Compute contrasts for a varFit object.

Description

Compute estimated coefficients, standard errors and LogVarRatios for a given set of contrasts.

Usage

contrasts.varFit(fit, contrasts=NULL)

Arguments

Details

This function calls the contrasts.fit function in limma to compute coefficients and standard errors for the specified contrasts corresponding to a linear model fit obtained from the varFit function. LogVarRatios are also computed in terms of the contrasts. A contrasts matrix can be computed using the makeContrasts function.

Value

A list object of the same class as fit.

Author(s)

Belinda Phipson

See Also

varFit, contrasts.fit, makeContrasts

Examples

# Randomly generate data for a 3 group problem with 100 CpG sites and 4 arrays in each group. 

library(limma)

y<-matrix(rnorm(1200),ncol=12)

group<-factor(rep(c(1,2,3),each=4))
design<-model.matrix(~0+group)
colnames(design)<-c("grp1","grp2","grp3")

# Fit linear model for differential variability
vfit<-varFit(y,design)

# Specify contrasts
contr<-makeContrasts(grp2-grp1,grp3-grp1,grp3-grp2,levels=colnames(design))

# Compute contrasts from fit object
vfit.contr<-contrasts.varFit(vfit,contrasts=contr)

summary(decideTests(vfit.contr))

# Look at top table of results for first contrast

topVar(vfit.contr,coef=1)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(missMethyl)
Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/missMethyl/contrasts.varFit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: contrasts.varFit
> ### Title: Compute contrasts for a varFit object.
> ### Aliases: contrasts.varFit
> 
> ### ** Examples
> 
> # Randomly generate data for a 3 group problem with 100 CpG sites and 4 arrays in each group. 
> 
> library(limma)
> 
> y<-matrix(rnorm(1200),ncol=12)
> 
> group<-factor(rep(c(1,2,3),each=4))
> design<-model.matrix(~0+group)
> colnames(design)<-c("grp1","grp2","grp3")
> 
> # Fit linear model for differential variability
> vfit<-varFit(y,design)
Warning messages:
1: Partial NA coefficients for 100 probe(s) 
2: In out$var.prior[is.na(out$var.prior)] <- 1/out$s2.prior :
  number of items to replace is not a multiple of replacement length
3: In ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim,  :
  Estimation of var.prior failed - set to default value
> 
> # Specify contrasts
> contr<-makeContrasts(grp2-grp1,grp3-grp1,grp3-grp2,levels=colnames(design))
> 
> # Compute contrasts from fit object
> vfit.contr<-contrasts.varFit(vfit,contrasts=contr)
Warning messages:
1: In out$var.prior[is.na(out$var.prior)] <- 1/out$s2.prior :
  number of items to replace is not a multiple of replacement length
2: In ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim,  :
  Estimation of var.prior failed - set to default value
> 
> summary(decideTests(vfit.contr))
   grp2 - grp1 grp3 - grp1 grp3 - grp2
-1           0           0           0
0            0           0           0
1            0           0           0
> 
> # Look at top table of results for first contrast
> 
> topVar(vfit.contr,coef=1)
   SampleVar LogVarRatio DiffLevene  t P.Value Adj.P.Value
1  0.6756536         NaN         NA NA      NA          NA
2  0.5361943         NaN         NA NA      NA          NA
3  1.1354274         NaN         NA NA      NA          NA
4  0.9988517         NaN         NA NA      NA          NA
5  0.5339442         NaN         NA NA      NA          NA
6  0.8884493         NaN         NA NA      NA          NA
7  0.4779402         NaN         NA NA      NA          NA
8  0.3822255         NaN         NA NA      NA          NA
9  1.1312203         NaN         NA NA      NA          NA
10 0.3287167         NaN         NA NA      NA          NA
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>