README.md
In frahik/IBCF.MTME: Item Based Collaborative Filtering for Multi-Trait and Multi-Environment Data
**I**tem **B**ased **C**ollaborative **F**ilterign For
**M**ulti-**T**rait and **M**ulti-**E**nvironment Data in R -
Development version 1.6.0.
\[Last README update: 2019-03-22\]
Table Of Contents
- NEWS
- Instructions
- How to cite this package
- Contributions
- Authors
News of this version (1.6.0)
- Fixed important issue with the predictions output.
- Fixed compatibility with dplyr 0.8.
- Fixed barplot function.
See the last updates in NEWS.
Instructions for proper implementation
Installation
To complete installation of dev version of the package IBCF.MTME from
GitHub, you must have previously installed the devtools package.
install.packages('devtools')
devtools::install_github('frahik/IBCF.MTME')
If you want to use the stable version of IBCF.MTME package, install it
from CRAN.
install.packages('IBCF.MTME')
Load the package
library(IBCF.MTME)
Example of Cross-validation with IBCF.MTME
Load available data from other package
library(BGLR)
data(wheat)
Generate a new data set in tidy data form
pheno <- data.frame(ID = gl(n = 599, k = 1, length = 599*4),
Response = as.vector(wheat.Y),
Env = paste0('Env', gl(n = 4, k = 599)))
head(pheno)
## ID Response Env
## 1 1 1.6716295 Env1
## 2 2 -0.2527028 Env1
## 3 3 0.3418151 Env1
## 4 4 0.7854395 Env1
## 5 5 0.9983176 Env1
## 6 6 2.3360969 Env1
Generate 10 partitions to do
cross-validation
CrossV <- CV.RandomPart(pheno, NPartitions = 10, PTesting = 0.25, Set_seed = 123)
Fitting the predictive model
pm <- IBCF(CrossV)
Show some results
All the predictive model printed output:
pm
## Item Based Collaborative Filtering Model:
## Fitted with 10 random partitions
## Runtime: 12.408 seconds
##
## Some predicted values:
## [1] -0.9554 -0.2731 -0.5007 -0.0909 -0.0501 -0.2599 -0.3494
## [8] 0.0913 -0.0215 -0.4023 -0.8106 0.5702 0.4918 -1.5810
## [15] -0.1540 -0.8060 -0.6665 -0.0671 -0.1934 -0.3210
##
## Predictive capacity of the model:
## Environment Trait Pearson SE_Pearson MAAPE SE_MAAPE
## 1 Env1 -0.131 0.024 0.931 0.011
## 2 Env2 0.686 0.010 0.674 0.012
## 3 Env3 0.612 0.017 0.682 0.007
## 4 Env4 0.307 0.027 0.762 0.011
##
## Use str() function to found more datailed information.
Predictions and observed data in tidy format
head(pm$predictions_Summary, 6)
## Position Partition Environment Trait Observed Predicted
## 1 1 1 Env1 1.6716 -0.9554
## 2 14 1 Env1 0.3160 -0.2731
## 3 25 1 Env1 -1.1272 -0.5007
## 4 26 1 Env1 -0.4852 -0.0909
## 5 28 1 Env1 2.5940 -0.0501
## 6 30 1 Env1 -0.5190 -0.2599
Predictions and observed data in matrix
format
head(pm$Data.Obs_Pred, 5)
## ID _Env1 _Env2 _Env3 _Env4 X_Env1.predicted
## 1 1 1.6716295 -1.72746986 -1.8902848 0.0509159 -0.9894943
## 2 2 -0.2527028 0.40952243 0.3093855 -1.7387588 -0.5478389
## 3 3 0.3418151 -0.64862633 -0.7995592 -1.0535691 -0.8596543
## 4 4 0.7854395 0.09394919 0.5704677 0.5517574 0.4040118
## 5 5 0.9983176 -0.28248062 1.6186819 -0.1142848 0.3243855
## X_Env2.predicted X_Env3.predicted X_Env4.predicted
## 1 -0.8744692 -0.635018256 -0.5894103
## 2 -0.4165869 -0.370835578 0.1360170
## 3 -0.6766007 NaN -0.3162934
## 4 0.5769577 0.347986885 0.5066996
## 5 1.0403187 0.001053535 0.7813698
Some plots
par(mai = c(2, 1, 1, 1))
plot(pm, select = 'Pearson')
plot(pm, select = 'MAAPE')
Example of Years prediction with IBCF.Years Function
Loading your data
load('DataExample.RData')
head(Data.Example)
## Years Gids Trait Response
## 1 2014 1 Trait1 15.14401
## 2 2014 2 Trait1 15.67879
## 3 2014 3 Trait1 14.85489
## 4 2014 4 Trait1 13.57002
## 5 2014 5 Trait1 15.01838
## 6 2014 6 Trait1 13.19616
Transforming the data from Tidy data to matrix form
Data.Example <- getMatrixForm(Data.Example, onlyTrait = TRUE)
head(Data.Example)
## Years Gids Trait1 Trait10 Trait11 Trait12 Trait2 Trait3
## 1 2014 1 15.14401 18.51428 17.08970 19.16776 16.21435 17.53858
## 2 2014 2 15.67879 18.21569 17.89645 19.94429 15.80614 17.89946
## 3 2014 3 14.85489 17.72576 15.78198 17.53058 14.06164 16.11997
## 4 2014 4 13.57002 18.57009 15.73343 17.49995 14.58312 15.22495
## 5 2014 5 15.01838 18.57348 16.97414 19.03081 14.98192 15.65125
## 6 2014 6 13.19616 16.83588 15.12312 17.39867 15.81264 14.80517
## Trait4 Trait5 Trait6 Trait7 Trait8 Trait9
## 1 15.51840 17.59132 17.14852 17.04474 17.48970 18.36118
## 2 15.13337 18.36446 17.32734 17.46764 18.08501 18.67266
## 3 15.04329 17.28942 16.50978 16.26685 17.02774 17.05612
## 4 14.93028 16.33687 15.11493 15.06632 17.56798 16.48810
## 5 16.70963 16.81113 17.24170 15.53379 16.07600 16.54047
## 6 14.82150 16.49238 15.37325 14.07796 15.98419 15.84705
Adjust the
model
pm <- IBCF.Years(Data.Example, colYears = 1, Years.testing = c('2014', '2015', '2016'),
Traits.testing = c('Trait1', 'Trait2', 'Trait3', 'Trait4', "Trait5"))
Show some results
summary(pm)
## Environment Trait Pearson MAAPE
## 1 2014 Trait1 0.7549 0.0409
## 2 2014 Trait2 0.1562 0.0473
## 3 2014 Trait3 0.6130 0.0353
## 4 2014 Trait4 0.5208 0.0447
## 5 2014 Trait5 0.7587 0.0240
## 6 2015 Trait1 0.8432 0.0277
## 7 2015 Trait2 0.6792 0.0371
## 8 2015 Trait3 0.7944 0.0327
## 9 2015 Trait4 0.7394 0.0384
## 10 2015 Trait5 0.7651 0.0298
## 11 2016 Trait1 0.7690 0.0343
## 12 2016 Trait2 0.7753 0.0286
## 13 2016 Trait3 0.6763 0.0369
## 14 2016 Trait4 0.8157 0.0325
## 15 2016 Trait5 0.8533 0.0250
par(mai = c(2, 1, 1, 1))
barplot(pm, las = 2)
barplot(pm, select = 'MAAPE', las = 2)
Load available data from the package
You can use the data sets in the package to test the functions
library(IBCF.MTME)
data('Wheat_IBCF')
head(Wheat_IBCF)
## GID Trait Env Response
## 1 6569128 DH Bed2IR -17.565895
## 2 6688880 DH Bed2IR -4.565895
## 3 6688916 DH Bed2IR -3.565895
## 4 6688933 DH Bed2IR -4.565895
## 5 6688934 DH Bed2IR -7.565895
## 6 6688949 DH Bed2IR -7.565895
data('Year_IBCF')
head(Year_IBCF)
## Years Gids Trait Response
## 1 2014 1 T1 5.144009
## 2 2014 2 T1 5.678792
## 3 2014 3 T1 4.854895
## 4 2014 4 T1 3.570019
## 5 2014 5 T1 5.018380
## 6 2014 6 T1 3.196160
Citation
First option, by the article paper
@article{IBCF2018,
author = {Montesinos-L{\'{o}}pez, Osval A. and Luna-V{\'{a}}zquez, Francisco Javier and Montesinos-L{\'{o}}pez, Abelardo and Juliana, Philomin and Singh, Ravi and Crossa, Jos{\'{e}}},
doi = {10.3835/plantgenome2018.02.0013},
issn = {1940-3372},
journal = {The Plant Genome},
number = {3},
pages = {16},
title = {{An R Package for Multitrait and Multienvironment Data with the Item-Based Collaborative Filtering Algorithm}},
url = {https://dl.sciencesocieties.org/publications/tpg/abstracts/0/0/180013},
volume = {11},
year = {2018}
}
Second option, by the manual package
citation('IBCF.MTME')
##
## To cite package 'IBCF.MTME' in publications use:
##
## Francisco Javier Luna-Vazquez, Osval Antonio Montesinos-Lopez,
## Abelardo Montesinos-Lopez and Jose Crossa (2019). IBCF.MTME:
## Item Based Collaborative Filtering for Multi-Trait and
## Multi-Environment Data. R package version 1.6-0.
## https://github.com/frahik/IBCF.MTME
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {IBCF.MTME: Item Based Collaborative Filtering for Multi-Trait and Multi-Environment Data},
## author = {Francisco Javier Luna-Vazquez and Osval Antonio Montesinos-Lopez and Abelardo Montesinos-Lopez and Jose Crossa},
## year = {2019},
## note = {R package version 1.6-0},
## url = {https://github.com/frahik/IBCF.MTME},
## }
Contributions
If you have any suggestions or feedback, I would love to hear about it.
Feel free to report new issues in this
link, also if you want
to request a feature/report a bug, or make a pull request if you can
contribute.
Authors
- Francisco Javier Luna-Vázquez (Author, Maintainer)
- Osval Antonio Montesinos-López (Author)
- Abelardo Montesinos-López (Author)
- José Crossa (Author)
frahik/IBCF.MTME documentation built on May 22, 2019, 2:39 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
**I**tem **B**ased **C**ollaborative **F**ilterign For **M**ulti-**T**rait and **M**ulti-**E**nvironment Data in R - Development version 1.6.0.
\[Last README update: 2019-03-22\]
Table Of Contents
- NEWS
- Instructions
- How to cite this package
- Contributions
- Authors
News of this version (1.6.0)
- Fixed important issue with the predictions output.
- Fixed compatibility with dplyr 0.8.
- Fixed barplot function.
See the last updates in NEWS.
Instructions for proper implementation
Installation
To complete installation of dev version of the package IBCF.MTME from
GitHub, you must have previously installed the devtools package.
install.packages('devtools')
devtools::install_github('frahik/IBCF.MTME')
If you want to use the stable version of IBCF.MTME package, install it
from CRAN.
install.packages('IBCF.MTME')
Load the package
library(IBCF.MTME)
Example of Cross-validation with IBCF.MTME
Load available data from other package
library(BGLR)
data(wheat)
Generate a new data set in tidy data form
pheno <- data.frame(ID = gl(n = 599, k = 1, length = 599*4),
Response = as.vector(wheat.Y),
Env = paste0('Env', gl(n = 4, k = 599)))
head(pheno)
## ID Response Env
## 1 1 1.6716295 Env1
## 2 2 -0.2527028 Env1
## 3 3 0.3418151 Env1
## 4 4 0.7854395 Env1
## 5 5 0.9983176 Env1
## 6 6 2.3360969 Env1
Generate 10 partitions to do cross-validation
CrossV <- CV.RandomPart(pheno, NPartitions = 10, PTesting = 0.25, Set_seed = 123)
Fitting the predictive model
pm <- IBCF(CrossV)
Show some results
All the predictive model printed output:
pm
## Item Based Collaborative Filtering Model:
## Fitted with 10 random partitions
## Runtime: 12.408 seconds
##
## Some predicted values:
## [1] -0.9554 -0.2731 -0.5007 -0.0909 -0.0501 -0.2599 -0.3494
## [8] 0.0913 -0.0215 -0.4023 -0.8106 0.5702 0.4918 -1.5810
## [15] -0.1540 -0.8060 -0.6665 -0.0671 -0.1934 -0.3210
##
## Predictive capacity of the model:
## Environment Trait Pearson SE_Pearson MAAPE SE_MAAPE
## 1 Env1 -0.131 0.024 0.931 0.011
## 2 Env2 0.686 0.010 0.674 0.012
## 3 Env3 0.612 0.017 0.682 0.007
## 4 Env4 0.307 0.027 0.762 0.011
##
## Use str() function to found more datailed information.
Predictions and observed data in tidy format
head(pm$predictions_Summary, 6)
## Position Partition Environment Trait Observed Predicted
## 1 1 1 Env1 1.6716 -0.9554
## 2 14 1 Env1 0.3160 -0.2731
## 3 25 1 Env1 -1.1272 -0.5007
## 4 26 1 Env1 -0.4852 -0.0909
## 5 28 1 Env1 2.5940 -0.0501
## 6 30 1 Env1 -0.5190 -0.2599
Predictions and observed data in matrix format
head(pm$Data.Obs_Pred, 5)
## ID _Env1 _Env2 _Env3 _Env4 X_Env1.predicted
## 1 1 1.6716295 -1.72746986 -1.8902848 0.0509159 -0.9894943
## 2 2 -0.2527028 0.40952243 0.3093855 -1.7387588 -0.5478389
## 3 3 0.3418151 -0.64862633 -0.7995592 -1.0535691 -0.8596543
## 4 4 0.7854395 0.09394919 0.5704677 0.5517574 0.4040118
## 5 5 0.9983176 -0.28248062 1.6186819 -0.1142848 0.3243855
## X_Env2.predicted X_Env3.predicted X_Env4.predicted
## 1 -0.8744692 -0.635018256 -0.5894103
## 2 -0.4165869 -0.370835578 0.1360170
## 3 -0.6766007 NaN -0.3162934
## 4 0.5769577 0.347986885 0.5066996
## 5 1.0403187 0.001053535 0.7813698
Some plots
par(mai = c(2, 1, 1, 1))
plot(pm, select = 'Pearson')
plot(pm, select = 'MAAPE')
Example of Years prediction with IBCF.Years Function
Loading your data
load('DataExample.RData')
head(Data.Example)
## Years Gids Trait Response
## 1 2014 1 Trait1 15.14401
## 2 2014 2 Trait1 15.67879
## 3 2014 3 Trait1 14.85489
## 4 2014 4 Trait1 13.57002
## 5 2014 5 Trait1 15.01838
## 6 2014 6 Trait1 13.19616
Transforming the data from Tidy data to matrix form
Data.Example <- getMatrixForm(Data.Example, onlyTrait = TRUE)
head(Data.Example)
## Years Gids Trait1 Trait10 Trait11 Trait12 Trait2 Trait3
## 1 2014 1 15.14401 18.51428 17.08970 19.16776 16.21435 17.53858
## 2 2014 2 15.67879 18.21569 17.89645 19.94429 15.80614 17.89946
## 3 2014 3 14.85489 17.72576 15.78198 17.53058 14.06164 16.11997
## 4 2014 4 13.57002 18.57009 15.73343 17.49995 14.58312 15.22495
## 5 2014 5 15.01838 18.57348 16.97414 19.03081 14.98192 15.65125
## 6 2014 6 13.19616 16.83588 15.12312 17.39867 15.81264 14.80517
## Trait4 Trait5 Trait6 Trait7 Trait8 Trait9
## 1 15.51840 17.59132 17.14852 17.04474 17.48970 18.36118
## 2 15.13337 18.36446 17.32734 17.46764 18.08501 18.67266
## 3 15.04329 17.28942 16.50978 16.26685 17.02774 17.05612
## 4 14.93028 16.33687 15.11493 15.06632 17.56798 16.48810
## 5 16.70963 16.81113 17.24170 15.53379 16.07600 16.54047
## 6 14.82150 16.49238 15.37325 14.07796 15.98419 15.84705
Adjust the model
pm <- IBCF.Years(Data.Example, colYears = 1, Years.testing = c('2014', '2015', '2016'),
Traits.testing = c('Trait1', 'Trait2', 'Trait3', 'Trait4', "Trait5"))
Show some results
summary(pm)
## Environment Trait Pearson MAAPE
## 1 2014 Trait1 0.7549 0.0409
## 2 2014 Trait2 0.1562 0.0473
## 3 2014 Trait3 0.6130 0.0353
## 4 2014 Trait4 0.5208 0.0447
## 5 2014 Trait5 0.7587 0.0240
## 6 2015 Trait1 0.8432 0.0277
## 7 2015 Trait2 0.6792 0.0371
## 8 2015 Trait3 0.7944 0.0327
## 9 2015 Trait4 0.7394 0.0384
## 10 2015 Trait5 0.7651 0.0298
## 11 2016 Trait1 0.7690 0.0343
## 12 2016 Trait2 0.7753 0.0286
## 13 2016 Trait3 0.6763 0.0369
## 14 2016 Trait4 0.8157 0.0325
## 15 2016 Trait5 0.8533 0.0250
par(mai = c(2, 1, 1, 1))
barplot(pm, las = 2)
barplot(pm, select = 'MAAPE', las = 2)
Load available data from the package
You can use the data sets in the package to test the functions
library(IBCF.MTME)
data('Wheat_IBCF')
head(Wheat_IBCF)
## GID Trait Env Response
## 1 6569128 DH Bed2IR -17.565895
## 2 6688880 DH Bed2IR -4.565895
## 3 6688916 DH Bed2IR -3.565895
## 4 6688933 DH Bed2IR -4.565895
## 5 6688934 DH Bed2IR -7.565895
## 6 6688949 DH Bed2IR -7.565895
data('Year_IBCF')
head(Year_IBCF)
## Years Gids Trait Response
## 1 2014 1 T1 5.144009
## 2 2014 2 T1 5.678792
## 3 2014 3 T1 4.854895
## 4 2014 4 T1 3.570019
## 5 2014 5 T1 5.018380
## 6 2014 6 T1 3.196160
Citation
First option, by the article paper
@article{IBCF2018,
author = {Montesinos-L{\'{o}}pez, Osval A. and Luna-V{\'{a}}zquez, Francisco Javier and Montesinos-L{\'{o}}pez, Abelardo and Juliana, Philomin and Singh, Ravi and Crossa, Jos{\'{e}}},
doi = {10.3835/plantgenome2018.02.0013},
issn = {1940-3372},
journal = {The Plant Genome},
number = {3},
pages = {16},
title = {{An R Package for Multitrait and Multienvironment Data with the Item-Based Collaborative Filtering Algorithm}},
url = {https://dl.sciencesocieties.org/publications/tpg/abstracts/0/0/180013},
volume = {11},
year = {2018}
}
Second option, by the manual package
citation('IBCF.MTME')
##
## To cite package 'IBCF.MTME' in publications use:
##
## Francisco Javier Luna-Vazquez, Osval Antonio Montesinos-Lopez,
## Abelardo Montesinos-Lopez and Jose Crossa (2019). IBCF.MTME:
## Item Based Collaborative Filtering for Multi-Trait and
## Multi-Environment Data. R package version 1.6-0.
## https://github.com/frahik/IBCF.MTME
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {IBCF.MTME: Item Based Collaborative Filtering for Multi-Trait and Multi-Environment Data},
## author = {Francisco Javier Luna-Vazquez and Osval Antonio Montesinos-Lopez and Abelardo Montesinos-Lopez and Jose Crossa},
## year = {2019},
## note = {R package version 1.6-0},
## url = {https://github.com/frahik/IBCF.MTME},
## }
Contributions
If you have any suggestions or feedback, I would love to hear about it. Feel free to report new issues in this link, also if you want to request a feature/report a bug, or make a pull request if you can contribute.
Authors
- Francisco Javier Luna-Vázquez (Author, Maintainer)
- Osval Antonio Montesinos-López (Author)
- Abelardo Montesinos-López (Author)
- José Crossa (Author)
R Package Documentation
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