ACE: Alternating Conditional Expectations

In gamlss.ggplots: Plotting Generalised Additive Model for Location, Scale and Shape

Alternating Conditional Expectations

Description

The function ACE() uses the alternating conditional expectations algorithm to find the transformations of y and x that maximise the proportion of variation in y explained by x. It is a less general function than the ace() function of the package acepack in that it takes only one explanatory variable. It uses by the function mcor() to calculate the maximal correlation between x and y.

Usage

ACE(x, y, weights, data = NULL, con_crit = 0.001, 
    fit.method = c("loess", "P-splines"), nseg = 10, 
    max.df = 6, ...)
    
mcor(x, y, data = NULL,  fit.method = c("loess", "P-splines"),  
        nseg = 10, max.df = 6,  ...)

Arguments

x	the unique x-variables
y	the y-variable
weights	prior weights
data	a data frame for y, x and weights
con_crit	the convergence criterio of the algorithm
fit.method	the method use to fit the smooth functions $t_1()$ and $t_2()$
nseg	the number of knots
max.df	the maximum od df allowed
...	arguments to pass to the fitted functions fir_PB or loess()

Details

The function ACE is a simplified version of the function ace() of the package agepack.

Value

A fitted ACE model with methods print.ACE() and plot.ACE()

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")}

See Also

fit_PB

Examples

data(rent)
ACE(Fl, R, data=rent)
pp <- ACE(Fl, R, data=rent)
pp
plot(pp)

mcor(Fl, R, data=rent)

gamlss.ggplots documentation built on Sept. 3, 2023, 5:08 p.m.