[R] print and coef Methods for survreg Differ

[Jeff Newmiller]

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, bill using denney.ws 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]]
>
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-- 
Sent from my phone. Please excuse my brevity.