BFR: BFR

In frahik/GFR: GFR (Genomic Functional Regression)

Description

Bayesian Functional Regression

Usage

BFR(data = NULL, datasetID = "Line", Multivariate = "Traditional",
  a = NULL, b = NULL, ETA = NULL, nIter = 1500, burnIn = 500,
  thin = 5, saveAt = "", S0 = NULL, df0 = 5, R2 = 0.5,
  weights = NULL, verbose = TRUE, rmExistingFiles = TRUE, groups = NULL,
  CrossValidation = NULL, set_seed = NULL, dec = 4)

Arguments

data	(data.frame) the data with the $n$ $Response, also needs $Line and $Env for Cross Validation defined on it (NAs allowed).
datasetID	(string) The name of the column with the identifiers of each line.
Multivariate	By default, when de dataset includes more than one Environment and more than one Trait (MTME) the solution is adjusted by the method traditional, also is possible adjust the MTME by "SVD" <doi: >
a	(numeric, $n$) Only requiered for censored outcomes. It's a vector specifying lower bounds for censored observation. By default is null.
b	(numeric, $n$) Only requiered for censored outcomes. It's a vector specifying upper bounds for censored observation. By default is null.
ETA	(list) Two level list used to specify the regression function, also could be generate by ETAGenerate() function for easy-use.
nIter	(integer) The number of iterations.
burnIn	(integer) The number of burn-in.
thin	(integer) The number of thinning.
saveAt	(character) This may include a path and a pre-fix that will be added to the name of the files that are saved as the program runs.
S0	(numeric) The scale parameter for the scaled inverse-chi squared prior assigned to the residual variance, only used with Gaussian outcomes.
df0	(numeric) The scale parameter for the scaled inverse-chi squared prior assigned to the residual variance, only used with Gaussian outcomes.
R2	(numeric,$0<R2<1$) The proportion of variance that one expects, a priori, to be explained by the regression. Only used if the hyper-parameters are not specified.
weights	(numeric, $n$) a vector of weights, may be NULL. The residual variance of each data-point is set to be proportional to the inverse of the squared-weight. Only used with Gaussian outcomes.
verbose	(logical) By default is TRUE and shows a fitting model progress bar if Folds <=1 or cross validation progress bar if Folds > 2.
rmExistingFiles	(logical) By default is TRUE and removes existing output files from previous runs.
groups	(factor) A vector of the same lenght of data$Response that associates observations with groups, each group will have an associated variance component for the error term.
CrossValidation	(list) Especified list to KFold Crossvalidation use list(Type = 'KFold', nFolds = 5), and to Random Partiton Cross validation use list(Type = 'RandomPartition', nPartitions = 5, pTesting = 0.20, Traits.testing = NULL)
set_seed	(integer) A seed for replicable research.
dec	(integer) Number of decimals to show on the predictions.

Details

More details about this function

frahik/GFR documentation built on May 25, 2019, 5:22 p.m.