ASSET (version 1.10.0)

h.summary: Summary results from subset-search.

Description

This function produces summary results from subset-based association analysis.

Usage

h.summary(rlist, level = 0.05, digits = 3)

Arguments

rlist

List returned by h.traits or h.types

level

Level for confidence intervals. Default is 0.05 for 95% confidence intervals. The confidence intervals are obtained by inverting the corresponding multiple-testing adjusted p-values.

digits

Number of significant digits to retain in odds ratios and confidence intervals in the summary table

Value

A list of data frames, one for each of the methods specified the original call of the functions h.traits or h.types. Each row of a data frame corresponds to a SNP and the values include p-values for overall association (including component-wise p-values for two-sided search), names of phenotypes or disease subtypes included in the best-subset, summary regression coefficients (e.g. log-odds-ratio for case-control studies) representing strength of association of a SNP with the identified subset of traits/subtype and corresponding confidence intervals.

Details

Returns a list of data frames containing p-values, odds-ratios, confidence intervals and the traits/types for each analysis. The number of data frames in the list will depend on which function (h.traits or h.types) was called and on the function options specified.

See Also

h.forestPlot, h.traits, h.types

Examples

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

 # 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=ncase, 
                 ncntl=ncntl, cor=cor, cor.numr=FALSE, search=NULL, 
                 side=2, meta=TRUE, zmax.args=NULL)
 
 h.summary(res)