perGeno: Genomic prediction using glmnet, with a genotype-specific...

In factReg: Multi-Environment Genomic Prediction with Penalized Factorial Regression

Genomic prediction using glmnet, with a genotype-specific penalized regression model.

Description

\loadmathjax

.... These models can be fitted either for the original data, or on the residuals of a model with only main effects.

Usage

perGeno(
  dat,
  Y,
  G,
  E,
  indices = NULL,
  indicesData = NULL,
  testEnv = NULL,
  weight = NULL,
  useRes = TRUE,
  outputFile = NULL,
  corType = c("pearson", "spearman"),
  partition = data.frame(),
  nfolds = 10,
  alpha = 1,
  scaling = c("train", "all", "no"),
  quadratic = FALSE,
  verbose = FALSE
)

Arguments

dat	A data.frame with data from multi-environment trials. Each row corresponds to a particular genotype in a particular environment. The data do not need to be balanced, i.e. an environment does not need to contain all genotypes. dat should contain the training as well as the test environments (see testEnv)
Y	The trait to be analyzed: either of type character, in which case it should be one of the column names in dat, or numeric, in which case the Yth column of dat will be analyzed.
G	The column in dat containing the factor genotype (either character or numeric).
E	The column in dat containing the factor environment (either character or numeric).
indices	The columns in dat containing the environmental indices (vector of type character). Alternatively, if the indices are always constant within environments (i.e. not genotype dependent), the environmental data can also be provided using the argument indicesData (see below).
indicesData	An optional data.frame containing environmental indices (covariates); one value for each environment and index. It should have the environment names as row names (corresponding to the names contained in dat$E); the column names are the indices. If indices (see before) is also provided, the latter will be ignored.
testEnv	vector (character). Data from these environments are not used for fitting the model. Accuracy is evaluated for training and test environments separately. The default is NULL, i.e. no test environments, in which case the whole data set is training. It is also possible that there are test environments, but without any data; in this case, no accuracy is reported for test environments (CHECK correctness).
weight	Numeric vector of length nrow(dat), specifying the weight (inverse variance) of each observation, used in glmnet. Default NULL, giving constant weights.
useRes	Indicates whether the genotype-specific regressions are to be fitted on the residuals of a model with main effects. If TRUE residuals of a model with environmental main effects are used, if FALSE the regressions are fitted on the original data.
outputFile	The file name of the output files, without .csv extension which is added by the function. If not NULL the prediction accuracies for training and test environments are written to separate files. If NULL the output is not written to file.
corType	type of correlation: Pearson (default) or Spearman rank sum.
partition	data.frame with columns E and partition. The column E should contain the training environments (type character); partition should be of type integer. Environments in the same fold should have the same integer value. Default is data.frame(), in which case the function uses a leave-one-environment out cross-validation. If NULL, the (inner) training sets used for cross-validation will be drawn randomly from all observations, ignoring the environment structure. In the latter case, the number of folds (nfolds) can be specified.
nfolds	Default NULL. If partition == NULL, this can be used to specify the number of folds to be used in glmnet.
alpha	Type of penalty, as in glmnet (1 = LASSO, 0 = ridge; in between = elastic net). Default is 1.
scaling	determines how the environmental variables are scaled. "train" : all data (test and training environments) are scaled using the mean and and standard deviation in the training environments. "all" : using the mean and standard deviation of all environments. "no" : No scaling.
quadratic	boolean; default FALSE. If TRUE, quadratic terms (i.e., squared indices) are added to the model.
verbose	boolean; default FALSE. If TRUE, the accuracies per environment are printed on screen.

Value

A list with the following elements:

predTrain

Vector with predictions for the training set (to do: Add the factors genotype and environment; make a data.frame)

predTest

Vector with predictions for the test set (to do: Add the factors genotype and environment; make a data.frame). To do: add estimated environmental main effects, not only predicted environmental main effects

mu

the estimated overall (grand) mean

envInfoTrain

The estimated environmental main effects, and the predicted effects, obtained when the former are regressed on the averaged indices, using penalized regression.

envInfoTest

The predicted environmental main effects for the test environments, obtained from penalized regression using the estimated main effects for the training environments and the averaged indices.

parGeno

data.frame containing the estimated genotypic main effects (first column) and sensitivities (subsequent columns)

testAccuracyEnv

a data.frame with the accuracy (r) for each test environment

trainAccuracyEnv

a data.frame with the accuracy (r) for each training environment

trainAccuracyGeno

a data.frame with the accuracy (r) for each genotype, averaged over the training environments

testAccuracyGeno

a data.frame with the accuracy (r) for each genotype, averaged over the test environments

RMSEtrain

The root mean squared error on the training environments

RMSEtest

The root mean squared error on the test environments

Y

The name of the trait that was predicted, i.e. the column name in dat that was used

G

The genotype label that was used, i.e. the argument G that was used

E

The environment label that was used, i.e. the argument E that was used

indices

The indices that were used, i.e. the argument indices that was used

lambdaOpt

pargeno

quadratic

The quadratic option that was used

factReg documentation built on July 9, 2023, 6:10 p.m.