update

Author: James Ramsay

.Rbuildignore

@@ -5,3 +5,4 @@ revdep
 ^.*\.Rproj$
 ^\.Rproj\.user$
 fda_release_to_CRAN.R
+^CRAN-SUBMISSION$

DESCRIPTION

@@ -1,10 +1,10 @@
 Package: fda
-Version: 6.1.3
-Date: 2023-04-24
+Version: 6.1.4
+Date: 2023-05-23
 Title: Functional Data Analysis
 Authors@R: c(person("James", "Ramsay", 
             role=c("aut","cre"),
-            email="james.ramsay@mcgill.ca"), 
+            email="ramsay@psych.mcgill.ca"), 
           person("Giles", "Hooker",
             role="ctb",
             email="gjh27@cornell.edu"),
@@ -29,5 +29,5 @@ Description: These functions were developed to support functional data
 License: GPL (>= 2)
 URL: http://www.functionaldata.org
 LazyData: true
-NeedsCompilation: no
+NeedsCompilation: yes
 VignetteBuilder: knitr

NAMESPACE

@@ -195,6 +195,8 @@ S3method(density,      fd)
 S3method(deriv,        fd)
 S3method(lines,        fd)
 S3method(mean,         fd)
+S3method(matrix,       matplot)
+S3method(numeric,      matplot)
 S3method(norder,       fd)
 S3method(plot,         fd)
 S3method(predict,      fd)

R/smooth.morph.R

@@ -40,12 +40,18 @@ smooth.morph <- function(x, y, ylim, WfdPar,
   #  WFD       Functional data object for W.  It's coefficient vector
   #               contains the optimized coefficients.
 
-  #  last modified 3 June 2022 by Jim Ramsay
+  #  last modified 17 May 2023 by Jim Ramsay
 
-  nobs <- length(x)        #  number of observations
+  #  number of observations and weights on x-values
+  
+  nobs <- length(x)        
   wt   <- matrix(1,nobs,1)
   wt[nobs] <- 10
 
+  #  -----------------------------------------------------
+  #                  Check arguments
+  #  -----------------------------------------------------
+  
   # check consistency of x and y and convert to column matrices
 
   if (length(y) != nobs) {
@@ -81,10 +87,13 @@ smooth.morph <- function(x, y, ylim, WfdPar,
   }
 
   #  -----------------------------------------------------
-  #      extract information from WfdPar
+  #                  Initialize optimization
   #  -----------------------------------------------------
 
+  #      extract information from WfdPar
+  
   Wfdobj   <- WfdPar$fd
+  cvec     <- Wfdobj$coef   #  initial coefficients
   Wbasis   <- Wfdobj$basis      #  basis for Wfdobj
   Wnbasis  <- Wbasis$nbasis      #  no. basis functions
   Wrange   <- Wbasis$rangeval
@@ -96,20 +105,22 @@ smooth.morph <- function(x, y, ylim, WfdPar,
     stop("One or more weights are negative.") 
   }
 
-  cvec <- Wfdobj$coef   #  initial coefficients
-  Zmat <- fda::zerobasis(length(cvec))
-  bvec <- t(Zmat) %*% cvec
-  cvec <- Zmat %*% bvec
-  
-  #  check range of x
+  #  check that values in x are within limits in xlim
 
   if (abs(x[1] - xlim[1]) > 1e-7 || abs(x[nobs] - xlim[2]) > 1e-7) {
-    stop("Values in x are out of bounds by more than 1e-7")
+    print("Argument vector x is out of range:")
+    stop("Values are out of bounds by more than 1e-7")
   }  else {
     x[1   ] <- xlim[1]
     x[nobs] <- xlim[2]
   }
 
+  #  transform coefficients to zero column sum 
+  
+  Zmat <- fda::zerobasis(length(cvec))
+  bvec <- t(Zmat) %*% cvec
+  cvec <- Zmat %*% bvec
+  
   #  initialize matrix Kmat defining penalty term
 
   if (Wlambda > 0) {
@@ -160,10 +171,6 @@ smooth.morph <- function(x, y, ylim, WfdPar,
   iterhist <- matrix(0,iterlim+1,length(status))
   iterhist[1,]  <- status
 
-  #  -----------------------------------------------------
-  #  -------------  Begin main iterations  ---------------
-  #  -----------------------------------------------------
-  
   STEPMAX <- 10
   itermax <- 20
   TOLX    <- 1e-10
@@ -220,7 +227,7 @@ smooth.morph <- function(x, y, ylim, WfdPar,
   }
 
   #  -----------------------------------------------------
-  #         construct output objects
+  #    construct output objects and return in a list
   #  -----------------------------------------------------
 
   Wfdobj  <- fd(cvec, Wbasis)
@@ -233,7 +240,8 @@ smooth.morph <- function(x, y, ylim, WfdPar,
   hwidth  <- hmax - hmin
   hfine   <- (ylim[1] - hmin) + hfine*(ywidth/hwidth)
 
-  return(list(Wfdobj=Wfdobj, f=f, grad=grad, hmat=hmat, norm=norm, hfine=hfine, 
+  return(list(Wfdobj=Wfdobj, f=f, grad=grad, hmat=hmat, 
+              norm=norm, hfine=hfine, 
               iternum=iternum, iterhist=iterhist))
 
 }

data/StatSciChinese.rda

@@ -67,10 +67,11 @@ as.array3(x)
   ##
   ## 4-d array 
   ##
-  
-  if(!CRAN()) {
-    as.array3(array(1:24, 1:4)) 
-  }
+  # These lines throw an error because the dimensionality woud be 4
+  # and as.array3 only allows dimensions 3 or less.
+  # if(!CRAN()) {
+  #   as.array3(array(1:24, 1:4)) 
+  # }
 }
 \keyword{utilities}
 

fda_release_to_CRAN.R

@@ -113,9 +113,9 @@ lines(x., eval.fd(x., fn.), col='green', lty='dotted', lwd=3)
 
 if(!CRAN()) {
 # Will NOT translate a periodic spline
-fp <- splinefun(x, y, method='periodic')
-as.fd(fp)
-#Error in as.fd.function(fp) :
+# fp <- splinefun(x, y, method='periodic')
+# as.fd(fp)
+# Error in as.fd.function(fp) :
 #  x (fp)  uses periodic B-splines, and as.fd is programmed
 #   to translate only B-splines with coincident boundary knots.
 }

man/StatSciChinese.Rd

@@ -19,8 +19,8 @@ eval.surp(evalarg, Wfdobj, nderiv = 0)
   the set \code{c(0,1,2)}.}
 }
 \details{A surprisal \code{M}-vector is information measured in \code{M}-bits.
-Since a multinomial probability vector must sum to one, it follows that the
-surprisal vector \code{S} must satisfy the constraint 
+Since a multinomial probability vector must sum to one, it follows 
+that the surprisal vector \code{S} must satisfy the constraint 
 \code{log_M(sum(M^(-S)) = 0.} That is, surprisal vectors lie within a
 curved \code{M-1}-dimensional manifold.
 

man/as.array3.Rd

@@ -106,9 +106,10 @@ all.equal(fd.bspl1.1, fd.bspl1.1a)
 \dontshow{ ) }
 # TRUE
 
-if(!CRAN()) {
-  fd.bspl1.1b <- fd(0)
-}
+# the following three lines shown an error in a non-cran check:
+# if(!CRAN()) {
+#   fd.bspl1.1b <- fd(0)
+# }
 
 ##
 ## Cubic spline:  4  basis functions

man/as.fd.Rd

@@ -1,65 +1,22 @@
-\name{fda-package}
-\alias{fda-package}
-\alias{fda}
 \docType{package}
-\title{Functional Data Analysis in R}
+\name{fda}
+\alias{fda}
+\title{Functions for statistical analyses of functions}
+
 \description{
-  Functions and data sets companion to Ramsay, J. O.; Hooker, Giles; and
-  Graves, Spencer (2010) Functional Data Analysis with R and Matlab,
-  plus Ramsay, J. O., and Silverman, B. W. (2006) Functional Data
-  Analysis, 2nd ed. and (2002) Applied Functional Data Analysis
-  (Springer).  This includes finite bases approximations (such as
-  splines and Fourier series) to functions fit to data smoothing on the
-  integral of the squared deviations from an arbitrary differential operator.
-}
-\details{
-  \tabular{ll}{
-    Package: \tab fda\cr
-    Type: \tab Package\cr
-    Version: \tab 2.2.6\cr
-    Date: \tab 2011-02-03\cr
-    License: \tab GPL-2\cr
-    LazyLoad: \tab yes\cr
-  }
-}
-\author{
-  J. O. Ramsay,  <ramsay@psych.mcgill.ca>, Hadley Wickham
-  <h.wickham@gmail.com>, Spencer Graves <spencer.graves@prodsyse.com>,
-  Giles Hooker <gjh27@cornell.edu>
+The data analyses that we use for data matrices or dataFrames, such as means, covariances, linear regressions, principal and canonical component analyses, can also be applied to samples of functions or curves.  This package provides functional versions of these analyses along with plotting and other assessment tools.  
+
+The functions themselves are often a consequence of smoothing discrete data values over domains like time, space, and other continuous variables.  Data smoothing tools are also provided.
+
+But there are transformations of functions that have no meaning for data matrices for which rows may be re-ordered.  Derivatives and integrals are often used to set up dynamic models, and can also play a constructive role in the data smoothing process.  Methods for these and other functional operations are also available in this package.
+
+There are now many texts and papers on functional data analysis.  The two resources provided by the buildeers of the fda package are:
+
+James Ramsay and Bernard Silverman (2005) Functional Data Analysis. New York, Springer.
 
-  Maintainer:  J. O. Ramsay <ramsay@psych.mcgill.ca>
-}
-\references{
-  Ramsay, J. O.; Hooker, Giles; and Graves, Spencer (2010)
-  \emph{Functional Data Analysis with R and Matlab}, Springer, New
-  York.
+James Ramsay, Giles Hooker and Spencer Graves (2009) Functional Data Analysis with R and Matlab.  New York: Springer.
 
-  Ramsay, James O., and Silverman, Bernard W. (2006), \emph{Functional
-    Data Analysis, 2nd ed.}, Springer, New York.
+Another relevant package is the package Data2LD that offers ann introduction and methods for constructing linear and nonlinear differential equation models, along with the text: 
 
-  Ramsay, James O., and Silverman, Bernard W. (2002), \emph{Applied
-    Functional Data Analysis}, Springer, New York.
-}
-\examples{
-##
-## As noted in the Preface to Ramsay, Hooker and Graves (p. v),
-## the fda package includes scripts to reproduce all but one of the
-## figures in the book.
-##
-## These figures can be found and run as follows:
-##
-  if(!CRAN()){
-    scriptsDir <- system.file('scripts', package='fda')
-    Rscripts   <- dir(scriptsDir, full.names=TRUE, pattern='R$')
-    fdarm      <- grep('fdarm', Rscripts, value=TRUE)
-    chapters   <- length(fdarm)
-    # NOTE:  If R fails in any of these scripts,
-    # this for loop will not end normally,
-    # and the abnormal termination will be displayed:
-    for(ch in 1:chapters){
-      cat('Running', fdarm[ch], '\n')
-      invisible(source(fdarm[ch]))
-    }
-  }
-}
-\keyword{smooth}
+James Ramsay and Giles Hooker, Dynamic Data Analysis, New York: Springer.
+}

man/eval.surp.Rd

@@ -70,11 +70,12 @@ norder(x, ...)
   # 21 breaks, 19 interior knots.
   stopifnot(norder(create.bspline.basis()) == 4)
 
-  if (!CRAN()) {
-    norder(create.fourier.basis(c(0,12) ))
+  # these five lines throw an error of for a nocran check 
+  # if (!CRAN()) {
+  #   norder(create.fourier.basis(c(0,12) ))
     # Error in norder.bspline(x) :
     # object x is of type = fourier;  'norder' is only defined for type = 'bspline'
-  }
+  # }
 
 }
 \keyword{smooth}