A symbolic wrapper to indicate a smooth term in a formala argument to gam
gam.lo( x, y, w = rep(1, length(y)), span = 0.5, degree = 1, ncols = p, xeval = x ) lo(..., span = 0.5, degree = 1)
a response variable passed to
the number of observations in a neighborhood. This is the
smoothing parameter for a
the degree of local polynomial to be fit; currently restricted
If this argument is present, then
A smoother in gam separates out the parametric part of the fit from the
non-parametric part. For local regression, the parametric part of the fit is
specified by the particular polynomial being fit locally. The workhorse
gam.lo fits the local polynomial, then strips off this
parametric part. All the parametric pieces from all the terms in the
additive model are fit simultaneously in one operation for each loop of the
lo returns a numeric matrix. The simplest case is when there
is a single argument to
degree=1; a one-column matrix
is returned, consisting of a normalized version of the vector. If
degree=2 in this case, a two-column matrix is returned, consisting of
a degree-2 polynomial basis. Similarly, if there are two arguments, or the
single argument is a two-column matrix, either a two-column matrix is
degree=1, or a five-column matrix consisting of powers
and products up to degree
2. Any dimensional argument is allowed,
but typically one or two vectors are used in practice.
The matrix is endowed with a number of attributes; the matrix itself is used
in the construction of the model matrix, while the attributes are needed for
the backfitting algorithms
general.wam (weighted additive model) or
lo.wam (currently not implemented). Local-linear curve or surface
fits reproduce linear responses, while local-quadratic fits reproduce
quadratic curves or surfaces. These parts of the
loess fit are
computed exactly together with the other parametric linear parts
When two or more smoothing variables are given, the user should make sure
they are in a commensurable scale;
lo() does no normalization. This
can make a difference, since
lo() uses a spherical (isotropic)
neighborhood when establishing the nearest neighbors.
lo itself does no smoothing; it simply sets things up for
gam.lo does the actual smoothing. of the model.
One important attribute is named
call. For example,
a call component
gam.lo(data[["lo(x)"]], z, w, span = 0.5, degree = 1,
ncols = 1). This is an expression that gets evaluated repeatedly in
general.wam (the backfitting algorithm).
gam.lo returns an object with components
residuals from the smooth fit. Note that the smoother removes the parametric
part of the fit (using a linear fit with the columns in
the nonlinear degrees of freedom
the pointwise variance for the nonlinear fit
gam.lo is evaluated with an
xeval argument, it returns a
matrix of predictions.
Written by Trevor Hastie, following closely the design in the "Generalized Additive Models" chapter (Hastie, 1992) in Chambers and Hastie (1992).
Hastie, T. J. (1992) Generalized additive models. Chapter 7 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models. London: Chapman and Hall.
y ~ Age + lo(Start) # fit Start using a loess smooth with a (default) span of 0.5. y ~ lo(Age) + lo(Start, Number) y ~ lo(Age, span=0.3) # the argument name span cannot be abbreviated.
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