lo: Specify a loess fit in a GAMLSS formula

In gamlss: Generalized Additive Models for Location Scale and Shape

Specify a loess fit in a GAMLSS formula

Description

Allows the user to specify a loess fit within a GAMLSS model. This function is similar to the lo function in the gam implementation of package gam see Chambers and Hastie (1991).

The function vis.lo() allows plotting the results.

Usage

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", "none"), 
          trace.hat = c("exact", "approximate"), 
          cell = 0.2, iterations = 4,iterTrace = FALSE, ...)
vis.lo(obj, se=-1, rug = FALSE, partial.resid = FALSE, 
          col.term = "darkred",  col.shaded = "gray", 
          col.res = "lightblue", col.rug = "gray", lwd.term = 1.5, 
          cex.res = 1, pch.res = par("pch"), 
          type = c("persp", "contour"), col.surface = "gray", 
          nlevels = 30, n.grid = 30, image = TRUE, ...)

Arguments

formula	a formula specifying the explanatory variables
control	a control to be passed to the loess function
...	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 loess. For consistency with the older version of lo the effective degrees of freedom df can be also specified instead of span, e.g. df=5
degree	the degree of local polynomial; can be 1 or 2. See also the help file of loess
parametric	should any terms be fitted globally rather than locally? See the help file of loess
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 loess
normalize	should the predictors be normalized to a common scale if there is more than one? See the help file of loess
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 loess
method	fit the model or just extract the model frame. See the help file of loess
surface	should the fitted surface be computed exactly or via interpolation from a kd tree? See also the help file of loess.control
statistics	should the statistics be computed exactly or approximately? See the help file of loess.control
trace.hat	should the trace of the smoother matrix be computed exactly or approximately? See the help file of loess.control
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 loess.control
iterations	the number of iterations used in robust fitting. See the help file of loess.control
iterTrace	logical (or integer) determining if tracing information during the robust iterations (iterations>= 2) is produced. See the help file of loess.control
obj	an lowss object fitted within gamlss
se	if se>0 then standard errors surfaces are drawn in the 3-dimensional plot. Set se at the required level i.e se=1.96 will be an approximated 95% CI.
rug	whether to plot a rug in the plot
partial.resid	whether to plot the partial residuals
col.term	the colour of the line of fitted term
cex.res	the shading of standard
col.shaded	the shading of standard error intervals
col.res	the colour of partial residuals
col.rug	the colour of the rug
lwd.term	the width of the line
pch.res	The character for the partial residuals
type	The type of the plot if the x's are two dimensional
col.surface	the colour of the fitted surface
nlevels	the number of levels used in cotour() plot.
n.grid	The number of points to evaluate the surface
image	whether to use image() or just contour

Details

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().

Value

a loess object is returned.

Warning

In this version the first argument is a formula NOT a list as in the previous one

Note

Note that lo itself does no smoothing; it simply sets things up for gamlss.lo() to do the backfitting.

Author(s)

Mikis Stasinopoulos, Bob Rigby, (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.

References

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.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

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, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

See Also

cs, random,

Examples

# 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) # 
term.plot(aids1, page=1)
## Not run: 
r1 <- gamlss(R~lo(~Fl)+lo(~A), data=rent, family=GA)
 term.plot(r1, pages=1)
 vis.lo(getSmo(r1, which=1), partial=T)
r2 <- gamlss(R~lo(~Fl+A), data=rent, family=GA)
 term.plot(r2, pages=1)
 vis.lo(getSmo(r2, which=1))
 vis.lo(getSmo(r2, which=1), se=1.97)
 vis.lo(getSmo(r2, which=1), partial.res=T)

## End(Not run)

gamlss documentation built on May 29, 2024, 6:08 a.m.