resids_diag: resids_diag
In willja16/GWAS.BAYES: GWAS for Selfing Species

Description Usage Arguments Value Examples

Tests residuals to see if they are normal. This looks at the model with all significant SNPs from the preselection phase.

1	resids_diag(Y,SNPs,significant,kinship = FALSE,principal_components = FALSE,plot_it = TRUE)

`Y`	The phenotype response of interest
`SNPs`	Standardized SNP data set where the values of each column are either 0 or 1
`significant`	A vector of 0's and 1's where the 1's indicate a significant SNP. This is returned in the output of the preselection function.
`kinship`	A kinship matrix, can be calculated from the rrBLUP package.
`principal_components`	A matrix or vector of the principal components one would like to include in the analysis.
`plot_it`	If TRUE a histogram of the residuals is returned.

`value 1`	The output of a Shapiro-Wilk test for the residuals. If the p-value is above .05, there is no evidence that the residuals are not normal. If the p-value is below .05 there is evidence that the residuals are not normal, and some transformation is suggested.
`value 2`	A histogram of the residuals when plot_it = TRUE

data("vignette_lm_dat")
Y <- vignette_lm_dat$Phenotype
SNPs <- vignette_lm_dat[,-1]
fullPreprocess <- preprocess_SNPs(SNPs = SNPs,Y = Y,MAF = 0.01,number_cores = 1)
SNPs <- fullPreprocess$SNPs
Y <- fullPreprocess$Y
fullPreprocess$SNPs_Dropped
principal_comp <- pca_function(SNPs = SNPs,number_components = 1,plot_it = FALSE)
Significant_SNPs <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = principal_comp,frequentist = TRUE,controlrate = "bonferroni",threshold = .05,kinship = FALSE,info = FALSE)

resids_diag(Y = Y,SNPs = SNPs,significant = Significant_SNPs$Significant,kinship = FALSE,principal_components = principal_comp,plot_it = TRUE)