# Predictions for relational life table model Predict the relational model for a life table correction model fitted by `flexrsuvclt` or for specified knots, degree and coefficients ```r predictCLT (...) ## S3 method for class 'flexrsurvclt' predictCLT(object, newdata= NULL, type = c("clt", "correction"), se.fit=FALSE, na.action=na.pass, newcoef = NULL, ...) ## Default S3 method: predictCLT(knots, degree, newdata, newcoef, ...) ``` ## Arguments - `object`: the results of a flexrsurvclt fit. - `newdata`: Optional new vector of logarithm of the cumulative distribution odds (LCDO) at which to do predictions. If absent predictions are for values of the LCDO used in the original fit (`logit_end` parameter in the call to `flexrsuvclt`)). - `newcoef`: Optional new coefficients for which to do predictions. If absent predictions are for the coefficients of the fitted model in `object`. - `type`: the type of predicted value. Choices are `"clt"` or `"correction"` to compute the corrected logarithm of the cumulative distribution odds. - `se.fit`: if TRUE, pointwise standard errors are produced for the predictions (not yet implemented). - `knots, degree`: knots and degree of the relational model. - `na.action`: function determining what should be done with missing values in `newdata`. The default is to predict `NA`. - ``...``: For future methods ## Returns a vector or a list containing the predicted relational model (element `"fit"`) and their standard errors (element `"se.fit"`) if the se.fit option is TRUE. ## Details `predictCLT` with `knots` and `degree` arguments computes corrected values of . ## See Also `predict.flexrsurvclt`, `flexrsurv`, `flexrsurvclt` ## Examples ```r if (requireNamespace("relsurv", quietly = TRUE) & requireNamespace("date", quietly = TRUE)) { library(date) # data from package relsurv data(rdata, package="relsurv") class(rdata$year)<-"integer" # rate table from package relsurv data(slopop, package="relsurv") # get the death rate at event (or end of followup) from slopop for rdata rdata$iage <- findInterval(rdata$age*365.24+rdata$time, attr(slopop, "cutpoints")[[1]]) rdata$iyear <- findInterval(rdata$year+rdata$time, attr(slopop, "cutpoints")[[2]]) therate <- rep(-1, dim(rdata)[1]) for( i in 1:dim(rdata)[1]){ therate[i] <- slopop[rdata$iage[i], rdata$iyear[i], rdata$sex[i]] } rdata$slorate <- therate # get the logit_start and logit_end # logit start at age 18 tmpsurv <- Surv(rep(0, length(rdata$time)), rdata$time, rdata$cens) HH <- getHazardFromTable(tmpsurv, startdate=rdata$year, startage=rdata$age*365.25 , matchdata=rdata, ratetable=slopop, age="age", year="year", rmap=list(sex=sex), agemin=18, ratename = "poprate", cumrateendname ="cumrateend", cumrateentername ="cumrateenter" ) rdata$slorate <- HH$poprate rdata$logit_start <- log(exp(HH$cumrateenter)-1) rdata$logit_end <- log(exp(HH$cumrateend)-1) rdata$Id <- 1:dim(rdata)[1] # change sex coding rdata$sex01 <- rdata$sex -1 # fit a relative survival model with a non linear effect of age # with correction of life table # full likelihood fit1 <- flexrsurvclt(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3, Boundary.knots = c(24, 95)), rate=slorate, logit_start=logit_start, logit_end=logit_end, data=rdata, Id=Id, knots.Bh=1850, # one interior knot at 5 years degree.Bh=3, Max_T=5400, Spline = "b-spline", Spline.CLT=flexrsurv:::R2bBSplineBasis(knots=c(-2.5,0,2.5), degree=3), initbyglm=TRUE, initbands=seq(0, 5400, 100), int_meth= "BANDS", bands=seq(0, 5400, 50) ) corrected_logit_end <- predictCLT(fit1) try_logit_end <- predictCLT(knots=c(-2.5,0,2.5), degree=3, newcoef = c(0.5, 2), newdata = rdata$logit_end ) plot(rdata$logit_end, corrected_logit_end) points(rdata$logit_end, try_logit_end, col = 2) } ```

predictCLT function