pb: Specify a Penalised B-Spline Fit in a pblm Formula

In pblm: Bivariate Additive Marginal Regression for Categorical Responses

Specify a Penalised B-Spline Fit in a pblm Formula

Description

Both pb and pbs are adaptations of function pb and ps from the gamlss package, respectively, to specify penalized B-spline.

Usage

pb(x, df = NULL, lambda = NULL, control = pb.control(...), ...)
pb.control(inter = 20, degree = 3, order = 2, quantiles = FALSE, ...) 
pbs(x, df = 3, lambda = NULL, ps.intervals = 20, degree = 3, order = 3)

Arguments

x	the univariate predictor.
df	the desidered equivalent number of degrees of freedom (trace of the smoother matrix minus two for the constant and linear fit.
lambda	the smoothing parameter.
control	setting the control parameters
ps.intervals	the number of break points in the x-axis.
inter	the number of break points (knots) in the x-axis.
degree	the degree of the piecewise polynomials.
order	the required difference in the vector of coefficients.
quantiles	if TRUE the quantile values of x are used to determine the knots.
...	for extra arguments.

Details

Basically, pb is a reduced-functionality version of the original one specified in gamlss with no performance iteration methods (i.e. by method specification) implemented. The only method implemented minimizes the GAIC by internal optim calls.

Value

The function returns the vector x, which includes several attached attributes. While x is directly used in building the model matrix, its attributes are crucial for the backfitting procedure implemented by additive.fit()

Author(s)

Marco Enea, based on the original versions of the corresponding functions contained in the gamlss package by Mikis Stasinopoulos and Bob Rigby.

Examples

#NOT RUN 
#Example 1. The Dale's model 
data(ulcer)
m1 <- pblm(fo1=cbind(pain,medication)~1, fo12=~I(operation=="vh"), RC.fo=~Col,
           data=ulcer, weights=freq,  contrasts=list(Col="contr.SAS"))
summary(m1)

# Example 2. An artificial data set: 
set.seed(1234)
da <- expand.grid("Y1"=1:3,"Y2"=1:3,"fat1"=0:9,"fat2"=0:1)
da$x1 <- seq(-5,5,l=180)
da$x2 <- rnorm(180)

da$Freq <- sample(5:30,180,replace=TRUE)

m1 <- pblm(fo1=cbind(Y1,Y2) ~ pbs(x1) + fat2, 
           fo2=~pb(x1) + x2, 
           fo12=~pb(x1) + x2, data=da, weights=Freq)

plot(m1)

pblm documentation built on June 19, 2025, 5:08 p.m.