric vector of probabilities for quantile diagnostic plots. Defaults to seq(0, 1, 0.01).
B	Positive integer scalar of the number of Monte Carlo replicates of the inner loop of the sim statistic function (see details).
parallel	logical scalar. Is parallel computing to be performed? Optional, defaults to FALSE.
conf	list of 5 fields containing the parameters values needed for creating the parallel backend (cluster configuration). See details below for usage. Optional, defaults to NULL, but all fields are required if used: type : character vector specifying the cluster type ("SOCKET", "MPI"). spec : A specification (character vector or integer scalar) appropriate to the type of cluster. homogeneous : logical scalar to be set to FALSE for inhomogeneous clusters. verbose : logical scalar to be set to FALSE for quiet mode. outfile : character vector of an output log file name to direct the stdout and stderr connection output from the workernodes. "" indicates no redirection.
verbose	logical scalar. Is the output to be verbose? Optional, defaults to TRUE.
seed	Positive integer scalar of the user seed to reproduce the results.

Details

Argument block will be converted to a factor, whose levels will match the data groups. It defaults to a single group situation, that is, under the assumption of equal variance between sample groups. All group sample sizes must be greater than 1, otherwise the program will stop.

Argument nc.max currently defaults to 30. Empirically, we found that this is enough for most datasets tested. This depends on (i) the dimensionality/sample size ratio \frac{p}{n}, (ii) the signal/noise ratio, and (iii) whether a pre-transformation has been applied (see Dazard, J-E. and J. S. Rao (2012) for more details). See the cluster diagnostic function cluster.diagnostic for more details, whether larger values of nc.max may be required.

The function mvr relies on the R package parallel to create a parallel backend within an R session. This enables access to a cluster of compute cores and/or nodes on a local and/or remote machine(s) and scaling-up with the number of CPU cores available and efficient parallel execution. To run a procedure in parallel (with parallel RNG), argument parallel is to be set to TRUE and argument conf is to be specified (i.e. non NULL). Argument conf uses the options described in function makeCluster of the R packages parallel and snow. PRIMsrc supports two types of communication mechanisms between master and worker processes: 'Socket' or 'Message-Passing Interface' ('MPI'). In PRIMsrc, parallel 'Socket' clusters use sockets communication mechanisms only (no forking) and are therefore available on all platforms, including Windows, while parallel 'MPI' clusters use high-speed interconnects mechanism in networks of computers (with distributed memory) and are therefore available only in these architectures. A parallel 'MPI' cluster also requires R package Rmpi to be installed first. Value type is used to setup a cluster of type 'Socket' ("SOCKET") or 'MPI' ("MPI"), respectively. Depending on this type, values of spec are to be used alternatively:

• For 'Socket' clusters (conf$type="SOCKET"), spec should be a character vector naming the hosts on which to run the job; it can default to a unique local machine, in which case, one may use the unique host name "localhost". Each host name can potentially be repeated to the number of CPU cores available on the local machine. It can also be an integer scalar specifying the number of processes to spawn on the local machine; or a list of machine specifications (a character value named host specifying the name or address of the host to use).

• For 'MPI' clusters (conf$type="MPI"), spec should be an integer scalar specifying the total number of processes to be spawned across the network of available nodes, counting the workernodes and masternode.

The actual creation of the cluster, its initialization, and closing are all done internally. For more details, see the reference manual of R package snow and examples below.

When random number generation is needed, the creation of separate streams of parallel RNG per node is done internally by distributing the stream states to the nodes. For more details, see the vignette of R package parallel. The use of a seed allows to reproduce the results within the same type of session: the same seed will reproduce the same results within a non-parallel session or within a parallel session, but it will not necessarily give the exact same results (up to sampling variability) between a non-parallelized and parallelized session due to the difference of management of the seed between the two (see parallel RNG and value of returned seed below).

Value

Xraw	numeric matrix of original data.
Xmvr	numeric matrix of MVR-transformed data.
centering	numeric vector of centering values for standardization (cluster mean of pooled sample mean).
scaling	numeric vector of scaling values for standardization (cluster mean of pooled sample std dev).
MVR	list (of size the number of groups) containing for each group: membership numeric vector of cluster membership of each variable nc Positive integer scalar of number of clusters found in optimal cluster configuration gap numeric vector of the similarity statistic values sde numeric vector of the standard errors of the similarity statistic values mu.std numeric matrix (K x p) of the vector of standardized means by groups (rows), where K = \#groups and p = \#variables sd.std numeric matrix (K x p) of the vector of standardized standard deviations by groups (rows), where K = \#groups and p = \#variables mu.quant numeric matrix (nc.max - nc.min + 1) x (length(probs)) of quantiles of means sd.quant numeric matrix (nc.max - nc.min + 1) x (length(probs)) of quantiles of standard deviations
block	Value of argument block.
tolog	Value of argument tolog.
nc.min	Value of argument nc.min.
nc.max	Value of argument nc.max.
probs	Value of argument probs.
seed	User seed(s) used: integer of a single value, if parallelization is used. integer vector of values, one for each replication, if parallelization is not used.

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

• makeCluster (R package parallel).

• justvsn (R package vsn) Variance stabilization and calibration for microarray data Huber, 2002

Examples

#===================================================
# Loading the library and its dependencies
#===================================================
library("MVR")

## Not run: 
    #===================================================
    # MVR package news
    #===================================================
    MVR.news()

    #================================================
    # MVR package citation
    #================================================
    citation("MVR")

    #===================================================
    # Loading of the Synthetic and Real datasets
    # Use help for descriptions
    #===================================================
    data("Synthetic", "Real", package="MVR")
    ?Synthetic
    ?Real

## End(Not run)

#===================================================
# Mean-Variance Regularization (Synthetic dataset)
# Single-Group Assumption
# Assuming equal variance between groups
# Without cluster usage
#===================================================
data("Synthetic", package="MVR")
nc.min <- 1
nc.max <- 10
probs <- seq(0, 1, 0.01)
n <- 10
mvr.obj <- mvr(data = Synthetic,
               block = rep(1,n),
               tolog = FALSE,
               nc.min = nc.min,
               nc.max = nc.max,
               probs = probs,
               B = 100,
               parallel = FALSE,
               conf = NULL,
               verbose = TRUE,
               seed = 1234)

## Not run: 
    #===================================================
    # Examples of parallel backend parametrization 
    #===================================================
    if (require("parallel")) {
       cat("'parallel' is attached correctly \n")
    } else {
       stop("'parallel' must be attached first \n")
    }
    #===================================================
    # Ex. #1 - Multicore PC
    # Running WINDOWS
    # SOCKET communication cluster
    # Shared memory parallelization
    #===================================================
    cpus <- parallel::detectCores(logical = TRUE)
    conf <- list("spec" = rep("localhost", cpus),
                 "type" = "SOCKET",
                 "homo" = TRUE,
                 "verbose" = TRUE,
                 "outfile" = "")
    #===================================================
    # Ex. #2 - Master node + 3 Worker nodes cluster
    # All nodes equipped with identical setups of multicores 
    # (8 core CPUs per machine for a total of 32)
    # SOCKET communication cluster
    # Distributed memory parallelization
    #===================================================
    masterhost <- Sys.getenv("HOSTNAME")
    slavehosts <- c("compute-0-0", "compute-0-1", "compute-0-2")
    nodes <- length(slavehosts) + 1
    cpus <- 8
    conf <- list("spec" = c(rep(masterhost, cpus),
                            rep(slavehosts, cpus)),
                 "type" = "SOCKET",
                 "homo" = TRUE,
                 "verbose" = TRUE,
                 "outfile" = "")
    #===================================================
    # Ex. #3 - Enterprise Multinode Cluster w/ multicore/node  
    # Running LINUX with SLURM scheduler
    # MPI communication cluster
    # Distributed memory parallelization
    # Below, variable 'cpus' is the total number of requested 
    # taks (threads/CPUs), which is specified from within a 
    # SLURM script.
    #==================================================
    if (require("Rmpi")) {
        print("Rmpi is loaded correctly \n")
    } else {
        stop("Rmpi must be installed first to use MPI\n")
    }
    cpus <- as.numeric(Sys.getenv("SLURM_NTASKS"))
    conf <- list("spec" = cpus,
                 "type" = "MPI",
                 "homo" = TRUE,
                 "verbose" = TRUE,
                 "outfile" = "")
    #===================================================
    # Mean-Variance Regularization (Real dataset)
    # Multi-Group Assumption
    # Assuming unequal variance between groups
    #===================================================
    data("Real", package="MVR")
    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,
                   tolog = FALSE,
                   nc.min = nc.min,
                   nc.max = nc.max,
                   probs = probs,
                   B = 100,
                   parallel = TRUE,
                   conf = conf,
                   verbose = TRUE,
                   seed = 1234)
    
## End(Not run)

jedazard/MVR documentation built on July 16, 2022, 10:55 p.m.