predict.bqtl: fitted values from QTL models

In bqtl: Bayesian QTL Mapping Toolkit

fitted values from QTL models

Description

The estimated coefficients and expected locus values are used to find fitted values for the QTL model

Usage

## S3 method for class 'bqtl'
predict(object, newdata, ...)
## S3 method for class 'bqtl'
fitted(object, newdata, ...)

Arguments

object	An object of class bqtl
newdata	An optional data.frame for which fitted values are to be found. If not specified, the a search for the original data frame for the fit will be made.
...	unused

Details

The estimated coefficients for a specific QTL model fit are used along with the expected locus values (conditionally on the marker values) are used to find fitted values for the QTL model. This is not the only way in which such fits could be obtained; one could condition the expect marker values on both the trait value and the marker values. One could also define fitted values for specific genotype combinations, e.g. for a backcross with k animals and a two gene model 4 fitted values could be determined for each animal leading to 2*2*k values. In fact, using newdata one can do this.

Value

A vector with as many elements as rows in newdata (after removing missing data) or in the original model.frame.

Author(s)

Charles C. Berry [e-mail]

See Also

bqtl

Examples

data(little.ana.bc)

fit.pheno <- bqtl(bc.phenotype~locus(15)+locus(42),little.ana.bc)

summary(predict(fit.pheno))

genotype.grid <- expand.grid( c(-1,1), c(-1,1) )     # set up a grid
names(genotype.grid) <- map.names( fit.pheno )       # use matching names

fit.vals <- predict( fit.pheno, genotype.grid )      # make predictions
cbind( genotype.grid, fit.vals )                     # print them!

bqtl documentation built on Sept. 25, 2024, 1:08 a.m.