The function fits a reduced conditional normal model (CNM)

Description

'CNM.simple' is used to fit the reduced (correlation only) conditional normal model using GEE.

Arguments

Details

The input object can be a numerical matrix with three columns with row representing observations and column representing three variables. It can also be an ExpressionSet object with three features. If input a matrix class data, all three columns of the object representing the variables should have column names. Each variable in the object will be standardized with mean 0 and variance 1 in the function. In addition, the third variable will be quantile normalized within the function. More detail example about the usage of geneMap is demonstrated in the vignette.

Value

'CNM.full' returns a object of CNM class with two Slots. The first slot describes the fitted model. The second slot is a matrix contains the CNM model fitting results. The row of this matrix represents the parameters in the CNM model. The first column, estimates, is the estimated value of the corresponding parameters. The second column, san.se, is the value of sandwich standard error estimator for the estimates. The third column, wald, is the wald test statistic as described in Ho et al (2009). The corresponding p value for the wald test statistic is represented in the fourth column. A more detailed interpretation of these values is illustrated in the vignette.

Author(s)

Yen-Yi Ho

References

Yen-Yi Ho, Leslie Cope, Thomas A. Louis, and Giovanni Parmigiani, GENERALIZED LIQUID ASSOCIATION (April 2009). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 183. http://www.bepress.com/jhubiostat/paper183.

Jun Yan and Jason Fine. Estimating equations for association structures Statistics in Medicine. 23(6): 859–74; discussion 875-7,879-80. http://dx.doi.org/10.1002/sim.1650

See Also

CNM.full-methods, CNM-class

Examples

data<-matrix(rnorm(300), ncol=3)

colnames(data)<-c("Gene1", "Gene2", "Gene3")

FitCNM.simple<-CNM.simple(data)

FitCNM.simple

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(LiquidAssociation)
Loading required package: geepack
Loading required package: yeastCC
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: org.Sc.sgd.db
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums


> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/LiquidAssociation/CNM.simple-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: CNM.simple-methods
> ### Title: The function fits a reduced conditional normal model (CNM)
> ### Aliases: CNM.simple-methods CNM.simple,eSet-method
> ###   CNM.simple,matrix-method CNM.simple
> ### Keywords: methods models
> 
> ### ** Examples
> 
> 
> data<-matrix(rnorm(300), ncol=3)
> 
> colnames(data)<-c("Gene1", "Gene2", "Gene3")
> 
> FitCNM.simple<-CNM.simple(data)
> 
> FitCNM.simple
Model: CNM(Gene1,Gene2|Gene3)
     estimates     san.se       wald   p value
a3 -0.01004731 0.09365976 0.01150783 0.9145712
a4 -0.01004731 0.09365976 0.01150783 0.9145712
a5 -0.22826148 0.19113776 1.42617325 0.2323900
b5  0.13718250 0.20834936 0.43352396 0.5102648
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>