QTL_pred_R2 {mppR} | R Documentation |
Predicted QTL global and partial R squared
Description
Compute predicted R squared in a validation set using QTLs detected in a
training set. These values are corrected by the heritability her
.
Usage
QTL_pred_R2(
mppData.ts,
mppData.vs,
trait = 1,
Q.eff = "cr",
QTL = NULL,
her = 1
)
Arguments
mppData.ts |
An object of class |
mppData.vs |
An object of class |
trait |
|
Q.eff |
|
QTL |
Object of class |
her |
|
Details
Compute QTLs predicted R squared in a validation set (mppData.vs
).
These QTLs have been previously detected in a training set
(mppData.ts
). The global R squared (R2 = cor(y.ts,y.pred.ts)^2) is
obtained using the Pearson squared correlation between the observed trait
values in the validation set (y.vs) and predicted values using estimated QTL
effects in the training set (y.pred.vs = X.vs * B.ts).
After that the values are corrected by the general or within cross
heritability her
. By default her = 1
which means that the
R squared represent the proportion of explained phenotypic variance. The
values are returned per cross (R2.cr
) or averaged at the population
level (glb.R2
).
Partial R squared statistics are also calculated for each individual position. The partial R squared are computed by making the difference between the global R squared and the R squared computed without the ith position.
Value
Return:
List
containing the following objects:
glb.R2 |
Global predicted R squared corrected for the heritability of all QTL terms. Doing the average of the within cross predicted R squared (R2.cr) |
R2.cr |
Within cross predicted R squared corrected for the heritability |
part.R2.diff |
Vector of predicted partial R squared corrected for the heritability doing the difference between the full model and a model minus the ith QTL. |
Author(s)
Vincent Garin
See Also
Examples
data(mppData)
folds <- CV_partition(cross.ind = mppData$cross.ind, k = 5)
mppData.ts <- subset(x = mppData, gen.list = folds[[1]]$train.set)
mppData.vs <- subset(x = mppData, gen.list = folds[[1]]$val.set)
SIM <- mpp_SIM(mppData = mppData)
QTL <- QTL_select(SIM)
QTL_pred_R2(mppData.ts = mppData.ts, mppData.vs = mppData.vs, QTL = QTL)