gamlss.ggplots (version 2.1-12)

ACE: Alternating Conditional Expectations

Description

The function ACE() uses the alternating conditional expectations algorithm to find a 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. The function ACE() is used 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,  ...)

Value

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

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()

Author

Mikis Stasinopoulos

Details

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

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. 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

fit_PB

Examples

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