predict.clr: Prediction from fitted CLR model(s)

In clr: Curve Linear Regression via Dimension Reduction

Description

Takes a fitted clr object produced by clr() and produces predictions given a new set of functions or the original values used for the model fit.

Usage

## S3 method for class 'clr'
predict(object, newX = NULL, newclust = NULL,
  newXmean = NULL, simplify = FALSE, ...)

Arguments

object	A fitted clr object produced by clr().
newX	An object of class clrdata or a matrix with one function a row. If this is not provided then predictions corresponding to the original data are returned. If newX is provided then it should contain the same type of functions as the original ones (same dimension, same clusters eventually, ...).
newclust	A new list of indices to obtain (approximately) homogeneous dependence structure inside each cluster of functions.
newXmean	To complete when done
simplify	If TRUE, one matrix of predicted functions is returned instead of a list of matrices (one matrix by cluster). In the final matrix, rows are sorted by increasing row numbers.
...	Further arguments are ignored.

Value

predicted functions

Examples

library(clr)
data(gb_load)

clr_load <- clrdata(x = gb_load$ENGLAND_WALES_DEMAND,
                    order_by = gb_load$TIMESTAMP,
                    support_grid = 1:48)

# data cleaning: replace zeros with NA
clr_load[rowSums((clr_load == 0) * 1) > 0, ] <- NA

Y <- clr_load[2:nrow(clr_load), ]
X <- clr_load[1:(nrow(clr_load) - 1), ]

begin_pred <- which(substr(rownames(Y), 1, 4) == '2016')[1]
Y_train <- Y[1:(begin_pred - 1), ]
X_train <- X[1:(begin_pred - 1), ]
Y_test <- Y[begin_pred:nrow(Y), ]
X_test <- X[begin_pred:nrow(X), ]


## Example without any cluster
model <- clr(Y = Y_train, X = X_train)

pred_on_train <- predict(model)
head(pred_on_train[[1]])

pred_on_test <- predict(model, newX = X_test)
head(pred_on_test[[1]])


## Example with clusters
model <- clr(Y = Y_train, X = X_train, clust = clust_train)

pred_on_train <- predict(model)
str(pred_on_train)
head(pred_on_train[[1]])

pred_on_test <- predict(model, newX = X_test, newclust = clust_test,
                        simplify = TRUE)
str(pred_on_test)
head(pred_on_test)

# With dates as row names
rownames(pred_on_test) <- rownames(Y_test)[as.numeric(rownames(pred_on_test))]

clr documentation built on July 29, 2019, 9:03 a.m.