general update to re-establish connection gitHub

Author: JamesRamsay5

.Rhistory

@@ -1,6 +1,6 @@
 Package: fda
 Version: 5.1.7
-Date: 2020-09-04
+Date: 2020-11-09
 Title: Functional Data Analysis
 Author: J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], 
  Spencer Graves <spencer.graves@effectivedefense.org> [ctb], 

DESCRIPTION

@@ -140,7 +140,6 @@ export(AmpPhaseDecomp,
 	     smooth.sparse.mean,
 	     sparse.list,
 	     sparse.mat,
-       svd2,
        symsolve,
        "[.fd",
        times.fd,
@@ -211,7 +210,6 @@ S3method(predict,      monfd)
 S3method(as.fd,        'function')
 S3method(as.fd,        smooth.spline)
 S3method(fRegress,     fd)
-S3method(fRegress,     numeric)
 S3method(fRegress,     double)
 S3method(fRegress,     formula)
 S3method(fRegress,     character)

NAMESPACE

@@ -171,7 +171,7 @@ CSTRfn <- function(parvec, datstruct, fitstruct,
 #
 #    Dcoef <- (lm.fit(Dres0, res0)$coefficients)
 #
-    Dres.svd <- svd2(Dres0)
+    Dres.svd <- svd(Dres0)
     ikeep <- with(Dres.svd, which(d > eps*max(d)))
     Dres.rank <- length(ikeep)
     if(Dres.rank < min(dim(Dres0)))
@@ -263,7 +263,7 @@ CSTRfn <- function(parvec, datstruct, fitstruct,
 #  Smat = Zmat*inv(Zmat'*Zmat + Rfac'*Rfac)*Zmat'
 # Use singular value decomposition so we never have to worry about
 # ill conditioning.
-  Zsvd <- svd2(Zmat)
+  Zsvd <- svd(Zmat)
 # Zmat = with(Zsvd, u %*% diag(d) %*% t(v))
 # so Z'Z+R'R = v d^2 v' + R'R
 #          = v %*% (d^2 + (R%*%v)'(R%*%v))

R/CSTRfn.R

@@ -3,11 +3,6 @@ fRegress <- function(y, ...) {
   UseMethod("fRegress")
 }
 
-#fRegress.fdPar <- function(y, xfdlist, betalist, wt=NULL,
-#                           y2cMap=NULL, SigmaE=NULL, returnMatrix=FALSE, ...){
-#  print("inside fRegress.fdPar")
-#  y = y$fd
-  
 fRegress.fd <- function(y, xfdlist, betalist, wt=NULL,
                         y2cMap=NULL, SigmaE=NULL, returnMatrix=FALSE,
                         method=c('fRegress', 'model'),
@@ -69,7 +64,7 @@ fRegress.fd <- function(y, xfdlist, betalist, wt=NULL,
   #  as predict(fRegressList).  In this call fRegressList can be any object of the
   #  "fRegress".
 
-  #  Last modified 28 October 2020 by Jim Ramsay
+  #  Last modified 5 November 2020 by Jim Ramsay
 
   if (is.fdPar(y)) y <- y$fd
 

R/fRegress.R

@@ -51,12 +51,10 @@ fRegress.double <- function(y, xfdlist, betalist, wt=NULL,
   #  as predict(List).  In this call List can be any object of the
   #  "".
 
-  # Last modified 3 November 2020 by Jim Ramsay
+  # Last modified 5 November 2020 by Jim Ramsay
 
   #  check Y and compute sample size N
 
-  print("inside fRegress.double")
-  
   if (!inherits(y, "numeric")) stop("Y is not a numeric vector.")
 
   #  ----------------------------------------------------------------
@@ -74,15 +72,15 @@ fRegress.double <- function(y, xfdlist, betalist, wt=NULL,
   N    <- dim(ymat)[1]
   p    <- length(xfdlist)
 
-  print("computing Rmat")
-  
   Zmat  <- NULL
   Rmat  <- NULL
   pjvec <- rep(0,p)
   ncoef <- 0
   for (j in 1:p) {
     xfdj       <- xfdlist[[j]]
-    print(class(xfdj))
+    if (!inherits(xfdj, "fd")) {
+      stop(paste("Independent variable",j,"is not of class fd."))
+    }
     xcoef      <- xfdj$coefs
     xbasis     <- xfdj$basis
     betafdParj <- betalist[[j]]
@@ -116,9 +114,6 @@ fRegress.double <- function(y, xfdlist, betalist, wt=NULL,
 
   #  solve for coefficients defining BETA
 
-  print("assembling Cmat and Dmat")
-  
-  
   if (any(wt != 1)) {
     rtwt   <- sqrt(wt)
     Zmatwt <- Zmat*rtwt
@@ -132,20 +127,16 @@ fRegress.double <- function(y, xfdlist, betalist, wt=NULL,
 
   eigchk(Cmat)
 
-  print("solving equation")
-  
   Cmatinv  <- solve(Cmat)
 
   betacoef <- Cmatinv %*% Dmat
 
-  #  compute and print degrees of freedom measure
+  #  compute degrees of freedom measure
 
   df <- sum(diag(Zmat %*% Cmatinv %*% t(Zmat)))
 
   #  set up fdPar object for BETAESTFDPAR
 
-  print("setting up beetaestlist")
-  
   betaestlist <- betalist
   mj2 <- 0
   for (j in 1:p) {
@@ -165,8 +156,6 @@ fRegress.double <- function(y, xfdlist, betalist, wt=NULL,
 
   #  set up fd object for predicted values
 
-  print("computing yhatmat")
-  
   yhatmat <- matrix(0,N,1)
   for (j in 1:p) {
     xfdj <- xfdlist[[j]]
@@ -203,8 +192,6 @@ fRegress.double <- function(y, xfdlist, betalist, wt=NULL,
 
     #  check dimensions of y2cMap and SigmaE
 
-    print("computing bvar, betastderrlist, and c2bMap")
-    
     y2cdim <- dim(y2cMap)
     if (y2cdim[2] != dim(SigmaE)[1])  stop(
       "Dimensions of Y2CMAP not correct.")