pvalmat {PSEA} | R Documentation |
Takes a list of fitted models (lm objects) and obtains the p-values associated with each fit.
pvalmat(lst,regressors)
lst |
list of lm objects. |
regressors |
character vector. Names of the coefficients for which p-values are extracted. |
Simple wrapper function that returns p-values extracted from summary(lm).
The coefficient names corresponding to the p-values to be extracted are matched based on their names in rownames(summary(lst[[i]])$coef).
pvalm |
numeric matrix. Matrix of extracted p-values with one row for each model in the list and one column for each targeted coefficient. |
Alexandre Kuhn alexandre.m.kuhn@gmail.com
## Load example expression data (variable "expression") ## and phenotype data (variable "groups") data("example") ## Four cell population-specific reference signals neuron_probesets <- list(c("221805_at", "221801_x_at", "221916_at"), "201313_at", "210040_at", "205737_at", "210432_s_at") neuron_reference <- marker(expression, neuron_probesets) astro_probesets <- list("203540_at",c("210068_s_at","210906_x_at"), "201667_at") astro_reference <- marker(expression, astro_probesets) oligo_probesets <- list(c("211836_s_at","214650_x_at"),"216617_s_at", "207659_s_at",c("207323_s_at","209072_at")) oligo_reference <- marker(expression, oligo_probesets) micro_probesets <- list("204192_at", "203416_at") micro_reference <- marker(expression, micro_probesets) ## Stepwise model selection for 2 transcripts (202429_s_at and ## 200850_s_at) and focusing on control samples (i.e. groups == 0) lmlist <- swlm(expression[c("202429_s_at", "200850_s_at"),], subset = which(groups == 0), upper = formula(~neuron_reference + astro_reference + oligo_reference + micro_reference)) pvalmat(lmlist, c("(Intercept)", "neuron_reference", "astro_reference", "oligo_reference", "micro_reference"))