Description: Demos for smoothing and gamlss.family distributions.
Title: Demos for GAMLSS
LazyLoad: yes
Version: 4.3-3
Date: 2015-07-16
Depends: R (≥ 2.4.0), rpanel (≥ 1.1-1), gamlss.dist, gamlss.tr, tcltk, graphics, stats, grDevices
Suggests: MASS
Author: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob Rigby <r.rigby@londonmet.ac.uk>, Paul Eilers <p.eilers@erasmusmc.nl>, Brian Marx \email{bmarx@LSU.EDU}, Konstantinos Pateras <kostas.pateras@gmail.com> with contributions from Larisa Kosidou.
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>
License: GPL-2 | GPL-3
URL: http://www.gamlss.org/
NeedsCompilation: no
Packaged: 2015-07-16 16:32:59 UTC; stasinom
Repository: CRAN
Date/Publication: 2015-07-17 00:23:27

Functions to fit local regression

Description

There are four function here to illustrate the fitting of local regressions. i) Locmean, which uses local means within a symmetric local window, ii) Locpoly, which uses a local polynomial fit within a symmetric local window. iii) WLocmean, which uses a Gaussian kernel and iv) WLocpoly, which uses local polynomials weighted by a Gaussian kernel

Usage

Locmean(y, x = seq(1, length(y)), w = rep(1, length(y)), span = 0.5)
Locpoly(y, x = seq(1, length(y)), w = rep(1, length(y)), span = 0.5, order = 1)
WLocmean(y, x = seq(1, length(y)), w = rep(1, length(y)), lambda = 0.5)
WLocpoly(y, x = seq(1, length(y)), w = rep(1, length(y)), lambda = 0.5, order = 1)

Arguments

y

the response variable

x

the x-variable

w

prior weights

span

the side of the local window compare as a proportion to the total number of observations

lambda

the smoothing parameter for the Gaussian kernel

order

the order of the polynomial

Details

Those functions can be used for illustration of the basic concepts of smoothing using small data sets. Do not use them with large data because are computationally inefficient.

Value

The functions return a locW object with values

fitted.values

the fitted valus

residuals

the residuals

edf

the effective degrees of freedom

rss

the residual sum of squares

lambda

the smoothing parameter

y

the y variable

x

the x variable

w

the prior weights

Author(s)

Mikis Stasinopoulos, d.stasinopoulos@londonmet.ac.uk

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/)

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, http://www.jstatsoft.org/v23/i07.

See Also

loess, ksmooth

Examples

library(MASS)
data(mcycle)
# local means
m0<-Locmean(mcycle$accel, mcycle$times, span=.1)
m1<-Locmean(mcycle$accel, mcycle$times, span=.2)
m2<-Locmean(mcycle$accel, mcycle$times, span=.3)
span <- c("span=0.1", "span=0.2", "span=0.3")
plot(accel~times, data=mcycle,main="local mean")
lines(fitted(m0)~mcycle$times, col=1, lty=1)
lines(fitted(m1)~mcycle$times, col=2, lty=2)
lines(fitted(m2)~mcycle$times, col=3, lty=3)
legend(1.5,50, legend = span, col = 1:3,
       lty = 1:3, cex = .8, y.intersp = 1)
#  kernel estimation      
k0<-WLocmean(mcycle$accel, mcycle$times, lambda=1)
k1<-WLocmean(mcycle$accel, mcycle$times,  lambda=2)
k2<-WLocmean(mcycle$accel, mcycle$times,  lambda=3)
lambda <- c("lambda=1", "lambda=2", "lambda=3")
plot(accel~times, data=mcycle,main="Gaussian kernel fit")
lines(fitted(k0)~mcycle$times, col=1, lty=1)
lines(fitted(k1)~mcycle$times, col=2, lty=2)
lines(fitted(k2)~mcycle$times, col=3, lty=3)
legend(1.5,50, legend = lambda, col = 1:3,
       lty = 1:3, cex = .8, y.intersp = 1)
# local polymials
l1<-Locpoly(mcycle$accel, mcycle$times, span=.1)
l2<-Locpoly(mcycle$accel, mcycle$times, span=.2)
l3<-Locpoly(mcycle$accel, mcycle$times, span=.3)

span <- c("span=0.1", "span=0.2", "span=0.3")
plot(accel~times, data=mcycle,main="local linear fit")
lines(fitted(l1)~mcycle$times, col=1, lty=1)
lines(fitted(l2)~mcycle$times, col=2, lty=2)
lines(fitted(l2)~mcycle$times, col=3, lty=3)
legend(1.5,50, legend = span, col = 1:3,
       lty = 1:3, cex = .8, y.intersp = 1)       
# weighted local polynomials  
lw1<-WLocpoly(mcycle$accel, mcycle$times, lambda=1.5, order=1)
lw2<-WLocpoly(mcycle$accel, mcycle$times, lambda=1.5, order=2)
lw3<-WLocpoly(mcycle$accel, mcycle$times, lambda=1.5, order=3)

span <- c("linear", "quadratic", "cubic")
plot(accel~times, data=mcycle,main="Weighted local linear, quadratic and cubic fits")
lines(fitted(lw1)~mcycle$times, col=1, lty=1)
lines(fitted(lw2)~mcycle$times, col=2, lty=2)
lines(fitted(lw3)~mcycle$times, col=3, lty=3)
legend(1.5,50, legend = span, col = 1:3,
       lty = 1:3, cex = .8, y.intersp = 1)

Demos for smoothing techniques

Description

These are demos for teaching smoothing techniques to students

Usage

demo.BSplines()
demo.RandomWalk(y = NULL, ...)
demo.histSmo(y = NULL, ...)
demo.interpolateSmo(y = NULL, w = NULL, ...)
demo.PSplines(y = NULL, x = NULL, ...)

Arguments

y

for y variable if needed otherwise it is generated

w

for weights if needed

x

for explanatory variable if needed

...

for adding parameters in the plot

Value

An rpanel plot

Author(s)

Paul Eirers p.eilers@erasmusmc.nl, Brian Marx bmarx@LSU.EDU, and Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk

References

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

Examples

demo.PSplines()

Local Regression Smoothing

Description

This function demonstrate some characteristics of local regression Smoothing

Usage

demo.LocalRegression(y = NULL, x = NULL, span = 0.5, 
                position = trunc((n - 1)/2), 
                deg = 1)
LPOL(y, x, span = 0.5, position = trunc((n - 1)/2), 
                w = rep(1, length(y)), deg = 1)
WLPOL(y, x, sd = 0.5, position = trunc((n - 1)/2), 
                w = rep(1, length(y)), deg = 1)

Arguments

y

The response variable

x

the explanatory variable

span

The smoothing parameters

sd

The standard deviation of a normal kernel used as smoothing parameter

position

The position of the target values in the x axis

w

weights

deg

The degree of the local polynomial

Details

The function demo.LocalRegression demonstrates some aspects of the Local (unweighed) polynomial regression. The functions LPOL() and WLPOL() produce plots related to unweighed and weighted local polynomial regression respectively.

Value

All function produce plots.

Author(s)

Mikis Stasinopoulos

References

R Development Core Team (2010) tcltk package, CRAN.

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

See Also

See also demoDist, gamlss.demo

Examples

demo.LocalRegression()
n <- 100
x <- seq(0, 1, length = n)*1.4
y <- 1.2 + .3*sin(5  * x) + rnorm(n) * 0.2
op <- par(mfrow=c(2,2))
LPOL(y,x, deg=0, position=5)
title("(a) moving average")
LPOL(y,x, deg=1,  position=75)
title("(b) linear poly")
WLPOL(y,x, deg=2, position=30)
title("(c) quadratic poly")
WLPOL(y,x, deg=3, position= 50)
title("(b) cubic poly")
par(op)

Demos for local polynomial smoothing

Description

Those are four demos to show weighed and unweighed local mean and polynomial smoothing.

Usage

 
demo.Locmean(y = NULL, x = NULL, ...) 
demo.Locpoly(y = NULL, x = NULL, ...)
demo.WLocpoly(y = NULL, x = NULL, ...)
demo.WLocmean(y = NULL, x = NULL, ...)

Arguments

y

the response variable. If null it generates its own data

x

explanatory variable

...

for extra argument in the plot

Value

It produces an rpanel plot

Author(s)

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk

References

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

See Also

demo.PSplines

Examples

 demo.Locmean()

Demos for different gamlss.family distributions

Description

The demo functions for showning the gamlss.family distributions. The functions use the package Rpanel.

Usage

demo.NO()
demo.LO()
demo.NO.LO()
demo.GU()
demo.RG()
demo.exGAUS()
demo.PE()
demo.PE.NO()
demo.TF()
demo.TF.NO()
demo.EGB2()
demo.GT()
demo.JSU()
demo.JSUo()
demo.NET()
demo.SHASH()
demo.SEP1()
demo.SEP2()
demo.SEP3() 
demo.SEP4()
demo.ST1()
demo.ST2()
demo.ST3()
demo.ST4()
demo.ST5()   
demo.EXP()
demo.GA()
demo.LOGNO()
demo.NO.LOGNO()
demo.IG()
demo.WEI()
demo.WEI2()
demo.WEI3()
demo.BCCG()
demo.GG()
demo.GIG()
demo.ZAGA()
demo.ZAIG()
demo.BCT() 
demo.BCPE()
demo.GB2()
demo.EGB2()
demo.BE()
demo.BEo() 
demo.GB1()  
demo.GT()
demo.BB()
demo.BEINF()
demo.BEINF0()
demo.BEINF1()
demo.BI()
demo.DEL()
demo.LG()
demo.NBI()
demo.NBII()
demo.PO()
demo.SICHEL()
demo.ZABI()
demo.ZAGA()
demo.ZALG()
demo.ZAP()
demo.ZIBI()
demo.ZIP()
demo.ZIP2()
demo.BCCG()
demo.GG()
demo.PIG()
demo.ZABB()
demo.ZIBB()
demo.ZANBI()
demo.ZINBI()
demo.ZIPIG()
demo.NOtr() 
demo.GAtr()
demo.YULE()
demo.WARING()
demo.GEOM()
demo.IGAMMA()
demo.PARETO2()
demo.PARETO2o()
demo.SHASHo()
demo.SHASHo2()
demo.LOGITNO()
demo.LOGNO2()
demo.SN1()
demo.SN2()
demo.SST()
demo.TF2()
demo.DPO()

Value

An rpanel plot

Author(s)

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby r.rigby@londonmet.ac.uk with contribution from Larisa Kosidou.

References

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

Examples

demo.NO()

Interface for demonstrating the gamlss.family distributions

Description

The function demoDist is an tcltk interface for plotting all the available gamlss.family distributions.

Usage

demoDist()

Value

It creates a tcltk menu

Author(s)

Konstantinos Pateras kostas.pateras@gmail.com

References

R Development Core Team (2010) tcltk package, CRAN.

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

Examples

## do not run 
demoDist()

Demo for local polynomial fits

Description

It starts the gamlss local plynomial demos demos. It is an tcltk interface for using the local polynolial demos.

Usage

demoLpolyS()

Value

It creates a tcltk menu

Author(s)

Konstantinos Pateras kostas.pateras@gmail.com

References

R Development Core Team (2010) tcltk package, CRAN.

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

See Also

See also demoDist, gamlss.demo,

Examples

demoLpolyS()

Interface for demonstrating the P-splines and other smoothers

Description

The function demoPsplines is an tcltk interface for P. Eilers and B. Marx demos for P-splines.

Usage

demoPsplines()

Value

Create an tcltk menu

Author(s)

Konstantinos Pateras kostas.pateras@gmail.com

References

R Development Core Team (2010) tcltk package, CRAN.

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

See Also

See also demoDist, ~~~

Examples

demoPsplines()

The demo for gamlss distributions and smoothing

Description

It starts the gamlss demos. It is an tcltk interface for using the gamlss demos.

Usage

gamlss.demo()

Value

It creates a tcltk menu

Author(s)

Konstantinos Pateras kostas.pateras@gmail.com

References

R Development Core Team (2010) tcltk package, CRAN.

Bowman, Bowman, Gibson and Crawford (2008) rpanel, CRAN

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

See Also

See also demoDist, gamlss.demo,

Examples

gamlss.demo()