\name{smooth.basis}
\alias{smooth.basis}
\title{
  Smooth Data with an Indirectly Specified Roughness Penalty 
}
\description{
  This is the main function for smoothing data using a roughness
  penalty.  Unlike function \code{data2fd}, which does not employ a
  rougness penalty, this function controls the nature and degree of
  smoothing by penalyzing a measure of rougness.  Roughness is definable
  in a wide variety of ways using either derivatives or a linear
  differential operator.
}
\usage{
smooth.basis(argvals, y, fdParobj, wtvec=rep(1,n),
             dffactor=1,
             fdnames=list(NULL, dimnames(y)[2], NULL))
}
\arguments{
  \item{argvals}{
    a vector of argument values correspond to the observations in array
    \code{y}.
  }
  \item{y}{
    an array containing values of curves at discrete sampling points or
    argument values. If the array is a matrix, the rows must correspond
    to argument values and columns to replications, and it will be
    assumed that there is only one variable per observation.  If
    \code{y} is a three-dimensional array, the first dimension
    corresponds to argument values, the second to replications, and the
    third to variables within replications.  If \code{y} is a vector,
    only one replicate and variable are assumed.  
  }
  \item{fdParobj}{
    a functional parameter object, a functional data object or a
    functional basis object.  If the object is a functional parameter
    object, then the linear differential operator object and the
    smoothing parameter in this object define the roughness penalty.  If
    the object is a functional data object, the basis within this object
    is used without a roughness penalty, and this is also the case if
    the object is a functional basis object.  In these latter two cases,
    \code{smooth.basis} is essentially the same as \code{data2fd}.
  }
  \item{wtvec}{
    a vector of the same length as \code{argvals} containing weights for
    the values to be smoothed. 
  }
  \item{dffactor}{
    Chong Gu in his book \emph{Smoothing Spline ANOVA Models} suggests a
    modification of the GCV criterion using a factor modifying the
    effective degrees of freedom of the smooth.  He suggests that values
    like 1.2 are effective at avoiding undersmoothing of the data.  The
    default value of 1 is the classic definition. 
  }
  \item{fdnames}{
    a list of length 3 with members containing
    \itemize{
      \item a single name for the argument domain, such as 'Time'
      
      \item a vector of names for the replications or cases
      
      \item a name for the function, or a vector of names if there are
      multiple functions. 
    }
  }
}
\value{
  a named list of length 7 containing:

  \item{fdobj}{
    a functional data object that smooths the data.
  }
  \item{df}{
    a degrees of freedom measure of the smooth
  }
  \item{gcv}{
    the value of the generalized cross-validation or GCV criterion.  If
    there are multiple curves, this is a vector of values, one per
    curve.  If the smooth is multivariate, the result is a matrix of gcv
    values, with columns corresponding to variables.  
  }
  \item{coef}{
    the coefficient matrix or array for the basis function expansion of
    the smoothing function 
  }
  \item{SSE}{
    the error sums of squares.  SSE is a vector or a matrix of the same
    size as GCV. 
  }
  \item{penmat}{
    the penalty matrix.
  }
  \item{y2cMap}{
    the matrix mapping the data to the coefficients.
  }
}
\details{
  If the smoothing parameter \code{lambda} is zero, there is no penalty
  on roughness.  As lambda increases, usually in logarithmic terms, the
  penalty on roughness increases and the fitted curves become more and
  more smooth.  Ultimately, the curves are forced to have zero roughness
  in the sense of being in the null space of the linear differential
  operator object \code{Lfdobj}that is a member of the \code{fdParobj}.
  
  For example, a common choice of roughness penalty is the integrated
  square of the second derivative.  This penalizes curvature.  Since the
  second derivative of a straight line is zero, very large values of
  \code{lambda} will force the fit to become linear.  It is also
  possible to control the amount of roughness by using a degrees of
  freedom measure.  The value equivalent to \code{lambda} is found in
  the list returned by the function.  On the other hand, it is possible
  to specify a degrees of freedom value, and then use function
  \code{df2lambda} to determine the equivalent value of \code{lambda}.
  One should not put complete faith in any automatic method for
  selecting \code{lambda}, including the GCV method. There are many
  reasons for this.  For example, if derivatives are required, then the
  smoothing level that is automatically selected may give unacceptably
  rough derivatives.  These methods are also highly sensitive to the
  assumption of independent errors, which is usually dubious with
  functional data.  The best advice is to start with the value
  minimizing the \code{gcv} measure, and then explore \code{lambda}
  values a few log units up and down from this value to see what the
  smoothing function and its derivatives look like.  The function
  \code{plotfit.fd} was designed for this purpose.
  
  An alternative to using \code{smooth.basis} is to first represent
  the data in a basis system with reasonably high resolution using
  \code{data2fd}, and then smooth the resulting functional data object
  using function \code{smooth.fd}.
}
\seealso{
  \code{\link{data2fd}}, 
  \code{\link{df2lambda}}, 
  \code{\link{lambda2df}}, 
  \code{\link{lambda2gcv}}, 
  \code{\link{plot.fd}}, 
  \code{\link{project.basis}}, 
  \code{\link{smooth.fd}}, 
  \code{\link{smooth.monotone}}, 
  \code{\link{smooth.pos}}
  \code{\link{smooth.basisPar}}
}
\examples{
# A toy example that creates problems with
# data2fd:  (0,0) -> (0.5, -0.25) -> (1,1)
b2.3 <- create.bspline.basis(norder=2, breaks=c(0, .5, 1))
# 3 bases, order 2 = degree 1 =
# continuous, bounded, locally linear
fdPar2 <- fdPar(b2.3, Lfdobj=2, lambda=1)
# Penalize excessive slope Lfdobj=1;  
# second derivative Lfdobj=2 is discontinuous.
#fd2.3s0 <- smooth.basis(0:1, 0:1, fdPar2)

b3.4 <- create.bspline.basis(norder=3, breaks=c(0, .5, 1))
# 4 bases, order 3 = degree 2 =
# continuous, bounded, locally quadratic 
fdPar3 <- fdPar(b3.4, lambda=1)
# Penalize excessive slope Lfdobj=1;  
# second derivative Lfdobj=2 is discontinuous.
fd3.4s0 <- smooth.basis(0:1, 0:1, fdPar3)
plot(fd3.4s0$fd)



#  Shows the effects of three levels of smoothing
#  where the size of the third derivative is penalized.
#  The null space contains quadratic functions.
x <- seq(-1,1,0.02)
y <- x + 3*exp(-6*x^2) + rnorm(rep(1,101))*0.2
#  set up a saturated B-spline basis
basisobj <- create.bspline.basis(c(-1,1), 101)

fdParobj <- fdPar(basisobj, 2, lambda=1)
result1  <- smooth.basis(x, y, fdParobj)
yfd1     <- result1$fd

with(result1, c(df, gcv, SSE))

fdParobj <- fdPar(basisobj, 2, lambda=1e-4)
result2  <- smooth.basis(x, y, fdParobj)
yfd2     <- result2$fd

with(result2, c(df, gcv, SSE))

fdParobj <- fdPar(basisobj, 2, lambda=0)
result3  <- smooth.basis(x, y, fdParobj)
yfd3     <- result3$fd

with(result3, c(df, gcv, SSE))

plot(x,y)           # plot the data
lines(yfd1, lty=2)  #  add heavily penalized smooth
lines(yfd2, lty=1)  #  add reasonably penalized smooth
lines(yfd3, lty=3)  #  add smooth without any penalty
legend(-1,3,c("1","0.0001","0"),lty=c(2,1,3))
plotfit.fd(y, x, yfd2)  # plot data and smooth

}
% docclass is function
\keyword{smooth}