Updates of 1 november 2020

Author: JamesRamsay5

R/fRegress.formula.R

@@ -12,9 +12,7 @@ fRegress.formula <- function(y, data=NULL, betalist=NULL,
                              method=c('fRegress', 'model'),
                              sep='.', ...) {
 
-  #  Last modified 30 October 2020 by Jim Ramsay
-  
-  print("inside fRegress.formula")
+  #  Last modified 1 November 2020 by Jim Ramsay
 
   ##
   ## 1.  get y = left hand side of the formula
@@ -376,11 +374,8 @@ fRegress.formula <- function(y, data=NULL, betalist=NULL,
     return(fRegressList)
   } else {
     if(inherits(y, 'fd')) {
-      print("dispatching fRegress")
-      # fRegress(y, xfdlist, betalist, wt)
       do.call('fRegress.fd',    fRegressList)
     } else {
-      print("dispatching fRegress.numeric")
       do.call('fRegress.double', fRegressList)
     }
   }

R/fRegress.numeric.R

@@ -51,12 +51,10 @@ fRegress.numeric <- function(y, xfdlist, betalist, wt=NULL,
   #  as predict(List).  In this call List can be any object of the
   #  "".
 
-  # Last modified 25 August 2020 by Jim Ramsay
+  # Last modified 1 November 2020 by Jim Ramsay
 
   #  check Y and compute sample size N
 
-  print("inside fRegress.numeric")
-  
   if (!inherits(y, "numeric")) stop("Y is not a numeric vector.")
 
   #  ----------------------------------------------------------------
@@ -74,16 +72,12 @@ fRegress.numeric <- function(y, xfdlist, betalist, wt=NULL,
   N    <- dim(ymat)[1]
   p    <- length(xfdlist)
 
-  print("preliminaries")
-  
   Zmat  <- NULL
   Rmat  <- NULL
   pjvec <- rep(0,p)
   ncoef <- 0
   for (j in 1:p) {
-    print(j)
     xfdj       <- xfdlist[[j]]
-    print(class(xfdj))
     xcoef      <- xfdj$coefs
     xbasis     <- xfdj$basis
     betafdParj <- betalist[[j]]
@@ -92,7 +86,6 @@ fRegress.numeric <- function(y, xfdlist, betalist, wt=NULL,
     pjvec[j]   <- bnbasis
     Jpsithetaj <- inprod(xbasis,bbasis)
     Zmat       <- cbind(Zmat,crossprod(xcoef,Jpsithetaj))
-    print("betafdParj$estimate")
     if (betafdParj$estimate) {
       lambdaj    <- betafdParj$lambda
       if (lambdaj > 0) {
@@ -116,8 +109,6 @@ fRegress.numeric <- function(y, xfdlist, betalist, wt=NULL,
   #          set up the linear equations for the solution
   #  -----------------------------------------------------------
 
-  print("Cmat and Dmat")
-  
   #  solve for coefficients defining BETA
 
   if (any(wt != 1)) {
@@ -131,21 +122,16 @@ fRegress.numeric <- function(y, xfdlist, betalist, wt=NULL,
     Dmat <- t(Zmat) %*% ymat
   }
 
-  #   print("solving")
-  
   eigchk(Cmat)
 
   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)))
 
-  print("betaestlist")
-  
   #  set up fdPar object for BETAESTFDPAR
 
   betaestlist <- betalist
@@ -167,8 +153,6 @@ fRegress.numeric <- function(y, xfdlist, betalist, wt=NULL,
 
   #  set up fd object for predicted values
 
-  print("yhatmat")
-  
   yhatmat <- matrix(0,N,1)
   for (j in 1:p) {
     xfdj <- xfdlist[[j]]
@@ -200,8 +184,6 @@ fRegress.numeric <- function(y, xfdlist, betalist, wt=NULL,
   #               if both y2cMap and SigmaE are supplied.
   #  -----------------------------------------------------------------------
 
-  print("standard errors")
-  
   if (!(is.null(y2cMap) || is.null(SigmaE))) {
 
     #  check dimensions of y2cMap and SigmaE

man/fRegress.Rd

@@ -503,17 +503,17 @@ TempRgn.f <- fRegress(Temp.fd ~ region, CanadianWeather)
 ## Get the default setup and possibly modify it
 ##
 
-TempRgn.mdl <- fRegress(tempfd ~ region, CanadianWeather, method='m')
+TempRgn.mdl <- fRegress(Temp.fd ~ region, CanadianWeather, method='m')
 
 # make desired modifications here
 # then run
 
 TempRgn.m <- do.call('fRegress', TempRgn.mdl)
 
 # no change, so match the first run
-# \dontshow{stopifnot(}
-# all.equal(TempRgn.m, TempRgn.f)
-# \dontshow{)}
+\dontshow{stopifnot(}
+all.equal(TempRgn.m, TempRgn.f)
+\dontshow{)}
 
 ##
 ## More detailed set up
@@ -536,16 +536,16 @@ region.fdlist <- list(const=rep(1, 35),
      region.Pacific=region.fd.Pacific)
 str(TempRgn.mdl$betalist)
 
-beta1 <- with(tempfd, fd(basisobj=basis, fdnames=fdnames))
+beta1 <- with(Temp.fd, fd(basisobj=basis, fdnames=fdnames))
 beta0 <- fdPar(beta1)
 betalist <- list(const=beta0, region.Atlantic=beta0,
              region.Continental=beta0, region.Pacific=beta0)
 
-TempRgn <- fRegress(tempfd, region.fdlist, betalist)
+TempRgn <- fRegress(Temp.fd, region.fdlist, betalist)
 
-#  \dontshow{stopifnot(}
-#  all.equal(TempRgn, TempRgn.f)
-#  \dontshow{)}
+\dontshow{stopifnot(}
+all.equal(TempRgn, TempRgn.f)
+\dontshow{)}
 
 ###
 ###
@@ -585,9 +585,9 @@ betalist  <- list(const=fdPar(beta0), hipfd=fdPar(beta1))
 
 fRegressout <- fRegress(kneefd, xfdlist, betalist)
 
-# #\dontshow{stopifnot(}
-# all.equal(fRegressout, knee.hip.f)
-# #\dontshow{)}
+\dontshow{stopifnot(}
+all.equal(fRegressout, knee.hip.f)
+\dontshow{)}
 
 #See also the following demos:
 

man/predict.fRegress.Rd

@@ -93,14 +93,14 @@ weight <- c(ctl, trt)
 
 fRegress.D9 <- fRegress(weight ~ group)
 
-#pred.fR.D9 <- predict(fRegress.D9)
+pred.fR.D9 <- predict(fRegress.D9)
 #  Now compare with 'lm'
-#lm.D9 <- lm(weight ~ group)
-#pred.lm.D9 <- predict(lm.D9)
+lm.D9 <- lm(weight ~ group)
+pred.lm.D9 <- predict(lm.D9)
 
-#\dontshow{stopifnot(}
-#all.equal(as.vector(pred.fR.D9), as.vector(pred.lm.D9))
-#\dontshow{)}
+\dontshow{stopifnot(}
+all.equal(as.vector(pred.fR.D9), as.vector(pred.lm.D9))
+\dontshow{)}
 
 ##
 ## vector response with functional explanatory variable