preselection_nopc: preselection_nopc

Description Usage Arguments Value

View source: R/preselection_nopc.R

Description

Finds significant SNP's when no principal components are present.

Usage

1
preselection_nopc(Y,X,number_cores,frequentist,controlrate,threshold,nullprob,alterprob,kinship = FALSE)

Arguments

Y

The reduced matrix of response values

X

The reduced SNP matrix where th columns are either 1's or 0's.

number_cores

The number of cores on which you would like to parallize this procedure

frequentist

A logical value to see whether one would like to use a frequentist multiple comparison test or Bayesian False Discovery based on BIC's. The value of this affects whether values of the next parameters are needed.

controlrate

Only used when frequentist = TRUE. This is for which multiple comparison method you would like to use. Examples are "bonferroni" and "BH". See p.adjust for a full list of methods.

threshold

The value at which type 1 error rate is held at. .05 in most common literature. Used when frequentist is TRUE or FALSE

nullprob

Used when frequentist = FALSE, the probability that is assigned to the null hypothesis.

alterprob

Used when frequentist = FALSE, the probability that is assigned to the alternate hypothesis.

kinship

The kinship matrix if a model with a kinship component is desired. If not set kinship = FALSE.

Value

Frequentist Matrix

The matrix of results when Frequentist = TRUE. The results are formated as a data.frame with the column Significant being 1 or 0 depending on if the SNP was significant (1 for significant). The P_values column will be the p-values that were calculated for each SNP.

Bayesian Matrix

The matrix of results when Frequentist = FALSE. The results are formated as a data.frame with the column Significant being 1 or 0 depending on if the SNP was significant (1 for significant). The ApprPosteriorProbs column will be the Approximate Posterior Probabilities that were calculated for each SNP.


GWAS.BAYES documentation built on Nov. 8, 2020, 7:47 p.m.