regression: Linear or logistic regression

In smgogarten/SeqVarTools: Tools for variant data

Linear or logistic regression

Description

Run linear or logistic regression on variants

Usage

## S4 method for signature 'SeqVarData'
regression(gdsobj, outcome, covar=NULL,
    model.type=c("linear", "logistic", "firth"),
    parallel=FALSE)

Arguments

gdsobj	A SeqVarData object
outcome	A character string with the name of the column in sampleData(gdsobj) containing the outcome variable
covar	A character vector with the name of the column(s) in sampleData(gdsobj) containing the covariates
model.type	the type of model to be run. "linear" uses lm, "logistic" uses glm with family=binomial(), and "firth" uses logistf.
parallel	Logical, numeric, or other value to control parallel processing; see seqParallel for details.

Details

regression tests the additive effect of the reference allele.

Value

a data.frame with the following columns (if applicable):

variant.id	variant identifier
n	number of samples with non-missing data
n0	number of controls (outcome=0) with non-missing data
n1	number of cases (outcome=1) with non-missing data
freq	reference allele frequency
freq0	reference allele frequency in controls
freq1	reference allele frequency in cases
Est	beta estimate for genotype
SE	standard error of beta estimate for the genotype
Wald.Stat	chi-squared test statistic for association
Wald.pval	p-value for association
PPL.Stat	firth only: profile penalized likelihood test statistic for association
PPL.pval	firth only: p-value for association

Author(s)

Stephanie Gogarten

See Also

SeqVarData, seqSetFilter, lm, glm, logistf

Examples

gds <- seqOpen(seqExampleFileName("gds"))

## create some phenotype data
library(Biobase)
sample.id <- seqGetData(gds, "sample.id")
n <- length(sample.id)
df <- data.frame(sample.id,
   sex=sample(c("M", "F"), n, replace=TRUE),
   age=sample(18:70, n, replace=TRUE),
   phen=rnorm(n),
   stringsAsFactors=FALSE)
meta <- data.frame(labelDescription=c("sample identifier",
   "sex", "age", "phenotype"), row.names=names(df))
sample.data <- AnnotatedDataFrame(df, meta)
seqData <- SeqVarData(gds, sample.data)

## select samples and variants
seqSetFilter(gds, sample.id=sample.id[1:50], variant.id=1:10)

res <- regression(seqData, outcome="phen", covar=c("sex", "age"))
res
seqClose(gds)

smgogarten/SeqVarTools documentation built on July 4, 2023, 2:34 a.m.