You can't. But in order to be able to predict for states that _were_ in the training data, the coefficient cannot be NA.
On February 23, 2021 8:40:43 AM PST, [email protected] wrote: >How should you be able to make a prediction (using this type of model) >from a state where there is no data such as treatment="C" in my >example? > >-----Original Message----- >From: Jeff Newmiller <[email protected]> >Sent: Tuesday, February 23, 2021 11:10 AM >To: [email protected]; [email protected] >Subject: Re: [R] print and coef Methods for survreg Differ > >Model equations do not normally have conditional forms dependent on >whether specific coefficients are NA or not. If you assign NA to a >coefficient then you will not be able to predict outputs for input >cases that you should be able to. Zero allows these expected cases to >work... NA would prevent any useful prediction output. > >On February 23, 2021 6:45:53 AM PST, [email protected] wrote: >>Hello, >> >> >> >>I'm working on a survreg model where the full data are subset for >>modeling individual parts of the data separately. When subsetting, >the >>fit variable ("treatment" in the example below) has levels that are >not >>in the data. >> A >>work-around for this is to drop the levels, but it seems inaccurate to > >>have the `coef()` method provide zero as the coefficient for the level > >>without data. >> >> >> >>Why does coef(model) provide zero as the coefficient for treatment >>instead of NA? Is this a bug? >> >> >> >>Thanks, >> >> >> >>Bill >> >> >> >>``` r >> >>library(survival) >> >>library(emmeans) >> >> >> >>my_data <- >> >> data.frame( >> >> value=c(rep(1, 5), 6:10), >> >> treatment=factor(rep(c("A", "B"), each=5), levels=c("A", "B", >"C")) >> >> ) >> >>my_data$cens <- c(0, 1)[(my_data$value == 1) + 1] >> >> >> >>model <- survreg(Surv(time=value, event=cens)~treatment, data=my_data) >> >>#> Warning in survreg.fit(X, Y, weights, offset, init = init, >>controlvals = >> >>#> control, : Ran out of iterations and did not converge >> >>coef(model) >> >>#> (Intercept) treatmentB treatmentC >> >>#> 0.08588218 2.40341893 0.00000000 >> >>model$coef >> >>#> (Intercept) treatmentB treatmentC >> >>#> 0.08588218 2.40341893 NA >> >>model$coefficients >> >>#> (Intercept) treatmentB treatmentC >> >>#> 0.08588218 2.40341893 0.00000000 >> >>print(model) >> >>#> Call: >> >>#> survreg(formula = Surv(time = value, event = cens) ~ treatment, >> >>#> data = my_data) >> >>#> >> >>#> Coefficients: (1 not defined because of singularities) >> >>#> (Intercept) treatmentB treatmentC >> >>#> 0.08588218 2.40341893 NA >> >>#> >> >>#> Scale= 0.09832254 >> >>#> >> >>#> Loglik(model)= 4.9 Loglik(intercept only)= -15 >> >>#> Chisq= 39.92 on 2 degrees of freedom, p= 2.15e-09 >> >>#> n= 10 >> >>summary(model) >> >>#> >> >>#> Call: >> >>#> survreg(formula = Surv(time = value, event = cens) ~ treatment, >> >>#> data = my_data) >> >>#> Value Std. Error z p >> >>#> (Intercept) 0.0859 0.0681 1.26 0.21 >> >>#> treatmentB 2.4034 0.2198 10.93 <2e-16 >> >>#> treatmentC 0.0000 0.0000 NA NA >> >>#> Log(scale) -2.3195 0.0000 -Inf <2e-16 >> >>#> >> >>#> Scale= 0.0983 >> >>#> >> >>#> Weibull distribution >> >>#> Loglik(model)= 4.9 Loglik(intercept only)= -15 >> >>#> Chisq= 39.92 on 2 degrees of freedom, p= 2.1e-09 >> >>#> Number of Newton-Raphson Iterations: 30 >> >>#> n= 10 >> >>ref_grid(model) >> >>#> Error in ref_grid(model): Something went wrong: >> >>#> Non-conformable elements in reference grid. >> >> >> >>my_data_correct_levels <- my_data >> >>my_data_correct_levels$treatment <- >>droplevels(my_data_correct_levels$treatment) >> >> >> >>model_correct <- survreg(Surv(time=value, event=cens)~treatment, >>data=my_data_correct_levels) >> >>#> Warning in survreg.fit(X, Y, weights, offset, init = init, >>controlvals = >> >>#> control, : Ran out of iterations and did not converge >> >>coef(model_correct) >> >>#> (Intercept) treatmentB >> >>#> 0.08588218 2.40341893 >> >>print(model_correct) >> >>#> Call: >> >>#> survreg(formula = Surv(time = value, event = cens) ~ treatment, >> >>#> data = my_data_correct_levels) >> >>#> >> >>#> Coefficients: >> >>#> (Intercept) treatmentB >> >>#> 0.08588218 2.40341893 >> >>#> >> >>#> Scale= 0.09832254 >> >>#> >> >>#> Loglik(model)= 4.9 Loglik(intercept only)= -15 >> >>#> Chisq= 39.92 on 1 degrees of freedom, p= 2.65e-10 >> >>#> n= 10 >> >>summary(model_correct) >> >>#> >> >>#> Call: >> >>#> survreg(formula = Surv(time = value, event = cens) ~ treatment, >> >>#> data = my_data_correct_levels) >> >>#> Value Std. Error z p >> >>#> (Intercept) 0.0859 0.0681 1.26 0.21 >> >>#> treatmentB 2.4034 0.2198 10.93 <2e-16 >> >>#> Log(scale) -2.3195 0.0000 -Inf <2e-16 >> >>#> >> >>#> Scale= 0.0983 >> >>#> >> >>#> Weibull distribution >> >>#> Loglik(model)= 4.9 Loglik(intercept only)= -15 >> >>#> Chisq= 39.92 on 1 degrees of freedom, p= 2.6e-10 >> >>#> Number of Newton-Raphson Iterations: 30 >> >>#> n= 10 >> >>ref_grid(model_correct) >> >>#> 'emmGrid' object with variables: >> >>#> treatment = A, B >> >>#> Transformation: "log" >> >>``` >> >> >> >><sup>Created on 2021-02-23 by the [reprex >>package](https://reprex.tidyverse.org) (v1.0.0)</sup> >> >> >> [[alternative HTML version deleted]] >> >>______________________________________________ >>[email protected] mailing list -- To UNSUBSCRIBE and more, see >>https://stat.ethz.ch/mailman/listinfo/r-help >>PLEASE do read the posting guide >>http://www.R-project.org/posting-guide.html >>and provide commented, minimal, self-contained, reproducible code. > >-- >Sent from my phone. Please excuse my brevity. -- Sent from my phone. Please excuse my brevity. ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
