asp: Fit a semiparametric regression model with spatially adaptive...
In AdaptFit: Adaptive Semiparametic Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

asp fits semiparametric regression models using the mixed model representation of penalized splines with spatially adaptive penalties, based on the "spm" function of the SemiPar-package.

1 2	asp(form,adap=TRUE,random=NULL,group=NULL,family="gaussian", spar.method="REML",omit.missing=NULL,niter=20,niter.var=50,tol=1e-06,returnFit=FALSE,weights=NULL,correlation=NULL,control=NULL)

`form`	a formula describing the model to be fit. Note, that an intercept is always included, whether given in the formula or not.
`adap`	TRUE (default) if an adaptive fit should be performed, otherwise the fit is identical to that of function "spm".
`random`	"random=~1" specifies inclusion of a random intercept according to the groups specified by the "group" argument.
`group`	a vector of labels for specifying groups.
`family`	for specification of the type of likelihood model assumed in the fitting. May be "gaussian","binomial" or "poisson".
`spar.method`	method for automatic smoothing parameter selection. May be "REML" (restricted maximum likelihood) or "ML" (maximum likelihood).
`omit.missing`	a logical value indicating whether fields with missing values are to be omitted.
`niter`	a maximum number of iterations for the mean estimation, default is 20.
`niter.var`	a maximum number of iterations for the variance of random effects estimation, default is 50.
`tol`	tolerance for the convergence criterion. Default is 1e-6.
`returnFit`	a logical value indicating whether the fitted object should be returned when the maximum number of iterations is reached without convergence of the algorithm. Default is FALSE.
`weights`	to use only with grouped binary data.
`correlation`	correlation structure of the response; see documentation to "nlme".
`control`	see lmeControl in the documentation to "nlme".

See the SemiPar Users' Manual for details and examples.

A list object of class "spm" containing the fitted model. The components are:

`fitted`	fitted values.
`coef.mean`	estimated mean coefficients.
`design.matrices`	design matrices both for knots und subknots.
`x`	x values.
`knots`	knots.
`y.cov`	estimated covariance matrix of the response.
`random.var`	estimated covariance matrix of the random effects.
`subknots`	subknots.
`coef.random`	estimated spline coefficients of the covariance matrix of the random effects.
`var.random.var`	estimated variance of the spline coefficients of the covariance matrix of the random effects.
`fit`	mimics fit object of lme() for family="gaussian" and glmmPQL() for family="binomial" or family="poisson".
`info`	information about the inputs.
`aux`	auxiliary information such as variability estimates.

Tatyana Krivobokova tkrivob at gwdg.de

Krivobokova, T., Crainiceanu, C.M. and Kauermann, G. (2008)
Fast Adaptive Penalized Splines. Journal of Computational and Graphical Statistics. 17(1) 1-20.

Ganguli, B. and Wand, M.P. (2005)
SemiPar 1.0 Users' Manual.
http://www.maths.unsw.edu.au/~wand/papers.html

Ruppert, D., Wand, M.P. and Carroll, R.J. (2003)
Semiparametric Regression Cambridge University Press.
http://stat.tamu.edu/~carroll/semiregbook/

gam (in package ‘mgcv’) lme (in package ‘nlme’) glmmPQL (in package ‘MASS’)

## scatterplot smoothing

x <- 1:1000/1000
mu <- exp(-400*(x-0.6)^2)+5*exp(-500*(x-0.75)^2)/3+2*exp(-500*(x-0.9)^2)
y <- mu+0.5*rnorm(1000)

#fit with default knots
y.fit <- asp(y~f(x))
plot(y.fit)

## one more scatterplot smoothing with specified knots and subknots

x <- 1:400/400
mu <- sqrt(x*(1-x))*sin((2*pi*(1+2^((9-4*6)/5)))/(x+2^((9-4*6)/5)))
y <- mu+0.2*rnorm(400)

kn <- default.knots(x,80)
kn.var <- default.knots(kn,20)

y.fit <- asp(y~f(x,knots=kn,var.knot=kn.var))
plot(y.fit)


## additive models

x1 <- 1:300/300
x2 <- runif(300)
mu1 <- exp(-400*(x1-0.6)^2)+5*exp(-500*(x1-0.75)^2)/3+2*exp(-500*(x1-0.9)^2)
mu2 <- sin(2*pi*x2)
y2 <- mu1+mu2+0.3*rnorm(300)

y2.fit <- asp(y2~f(x1)+f(x2))
par(mfrow=c(2,2))
y21.fit <- asp(y2~f(x1,adap=FALSE)+f(x2)) #switch off adaptive fitting for the first function
plot(y2.fit)
plot(y21.fit)
par(mfrow=c(1,1))

## spatial smoothing

mu3 <- x1*sin(4*pi*x2)
y3 <- mu3+diff(range(mu3))*rnorm(300)/4


#for the specified knots and subknots use
	# kn <- default.knots.2D(x1,x2,12^2) # !!! interactive function !!!
	# kn.var <- default.knots.2D(kn[,1],kn[,2],5^2)
	# y3.fit <- asp(y3~f(x1,x2,knots=kn,var.knot=kn.var))

## non-normal response

x <- 1:1000/1000
mu <- exp(-400*(x-0.6)^2)+5*exp(-500*(x-0.75)^2)/3+2*exp(-500*(x-0.9)^2)
y4 <- rbinom(1000,5,1/(1+exp(-mu)))
nn <- rep(5,1000)
y4.fit <- asp(cbind(y4,nn-y4)~f(x),family="binomial")
### same as ###  y4.fit <- asp(y4/nn~f(x),family="binomial",weights=nn)
plot(y4.fit) #plot of systematic component


## correlated errors

y5 <- sin(2*pi*x1)+0.3*arima.sim(300,model=list(ar=0.6))

y5.fit <- asp(y5~f(x1),adap=FALSE,correlation=corAR1())
plot(y5.fit)

#see also SemiPar User Manual

#
# The current version of the SemiPar User Manual is posted on the web-site:
#
#     www.maths.unsw.edu.au/~wand/papers.html