fit_PB: P-spline smoother

In gamlss.ggplots: Plotting Functions for Generalized Additive Model for Location Scale and Shape

P-spline smoother

Description

The function fit_PB() fits a P-spline univariate smoother [Eilers and Marx (1996)] to y against the x with prior weights weights.

Usage

fit_PB(x, y, weights, data, xmin, xmax, nseg = 20, 
      lambda = 10, order = 2, degree = 3, max.df = 20, 
      ylim, plot = TRUE, col.ribbon = "pink")

Arguments

x	the explanatory variable
y	the response
weights	possible prior weights (set to one by default)
data	the data frame where x, y and weights are coming from
xmin	the x minimum if different from min(x)
xmax	the x maximum if different from max(x)
nseg	the number of knots
lambda	the smotthing parameter
order	the ordr of the difference
degree	the degree of the piewise polynonmial
max.df	the maximum allowed degress of freedom
ylim	the ylim in the plot
plot	whether to plot the results
col.ribbon	the color in the se of the fitted values

Value

A object Psplines is produced with methods print(), coef() deviance(), fitted(), predict() and resid().

Note

The functionfit_PB() is an engine for getting the maximal correlation between two continuous variables. It can be also used on its own as a smoother.

Author(s)

Mikis Stasinopoulos

References

Eilers, P. H. C. and Marx, B. D. (1996). Flexible smoothing with B-splines and penalties (with comments and rejoinder). Statist. Sci, 11, 89-121.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/9780429298547")}.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/b21973")}

Stasinopoulos, M.D., Kneib, T., Klein, N., Mayr, A. and Heller, G.Z., (2024). Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Vol. 56). Cambridge University Press.

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

See Also

ACE

Examples

data(abdom)
m1 <- fit_PB(x,y, data=abdom)

gamlss.ggplots documentation built on May 29, 2024, 1:34 a.m.