regression {SeqVarTools} | R Documentation

## 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"))

### 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`.

### 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)

[Package SeqVarTools version 1.24.1 Index]