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#' Remove linear trends
#'
#' Removes the linear function from the input and output matrices.
#'
#' @param data an object of class \code{idframe}
#'
#' @return
#' A list containing the following elements
#'
#' \tabular{ll}{
#' \code{fitted.values} \tab \code{idframe} object with detrended variables \cr
#' \code{output.trend} \tab \code{list} containing trend fits for each output variable \cr
#' \code{input.trend} \tab \code{list} containing trend fits for each input variable
#' }
#'
#' @examples
#' data(cstr)
#' fit <- detrend.idframe(cstr)
#' cstr_detrend <- predict(fit)
#'
#' @seealso \code{\link{predict.detrend.idframe}}, \code{\link[stats]{lm}}
#' @export
detrend.idframe <- function(data){
data_detrend <- data
out <- data$output;output_trend <- list()
t <- time(out[,1])
for(i in 1:ncol(out)){
output_trend[[i]] <- lm(out[,i]~t)
out[,i] <- fitted(output_trend[[i]])
}
input <- data$input;input_trend <- list()
for(i in 1:ncol(input)){
input_trend[[i]] <- lm(input[,i]~t)
input[,i] <- fitted(input_trend[[i]])
}
data_detrend$output <- out;data_detrend$input <- input
est <- list(fitted.values=data_detrend,output.trend = output_trend,
input.trend = input_trend)
class(est) <- "detrend.idframe"
return(est)
}
#' Predict method for trend fits on idframe objects
#'
#' Detrended \code{idframe} object based on linear trend fit
#'
#' @param object an object of class \code{idframe}
#' @param newdata An optional idframe object in whic to look for variables with which
#' to predict. If ommited, the original detrended idframe object is used
#'
#' @return an \code{idframe} object
#'
#' @examples
#' ## Examples for train and test sets
#' data(cstr)
#' splitList <- dataPartition(cstr,p=0.6)
#' train <- splitList$estimation # training set
#' test <- splitList$validation # testing set
#' fit <- detrend.idframe(train)
#' train_detrend <- predict(fit)
#' test_detrend <- predict(fit,newdata=test)
#' @export
predict.detrend.idframe <- function(object,newdata=NULL,...){
if(is.null(newdata)){
data <- fitted(object)
} else{
data <- newdata
out <- detrend.predict(object$output.trend,data$output)
input <- detrend.predict(object$input.trend,data$input)
data$output <- out;data$input <- input
}
return(data)
}
detrend.predict <- function(object,data){
pred_list <- list()
for(i in 1:ncol(data)){
pred_list[[i]] <- predict(object[[i]],newdata=data.frame(t = time(data[,i])))
}
pred <- data.frame(matrix(unlist(pred_list),ncol=ncol(data),byrow=T))
colnames(pred) <- colnames(data)
return(pred)
}
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