diff options
| -rw-r--r-- | R/preprocess.R | 19 |
1 files changed, 9 insertions, 10 deletions
diff --git a/R/preprocess.R b/R/preprocess.R index bbe5f52..17afdb1 100644 --- a/R/preprocess.R +++ b/R/preprocess.R @@ -32,7 +32,7 @@ detrend <- function(data,type=c("constant","linear")[1]){ stop("Error: Invalid trend type") } - reg <- time(data$output[,1]) + reg <- time(data) if(type=="linear"){ formula <- X ~ reg @@ -99,7 +99,7 @@ predict.detrend <- function(object,newdata=NULL,...){ detrend.predict <- function(object,data){ pred_list <- list() for(i in 1:ncol(data)){ - pred_list[[i]] <- predict(object[[i]],newdata=data.frame(reg = time(data[,i]))) + pred_list[[i]] <- predict(object[[i]],newdata=data.frame(reg = time(data))) } pred <- data.frame(matrix(unlist(pred_list),ncol=ncol(data),byrow=T)) colnames(pred) <- colnames(data) @@ -128,18 +128,17 @@ misdata <- function(data){ require(zoo) f <- function(var,start,end,Ts){ - var <- ts(data=var,start=start,end=end,frequency=floor(1/Ts)) + var <- ts(data=var,start=start,end=end,frequency=1/Ts) out <- na.approx(var,na.rm=F) return(as.numeric(out)) } - dataout <- data - dataout$output <- data.frame(apply(data$output,2,f,start=data$t.start, - end=data$t.end,Ts= data$Ts)) - dataout$input <- data.frame(apply(data$input,2,f,start=data$t.start, - end=data$t.end,Ts= data$Ts)) - - return(dataout) + Z <- data + outputData(Z) <- apply(outputData(data),2,f,start=time(data)[1], + end=tail(time(data),n=1),Ts= data$Ts)) + inputData(Z) <- apply(inputData(data),2,f,start=time(data)[1], + end=tail(time(data),n=1),Ts= data$Ts)) + Z } #' Subset or Resample idframe data |