thr3ads.net - R help - [R] print and coef Methods for survreg Differ [Feb 2021]

If this information is useful, please help other people find it:
Share via:

biii m@iii@g oii de@@ey@ws

2021-Feb-23 14:45 UTC

[R] print and coef Methods for survreg Differ

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]]

Jeff Newmiller

2021-Feb-23 16:09 UTC

head link

[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, bill at 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]]
>
>______________________________________________
>R-help at r-project.org 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.

R help - Feb 2021 - print and coef Methods for survreg Differ

[R] print and coef Methods for survreg Differ

[R] print and coef Methods for survreg Differ