Title: Item Based Collaborative Filtering for Multi-Trait and Multi-Environment Data
Version: 1.6-0
Language: en-US
Date: 2019-03-17
Maintainer: Francisco Javier Luna-Vazquez <frahik@gmail.com>
Description: Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) <doi:10.1534/g3.117.300309>.
Depends: R (≥ 3.0.0)
License: LGPL-3
Encoding: UTF-8
LazyData: true
Type: Package
RoxygenNote: 6.1.1
URL: https://github.com/frahik/IBCF.MTME
BugReports: https://github.com/frahik/IBCF.MTME/issues/new
NeedsCompilation: no
Collate: CrossValidation.R IBCF.R IBCF.MTME.R IBCFYear.R methods.R Td2M.R Title.R Wheat_IBCF.R Year_IBCF.R
Imports: lsa, tidyr, dplyr
Suggests: testthat, knitr, rmarkdown, covr
Packaged: 2019-03-23 04:37:48 UTC; frahik
Author: Francisco Javier Luna-Vazquez ORCID iD [aut, cre], Osval Antonio Montesinos-Lopez ORCID iD [aut], Abelardo Montesinos-Lopez [aut], Jose Crossa ORCID iD [aut]
Repository: CRAN
Date/Publication: 2019-03-23 05:18:07 UTC

IBCF.MTME: Item Based Collaborative Filtering for Multi-Trait and Multi-Environment Data.

Description

The Item Based Collaborative Filtering for Multi-Trait and Multi-Environment Data (IBCF.MTME) package was developed to implement the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments. It is important to point out that the main difference of this package with the available packages that can implement IBCF is that this package was developed for continuous phenotypes which cannot be implemented in the current packages that can implement IBCF that only work for binary and ordinary phenotypes.

Cross-Validation with Random Partitions

Description

This method consists of randomly dividing the training data set and the test data set. For each division, the approximation function is adjusted from the training data and calculates the output values for the test data set. The result corresponds to the arithmetic mean of the values obtained for the different divisions.

Usage

CV.RandomPart(DataSet, NPartitions = 10, PTesting = 0.35,
  Traits.testing = NULL, Set_seed = NULL)

Arguments

DataSet

data.frame The data set object is a data.frame object that contains 4 columns in the Tidy data format: $Line is the Line or genotype identifier, and the name of this column could change. $Env is the name of the evaluated environment (s). $Trait is the name of the evaluated trait (s). $Response Variable response obtained for the row corresponding to line and environment.

NPartitions

integer Number of Partitions for the Cross-Validation. Is 10 by default.

PTesting

Double Percentage of Testing for the Cross-Validation. Is 0.35 by default.

Traits.testing

character By default is null and use all the traits to fit the model, else only part of the traits specified be used to fit the model.

Set_seed

integer Number of seed for reproducible research. Is NULL by default.

Value

List A list object with length of NPartitions, every index has a matrix n \times x, where n is the number of NLines and x is the number of NEnv \times NTraits. The values inside is 1 for training and 2 for testing.

Examples

## Not run: 
  library(IBCF.MTME)
  data('Wheat_IBCF')

  CV.RandomPart(Wheat_IBCF)
  CV.RandomPart(Wheat_IBCF, NPartitions = 10)
  CV.RandomPart(Wheat_IBCF, Traits.testing = 'DH')
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35)
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, Traits.testing = 'DH')
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35, Set_seed = 5)
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35, Traits.testing = 'DH')
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35, Traits.testing = 'DH', Set_seed = 5 )

## End(Not run)

IBCF

Description

Item Based Collaborative Filtering for multi-trait and multi-environment data.

Usage

IBCF(object, dec = 4)

Arguments

object

list CrossValidation object, is obtained from CV.RandomPartition function.

dec

integer Number of decimals to print in the results.

Value

A list with the next components

NPartitions

integer Number of partitions used for testing data

predictions_Summary

data.frame A data.frame with the results of the test

Predictions

list A list with the predicted results for each partition

Examples

 ## Not run: 
  library(IBCF.MTME)
  data('Wheat_IBCF')

  CV <- CV.RandomPart(Wheat_IBCF)
  IBCF(CV)

## End(Not run)

IBCF.Years

Description

Item Based Collaborative Filtering for Years data

Usage

IBCF.Years(DataSet, colYears = 1, colID = 2, Years.testing = "",
  Traits.testing = "", dec = 4)

Arguments

DataSet

data.frame A data set in Matrix Form.

colYears

string or integer A name or the position of the 'Years' column just in case that is not the first column.

colID

string or integer A name or the position of the 'ID' column, just in case that is not the second column.

Years.testing

vector A vector with the names of the years to use in test.

Traits.testing

vector A vector with the names of the traits to use in test.

dec

integer Number of decimals to print in the results.

Value

A list with the next components

Year.testing

vector a vector with the Years used for the testing data

Traits.testing

vector a vector with the Traits used for the testing data

Data_Obs_Pred

data.frame Contains the values observed and predicted (the predicted values has '.1' after the name)

predictions_Summary

data.frame Contains the summary of the correlation of the predictions and the MAAPE

Examples

## Not run: 
  library(IBCF.MTME)
  data('Year_IBCF')
  DataSet <- getMatrixForm(Year_IBCF, onlyTrait = TRUE)
  IBCF.Years(DataSet , Years.testing = c('2015', '2016'), Traits.testing = c('T5', 'T6'))


## End(Not run)

Wheat Data

Description

The package includes a data set based on a portion of the data used in the study of Montesinos-Lopez, O. A.; Montesinos-Lopez, A.; Crossa, J.; Toledo, F. H.; Montesinos-Lopez, J. C.; Singh, P. & Salinas-Ruiz, J. (2017). A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction. G3: Genes|Genomes|Genetics 7(5):1595–1606. http://doi.org/10.1534/g3.117.039974. The data set consists of 250 wheat lines evaluated in 3 environments and 4 distinct traits, i.e. 3000 observations.

Usage

data(Wheat_IBCF)

Format

a TidyData format, 3000 row per 4 columns.

Author(s)

Montesinos-Lopez, O. A.

Year_IBCF Data

Description

Dataset based on simulated data with the next code: 

set.seed(2)
A <- matrix(0.65,ncol=12,nrow=12)
diag(A) <- 1
Sdv <- diag(c(0.9^0.5,0.8^0.5,0.9^0.5,0.8^0.5,0.86^0.5,0.7^0.5,0.9^0.5,0.8^0.5,0.9^0.5,0.7^0.5,0.7^0.5,0.85^0.5))

Sigma <- Sdv
No.Lines <- 60
Z <- rmvnorm(No.Lines,mean=c(5,5.5,6,5.5,7,6.5,6.0,7,6.6,8,6.3,8),sigma=Sigma)
Years <- c(rep(2014,20),rep(2015,20),rep(2016,20))
Gids <- c(1:No.Lines)

Data.Final <- data.frame(cbind(Years,Gids,Z))

colnames(Data.Final) <- c("Years","Gids","T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12")
head(Data.Final)
Year_IBCF <- getTidyForm(Data.Final, onlyTrait = T)

Usage

data(Year_IBCF)

Format

a TidyData format, 750 row per 4 columns.

Author(s)

Montesinos-Lopez, O. A.

barplot.IBCFY

Description

Barplot of the results from IBCFY object

Usage

## S3 method for class 'IBCFY'
barplot(height, select = "Pearson", ...)

Arguments

height

IBCFY object IBCFY object, result of use the IBCF.Years() function

select

character By default ('Pearson'), plot the Pearson Correlations of the IBCF Object, else ('MAAPE'), plot the MAAPE of the IBCF Object.

...

Further arguments passed to or from other methods.

Tidy data format to Matrix format

Description

Tidy data format to Matrix format

Usage

getMatrixForm(Tidy_DataSet, onlyTrait = FALSE)

Arguments

Tidy_DataSet

data.frame object that contains 4 columns: $Line: Line or genotype identifier, and the name of this column could change. $Env: Name of the evaluated environment (s). $Trait: Name of the evaluated trait (s). $Response: Variable response obtained for the row corresponding to line and environment.

onlyTrait

logical by default is FALSE, if is TRUE only the column $Trait is transformed.

Value

A data.frame object with the $Response divided by $Traits columns.

Examples

## Not run: 
  data('Wheat_IBCF')
  M <- getMatrixForm(Wheat_IBCF)

## End(Not run)

## Not run: 
  data('Year_IBCF')
  M.Y <- getMatrixForm(Year_IBCF, onlyTrait = T)

## End(Not run)

Matrix format to Tidy data format

Description

Matrix format to Tidy data format

Usage

getTidyForm(Matrix_DataSet, onlyTrait = FALSE)

Arguments

Matrix_DataSet

A data.frame object with the response values divided in n environments or traits columns

onlyTrait

logical by default is FALSE, if is TRUE only is considered the $Trait column.

Value

A data.frame object with the $Response divided by $Traits columns.

Examples

## Not run: 
  data('Wheat_IBCF')
  M <- getMatrixForm(Wheat_IBCF)
  Tidy <- getTidyForm(M)

## End(Not run)

## Not run: 
  data('Year_IBCF')
  M.Y <- getMatrixForm(Year_IBCF, onlyTrait = T)
  Tidy.Y <- getTidyForm(M.Y, onlyTrait = T)

## End(Not run)

Plot IBCF graph

Description

Plot from IBCF object

Usage

## S3 method for class 'IBCF'
plot(x, select = "Pearson", ...)

Arguments

x

IBCF object IBCF object, result of use the IBCF() function

select

character By default ('Pearson'), plot the Pearson Correlations of the IBCF Object, else ('MAAPE'), plot the MAAPE of the IBCF Object.

...

Further arguments passed to or from other methods.

Print IBCF information object

Description

Print IBCF information object

Usage

## S3 method for class 'IBCF'
print(x, ...)

Arguments

x

IBCF object

...

Further arguments passed to or from other methods.

Value

printeable object

Print IBCFY information object

Description

Print IBCFY information object

Usage

## S3 method for class 'IBCFY'
print(x, ...)

Arguments

x

IBCFY object

...

Further arguments passed to or from other methods.

Value

printeable object

Summary

Description

Summary of IBCF object

Usage

## S3 method for class 'IBCF'
summary(object, information = "compact", digits = 4,
  ...)

Arguments

object

IBCF object IBCF object, result of use the IBCF() function

information

string ...

digits

numeric ...

...

Further arguments passed to or from other methods.

Summary

Description

Summary of IBCFY object

Usage

## S3 method for class 'IBCFY'
summary(object, digits = 4, ...)

Arguments

object

IBCFY object IBCFY object, result of use the IBCF.Years() function

digits

numeric Number of digits of the output.

...

Further arguments passed to or from other methods.