pe_param: Partial Effect of a term on the parameters and predictors

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

Partial Effect of a term on the parameters and predictors

Description

The function pe_param() is similar to the function getPEF() of the gamlss package. It plot the partial effect that a particular term has one of the parameters of the distribution or its predictor eta. The function pe2_param() is build for partial effects from two terms and it is suitable to display first order interactions.

Usage

pe_param(obj = NULL, term = NULL, data = NULL, n.points = 100, 
               parameter = c("mu", "sigma", "nu", "tau"), 
               type = c("parameter", "eta"), scenario = list(),  
               how = c("median", "last", "fixed"),
               col = "darkblue", size = 1.3, name.obj = NULL,
               rug.plot = TRUE, rug.col = "gray", rug.size = 0.5,  
               data.plot = FALSE, data.col = "lightblue", 
               data.size = 0.1, factor.size = 15,
               data.alpha = 0.9, bins = 30, 
               filled = FALSE, ylim = NULL,
                    title) 

pe_1_param(obj = NULL, term = NULL, data = NULL, n.points = 100,  
                parameter = c("mu", "sigma", "nu", "tau"), 
                type = c("parameter", "eta"),
                how = c("median", "last", "fixed"),
                scale.from = c("mean", "median", "none"),
                scenario = list(), col = "darkblue", size = 1.3,
                name.obj = NULL, data.plot = FALSE, 
                data.col = "lightblue",data.size = 0.1,
                data.alpha = 0.9, rug.plot = TRUE, rug.col = "gray",
                rug.size = 0.5, factor.size = 15,
                ylim = NULL, title) 

pe_2_param(obj = NULL, terms = NULL, data = NULL, n.points = 100, 
                parameter = c("mu", "sigma", "nu", "tau"), 
                type = c("parameter", "eta"),
                how = c("median", "last", "fixed"),
                scenario = list(), col = "darkblue",
                size = 1.3, data.plot = TRUE,
                data.col = "lightblue", data.size = 0.1,
                data.alpha = 0.9,bins = 30, 
                filled = FALSE, name.obj = NULL, title) 

pe_param_grid(model, terms, maxcol = 2, maxrow = 3, ylim=NULL, ...)

Arguments

obj	a GAMLSS fitted object
model	a GAMLSS fitted model
term	the model term we want to investigate can be one i.e. "Fl" or two c("Fl", "A")
terms	a list of model terms for example list(c("Fl","A"), "H", "loc" ))
data	the data frame used otherwise it takes it from the fitted model
n.points	the number of points for the evaluation of the term
parameter	the distribution parameter in which the term is fitted
type	here you specify or the distribution parameter i.e "parameter" or its prediction, "eta"
how	how to set all the other terms in the model
scenario	this can be a list of values for the rest of the terms in the model for the distribution parameter
plot	whether to plot the result
col	the colour of the partial effect of the term
size	the size of the line of partial effect of the term
bins	the number of binds for the contour plot
filled	whether to display the values in the contour
title	the title if different from the default
name.obj	this is a way to pass the name of the object
maxcol	the maximum columns in the grid plot
maxrow	the maximum rowss in the grid plot
data.plot	whether to plot the data
rug.plot	whether to print the rug bellow the figure
rug.size	the size of the rug
rug.col	the colour of the rug
data.col	the color of the data points
data.size	the size of the data points
data.alpha	the trnsparance constant of the data points
factor.size	the size of the symbol if a factor is plotted
ylim	if a common y limit is required
scale.from	whethet to substact from the mean the median or from zero
...	for passing argument from the function pe_param_grid to the function pe_param

Details

The functions pe_param() and pe_param_grid() can be used to help the use the interpretation of a GAMLSS model. The functions pe_param() provides the partial effect of one or two terms of a specified parameter of the distribution while the rest of the terms in the model are set on specific values or scenarios. The function pe_param() calls pe_1param() if the argument terms is one i.e. "Fl" or the function pe_2param() if the terms are two i.e. c("Fl"","A"). The pe_param_grid() plots multiple plots specified by the list used in the term argument.

Similar functions are pe_quantile() and pe_moment().

Value

It is plotting the partial effect or is producing the resulting function

Author(s)

Mikis Stasinopulos, Rober Rigby and Fernanda de Bastiani

References

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.

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

getPEF

Examples

m1 <- gamlss(R~pb(Fl)+pb(A)+loc+H, data=rent, gamily=GA)
pe_param(m1, "A")
pe_param(m1, c("Fl","A"), filled=TRUE)
pe_param_grid(m1, list(c("Fl","A"), c("H","loc")), filled=TRUE)
# the terms are additive no interaction

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