smoothers {analogue}  R Documentation 
Functions to be used as plugins to prcurve
that fit
smooth functions to each variable that, when combined, give the
principal curve. The functions act as wrappers to the main fitting
functions, which currently include smooth.spline
and
gam
.
smoothSpline(lambda, x, choose = TRUE, complexity, ..., penalty = 1, cv = FALSE, keep.data = FALSE, control.spar = list(low = 0)) smoothGAM(lambda, x, choose = TRUE, complexity, bs = "tp", ..., family = gaussian(), method = "REML", select = FALSE, control = gam.control())
lambda 
the current projection function; the position that each sample projects to on the current principal curve. This is the predictor variable or covariate in the smooth function. 
x 
numeric vector; used as the response variable in the smooth
function. The principal curve algorithm fits a separate scatterplot
smoother (or similar smoother) to each variable in 
choose 
logical; should the underlying smoother function be allowed to choose the degree of smooth complexity for each variable? 
complexity 
numeric; the complexity of the fitted smooth functions. 
penalty, cv, keep.data, control.spar 
arguments to

bs, family 
arguments to 
method, select, control 
arguments to 
... 
arguments passed on the the underlying function

An object of class "prcurveSmoother"
with the following
components:
lambda 
for each observations, its arclength from the beginning of the curve. 
x 
numeric vector of response values. 
fitted.values 
numeric vector of fitted values for the observations generated from the fitted smooth function. 
complexity 
numeric; the degrees of freedom used for the smooth
function. The exact details of what these pertain to are in the help
for the respective fitting functions 
model 
the object fitted by the wrapped fitting function. 
Gavin L. Simpson
prcurve
for how these functions are used.