mpp_CV | R Documentation |
Evaluation of MPP QTL detection procedure by cross-validation (CV).
mpp_CV(
pop.name = "MPP_CV",
trait.name = "trait1",
mppData,
trait = 1,
her = 1,
Rep = 10,
k = 5,
Q.eff = "cr",
thre.cof = 3,
win.cof = 50,
N.cim = 1,
window = 20,
thre.QTL = 3,
win.QTL = 20,
backward = TRUE,
alpha.bk = 0.05,
n.cores = 1,
verbose = TRUE,
output.loc
)
pop.name |
|
trait.name |
|
mppData |
An object of class |
trait |
|
her |
|
Rep |
|
k |
|
Q.eff |
|
thre.cof |
|
win.cof |
|
N.cim |
|
window |
|
thre.QTL |
|
win.QTL |
|
backward |
|
alpha.bk |
|
n.cores |
|
verbose |
|
output.loc |
Path where a folder will be created to save the results. |
For details on the MPP QTL detection models see mpp_SIM
documentation. The CV scheme is adapted from Utz et al. (2000) to the MPP
context. A single CV run works like that:
Generation of a k-fold partition of the data. The partition is done within crosses. Each cross is divided into k subsets. Then for the kth repetition, the kth subset is used as validation set, the rest goes into the training set.
For the kth repetition, utilization of the training set for cofactor
selection and multi-QTL model determination (mpp_SIM
and
mpp_CIM
). If backward = TRUE
, the final list of QTLs is
tested simultaneously using a backward elimination
(mpp_back_elim
).
Use the list of detected QTLs in the training set to calculate
the proportion of genetic variance explained by all detected QTLs in the
training set (p.ts = R2.ts/h2). Where R2.ts is the adjusted
R squared and h2 is the average within cross heritability (her
). By
default, her = 1, which mean that
For each single QTL effect, difference partial R squared are also
calculated. Difference R squared are computed by doing the difference between
a model with all QTLs and a model without the ith position. For details about R
squared computation and adjustment look at QTL_R2
.
Use the estimates of the QTL effects in the training set (B.ts) to
predict the phenotypic values of the validation set. y.pred.vs = X.vs*B.ts.
Computes the predicted R squared in the validation set using the squared
Pearson correlation coefficient between the real values (y.vs) and the
predicted values (y.pred.vs). R2.vs = cor(y.ts,y.pred.ts)^2. Then
the predicted genetic variance in the validation set (p.vs) is equal to
p.vs = R2.vs/h2. For heritability correction, the user can provide a single
value for the average within cross heritability or a vector specifying each
within cross heritability. By default, her = 1
, which means that the
results represent the proportion of phenotypic variance explained (predicted)
in the training (validation) sets.
The predicted R squared is computed per cross and then averaged at the population level (p.ts). Both results are returned. Partial QTL predicted R squared are also calculated using the difference between the predicted R squared using all QTL and the predicted R squared without QTL i. The bias between p.ts and p.vs is calculated as bias = 1 - (p.vs/p.ts).
List
containing the following results items:
CV_res |
|
p.vs.cr |
|
QTL |
|
QTL.profiles |
|
The results elements return as R object are also saved as text
files at the specified output location (output.loc
). A transparency
plot of the CV results (plot.pdf) is also saved.
Vincent Garin
Utz, H. F., Melchinger, A. E., & Schon, C. C. (2000). Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics, 154(4), 1839-1849.
mpp_back_elim
,
mpp_CIM
,
mpp_perm
,
mpp_SIM
,
QTL_R2
## Not run:
data(mppData)
# Specify a location where your results will be saved
my.loc <- tempdir()
CV <- mpp_CV(pop.name = "USNAM", trait.name = "ULA", mppData = mppData,
her = .4, Rep = 1, k = 3, verbose = FALSE, output.loc = my.loc)
## End(Not run)
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