| lo {gamlss} | R Documentation |
Allows the user to specify a loess fit in a GAMLSS model. This function is similar to the lo function in the gam implementation of package gam see Chambers and Hastie (1991).
lo(formula, control = lo.control(...), ...)
lo.control(span = 0.75, enp.target = NULL,
degree = 2, parametric = FALSE, drop.square = FALSE,
normalize = TRUE, family = c("gaussian", "symmetric"),
method = c("loess", "model.frame"),
surface = c("interpolate", "direct"),
statistics = c("approximate", "exact"),
trace.hat = c("exact", "approximate"),
cell = 0.2, iterations = 4, ...)
formula |
a formula specifying the explanatory variables |
control |
a control to be passed to the |
... |
extra arguments |
span |
the number of observations in a neighbourhood. This is the smoothing parameter for a loess fit. |
enp.target |
an alternative way to specify span, as the approximate equivalent number degrees of freedom to be used. See also the help file of the R function |
degree |
the degree of local polynomial; can be 1 or 2. See also the help file of |
parametric |
should any terms be fitted globally rather than locally? See the help file of |
drop.square |
for fits with more than one predictor and degree=2, should the quadratic term be dropped for particular predictors?. See also help file of |
normalize |
should the predictors be normalized to a common scale if there is more than one? See the help file of |
family |
if "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function. See the help file of |
method |
fit the model or just extract the model frame. See the help file of |
surface |
should the fitted surface be computed exactly or via interpolation from a kd tree? See also
the help file of |
statistics |
should the statistics be computed exactly or approximately? See the help file of |
trace.hat |
should the trace of the smoother matrix be computed exactly or approximately? See the help file of |
cell |
if interpolation is used this controls the accuracy of the approximation via the maximum number of points in a cell in the kd tree. See the help file of |
iterations |
the number of iterations used in robust fitting. See the help file of |
Note that lo itself does no smoothing; it simply sets things up for the function gamlss.lo() which is used by the backfitting function gamlss.add().
a loess object is returned.
In this version the first argument is a formula NOT a list as in the previous one
Note that lo itself does no smoothing; it simply sets things up for gamlss.lo() to do the backfitting.
Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby r.rigby@londonmet.ac.uk, (The original lo() function was based on the Trevor Hastie's S-plus lo() function. See also the documentation of the loess function for the authorship of the function.
Chambers, J. M. and Hastie, T. J. (1991). Statistical Models in S, Chapman and Hall, London.
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.
data(aids) attach(aids) # fitting a loess curve with span=0.4 plus the a quarterly effect aids1<-gamlss(y~lo(~x,span=0.4)+qrt,data=aids,family=PO) # plot(x,y) lines(x,fitted(aids1)) rm(aids1) detach(aids)