From 1b5e70091851261739757c472bacaeedca324d76 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Francisco=20Javier=20Luna=20V=C3=A1zquez?=
 <frahik@gmail.com>
Date: Sun, 21 Oct 2018 12:25:52 -0500
Subject: [PATCH 1/3] include position of prediction in output

---
 R/IBCF.R | 250 ++++++++++++++++++++++++++++---------------------------
 1 file changed, 126 insertions(+), 124 deletions(-)

diff --git a/R/IBCF.R b/R/IBCF.R
index 561fcc0..521757a 100644
--- a/R/IBCF.R
+++ b/R/IBCF.R
@@ -1,124 +1,126 @@
-#' @title IBCF
-#' @description Item Based Collaborative Filtering for multi-trait and multi-environment data.
-#'
-#' @param object \code{list} CrossValidation object, is obtained from CV.RandomPartition function.
-#' @param dec \code{integer} Number of decimals to print in the results.
-#'
-#' @return A list with the next components
-#' \item{NPartitions}{\code{integer} Number of partitions used for testing data}
-#' \item{predictions_Summary}{\code{data.frame} A data.frame with the results of the test}
-#' \item{Predictions}{\code{list} A list with the predicted results for each partition}
-#'
-#' @export
-#'
-#' @importFrom stats cor na.omit sd
-#' @examples
-#'  \dontrun{
-#'   library(IBCF.MTME)
-#'   data('Wheat_IBCF')
-#'
-#'   CV <- CV.RandomPart(Wheat_IBCF)
-#'   IBCF(CV)
-#' }
-#'
-IBCF <- function(object, dec = 4) {
-  if (!inherits(object, 'CrossValidation')) stop("This function only works for objects of class 'CrossValidation'")
-  time.init <- proc.time()[3]
-  nIL <- ncol(object$DataSet) - 1
-
-  Y_avr <- matrix(0, ncol = nIL, nrow = nrow(object$DataSet))
-  NPartitions <- length(object$CrossValidation_list)
-
-  predicted <- vector('list', NPartitions)
-  names(predicted) <- paste0('Partition', 1:NPartitions)
-
-  results <- data.frame()
-  for (j in seq_len(NPartitions)) {
-    Part <- object$CrossValidation_list[[j]]
-
-    pos.NA <- which(Part == 2, arr.ind = T)
-    pos.NA[, 2] <- c(pos.NA[, 2]) + 1
-    pos.No_NA <- which(Part == 1, arr.ind = T)
-    # pos.No_NA[, 2] <- c(pos.No_NA[, 2])
-
-    if (length(pos.NA) == 0) {
-      stop('An error ocurred with the CrossValidation data')
-    }
-
-    Data.trn <- object$DataSet
-
-    Data.trn[pos.NA] <- NA
-
-    rows.Na <- which(apply(Data.trn, 1, function(x) any(is.na(x))) == TRUE)
-
-    Means_trn <- apply(Data.trn[, -c(1)], 2, mean, na.rm = T)
-    SDs_trn <- apply(Data.trn[, -c(1)], 2, sd, na.rm = T)
-
-    Scaled_Col <- scale(Data.trn[, -c(1)])
-
-    Means_trn_Row <- apply(Scaled_Col, 1, mean, na.rm = T)
-    SDs_trn_Row <- apply(Scaled_Col, 1, sd, na.rm = T)
-
-    if (any(is.na(SDs_trn_Row))) {
-      Data.trn_scaled <- data.frame(ID = as.character(Data.trn[, c(1)]), Scaled_Col)
-    } else {
-      Scaled_Row <- t(scale(t(Scaled_Col)))
-      Data.trn_scaled <- data.frame(ID = as.character(Data.trn[, c(1)]), Scaled_Row)
-    }
-
-    Hybrids.New <- Data.trn_scaled
-    Hybrids.New[, 2:ncol(Data.trn_scaled)] <- NA
-
-    ratings <- Data.trn_scaled
-
-    x <- ratings[, 2:(ncol(ratings))]
-
-    x[is.na(x)] <- 0
-
-    item_sim <- lsa::cosine(as.matrix((x)))
-
-    for (i in seq_len(length(rows.Na))) {
-      pos <- rows.Na[i]
-      ratings[pos, 2:ncol(ratings)] <- rec_itm_for_geno(pos, item_sim, ratings[,2:ncol(ratings)])
-    }
-
-    All.Pred <- data.matrix(ratings[,-1])
-
-
-    if (any(is.na(SDs_trn_Row))) {
-      All.Pred_O <- sapply(1:ncol(All.Pred), function(i) (All.Pred[,i]*SDs_trn[i] + Means_trn[i]))
-    } else {
-      All.Pred_O_Row <- t(sapply(1:nrow(All.Pred), function(i) (All.Pred[i,]*SDs_trn_Row[i] + Means_trn_Row[i])) )
-      All.Pred_O <- sapply(1:ncol(All.Pred_O_Row), function(i) (All.Pred_O_Row[,i]*SDs_trn[i] + Means_trn[i]))
-    }
-
-    colnames(All.Pred_O) <- colnames(Data.trn_scaled[,-c(1)])
-    All.Pred_O[pos.No_NA] <- NA
-    All.Pred_O_tst <- All.Pred_O[rows.Na, ]
-
-    predicted[[paste0('Partition', j)]] <- c(All.Pred_O)
-    pos.No_NA[,2] <- pos.No_NA[,2] + 1
-    DataSet_tst <- object$DataSet
-    DataSet_tst[pos.No_NA] <- NA
-    DataSet_tst <- DataSet_tst[rows.Na, ]
-
-    Data.Obs_tst <- getTidyForm(DataSet_tst)
-    posTST <- which(complete.cases(Data.Obs_tst) == TRUE)
-    results <- rbind(results, data.frame(Partition = j,
-      Environment = Data.Obs_tst$Env[posTST],
-      Trait = Data.Obs_tst$Trait[posTST],
-      Observed = round(Data.Obs_tst$Response[posTST],dec),
-      Predicted = round(c(All.Pred_O_tst[which(!is.na(All.Pred_O_tst))]), dec)))
-  }
-  Yhat_Obs_pred <- data.frame(object$DataSet, Y_avr)
-
-  out <- list(NPartitions = NPartitions,
-              predictions_Summary = results,
-              observed = getTidyForm(object$DataSet)$Response,
-              yHat = Y_avr,
-              predicted_Partition = predicted,
-              Data.Obs_Pred = Yhat_Obs_pred,
-              executionTime = proc.time()[3] - time.init)
-  class(out) <- 'IBCF'
-  return(out)
-}
+#' @title IBCF
+#' @description Item Based Collaborative Filtering for multi-trait and multi-environment data.
+#'
+#' @param object \code{list} CrossValidation object, is obtained from CV.RandomPartition function.
+#' @param dec \code{integer} Number of decimals to print in the results.
+#'
+#' @return A list with the next components
+#' \item{NPartitions}{\code{integer} Number of partitions used for testing data}
+#' \item{predictions_Summary}{\code{data.frame} A data.frame with the results of the test}
+#' \item{Predictions}{\code{list} A list with the predicted results for each partition}
+#'
+#' @export
+#'
+#' @importFrom stats cor na.omit sd
+#' @examples
+#'  \dontrun{
+#'   library(IBCF.MTME)
+#'   data('Wheat_IBCF')
+#'
+#'   CV <- CV.RandomPart(Wheat_IBCF)
+#'   IBCF(CV)
+#' }
+#'
+IBCF <- function(object, dec = 4) {
+  if (!inherits(object, 'CrossValidation')) stop("This function only works for objects of class 'CrossValidation'")
+  time.init <- proc.time()[3]
+  nIL <- ncol(object$DataSet) - 1
+
+  Y_avr <- matrix(0, ncol = nIL, nrow = nrow(object$DataSet))
+  NPartitions <- length(object$CrossValidation_list)
+
+  predicted <- vector('list', NPartitions)
+  names(predicted) <- paste0('Partition', 1:NPartitions)
+
+  results <- data.frame()
+  for (j in seq_len(NPartitions)) {
+    Part <- object$CrossValidation_list[[j]]
+
+    pos.NA <- which(Part == 2, arr.ind = T)
+    pos.NA[, 2] <- c(pos.NA[, 2]) + 1
+    pos.No_NA <- which(Part == 1, arr.ind = T)
+    # pos.No_NA[, 2] <- c(pos.No_NA[, 2])
+
+    if (length(pos.NA) == 0) {
+      stop('An error ocurred with the CrossValidation data')
+    }
+
+    Data.trn <- object$DataSet
+
+    Data.trn[pos.NA] <- NA
+
+    rows.Na <- which(apply(Data.trn, 1, function(x) any(is.na(x))) == TRUE)
+
+    Means_trn <- apply(Data.trn[, -c(1)], 2, mean, na.rm = T)
+    SDs_trn <- apply(Data.trn[, -c(1)], 2, sd, na.rm = T)
+
+    Scaled_Col <- scale(Data.trn[, -c(1)])
+
+    Means_trn_Row <- apply(Scaled_Col, 1, mean, na.rm = T)
+    SDs_trn_Row <- apply(Scaled_Col, 1, sd, na.rm = T)
+
+    if (any(is.na(SDs_trn_Row))) {
+      Data.trn_scaled <- data.frame(ID = as.character(Data.trn[, c(1)]), Scaled_Col)
+    } else {
+      Scaled_Row <- t(scale(t(Scaled_Col)))
+      Data.trn_scaled <- data.frame(ID = as.character(Data.trn[, c(1)]), Scaled_Row)
+    }
+
+    Hybrids.New <- Data.trn_scaled
+    Hybrids.New[, 2:ncol(Data.trn_scaled)] <- NA
+
+    ratings <- Data.trn_scaled
+
+    x <- ratings[, 2:(ncol(ratings))]
+
+    x[is.na(x)] <- 0
+
+    item_sim <- lsa::cosine(as.matrix((x)))
+
+    for (i in seq_len(length(rows.Na))) {
+      pos <- rows.Na[i]
+      ratings[pos, 2:ncol(ratings)] <- rec_itm_for_geno(pos, item_sim, ratings[,2:ncol(ratings)])
+    }
+
+    All.Pred <- data.matrix(ratings[,-1])
+
+
+    if (any(is.na(SDs_trn_Row))) {
+      All.Pred_O <- sapply(1:ncol(All.Pred), function(i) (All.Pred[,i]*SDs_trn[i] + Means_trn[i]))
+    } else {
+      All.Pred_O_Row <- t(sapply(1:nrow(All.Pred), function(i) (All.Pred[i,]*SDs_trn_Row[i] + Means_trn_Row[i])) )
+      All.Pred_O <- sapply(1:ncol(All.Pred_O_Row), function(i) (All.Pred_O_Row[,i]*SDs_trn[i] + Means_trn[i]))
+    }
+
+    colnames(All.Pred_O) <- colnames(Data.trn_scaled[,-c(1)])
+    All.Pred_O[pos.No_NA] <- NA
+    All.Pred_O_tst <- All.Pred_O[rows.Na, ]
+
+    predicted[[paste0('Partition', j)]] <- c(All.Pred_O)
+    pos.No_NA[,2] <- pos.No_NA[,2] + 1
+    DataSet_tst <- object$DataSet
+    DataSet_tst[pos.No_NA] <- NA
+    DataSet_tst <- DataSet_tst[rows.Na, ]
+
+    Data.Obs_tst <- getTidyForm(DataSet_tst)
+    posTST <- which(complete.cases(Data.Obs_tst) == TRUE)
+
+    results <- rbind(results, data.frame(Position = posTST,
+      Partition = j,
+      Environment = Data.Obs_tst$Env[posTST],
+      Trait = Data.Obs_tst$Trait[posTST],
+      Observed = round(Data.Obs_tst$Response[posTST],dec),
+      Predicted = round(c(All.Pred_O_tst[which(!is.na(All.Pred_O_tst))]), dec)))
+  }
+  Yhat_Obs_pred <- data.frame(object$DataSet, Y_avr)
+
+  out <- list(NPartitions = NPartitions,
+              predictions_Summary = results,
+              observed = getTidyForm(object$DataSet)$Response,
+              yHat = Y_avr,
+              predicted_Partition = predicted,
+              Data.Obs_Pred = Yhat_Obs_pred,
+              executionTime = proc.time()[3] - time.init)
+  class(out) <- 'IBCF'
+  return(out)
+}

From 414f8f9383a1fc44edfecbdbe1b65f08dc1cc50f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Francisco=20Javier=20Luna=20V=C3=A1zquez?=
 <frahik@gmail.com>
Date: Sun, 21 Oct 2018 12:32:22 -0500
Subject: [PATCH 2/3] update cran comments

---
 cran-comments.md | 31 ++++++++++---------------------
 1 file changed, 10 insertions(+), 21 deletions(-)

diff --git a/cran-comments.md b/cran-comments.md
index 66d0291..b69bd08 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,21 +1,10 @@
-## Test environments
-* Linux - Manjaro R-base [3.4.3]
-* Windows 10 R [3.4.3]
-* win-builder (devel and release)
-
-## R CMD check results
-There were no ERRORs or WARNINGs.
-
-There was 1 NOTE:
-
-* checking CRAN incoming feasibility ... NOTE
-Maintainer: 'Francisco Javier Luna-Vazquez <frahik@gmail.com>'
-
-## Special Comments
-
-The last version have a several issue with the predictions, this new version has already corrected that detail.
-Sorry for this detail.
-
-## Last round comments
-For your next version:
-Please do not capitalize "Item" in your description. [Solved]
\ No newline at end of file
+## Test environments
+* Linux - Manjaro R-base [3.5.1]
+* Windows 10 R [3.5.1]
+* win-builder (devel and release)
+
+## R CMD check results
+
+0 errors | 0 warnings | 0 notes
+
+R CMD check succeeded

From cc12cd6643bf587d0910870a4742d42184c7d640 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Francisco=20Javier=20Luna=20V=C3=A1zquez?=
 <frahik@gmail.com>
Date: Sun, 21 Oct 2018 12:32:58 -0500
Subject: [PATCH 3/3] update functions

---
 R/methods.R | 364 ++++++++++++++++++++++++++--------------------------
 1 file changed, 182 insertions(+), 182 deletions(-)

diff --git a/R/methods.R b/R/methods.R
index 7c65cab..b237e83 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -1,183 +1,183 @@
-#' @title Summary
-#'
-#' @description Summary of IBCF object
-#'
-#' @param object \code{IBCF object} IBCF object, result of use the IBCF() function
-#' @param information \code{string} ...
-#' @param digits \code{numeric} ...
-#' @param ... Further arguments passed to or from other methods.
-#'
-#' @importFrom stats cor
-#' @importFrom dplyr summarise group_by select '%>%' mutate_if funs
-#'
-#' @export
-summary.IBCF <- function(object, information = 'compact', digits = 4, ...){
-  if (!inherits(object, "IBCF")) stop("This function only works for objects of class 'IBCF'")
-
-  object$predictions_Summary %>%
-    group_by(Environment, Trait, Partition) %>%
-    summarise(Pearson = cor(Predicted, Observed, use = 'pairwise.complete.obs'),
-              MAAPE = mean(atan(abs(Observed-Predicted)/abs(Observed)))) %>%
-    select(Environment, Trait, Partition, Pearson, MAAPE) %>%
-    mutate_if(is.numeric, funs(round(., digits))) %>%
-    as.data.frame() -> presum
-
-  presum %>%  group_by(Environment, Trait) %>%
-    summarise(SE_MAAPE = sd(MAAPE, na.rm = T)/sqrt(n()), MAAPE = mean(MAAPE, na.rm = T),
-              SE_Pearson = sd(Pearson, na.rm = T)/sqrt(n()), Pearson = mean(Pearson, na.rm = T))  %>%
-    select(Environment, Trait, Pearson, SE_Pearson, MAAPE, SE_MAAPE) %>%
-    mutate_if(is.numeric, funs(round(., digits))) %>%
-    as.data.frame() -> finalSum
-
-  out <- switch(information,
-                compact = finalSum,
-                complete = presum,
-                extended = {
-                  finalSum$Partition <- 'All'
-                  presum$Partition <- as.character(presum$Partition)
-                  presum$SE_Pearson <- NA
-                  presum$SE_MAAPE <- NA
-                  rbind(presum, finalSum)
-                }
-  )
-  return(out)
-}
-
-#' @title Summary
-#'
-#' @description Summary of IBCFY object
-#'
-#' @param object \code{IBCFY object} IBCFY object, result of use the IBCF.Years() function
-#' @param digits \code{numeric} Number of digits of the output.
-#' @param ... Further arguments passed to or from other methods.
-#'
-#' @importFrom stats cor
-#' @importFrom dplyr summarise group_by select '%>%' mutate_if funs
-#'
-#' @export
-summary.IBCFY <- function(object, digits = 4, ...) {
-  if (!inherits(object, "IBCFY")) stop("This function only works for objects of class 'IBCFY'")
-
-  object$predictions_Summary %>%
-    group_by(Environment, Trait) %>%
-    summarise(Pearson = cor(Predicted, Observed, use = 'pairwise.complete.obs'),
-              MAAPE = mean(atan(abs(Observed-Predicted)/abs(Observed)))) %>%
-    select(Environment, Trait, Pearson, MAAPE) %>%
-    mutate_if(is.numeric, funs(round(., digits))) %>%
-    as.data.frame() -> out
-
-  return(out)
-}
-
-#' @title Plot IBCF graph
-#'
-#' @description Plot from IBCF object
-#'
-#' @param x \code{IBCF object} IBCF object, result of use the IBCF() function
-#' @param select \code{character} By default ('Pearson'), plot the Pearson Correlations of the IBCF Object, else ('MAAPE'), plot the MAAPE of the IBCF Object.
-#' @param ... Further arguments passed to or from other methods.
-#'
-#' @importFrom graphics arrows axis plot
-#' @export
-plot.IBCF <- function(x, select = 'Pearson', ...){
-  ### Check that object is compatible
-  if (!inherits(x, "IBCF")) stop("This function only works for objects of class 'IBCF'")
-
-  results <- summary(x)
-  results[, select] <- results[order(results[, select]), select]
-
-  if (select == "Pearson") {
-    results$SE <- results$SE_Pearson * 1.96
-    ylab <- "Pearson's Correlation"
-  } else if (select == "MAAPE") {
-    results$SE <- results$SE_MAAPE * 1.96
-    ylab <- select
-  }
-
-  x.labels <- paste0(results$Trait, '_', results$Env)
-  plot.x <- 1:length(x.labels)
-
-  plot(plot.x, results[, select], ylim = range(c(results[, select] - results$SE, results[, select] + results$SE)),
-      type = 'p', ylab = ylab, xlab = '', xaxt = "n", ...)
-  axis(1, at = plot.x, labels = x.labels, las = 2)
-  arrows(plot.x, results[, select] - results$SE, plot.x, results[, select] + results$SE, code = 3, length = 0.02, angle = 90)
-}
-
-
-#' @title barplot.IBCFY
-#'
-#' @description Barplot of the results from IBCFY object
-#'
-#' @param height \code{IBCFY object} IBCFY object, result of use the IBCF.Years() function
-#' @param select \code{character} By default ('Pearson'), plot the Pearson Correlations of the IBCF Object, else ('MAAPE'), plot the MAAPE of the IBCF Object.
-#' @param ... Further arguments passed to or from other methods.
-#'
-#' @importFrom graphics barplot
-#' @export
-barplot.IBCFY <- function(height, select = 'Pearson', ...){
-  ### Check that object is compatible
-  if (!inherits(height, "IBCFY")) stop("This function only works for objects of class 'IBCF'")
-
-  results <- summary(height)
-  vector <- as.numeric(paste(results[, select]))
-  names(vector) <- results[, 1]
-  vector <- vector[order(vector)]
-
-  if (select == 'Pearson')
-    select <- 'Pearson Correlation'
-  else
-    select <- 'MAAPE'
-
-  barplot(vector, ylab = select, ...)
-}
-
-#' Print IBCF information object
-#'
-#' @param x object a
-#' @param ...  more objects
-#'
-#' @return test
-#' @importFrom utils head
-#' @export
-#'
-print.IBCF <- function(x, ...){
-  cat('Item Based Collaborative Filtering Model: \n',
-      'Fitted with ', x$NPartitions, ' random partitions\n',
-      'Runtime: ', x$executionTime ,' seconds \n\n',
-      'Some predicted values: \n')
-
-  print.default(format(head(x$predictions_Summary$Predicted, 20), digits = 3), print.gap = 2L, quote = FALSE)
-
-  cat('\nPredictive capacity of the model: \n')
-
-  print.data.frame(summary(x, 'compact', digits = 3), print.gap = 2L, quote = FALSE)
-
-  cat('\n Use str() function to found more datailed information.')
-  invisible(x)
-}
-
-#' Print IBCFY information object
-#'
-#' @param x object a
-#' @param ...  more objects
-#'
-#' @return test
-#' @importFrom utils head
-#' @export
-#'
-print.IBCFY <- function(x, ...){
-  cat('Item Based Collaborative Filtering Model: \n',
-      'Evaluated Environment/Year (s): ', x$Years.testing, '\n',
-      'Evaluated Trait (s): ', x$Traits.testing, '\n',
-      'Runtime: ', x$executionTime ,' seconds \n\n',
-      'Some predicted values: \n')
-
-  print.default(format(head(x$predictions_Summary$Predicted, 20), digits = 3), print.gap = 2L, quote = FALSE)
-
-  cat('\nPredictive capacity of the model: \n')
-
-  print.data.frame(summary(x, 'compact', digits = 3), print.gap = 2L, quote = FALSE)
-
-  cat('\n Use str() function to found more datailed information.')
-  invisible(x)
+#' @title Summary
+#'
+#' @description Summary of IBCF object
+#'
+#' @param object \code{IBCF object} IBCF object, result of use the IBCF() function
+#' @param information \code{string} ...
+#' @param digits \code{numeric} ...
+#' @param ... Further arguments passed to or from other methods.
+#'
+#' @importFrom stats cor
+#' @importFrom dplyr summarise group_by select '%>%' mutate_if funs
+#'
+#' @export
+summary.IBCF <- function(object, information = 'compact', digits = 4, ...){
+  if (!inherits(object, "IBCF")) stop("This function only works for objects of class 'IBCF'")
+
+  object$predictions_Summary %>%
+    group_by(Environment, Trait, Partition) %>%
+    summarise(Pearson = cor(Predicted, Observed, use = 'pairwise.complete.obs'),
+              MAAPE = mean(atan(abs(Observed-Predicted)/abs(Observed)))) %>%
+    select(Environment, Trait, Partition, Pearson, MAAPE) %>%
+    mutate_if(is.numeric, funs(round(., digits))) %>%
+    as.data.frame() -> presum
+
+  presum %>%  group_by(Environment, Trait) %>%
+    summarise(SE_MAAPE = sd(MAAPE, na.rm = T)/sqrt(n()), MAAPE = mean(MAAPE, na.rm = T),
+              SE_Pearson = sd(Pearson, na.rm = T)/sqrt(n()), Pearson = mean(Pearson, na.rm = T))  %>%
+    select(Environment, Trait, Pearson, SE_Pearson, MAAPE, SE_MAAPE) %>%
+    mutate_if(is.numeric, funs(round(., digits))) %>%
+    as.data.frame() -> finalSum
+
+  out <- switch(information,
+                compact = finalSum,
+                complete = presum,
+                extended = {
+                  finalSum$Partition <- 'All'
+                  presum$Partition <- as.character(presum$Partition)
+                  presum$SE_Pearson <- NA
+                  presum$SE_MAAPE <- NA
+                  rbind(presum, finalSum)
+                }
+  )
+  return(out)
+}
+
+#' @title Summary
+#'
+#' @description Summary of IBCFY object
+#'
+#' @param object \code{IBCFY object} IBCFY object, result of use the IBCF.Years() function
+#' @param digits \code{numeric} Number of digits of the output.
+#' @param ... Further arguments passed to or from other methods.
+#'
+#' @importFrom stats cor
+#' @importFrom dplyr summarise group_by select '%>%' mutate_if funs
+#'
+#' @export
+summary.IBCFY <- function(object, digits = 4, ...) {
+  if (!inherits(object, "IBCFY")) stop("This function only works for objects of class 'IBCFY'")
+
+  object$predictions_Summary %>%
+    group_by(Environment, Trait) %>%
+    summarise(Pearson = cor(Predicted, Observed, use = 'pairwise.complete.obs'),
+              MAAPE = mean(atan(abs(Observed-Predicted)/abs(Observed)))) %>%
+    select(Environment, Trait, Pearson, MAAPE) %>%
+    mutate_if(is.numeric, funs(round(., digits))) %>%
+    as.data.frame() -> out
+
+  return(out)
+}
+
+#' @title Plot IBCF graph
+#'
+#' @description Plot from IBCF object
+#'
+#' @param x \code{IBCF object} IBCF object, result of use the IBCF() function
+#' @param select \code{character} By default ('Pearson'), plot the Pearson Correlations of the IBCF Object, else ('MAAPE'), plot the MAAPE of the IBCF Object.
+#' @param ... Further arguments passed to or from other methods.
+#'
+#' @importFrom graphics arrows axis plot
+#' @export
+plot.IBCF <- function(x, select = 'Pearson', ...){
+  ### Check that object is compatible
+  if (!inherits(x, "IBCF")) stop("This function only works for objects of class 'IBCF'")
+
+  results <- summary(x)
+  results[, select] <- results[order(results[, select]), select]
+
+  if (select == "Pearson") {
+    results$SE <- results$SE_Pearson * 1.96
+    ylab <- "Pearson's Correlation"
+  } else if (select == "MAAPE") {
+    results$SE <- results$SE_MAAPE * 1.96
+    ylab <- select
+  }
+
+  x.labels <- paste0(results$Trait, '_', results$Env)
+  plot.x <- 1:length(x.labels)
+
+  plot(plot.x, results[, select], ylim = range(c(results[, select] - results$SE, results[, select] + results$SE)),
+      type = 'p', ylab = ylab, xlab = '', xaxt = "n", ...)
+  axis(1, at = plot.x, labels = x.labels, las = 2)
+  arrows(plot.x, results[, select] - results$SE, plot.x, results[, select] + results$SE, code = 3, length = 0.02, angle = 90)
+}
+
+
+#' @title barplot.IBCFY
+#'
+#' @description Barplot of the results from IBCFY object
+#'
+#' @param height \code{IBCFY object} IBCFY object, result of use the IBCF.Years() function
+#' @param select \code{character} By default ('Pearson'), plot the Pearson Correlations of the IBCF Object, else ('MAAPE'), plot the MAAPE of the IBCF Object.
+#' @param ... Further arguments passed to or from other methods.
+#'
+#' @importFrom graphics barplot
+#' @export
+barplot.IBCFY <- function(height, select = 'Pearson', ...){
+  ### Check that object is compatible
+  if (!inherits(height, "IBCFY")) stop("This function only works for objects of class 'IBCF'")
+
+  results <- summary(height)
+  vector <- as.numeric(paste(results[, select]))
+  names(vector) <- results[, 1]
+  vector <- vector[order(vector)]
+
+  if (select == 'Pearson')
+    select <- 'Pearson Correlation'
+  else
+    select <- 'MAAPE'
+
+  barplot(vector, ylab = select, ...)
+}
+
+#' Print IBCF information object
+#'
+#' @param x IBCF object
+#' @param ...   Further arguments passed to or from other methods.
+#'
+#' @return printeable object
+#' @importFrom utils head
+#' @export
+#'
+print.IBCF <- function(x, ...){
+  cat('Item Based Collaborative Filtering Model: \n',
+      'Fitted with ', x$NPartitions, ' random partitions\n',
+      'Runtime: ', x$executionTime ,' seconds \n\n',
+      'Some predicted values: \n')
+
+  print.default(format(head(x$predictions_Summary$Predicted, 20), digits = 3), print.gap = 2L, quote = FALSE)
+
+  cat('\nPredictive capacity of the model: \n')
+
+  print.data.frame(summary(x, 'compact', digits = 3), print.gap = 2L, quote = FALSE)
+
+  cat('\n Use str() function to found more datailed information.')
+  invisible(x)
+}
+
+#' Print IBCFY information object
+#'
+#' @param x IBCFY object
+#' @param ...   Further arguments passed to or from other methods.
+#'
+#' @return printeable object
+#' @importFrom utils head
+#' @export
+#'
+print.IBCFY <- function(x, ...){
+  cat('Item Based Collaborative Filtering Model: \n',
+      'Evaluated Environment/Year (s): ', x$Years.testing, '\n',
+      'Evaluated Trait (s): ', x$Traits.testing, '\n',
+      'Runtime: ', x$executionTime ,' seconds \n\n',
+      'Some predicted values: \n')
+
+  print.default(format(head(x$predictions_Summary$Predicted, 20), digits = 3), print.gap = 2L, quote = FALSE)
+
+  cat('\nPredictive capacity of the model: \n')
+
+  print.data.frame(summary(x, 'compact', digits = 3), print.gap = 2L, quote = FALSE)
+
+  cat('\n Use str() function to found more datailed information.')
+  invisible(x)
 }
\ No newline at end of file