phenoRegressor.BGLR | R Documentation |
This is a wrapper around BGLR
. As such, it won't work if BGLR package
is not installed.
Genotypes are modeled using the specified type
. If type
is 'RKHS' (and only
in this case) the covariance/kinship matrix covariances
is required, and it will be modeled
as matrix K in BGLR terms. In all other cases genotypes and covariances are put in the model
as X matrices.
Extra covariates, if present, are modeled as FIXED effects.
phenoRegressor.BGLR( phenotypes, genotypes, covariances, extraCovariates, type = c("FIXED", "BRR", "BL", "BayesA", "BayesB", "BayesC", "RKHS"), ... )
phenotypes |
phenotypes, a numeric array (n x 1), missing values are predicted |
genotypes |
SNP genotypes, one row per phenotype (n), one column per marker (m), values in 0/1/2 for
diploids or 0/1/2/...ploidy for polyploids. Can be NULL if |
covariances |
square matrix (n x n) of covariances. Can be NULL if |
extraCovariates |
extra covariates set, one row per phenotype (n), one column per covariate (w). If NULL no extra covariates are considered. |
type |
character literal, one of the following: FIXED (Flat prior), BRR (Gaussian prior), BL (Double-Exponential prior), BayesA (scaled-t prior), BayesB (two component mixture prior with a point of mass at zero and a scaled-t slab), BayesC (two component mixture prior with a point of mass at zero and a Gaussian slab) |
... |
extra parameters are passed to |
The function returns a list with the following fields:
predictions
: an array of (n) predicted phenotypes, with NAs filled and all other positions repredicted (useful for calculating residuals)
hyperparams
: empty, returned for compatibility
extradata
: list with information on trained model, coming from BGLR
BGLR
Other phenoRegressors:
phenoRegressor.RFR()
,
phenoRegressor.SVR()
,
phenoRegressor.dummy()
,
phenoRegressor.rrBLUP()
,
phenoregressor.BGLR.multikinships()
## Not run: #using the GROAN.KI dataset, we regress on the dataset and predict the first ten phenotypes phenos = GROAN.KI$yield phenos[1:10] = NA #calling the regressor with Bayesian Lasso results = phenoRegressor.BGLR( phenotypes = phenos, genotypes = GROAN.KI$SNPs, covariances = NULL, extraCovariates = NULL, type = 'BL', nIter = 2000 #BGLR-specific parameters ) #examining the predictions plot(GROAN.KI$yield, results$predictions, main = 'Train set (black) and test set (red) regressions', xlab = 'Original phenotypes', ylab = 'Predicted phenotypes') points(GROAN.KI$yield[1:10], results$predictions[1:10], pch=16, col='red') #printing correlations test.set.correlation = cor(GROAN.KI$yield[1:10], results$predictions[1:10]) train.set.correlation = cor(GROAN.KI$yield[-(1:10)], results$predictions[-(1:10)]) writeLines(paste( 'test-set correlation :', test.set.correlation, '\ntrain-set correlation:', train.set.correlation )) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.