# Extract Predictor Values and Standard Errors For New Data in a gamlssZadj Model `predict.gamlssZadj` is the specific method which produce predictors for a new data set for a specified parameter from a gamlssZadj objects. The `predict.gamlssZadj` can be used to extract the linear predictors, fitted values and specific terms in the model at new data values in the same way that the `predict.lm()` and `predict.glm()` functions can be used for `lm` or `glm` objects. Note that linear predictors, fitted values and specific terms in the model at the current data values can also be extracted using the function `lpred()` (which is called from predict if new data is NULL). ```r ## S3 method for class 'gamlssZadj' predict(object, parameter = c("mu", "sigma", "nu", "tau", "xi0"), newdata = NULL, type = c("link", "response", "terms"), terms = NULL, se.fit = FALSE,data = NULL, ...) ``` ## Arguments - `object`: a gamlssZadj fitted model - `parameter`: which distribution (or inflation) parameter is required, default `parameter="mu"` - `newdata`: a data frame containing new values for the explanatory variables used in the model - `type`: the default, gets the linear predictor for the specified distribution (or inflation) parameter. `type="response"` gets the fitted values for the parameter while `type="terms"` gets the fitted terms contribution - `terms`: if `type="terms"`, which terms to be selected (default is all terms) - `se.fit`: if TRUE the approximate standard errors of the appropriate type are extracted if exist - `data`: the data frame used in the original fit if is not defined in the call - ``...``: for extra arguments ## Details The predict function assumes that the object given in `newdata` is a data frame containing the right x-variables used in the model. This could possible cause problems if transformed variables are used in the fitting of the original model. For example, let us assume that a transformation of age is needed in the model i.e. `nage<-age^.5`. This could be fitted as `mod<-gamlss(y~cs(age^.5),data=mydata)` or as `nage<-age^.5; mod<-gamlss(y~cs(nage), data=mydata)`. The later could more efficient if the data are in thousands rather in hundreds. In the first case, the code `predict(mod,newdata=data.frame(age=c(34,56)))` would produce the right results. In the second case a new data frame has to be created containing the old data plus any new transform data. This data frame has to be declared in the `data` option. The option `newdata` should contain a data.frame with the new names and the transformed values in which prediction is required, (see the last example). ## Returns A vector or a matrix depending on the options. ## References Hossain, A., Stasinopoulos, M., Rigby, R. and Enea, M. (2015). Centile estimation for a proportion response variable. **Statistics in Medicine**, doi: 10.1002/sim.6748. 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. (2003) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also [http://www.gamlss.org/](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](http://www.jstatsoft.org/v23/i07). ## Author(s) Abu Hossain, Mikis Stasinopoulos mikis.stasinopoulos@gamlss.org , Bob Rigby and Marco Enea ## See Also `gamlssZadj` ## Examples ```r set.seed(3210) x <- (runif(800)*4)-2 data(sda) fmu <- splinefun(sda$x, sda$mu) curve(fmu, -2,2) fsigma <- splinefun(sda$x, sda$sigma) curve(fsigma, -2,2) fnu <- function(x) {f <- splinefun(sda$x, sda$nu) f(x)/6 } curve(fnu, -2,2) set.seed(321) y0 <- rZAGA(800, mu=fmu(x), sigma=fsigma(x), nu=fnu(x)) da <- data.frame(y0,x) g0p <- gamlss(y0~pb(x), sigma.fo=~pb(x), nu.fo=~pb(x), data=da, family=ZAGA) t0p <- gamlssZadj(y=y0, mu.fo=~pb(x), sigma.fo=~pb(x),data=da, trace=TRUE, xi0.fo=~pb(x), family="GA") plot(predict(g0p,"nu",type="link"), predict(t0p,"xi0",type="link")) ```

predict.gamlssZadj function