correct lean=TRUE & plot=TRUE

R/FCReg.R

@@ -18,8 +18,8 @@
 #' @details If measurement error is assumed, the diagonal elements of the raw covariance will be removed. This could result in highly unstable estimate if the design is very sparse, or strong seasonality presents. 
 #' @references
 #' \cite{Yao, F., M\"{u}ller, H.G., Wang, J.L. "Functional Linear Regression Analysis for Longitudinal Data." Annals of Statistics 33, (2005): 2873-2903.(Dense data)} 
-#' \cite{Sent\"{u}rk, D., M\"{u}ller, H.G. "Functional varying coefficient models for longitudinal data." J. American Statistical Association, 10, (2010): 1256--1264.
-#' \cite{Sent\"{u}rk, D., Nguyen, D.V. "Varying Coefficient Models for Sparse Noise-contaminated Longitudinal Data", Statistica Sinica 21(4), (2011): 1831-1856. (Sparse data)} 
+#' \cite{Sent\"{u}rk, D., M\"{u}ller, H.G. "Functional varying coefficient models for longitudinal data." J. American Statistical Association, 10, (2010): 1256--1264.}
+#' \cite{Sent\"{u}rk, D., Nguyen, D.V. "Varying Coefficient Models for Sparse Noise-contaminated Longitudinal Data", Statistica Sinica 21(4), (2011): 1831-1856. (Sparse data)}
 #' @export
 #' @examples 
 #' # Y(t) = \beta_0(t) + \beta_1(t) X_1(t) + \beta_2(t) Z_2 + \epsilon

R/FPCA.R

@@ -249,6 +249,10 @@ FPCA = function(Ly, Lt, optns = list()){
     plot.FPCA(ret)
   }
 
+  if (optns$lean) {
+    ret$inputData <- NULL
+  }
+
   return(ret); 
 }
 

R/MakeResultFPCA.R

@@ -13,7 +13,7 @@
 #  rho: regularization parameter for sigma2
 #  fitLambda: eigenvalues by least squares fit method
 #  timestamps: time-stamps on how much time specific parts of FPCA needed
-#  inputData: input data to return (if lean: FALSE)
+#  inputData: input data to return
 ######
 # Output: 
 ######
@@ -66,17 +66,17 @@ MakeResultFPCA <- function(optns, smcObj, mu, scsObj, eigObj,
     ret$fitLambda <- fitLambda
   }
 
-  ret$inputData <- inputData; # This will be potentially be NULL if `lean`
+  ret$inputData <- inputData
   class(ret) <- 'FPCA'
 
   # select number of components based on specified criterion # This should be move within MakeResultFPCA
   selectedK <- SelectK(fpcaObj = ret, criterion = optns$methodSelectK, FVEthreshold = optns$FVEthreshold)  
 
-  if(!optns$lean){
+  # if(!optns$lean){
     ret$inputData <- inputData;
-  } else {
-    ret$inputData <- NULL
-  }
+  # } else {
+    # ret$inputData <- NULL
+  # }
 
   ret <- append(ret, list(selectK = selectedK$K, criterionValue = selectedK$criterion))
   class(ret) <- 'FPCA'