cw.tenfold: F-Fold cross-validation for clusterwise multiblock analyses

In mbclusterwise: Clusterwise Multiblock Analyses

Description

Function to perform a F-fold cross-validation applied to clusterwise multiblock analyses. This function is usually applied to various numbers of clusters and of dimensions to select their optimal values.

Usage

cw.tenfold(Y, X, blo, option = c("none", "uniform"), G, H, FOLD = 10, INIT = 20, 
   method = c("mbpls", "mbpcaiv", "mbregular"), Gamma = NULL,
   parallel.level = c("high", "low"))

Arguments

Y	a matrix or data frame containing the dependent variable(s)
X	a matrix or data frame containing the explanatory variables
blo	vector of the numbers of variables in each explanatory dataset
option	an option for the block weighting (by default, the first option is chosen): none the block weight is equal to the block inertia uniform the block weight is equal to 1/K for (X_1, …, X_K) and to 1 for X and Y
G	an integer giving the number of clusters
H	an integer giving the number of dimensions of the component-based model
FOLD	an integer giving the number of folds of the F-Fold cross-validation procedure comprised between 2 and 10 (10 by default)
INIT	an integer giving the number of initializations required for the clusterwise analysis (20 by default)
method	an option for the multiblock method to be applied (by default, the first option is chosen): mbpls multiblock Partial Least Squares is applied mbpcaiv multiblock Redundancy Analysis is applied mbregular multiblock regularized regression is applied
Gamma	a numeric value of the regularization parameter for the multiblock regularized regression comprised between 0 and 1 (NULL by default). The value (Gamma=0) leads to multiblock Redundancy Analysis and (Gamma=1) to multiblock PLS
parallel.level	Level of parallel computing, i.e. initializations are carried out simultaneously (high by default) high includes all the processing units of your computer low includes only two processing units of your computer

Value

A list containing the following components is returned:

call	the matching call
sqrmse.cal	the squared Root Mean Squared Error from the F calibration datasets
sqrmse.val	the squared Root Mean Squared Error from the F prediction datasets

Author(s)

Stephanie Bougeard ([e-mail])

References

Bougeard, S., Abdi, H., Saporta, G., Niang, N., Submitted, Clusterwise analysis for multiblock component methods.

See Also

cw.multiblock, cw.predict

Examples

  data(simdata.red) 
  Data.X <- simdata.red[c(1:8, 21:28), 1:10]
  Data.Y <- simdata.red[c(1:8, 21:28), 11:13]
  res1   <- list()
  res2   <- list()

  ## Note that the options (INIT=2) and (parallel.level = "low") are chosen to quickly
  ## illustrate the function. 
  ## For real data, instead choose (INIT=20) to avoid local optima and (parallel.level = "high")
  ## to improve the computing speed.

  for (H in c(1:2)){
    print(paste("H=", H, sep=""))
    res1[[H]] <- cw.tenfold(Y = Data.Y, X = Data.X, blo = c(5, 5), option = "none", G = 1, H,  
      FOLD = 2, INIT = 2, method = "mbpls", Gamma = NULL, parallel.level = "low")
    res2[[H]] <- cw.tenfold(Y = Data.Y, X = Data.X, blo = c(5, 5), option = "none", G = 2, H,
       FOLD = 2, INIT = 2, method = "mbpls", Gamma = NULL, parallel.level = "low")
  }
  res1.cal <- unlist(lapply(1:2, function(x) mean(sqrt(res1[[x]]$sqrmse.cal), na.rm=TRUE))) 
  res1.val <- unlist(lapply(1:2, function(x) mean(sqrt(res1[[x]]$sqrmse.val), na.rm=TRUE)))
  res2.cal <- unlist(lapply(1:2, function(x) mean(sqrt(res2[[x]]$sqrmse.cal), na.rm=TRUE))) 
  res2.val <- unlist(lapply(1:2, function(x) mean(sqrt(res2[[x]]$sqrmse.val), na.rm=TRUE)))
  
  rmse.cal <- rbind(res1.cal, res2.cal)
  rmse.val <- rbind(res1.val, res2.val)
  rownames(rmse.cal) <- rownames(rmse.val) <- paste("G", 1:2, sep = "=")
  colnames(rmse.cal) <- colnames(rmse.val) <- paste("H", 1:2, sep = "=")

  par(mfrow=c(1,2))
  matplot(t(rmse.cal), type = "o", ylab = "RMSE of calibration", xlab = "Model dimension (H)", 
        main = "Calibration", col = c("steelblue", "darkorange"), pch = c(0, 5), lwd = c(3, 3))
        legend("center", inset = .05, legend = rownames(rmse.cal), pch = c(0, 5), lwd = c(3, 3),
        col = c("steelblue", "darkorange"), horiz = TRUE, title = "Cluster number (G)")
  matplot(t(rmse.val), type = "o", ylab = "RMSE of prediction", xlab = "Model dimension (H)", 
        main = "Prediction", col = c("steelblue", "darkorange"), pch = c(0, 5), lwd = c(3, 3))
        legend("center", inset = .05, legend = rownames(rmse.val), pch = c(0, 5), lwd = c(3, 3), 
        col = c("steelblue", "darkorange"), horiz = TRUE, title = "Cluster number (G)")

mbclusterwise documentation built on May 2, 2019, 9:19 a.m.