h.forestPlot {ASSET} | R Documentation |
Forest Plot for meta-analysis of heterogenerous traits or types.
h.forestPlot(rlist, snp.var, level=0.05, p.adj=TRUE, digits=2)
rlist |
The list of results returned by |
snp.var |
A character string giving the name of the SNP variable to be plotted. No default. |
level |
Level for confidence intervals. Default is |
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. |
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).
# 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)