# Estimated mean/proportion and confidence intervals derived from the maximum likelihood fit of a 3- or 4- parameter Emax model. The estimated means from an Emax model is computed along with confidence bounds. The results are computed for a vector of input dose levels. For binary outcomes, the results are computed on the logit scale and then back-transformed. ```r ## S3 method for class 'fitEmax' predict(object,dosevec,clev=0.9, int=1,dref=0, xvec=NULL, ...) ``` ## Arguments - `object`: Output of `fitEmax` with class "fitEmax". - `dosevec`: Vector of doses to be evaluated. - `clev`: Confidence level for intervals about the estimated mean/proportion at each dosevec. - `int`: The index for the protocol (intercept) to use for the predictions - `dref`: Differences in response between `doselev` and `dref` are computed. - `xvec`: The vector of centered baseline values for the prediction model when `xbase` was specified in the model fit. Centering must be done using the protocol-specific means consistent with `int`. See details for the default calculations when `xvec` is not specified. - `...`: No additonal parameters will be utilized. ## Details Model estimates, standard errors, and confidence bounds are computed with the function `SeEmax`. If baseline covariates were included in the fit and `xvec` is not specified, then the predicted value is the mean of the predictions for all patients in the specified protocol. Note that the protocol must be specified, or the prediction defaults to patients from the first protocol. Note that for binary data, the distinction between the mean of the predicted values and the predicted value as the mean of the covariates can be important. ## Returns A list with estimated dose group means/proportions, lower bound, upper bound, SE, and corresponding values for differences with the reference dose. One value for each dose in dosevec. ## Author(s) Neal Thomas ## See Also `nls` ## Examples ```r ## Not run: ## this example changes the random number seed doselev<-c(0,5,25,50,100,350) n<-c(78,81,81,81,77,80) ### population parameters for simulation e0<-2.465375 ed50<-67.481113 dtarget<-100 diftarget<-9.032497 emax<-solveEmax(diftarget,dtarget,log(ed50),1,e0) sdy<-8.0 pop.parm<-c(log(ed50),emax,e0) dose<-rep(doselev,n) meanlev<-emaxfun(dose,pop.parm) y<-rnorm(sum(n),meanlev,sdy) testout<-fitEmax(y,dose,modType=4) predout<-predict(testout,dosevec=c(20,80),int=1) ## End(Not run) ```

predict.fitEmax function