This function performs Polygenic Prioritisation Scoring (POPS) using Bayesian regression (`bayesC` or `bayesR`) or ridge regression (`rr`). It maps features to sets, performs optional feature selection based on p-value thresholds, and calculates predictive scores for prioritisation.
pops(
stat = NULL,
sets = NULL,
validate = NULL,
threshold = NULL,
method = "bayesC",
pi = 0.001,
nit = 5000,
nburn = 1000,
updateB = TRUE,
updateE = TRUE,
updatePi = TRUE,
updateG = TRUE
)A matrix of predicted prioritisation scores (`ypred`) for each feature, ordered by their predictive values. If a validation set is provided, cross-validation results are returned instead.
A numeric vector or matrix of summary statistics (e.g., phenotypic values or effect sizes), where rows represent features (e.g., SNPs) and columns represent traits. Required.
A list of feature sets (e.g., genes or SNP groups) to map to the rows of `stat`. Required.
An optional validation set. If provided, cross-validation results are returned instead of fitting the model.
A numeric value specifying a p-value threshold for feature selection. If provided, only features with p-values below this threshold are included in the model.
A string specifying the regression method. Options are `"bayesC"` (default), `"bayesR"`, or `"rr"` (ridge regression).
A numeric value specifying the proportion of non-zero effects for Bayesian methods. Default is `0.001`.
An integer specifying the number of iterations for Bayesian methods. Default is `5000`.
An integer specifying the number of burn-in iterations for Bayesian methods. Default is `1000`.
A logical value indicating whether to update marker effects in Bayesian methods. Default is `TRUE`.
A logical value indicating whether to update residual variances in Bayesian methods. Default is `TRUE`.
A logical value indicating whether to update the proportion of non-zero effects in Bayesian methods. Default is `TRUE`.
A logical value indicating whether to update the genomic variances in Bayesian methods. Default is `TRUE`.