Writing up the doc for CreateOptions

HBGDki · Jul 6, 2015 · 8215857 · 8215857
1 parent 34bda7d
commit 8215857
Show file tree

Hide file tree

Showing 4 changed files with 56 additions and 48 deletions.
diff --git a/R/CheckOptions.R b/R/CheckOptions.R
@@ -10,7 +10,7 @@ CheckOptions = function(t,optns,n){
 
   bwmu = optns$bwmu;                bwmuGcv = optns$bwmuGcv; 
   bwuserCov = optns$bwuserCov;            bwuserCovGcv = optns$bwuserCovGcv;
-  ntest1 = optns$ntest1;            ngrid1 = optns$ngrid1; 
+  ntest1 = optns$ntest1;            # ngrid1 = optns$ngrid1; 
   selectionMethod = optns$selectionMethod;  FVEthreshold = optns$FVEthreshold;
   maxK = optns$maxK;                
   dataType = optns$dataType;          error = optns$error; 
@@ -54,11 +54,11 @@ CheckOptions = function(t,optns,n){
     cat("Error: FPCA is aborted because the argument: ntest1 is invalid!\n");  
     return(TRUE);   
   }
-  if( !( (length(optns$ngrid1)==1) &&  is.numeric(optns$ngrid1) && (1<=optns$ngrid1) && (optns$ngrid1<90) ) ){ 
+  # if( !( (length(optns$ngrid1)==1) &&  is.numeric(optns$ngrid1) && (1<=optns$ngrid1) && (optns$ngrid1<90) ) ){ 
     # number of support points for the covariance surface 
-    cat("Error: FPCA is aborted because the argument: ngrid1 is invalid!\n");  
-    return(TRUE);   
-  }
+  #   cat("Error: FPCA is aborted because the argument: ngrid1 is invalid!\n");  
+  #   return(TRUE);   
+  # }
   if( !(any(optns$selectionMethod == c('FVE','AIC','BIC')))){
     if ( !( is.numeric(optns$selectionMethod) &&  (length(optns$selectionMethod)==1) && (1>=optns$selectionMethod) && (optns$selectionMethod<n) )){          
       # the method of choosing the number of principal components K

diff --git a/R/CreateOptions.R b/R/CreateOptions.R
@@ -1,15 +1,51 @@
-## To be merged into FPCA.R
+#' Create the options list used by FPCA
+#' 
+#' @param bwcov : bandwidth value for covariance function (positive numeric - default: determine automatically based on 'bwcovGcv')
+#' @param bwcovGcv : bandwidth choice method for covariance function ('GMeanAndGCV','CV','GCV - default: 'GMeanAndGCV'')
+#' @param bwmu : bandwidth choice for mean function is using CV or GCV (positive numeric - default: determine automatically based on 'bwmuGcv')
+#' @param bwmuGcv : bandwidth choice method for mean function ('GMeanAndGCV','CV','GCV - default: 'GMeanAndGCV'')
+#' @param corrPlot : make correlation plot (logical - default: FALSE)
+#' @param corrPlotType: which type of correlation plot to show ('Fitted', 'Raw', 'Smoothed' - default : 'Fitted')
+#' @param dataType : do we have sparse or dense functional data ('Sparse', 'Dense', 'DenseWithMV', 'p>>n' - default :  determine automatically based on 'IsRegular')
+#' @param designPlot : make design plot (logical - default: FALSE)
+#' @param error : assume measurement error in the dataset (logical - default: TRUE)
+#' @param FVEthreshold : Fraction-of-Variance-Explained threshold used during the SVD of the fitted covar. function ( numeric (0,1] - default: 0.9999) 
+#' @param kernel : smoothing kernel choice, common for mu and covariance, ("rect", "gauss", "epan", "gausvar", "quar" - default : "epan" for dense data else "gauss")
+#' @param methodCov :  method to estimate covariance ('PACE','RARE','CrossSectional' - automatically determined, user input ignored)
+#' @param methodMu :  method to estimate mu ('PACE','RARE','CrossSectional' - automatically determined, user input ignored)
+#' @param maxK : maximum number of principal components to consider (positive integer - default : min(20, N-1), N : # of curves)
+#' @param method : method to estimate the PC scores. Either 'CE' (default) or 'IN'
+#' @param newdata : new data points to estimate (numeric - default : NULL )
+#' @param ntest1 : number of curves used for CV when choosing bandwidth ( [1,N] - default : min(30, N-1), N : # of curves )
+#' @param nRegGrid : number of support points in each direction of covariance surface (numeric - default : 51 )
+#' @param numBins : number of bins to bin the data into (default : NULL)
+#' @param screePlot : make scree plot (logical - default : FALSE )
+#' @param selectionMethod : the method of choosing the number of principal components K ('FVE','AIC','BIC' : default 'FVE' - only 'FVE' avaiable now)
+#' @param shrink : apply shrinkage to estimates of random coefficients (dataType data only)
+#' @param outPercent : 2-element vector in [0,1] indicating the outPercent data in the boundary default(0,1)
+#' @param rho : truncation threshold for the iterative residual. Either 'cv': choose rho by leave-one-observation out cross-validation; 'no': for not using the iterative sigma2 estimate, or a numerical value.
+#' @param rotationCut : 2-element vector in [0,1] indicating the percent of data truncated during sigma^2 estimation (default c(1/4,3/4))
+#' @param useBinnedData : 'FORCE' (Enforce the # of bins), 'AUTO' (Select the # of  bins automatically), 'OFF' (Do not bin)
+#' @param useBins: testing purpose: whether to bin the same observed time points when 2D smoothing
+#' @param userCov : user-defined smoothed covariance function
+#' @param userMu : user-defined smoothed mean function 
+#' @param verbose : display diagnostic messages (default = FALSE)
+#' @return an option list
+#' @examples 
+#' optLst = CreateOptions(kernel='rect');  # Create options list with rectangular kernel 
+
+
 
 CreateOptions = function(bwmu = NULL, bwmuGcv = NULL, bwuserCov = NULL, bwuserCovGcv = NULL, 
-    ntest1 = NULL, ngrid1 = NULL, selectionMethod = NULL, FVEthreshold = NULL,
+    ntest1 = NULL,  selectionMethod = NULL, FVEthreshold = NULL,
     maxK = NULL, dataType = NULL, error = NULL, nRegGrid = NULL,
     method = NULL, shrink = NULL, newdata = NULL, kernel = NULL, 
     numBins = NULL, yname = NULL, screePlot = NULL, designPlot = NULL, 
     corrPlot = NULL, corrPlotType =NULL,  rho = NULL, verbose = NULL, userMu = NULL, userCov = NULL, methodCov = NULL,
     methodMu = NULL, outPercent = NULL, useBinnedData = NULL, rotationCut = NULL){ 
 
  return( list(bwmu = bwmu, bwmuGcv = bwmuGcv, bwuserCov = bwuserCov, bwuserCovGcv = bwuserCovGcv,
-          ntest1 = ntest1, ngrid1 = ngrid1, selectionMethod = selectionMethod, FVEthreshold = FVEthreshold,
+          ntest1 = ntest1,  selectionMethod = selectionMethod, FVEthreshold = FVEthreshold,
           maxK = maxK, dataType = dataType, error = error, nRegGrid = nRegGrid, 
           method = method, shrink = shrink, newdata = newdata, kernel = kernel, corrPlot = corrPlot, corrPlotType = corrPlotType,	
           numBins = numBins, yname = yname, screePlot = screePlot, designPlot = designPlot, rho = rho, rotationCut = rotationCut,

diff --git a/R/SetOptions.R b/R/SetOptions.R
@@ -1,45 +1,12 @@
 #' Set the PCA option list
 #' 
-#' @param bwmu : bandwidth choice for mean function is using CV or GCV
-#' @param bwmuGcv : bandwidth choice method for mean function is GCV if bwmu = 0
-#' @param bwcov : bandwidth choice for covariance function is CV or GCV
-#' @param bwcovGcv : bandwidth choice method for covariance function is GCV if bwuserCov = 0
-#' @param ntest1 : number of curves used for CV when choosing bandwidth 
-#' @param ngrid1 : number of support points for the covariance surface 
-#' @param selectionMethod : the method of choosing the number of principal components K
-#' @param FVEthreshold : Fraction-of-Variance-Explained
-#' @param maxK : maximum number of principal components to consider
-#' @param dataType : do we have dataType or sparse functional data ('Sparse', 'Dense', 'DenseWithMV', 'p>>n')
-#' @param error : having measurement error
-#' @param nRegGrid : number of support points in each direction of covariance surface 
-#' @param method : method to estimate the PC scores. Either 'CE' (default) or 'IN'
-#' @param shrink : apply shrinkage to estimates of random coefficients (dataType data only)
-#' @param newdata : new data points to estimate
-#' @param kernel : smoothing kernel choice
-#' @param numBins : number of bins
-#' @param useBins: testing purpose: whether to bin the same observed time points when 2D smoothing
-#' @param screePlot : make scree plot
-#' @param designPlot : make design plot
-#' @param corrPlotType: which correlation plot ('Fitted', 'Raw', 'Smoothed')
-#' @param corrPlot : make correlation plot
-#' @param rho : truncation threshold for the iterative residual. Either 'cv': choose rho by leave-one-observation out cross-validation; 'no': for not using the iterative sigma2 estimate, or a numerica value.
-#' @param verbose : display diagnostic messages (default = FALSE)
-#' @param userMu : user-defined smoothed mean function 
-#' @param userCov : user-defined smoothed covariance function
-#' @param methodMu :  method to estimate mu ('PACE','RARE','CrossSectional')
-#' @param methodCov :  method to estimate covariance ('PACE','RARE','CrossSectional')
-#' @param outPercent : 2-element vector in [0,1] indicating the outPercent data in the boundary default(0,1)
-#' @param rotationCut : 2-element vector in [0,1] indicating the percent of data truncated during sigma^2 estimation (default c(1/4,3/4))
-#' @param useBinnedData : 'FORCE' (Enforce the # of bins), 'AUTO' (Select the # of  bins automatically), 'OFF' (Do not bin)
-#' @return an option list
-#' @examples 
-#' 1 + 3
+#' See '?CreateOptions for more details
 
 SetOptions = function(y, t, optns){ 
 
   bwmu =optns[['bwmu']];                bwmuGcv =optns[['bwmuGcv']]; 
   bwuserCov =optns[['bwuserCov']];            bwuserCovGcv =optns[['bwuserCovGcv']];
-  ntest1 =optns[['ntest1']];            ngrid1 =optns[['ngrid1']]; 
+  ntest1 =optns[['ntest1']];           # ngrid1 =optns[['ngrid1']]; 
   selectionMethod =optns[['selectionMethod']];  FVEthreshold =optns[['FVEthreshold']];
   maxK =optns[['maxK']];                
   dataType =optns[['dataType']];          error =optns[['error']];
@@ -69,9 +36,9 @@ SetOptions = function(y, t, optns){
   if(is.null(ntest1)){ # number of curves used for CV when choosing bandwidth 
     ntest1 = min(30, length(y)-1);
   }
-  if(is.null(ngrid1)){ # number of support points for the covariance surface 
-    ngrid1 = 30;
-  }
+  #if(is.null(ngrid1)){ # number of support points for the covariance surface 
+  #  ngrid1 = 30;
+  #}
   if(is.null(selectionMethod)){ # the method of choosing the number of principal components K
     #  TODO : Possibly have user-defined selection methods for the # of FPCs and we keep
     # an internal FVE-based method for us
@@ -97,6 +64,11 @@ SetOptions = function(y, t, optns){
       nRegGrid = 51;
     }    
   }
+  methodNames = c("IN", "CE");
+  if(!is.null(method) && !(method %in% methodNames)){ # Check suitability of kernel
+    cat(paste('method', kernel, 'is unrecognizable! Reset to automatic selection now!\n')); 
+    method = NULL; 
+  }   
   if(is.null(method)){ # method to estimate the PC scores
     if(dataType == 'Dense'){
       shrink = FALSE;
@@ -203,7 +175,7 @@ SetOptions = function(y, t, optns){
   }
 
   return( list(bwmu = bwmu, bwmuGcv = bwmuGcv, bwuserCov = bwuserCov, bwuserCovGcv = bwuserCovGcv,
-          ntest1 = ntest1, ngrid1 = ngrid1, selectionMethod = selectionMethod, FVEthreshold = FVEthreshold,
+          ntest1 = ntest1, selectionMethod = selectionMethod, FVEthreshold = FVEthreshold,
           maxK = maxK, dataType = dataType, error = error, nRegGrid = nRegGrid, rotationCut = rotationCut,
           method = method, shrink = shrink, newdata = newdata, kernel = kernel, corrPlot = corrPlot, corrPlotType = corrPlotType,	
           numBins = numBins, useBins = useBins, yname = yname, screePlot = screePlot, designPlot = designPlot, rho = rho, 

diff --git a/src/Rlwls1d.cpp b/src/Rlwls1d.cpp
@@ -7,7 +7,7 @@
 // [[Rcpp::depends(RcppEigen)]]
 // [[Rcpp::export]]
 
-Eigen::VectorXd Rlwls1d( const double & bw, const std::string kernel_type, const Eigen::Map<Eigen::VectorXd> & win, const Eigen::Map<Eigen::VectorXd> & xin, const Eigen::Map<Eigen::VectorXd> & yin, const Eigen::Map<Eigen::VectorXd> & xout, const int & npoly = 1, const int & nder = 0){
+Eigen::VectorXd Rlwls1d( const double & bw, const std::string kernel_type, const Eigen::Map<Eigen::VectorXd> & win, const Eigen::Map<Eigen::VectorXd> & xin, const Eigen::Map<Eigen::VectorXd> & yin, const Eigen::Map<Eigen::VectorXd> & xout, const unsigned int & npoly = 1, const unsigned int & nder = 0){
 
 
   // Convenient constants