# Fixed means (proportions) random data constructor for emaxsim for continuous or binary data Creates a list object that contains inputs and a function to create simulated data sets with a common mean (proportion) for use in emaxsim with normal or continuous data ```r FixedMean(n, doselev, meanlev, resSD, parm = NULL, binary=FALSE) ``` ## Arguments - `n`: Sample size for each dose group - `doselev`: Dose levels (including 0 for placebo) in the study corresponding to `n`. Must be in increasing order. - `meanlev`: Mean response at each doselev. For binary data, these are the proportion of responders (no logit transformation). - `resSD`: Standard deviation for residuals within each dose group (assumed common to all dose groups) - `parm`: Population parameters that are saved for later reference, but are not used when creating simulated data. `parm` can contain parameters for a 3- or 4- parameter Emax model that generated `meanlev`. They should be stored in the order given in `emaxfun`. Default is `NULL`. - `binary`: Normal data with homogeneous variance are generated unless `binary` is `TRUE`, and then means are interpreted as proportions and `0/1` data are generated. ## Returns A list of length `2`. The first element is itself a list named `genP` that contains named elments `n`, `resSD`, `doselev`, `dose`, `parm`, `binary`, and the element `meanlev`, which is specific to `FixedMean`. The second element is a function named `genFun` that takes `genP` as input and returns a list with named elements `meanlev`, `parm`, `resSD`, `y`. ## Author(s) Neal Thomas ## See Also `emaxsim`, `RandEmax` ## Examples ```r ## Not run: ## example changes the random number seed doselev<-c(0,5,25,50,100) n<-c(78,81,81,81,77) ### population parameters for simulation e0<-2.465375 ed50<-67.481113 dtarget<-100 diftarget<-9.032497 emax<-solveEmax(diftarget,dtarget,log(ed50),1,e0) sdy<-7.967897 pop<-c(log(ed50),emax,e0) meanlev<-emaxfun(doselev,pop) ###FixedMean is specialized constructor function for emaxsim genp<-FixedMean(n,doselev,meanlev,sdy,pop) ### binary example n<-rep(500,5) doselev<-c(0,5,25,50,1000) dose<-rep(doselev,n) e0<- qlogis(0.2) ed50<-20 diftarget<-qlogis(0.6)-qlogis(0.2) lambda<-2 dtarget<-100 emax<-solveEmax(diftarget,dtarget,log(ed50),lambda,e0) pop<-c(log(ed50),lambda,emax,e0) meanlev<-plogis(emaxfun(doselev,pop)) genp<-FixedMean(n,doselev,meanlev,sdy,pop,binary=TRUE) tapply(genp$genFun(genp$genP)$y,dose,mean) meanlev ## End(Not run) ```

FixedMean function