plot.gamlss
(Package: gamlss) :
Plot Residual Diagnostics for an GAMLSS Object
This function provides four plots for checking the normalized (randomized for a discrete response distribution) quantile residuals of a fitted GAMLSS object, referred to as residuals below : a plot of residuals against fitted values, a plot of the residuals against an index or a specific explanatory variable, a density plot of the residuals and a normal Q-Q plot of the residuals. If argument ts=TRUE then the first two plots are replaced by the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the residuals
● Data Source:
CranContrib
● Keywords: regression
● Alias: plot.gamlss
●
0 images
|
bfp
(Package: gamlss) :
Functions to fit fractional polynomials in GAMLSS
The function bfp generate a power polynomial basis matrix which (for given powers) can be used to fit power polynomials in one x-variable. The function fp takes a vector and returns it with several attributes. The vector is used in the construction of the model matrix. The function fp() is not used for fitting the fractional polynomial curves but assigns the attributes to the vector to aid gamlss in the fitting process. The function doing the fitting is gamlss.fp() which is used at the backfitting function additive.fit (but never used on its own). The (experimental) function pp can be use to fit power polynomials as in a+b1*x^p1+b2*x^p2., where p1 and p2 have arbitrary values rather restricted as in the fp function.
● Data Source:
CranContrib
● Keywords: regression
● Alias: bfp, fp, pp
●
0 images
|
cs
(Package: gamlss) :
Specify a Smoothing Cubic Spline Fit in a GAMLSS Formula
The functions cs() and scs() are using the cubic smoothing splines function smooth.spline() to do smoothing. They take a vector and return it with several attributes. The vector is used in the construction of the model matrix. The functions do not do the smoothing, but assigns the attributes to the vector to aid gamlss in the smoothing. The function doing the smoothing is gamlss.cs(). This function use the R function smooth.spline() which is then used by the backfitting function additive.fit() which is based on the original GAM implementation described in Chambers and Hastie (1992). The function gamlss.scs() differs from the function cs() in that allows cross validation of the smoothing parameters unlike the cs() which fixes the effective degrees of freedom, df. Note that the recommended smoothing function is now the function pb() which allows the estimation of the smoothing parameters using a local maximum likelihood. The function pb() is based on the penalised beta splines (P-splines) of Eilers and Marx (1996).
● Data Source:
CranContrib
● Keywords: regression
● Alias: cs, cs.control, scs
●
0 images
|
gamlss.fp
(Package: gamlss) :
Support for Function fp()
Those are support for the functions fp() and pp. It is not intended to be called directly by users.
● Data Source:
CranContrib
● Keywords: regression
● Alias: gamlss.fp, gamlss.pp
●
0 images
|
The function getSmo() extracts information from a fitted smoothing additive term.
● Data Source:
CranContrib
● Keywords: regression
● Alias: getSmo
●
0 images
|
The functions edf() and edfAll() can be used to obtained the effective degrees of freedom for different additive terms for the distribution parameters in a gamlss model.
● Data Source:
CranContrib
● Keywords: regression
● Alias: edf, edfAll
●
0 images
|
histDist
(Package: gamlss) :
This function plots the histogram and a fitted (GAMLSS family) distribution to a variable
This function fits constants to the parameters of a GAMLSS family distribution and them plot the histogram and the fitted distribution.
● Data Source:
CranContrib
● Keywords: regression
● Alias: histDist
●
0 images
|
find.hyper
(Package: gamlss) :
A function to select values of hyper-parameters in a GAMLSS model
This function selects the values of hyper parameters and/or non-linear parameters in a GAMLSS model. It uses the R function optim which then minimises the generalised Akaike information criterion (GAIC) with a user defined penalty.
● Data Source:
CranContrib
● Keywords: regression
● Alias: find.hyper
●
0 images
|
lpred
(Package: gamlss) :
Extract Linear Predictor Values and Standard Errors For A GAMLSS Model
lpred is the GAMLSS specific method which extracts the linear predictor and its (approximate) standard errors for a specified parameter from a GAMLSS objects. The lpred can be also used to extract the fitted values (with its approximate standard errors) or specific terms in the model (with its approximate standard errors) in the same way that the predict.lm() and predict.glm() functions can be used for lm or glm objects. The function lp extract only the linear predictor. If prediction is required for new data values then use the function predict.gamlss().
● Data Source:
CranContrib
● Keywords: regression
● Alias: lp, lpred
●
0 images
|
polyS
(Package: gamlss) :
Auxiliary support for the GAMLSS
These two functions are similar to the poly and polym in R. Are needed for the gamlss.lo function of GAMLSS and should not be used on their own.
● Data Source:
CranContrib
● Keywords: regression
● Alias: poly.matrix, polyS
●
0 images
|
providing 
.