h.forestPlot: Forest plot for meta-analysis of heterogenerous traits or...

In ASSET: An R package for subset-based association analysis of heterogeneous traits and subtypes

Description

Forest Plot for meta-analysis of heterogenerous traits or types.

Usage

h.forestPlot(rlist, snp.var, level=0.05, p.adj=TRUE, digits=2)

Arguments

rlist	The list of results returned by h.traits or h.types. SNPs other than snp.var are ignored.
snp.var	A character string giving the name of the SNP variable to be plotted. No default.
level	Level for confidence intervals. Default is 0.05 for 95% confidence intervals.
p.adj	Logical. Whether to report Bonferroni adjusted p-values for each individual subtype. Default is TRUE.
digits	Number of significant digits to display the odds ratios in the plot.

Value

Forest plot for a SNP showing regression coefficients (e.g. log-odds-ratio for case-control studies) for individual studies/traits and confidence intervals, estimate of an overall regression coefficient and confidence interval based on standard fixed-effect meta-analysis and estimate of regression coefficient(s) and confidence intervals associated with the identified best subset(s).

See Also

h.summary, h.traits, h.types

Examples

 # Use the example data
 data(ex_trait, package="ASSET")
 data

 # Define the input arguments to h.traits
 snps       <- as.vector(data[, "SNP"])
 traits.lab <- paste("Trait_", 1:6, sep="")
 beta.hat   <- as.matrix(data[, paste(traits.lab, ".Beta", sep="")])
 sigma.hat  <- as.matrix(data[, paste(traits.lab, ".SE", sep="")])
 cor        <- list(N11=N11, N00=N00, N10=N10)
 ncase      <- diag(N11)
 ncntl      <- diag(N00)

 # Now let us call h.traits on these summary data. 
 res <- h.traits(snps, traits.lab, beta.hat, sigma.hat, ncase, ncntl, cor=cor, 
                 cor.numr=FALSE, search=NULL, side=2, meta=TRUE, zmax.args=NULL, 
                 meth.pval="DLM")

 h.forestPlot(res, "SNP_1", digits=3)

ASSET documentation built on Nov. 8, 2020, 5:20 p.m.