preselection_nopc: preselection_nopc
In GWAS.BAYES: GWAS for Selfing Species

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

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

`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.

`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.