fplsr <- function (data, order = 6, type = c("simpls", 
    "nipals"), unit.weights = TRUE, weight = FALSE, beta = 0.1, 
    interval = FALSE, method = c("delta", "boota"), alpha = 0.05,
    B = 100, adjust = FALSE, backh = 10) 
{
    type = match.arg(type)
    rawdata = t(data$y)
    n = dim(rawdata)[1]
    Xtrain = rawdata[1:(n-1),]
    Ytrain = rawdata[2:n,]
    Xtest = as.numeric(rawdata[n,])
    if (interval == FALSE){
        if (type == "simpls") {
            if (unit.weights == TRUE) {
                output = unitsimpls(Xtrain, Ytrain, Xtest, order, weight = weight, 
                                    beta = beta)
                fitted = t(output$T%*%t(output$Q)) + colMeans(Ytrain)
                residuals = t(Ytrain) - fitted
                out = list(x1 = as.numeric(rownames(Xtrain)), y1 = as.numeric(colnames(Xtrain)),
                          y = fts(1:dim(Ytrain)[2], t(Ytrain), xname = data$xname, yname = data$yname),
                          B = output$B, Ypred = fts(1:dim(Ytrain)[2], as.matrix(output$Ypred), xname = data$xname, yname = data$yname), 
                          P = output$P, Q = output$Q, T = output$T, R = output$R,
                          fitted = fts(1:dim(Xtrain)[2], fitted, xname = data$xname, yname = "Fitted values"),
                          residuals = fts(1:dim(Xtrain)[2], residuals, xname = data$xname, yname = "Residual"), 
                          meanX = fts(1:dim(Xtrain)[2], as.matrix(colMeans(Xtrain)), xname = data$xname, yname = data$yname), 
                          meanY = fts(1:dim(Ytrain)[2], as.matrix(colMeans(Ytrain)), xname = data$xname, yname = data$yname), 
                                      call = match.call())
                return(structure(out, class = "fm"))
            }
            else {
                output = simpls(Xtrain, Ytrain, Xtest, order, weight = weight, 
                                beta = beta)
                fitted = t(output$T%*%t(output$Q)) + colMeans(Ytrain)
                residuals = t(Ytrain) - fitted
                out = list(x1 = as.numeric(rownames(Xtrain)), y1 = as.numeric(colnames(Xtrain)),
                           y = fts(1:dim(Ytrain)[2], t(Ytrain), xname = data$xname, yname = data$yname),
                           B = output$B, Ypred = fts(1:dim(Ytrain)[2], as.matrix(output$Ypred), xname = data$xname, yname = data$yname), 
                           P = output$P, Q = output$Q, T = output$T, R = output$R,
                           fitted = fts(1:dim(Xtrain)[2], fitted, xname = data$xname, yname = "Fitted values"),
                           residuals = fts(1:dim(Xtrain)[2], residuals, xname = data$xname,
                           yname = "Residual"), meanX = fts(1:dim(Xtrain)[2], as.matrix(colMeans(Xtrain)), xname = data$xname, yname = data$yname), 
                           meanY = fts(1:dim(Ytrain)[2], as.matrix(colMeans(Ytrain)), xname = data$xname, yname = data$yname), call = match.call())
                return(structure(out, class = "fm"))
            }
        }
        else {
             output = nipals(Xtrain, Ytrain, Xtest, order, weight = weight, beta = beta)
             out = list(x1 = as.numeric(rownames(Xtrain)), y1 = as.numeric(colnames(Xtrain)),
                    y = fts(1:dim(Ytrain)[2], t(Ytrain), xname = data$xname, yname = data$yname),
                    B = output$B, Ypred = fts(1:dim(Ytrain)[2], matrix(output$Ypred, dim(Ytrain)[2],), xname = data$xname, yname = data$yname), 
                    P = output$P, Q = output$Q, T = output$T,
                    R = output$R, meanX = fts(1:dim(Xtrain)[2], as.matrix(colMeans(Xtrain)), xname = data$xname, yname = data$yname), 
                    meanY = fts(1:dim(Ytrain)[2], as.matrix(colMeans(Ytrain)), xname = data$xname, yname = data$yname),
                    Yscores = output$Yscores, projection = output$projection, 
                    fitted = fts(1:dim(Xtrain)[2],t(output$fitted.values[,,order]), xname = data$xname, yname = "Fitted values"),
                    residuals = fts(1:dim(Xtrain)[2], t(output$residuals[,,order]), xname = data$xname, yname = "Residual"),
                    Xvar = output$Xvar, Xtotvar = output$Xtotvar, call = match.call())
             return(structure(out, class = "fm"))             
        }
   }
   else{
       fplsrPI(t(Xtrain), t(Ytrain), Xtest, order, method = method, alpha = alpha, B = B, weight = weight,
               beta = beta, adjust = adjust, backh = backh)
   }
}