gam (version 1.02)

predict.gam: Predict method for GAM fits

Description

Obtains predictions and optionally estimates standard errors of those predictions from a fitted generalized additive model object.

Usage

predict.gam(object, newdata, type, dispersion, se.fit = FALSE,na.action, terms,...)

Arguments

object

a fitted gam object, or one of its inheritants, such as a glm or lm object.

newdata

a data frame containing the values at which predictions are required. This argument can be missing, in which case predictions are made at the same values used to compute the object. Only those predictors, referred to in the right side of the formula in o

type

type of predictions, with choices "link" (the default), "response", or "terms". The default produces predictions on the scale of the additive predictors, and with newdata missing, predict is

se.fit

if TRUE, pointwise standard errors are computed along with the predictions.

dispersion

the dispersion of the GLM fit to be assumed in computing the standard errors. If omitted, that returned by 'summary' applied to the object is used

terms

if type="terms", the terms= argument can be used to specify which terms should be included; the default is labels(object).

na.action

function determining what should be done with missing values in 'newdata'. The default is to predict 'NA'.

...

Placemark for additional arguments to predict

Value

• a vector or matrix of predictions, or a list consisting of the predictions and their standard errors if se.fit = TRUE. If type="terms", a matrix of fitted terms is produced, with one column for each term in the model (or subset of these if the terms= argument is used). There is no column for the intercept, if present in the model, and each of the terms is centered so that their average over the original data is zero. The matrix of fitted terms has a "constant" attribute which, when added to the sum of these centered terms, gives the additive predictor. See the documentation of predict for more details on the components returned.

  When newdata are supplied, predict.gam simply invokes inheritance and gets predict.glm to produce the parametric part of the predictions. For each nonparametric term, predict.gam reconstructs the partial residuals and weights from the final iteration of the local scoring algorithm. The appropriate smoother is called for each term, with the appropriate xeval argument (see s or lo), and the prediction for that term is produced.

  The standard errors are based on an approximation given in Hastie (1992). Currently predict.gam does not produce standard errors for predictions at newdata.

  Warning: naive use of the generic predict can produce incorrect predictions when the newdata argument is used, if the formula in object involves transformations such as sqrt(Age - min(Age)).

References

Hastie, T. J. (1992) Generalized additive models. Chapter 7 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models. London: Chapman and Hall.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.

See Also

predict.glm, fitted, expand.grid

Examples

data(gam.data)
gam.object <- gam(y ~ s(x,6) + z, data=gam.data)
predict(gam.object) # extract the additive predictors
data(gam.newdata)
predict(gam.object, gam.newdata, type="terms")