lkhrWrapper {iCheck} | R Documentation |
Perform glm test for all gene probes.
lkhrWrapper(es, formulaReduced = FEV1 ~ xi + gender, formulaFull = FEV1 ~ xi + age + gender, family = gaussian, logit = FALSE, pvalAdjMethod = "fdr", alpha = 0.05, probeID.var = "ProbeID", gene.var = "Symbol", chr.var = "Chromosome", applier = lapply, verbose = TRUE)
es |
An LumiBatch object.
|
formulaReduced |
An object of class |
formulaFull |
An object of class |
family |
By default is gaussian. refer to |
logit |
logical. Indicate if the gene probes will be logit transformed. For example, for DNA methylation data, one might want to logit transformation for the beta-value (methylated/(methylated+unmethylated)). |
pvalAdjMethod |
One of p-value adjustment methods provided by
the R function |
alpha |
Significance level. A test is claimed to be significant
if the adjusted p-value < |
probeID.var |
character string. Name of the variable indicating probe ID in feature data set. |
gene.var |
character string. Name of the variable indicating gene symbol in feature data set. |
chr.var |
character string. Name of the variable indicating chromosome number in feature data set. |
applier |
By default, it is lapply. If the library multicore is available, can use mclapply to replace lappy. |
verbose |
logical. Determine if intermediate output need to be suppressed. By default
|
This function applies R functions lrtest
in R package lmtest
and glm
for each gene probe.
A list with the following elements:
frame |
A data frame containing test results sorted according
to the ascending order of unadjusted p-values for the covariate
of the interest. The data frame contains
8 columns: |
frame.unsorted |
A data frame containing test results.
unordered |
Weiliang Qiu <stwxq@channing.harvard.edu>, Brandon Guo <brandowonder@gmail.com>, Christopher Anderson <christopheranderson84@gmail.com>, Barbara Klanderman <BKLANDERMAN@partners.org>, Vincent Carey <stvjc@channing.harvard.edu>, Benjamin Raby <rebar@channing.harvard.edu>
# generate simulated data set from conditional normal distribution set.seed(1234567) es.sim = genSimData.BayesNormal(nCpGs = 100, nCases = 20, nControls = 20, mu.n = -2, mu.c = 2, d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var", outlierFlag = FALSE, eps = 1.0e-3, applier = lapply) print(es.sim) set.seed(1234567) es.sim$age = rnorm(ncol(es.sim), mean=50, sd=5) res.lkh = lkhrWrapper( es = es.sim, formulaReduced = xi ~ memSubj, formulaFull = xi ~ memSubj + age, family = gaussian(), logit = FALSE, pvalAdjMethod = "fdr", alpha = 0.05, probeID.var = "probe", gene.var = "gene", chr.var = "chr", applier = lapply, verbose = TRUE)