predict.cozigam {COZIGAM}R Documentation

Prediction from fitted COZIGAM

Description

Takes a fitted cozigam object produced by cozigam() and produces predictions given a new set of values for the model covariates or the original values used for the model fit. Predictions can be accompanied by standard errors, based on the distribution of the model coefficients obtained by Louis' method.

Usage

## S3 method for class 'cozigam':
predict(object, newdata, type="link", se.fit=FALSE, ...)

Arguments

object A fitted cozigam object as produced by cozigam().
newdata A data frame containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned. If newdata is provided then it must contain all the variables needed for prediction.
type When this has the value "link" (default) the linear predictor (possibly with associated standard errors) is returned. When type="terms" each component of the linear predictor is returned seperately (possibly with standard errors): this excludes any intercept. When type="response" predictions on the scale of the response are returned (possibly with approximate standard errors).
se.fit Logical. If TRUE (not default), standard error estimates are returned for each prediction.
... Other arguments.

Details

The standard errors produced by predict.cozigam() are based on the covariance matrix of the parameters obtained by Louis' method in the fitted gam object.

Value

If se.fit is TRUE then a 3 item list is returned with items (both arrays) fit, se.fit containing predictions and associated standard error estimates and p containing predictions of associated zero-inflation rates, otherwise a 2 item list without the array of standard error estimated is returned. The dimensions of the returned arrays depends on whether type is "terms" or not: if it is then the array is 2 dimensional with each term in the linear predictor separate, otherwise the array is 1 dimensional and contains the linear predictor/predicted values (or corresponding s.e.s). The linear predictor returned termwise will not include the intercept.

Author(s)

Hai Liu and Kung-Sik Chan

References

Liu, H and Chan, K.S. (2008) Constrained Generalized Additive Model with Zero-Inflated Data. Technical Report 388, Department of Statisics and Actuarial Science, The Unversity of Iowa. http://www.stat.uiowa.edu/techrep/tr388.pdf

Louis, T. A. (1982) Finding the Observed Information Matrix When Using EM Algorithm. J. R. Statist. Soc. B, 44, 226-233

See Also

cozigam, plot.cozigam

Examples

set.seed(11)
n <- 400
x1 <- runif(n, 0, 1)
x2 <- runif(n, 0, 1)
x3 <- runif(n, 0, 1)

f <- test(x1,x2)*4-mean(test(x1,x2)*4) + f0(x3)/2-mean(f0(x3)/2)
sig <- 0.5
mu0 <- f + 3
y <- mu0 + rnorm(n, 0, sig)

alpha0 <- -2.2
delta0 <- 1.2
p0 <- .Call("logit_linkinv", alpha0 + delta0 * mu0, PACKAGE = "stats")
z <- rbinom(rep(1,n), 1, p0)
y[z==0] <- 0

res <- cozigam(y~s(x1,x2)+s(x3), constraint = "proportional", family = gaussian)

newdata <- data.frame(x1=c(0.5,0.8), x2=c(0.2,0.1), x3=c(0.3,0.7))
predict(res, newdata=newdata, se.fit=TRUE, type="response")

[Package COZIGAM version 2.0-1 Index]