False convergence in competing risks regression when using the cmprsk packageCompeting risk analysis of interval dataSimulate competing risk dataGLMNet convergence issue for penalized regressionnomogram for competing risk regressionCompeting risk regression with RCompeting risk survival random forest with large dataSurvival competing risk cox modelConvergence of Tobit and truncated regression modelLogistic regression not converging in R
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False convergence in competing risks regression when using the cmprsk package
Competing risk analysis of interval dataSimulate competing risk dataGLMNet convergence issue for penalized regressionnomogram for competing risk regressionCompeting risk regression with RCompeting risk survival random forest with large dataSurvival competing risk cox modelConvergence of Tobit and truncated regression modelLogistic regression not converging in R
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I was using the crr() function in the cmprsk package to perform the competing risks regression. However, it fails with the warning "crr converged: FALSE". I have performed the competing risk regression for many times and this is the first time I have met the question.
x_total2 <- cbind(factor2ind(m1_sub3), factor2ind(age1, "1"), factor2ind(sex2, "1"), factor2ind(race2, "1"),
factor2ind(marital2, "1"), factor2ind(grade2, "1"), factor2ind(t_stage2, "1"),
factor2ind(n_stage2, "1"), factor2ind(ssct2, "0"))
mod_nrt1 <- crr(surv_mo, sum_cod3, x_total2[,c(1:24)])
summary.crr(mod_nrt1, digits = max(options()$digits - 4, 3))
Here is the warning.
Competing Risks Regression
Call:
crr(ftime = surv_mo, fstatus = sum_cod3, cov1 = x_total2[, c(1:24)])
crr converged: FALSE
After this warning, I used
mod_nrt1[["converged"]] <- TRUE
Through this function, I could get the results as follows.
exp(coef) exp(-coef) 2.5% 97.5%
sex2:2 4.36e+00 2.30e-01 1.28e+00 1.48e+01
race2:2 1.30e+00 7.67e-01 8.71e-01 1.95e+00
race2:3 1.17e+00 8.58e-01 6.58e-01 2.07e+00
race2:4 2.42e+00 4.13e-01 1.18e+00 4.98e+00
race2:5 3.57e-05 2.80e+04 4.15e-06 3.07e-04
t_stage2:2 2.60e-01 3.85e+00 1.10e-01 6.12e-01
t_stage2:3 2.75e-01 3.64e+00 1.16e-01 6.51e-01
t_stage2:4 4.08e-01 2.45e+00 1.53e-01 1.09e+00
t_stage2:5 2.74e-01 3.65e+00 1.05e-01 7.14e-01
t_stage2:6 8.58e-01 1.17e+00 2.80e-01 2.63e+00
n_stage2:2 1.01e+00 9.91e-01 4.70e-01 2.16e+00
n_stage2:3 6.97e-01 1.44e+00 2.74e-01 1.77e+00
n_stage2:4 1.26e+00 7.95e-01 5.30e-01 2.98e+00
n_stage2:5 3.22e+00 3.10e-01 1.01e+00 1.02e+01
ssct2:0 3.74e+00 2.68e-01 1.75e+00 7.96e+00
ssct2:1 2.92e+00 3.42e-01 1.36e+00 6.27e+00
ssct2:3 9.93e-01 1.01e+00 6.49e-01 1.52e+00
I know it's wrong. What's the perfect solution? Is there anything wrong with the original data?
r
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
add a comment |
I was using the crr() function in the cmprsk package to perform the competing risks regression. However, it fails with the warning "crr converged: FALSE". I have performed the competing risk regression for many times and this is the first time I have met the question.
x_total2 <- cbind(factor2ind(m1_sub3), factor2ind(age1, "1"), factor2ind(sex2, "1"), factor2ind(race2, "1"),
factor2ind(marital2, "1"), factor2ind(grade2, "1"), factor2ind(t_stage2, "1"),
factor2ind(n_stage2, "1"), factor2ind(ssct2, "0"))
mod_nrt1 <- crr(surv_mo, sum_cod3, x_total2[,c(1:24)])
summary.crr(mod_nrt1, digits = max(options()$digits - 4, 3))
Here is the warning.
Competing Risks Regression
Call:
crr(ftime = surv_mo, fstatus = sum_cod3, cov1 = x_total2[, c(1:24)])
crr converged: FALSE
After this warning, I used
mod_nrt1[["converged"]] <- TRUE
Through this function, I could get the results as follows.
exp(coef) exp(-coef) 2.5% 97.5%
sex2:2 4.36e+00 2.30e-01 1.28e+00 1.48e+01
race2:2 1.30e+00 7.67e-01 8.71e-01 1.95e+00
race2:3 1.17e+00 8.58e-01 6.58e-01 2.07e+00
race2:4 2.42e+00 4.13e-01 1.18e+00 4.98e+00
race2:5 3.57e-05 2.80e+04 4.15e-06 3.07e-04
t_stage2:2 2.60e-01 3.85e+00 1.10e-01 6.12e-01
t_stage2:3 2.75e-01 3.64e+00 1.16e-01 6.51e-01
t_stage2:4 4.08e-01 2.45e+00 1.53e-01 1.09e+00
t_stage2:5 2.74e-01 3.65e+00 1.05e-01 7.14e-01
t_stage2:6 8.58e-01 1.17e+00 2.80e-01 2.63e+00
n_stage2:2 1.01e+00 9.91e-01 4.70e-01 2.16e+00
n_stage2:3 6.97e-01 1.44e+00 2.74e-01 1.77e+00
n_stage2:4 1.26e+00 7.95e-01 5.30e-01 2.98e+00
n_stage2:5 3.22e+00 3.10e-01 1.01e+00 1.02e+01
ssct2:0 3.74e+00 2.68e-01 1.75e+00 7.96e+00
ssct2:1 2.92e+00 3.42e-01 1.36e+00 6.27e+00
ssct2:3 9.93e-01 1.01e+00 6.49e-01 1.52e+00
I know it's wrong. What's the perfect solution? Is there anything wrong with the original data?
r
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
add a comment |
I was using the crr() function in the cmprsk package to perform the competing risks regression. However, it fails with the warning "crr converged: FALSE". I have performed the competing risk regression for many times and this is the first time I have met the question.
x_total2 <- cbind(factor2ind(m1_sub3), factor2ind(age1, "1"), factor2ind(sex2, "1"), factor2ind(race2, "1"),
factor2ind(marital2, "1"), factor2ind(grade2, "1"), factor2ind(t_stage2, "1"),
factor2ind(n_stage2, "1"), factor2ind(ssct2, "0"))
mod_nrt1 <- crr(surv_mo, sum_cod3, x_total2[,c(1:24)])
summary.crr(mod_nrt1, digits = max(options()$digits - 4, 3))
Here is the warning.
Competing Risks Regression
Call:
crr(ftime = surv_mo, fstatus = sum_cod3, cov1 = x_total2[, c(1:24)])
crr converged: FALSE
After this warning, I used
mod_nrt1[["converged"]] <- TRUE
Through this function, I could get the results as follows.
exp(coef) exp(-coef) 2.5% 97.5%
sex2:2 4.36e+00 2.30e-01 1.28e+00 1.48e+01
race2:2 1.30e+00 7.67e-01 8.71e-01 1.95e+00
race2:3 1.17e+00 8.58e-01 6.58e-01 2.07e+00
race2:4 2.42e+00 4.13e-01 1.18e+00 4.98e+00
race2:5 3.57e-05 2.80e+04 4.15e-06 3.07e-04
t_stage2:2 2.60e-01 3.85e+00 1.10e-01 6.12e-01
t_stage2:3 2.75e-01 3.64e+00 1.16e-01 6.51e-01
t_stage2:4 4.08e-01 2.45e+00 1.53e-01 1.09e+00
t_stage2:5 2.74e-01 3.65e+00 1.05e-01 7.14e-01
t_stage2:6 8.58e-01 1.17e+00 2.80e-01 2.63e+00
n_stage2:2 1.01e+00 9.91e-01 4.70e-01 2.16e+00
n_stage2:3 6.97e-01 1.44e+00 2.74e-01 1.77e+00
n_stage2:4 1.26e+00 7.95e-01 5.30e-01 2.98e+00
n_stage2:5 3.22e+00 3.10e-01 1.01e+00 1.02e+01
ssct2:0 3.74e+00 2.68e-01 1.75e+00 7.96e+00
ssct2:1 2.92e+00 3.42e-01 1.36e+00 6.27e+00
ssct2:3 9.93e-01 1.01e+00 6.49e-01 1.52e+00
I know it's wrong. What's the perfect solution? Is there anything wrong with the original data?
r
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
I was using the crr() function in the cmprsk package to perform the competing risks regression. However, it fails with the warning "crr converged: FALSE". I have performed the competing risk regression for many times and this is the first time I have met the question.
x_total2 <- cbind(factor2ind(m1_sub3), factor2ind(age1, "1"), factor2ind(sex2, "1"), factor2ind(race2, "1"),
factor2ind(marital2, "1"), factor2ind(grade2, "1"), factor2ind(t_stage2, "1"),
factor2ind(n_stage2, "1"), factor2ind(ssct2, "0"))
mod_nrt1 <- crr(surv_mo, sum_cod3, x_total2[,c(1:24)])
summary.crr(mod_nrt1, digits = max(options()$digits - 4, 3))
Here is the warning.
Competing Risks Regression
Call:
crr(ftime = surv_mo, fstatus = sum_cod3, cov1 = x_total2[, c(1:24)])
crr converged: FALSE
After this warning, I used
mod_nrt1[["converged"]] <- TRUE
Through this function, I could get the results as follows.
exp(coef) exp(-coef) 2.5% 97.5%
sex2:2 4.36e+00 2.30e-01 1.28e+00 1.48e+01
race2:2 1.30e+00 7.67e-01 8.71e-01 1.95e+00
race2:3 1.17e+00 8.58e-01 6.58e-01 2.07e+00
race2:4 2.42e+00 4.13e-01 1.18e+00 4.98e+00
race2:5 3.57e-05 2.80e+04 4.15e-06 3.07e-04
t_stage2:2 2.60e-01 3.85e+00 1.10e-01 6.12e-01
t_stage2:3 2.75e-01 3.64e+00 1.16e-01 6.51e-01
t_stage2:4 4.08e-01 2.45e+00 1.53e-01 1.09e+00
t_stage2:5 2.74e-01 3.65e+00 1.05e-01 7.14e-01
t_stage2:6 8.58e-01 1.17e+00 2.80e-01 2.63e+00
n_stage2:2 1.01e+00 9.91e-01 4.70e-01 2.16e+00
n_stage2:3 6.97e-01 1.44e+00 2.74e-01 1.77e+00
n_stage2:4 1.26e+00 7.95e-01 5.30e-01 2.98e+00
n_stage2:5 3.22e+00 3.10e-01 1.01e+00 1.02e+01
ssct2:0 3.74e+00 2.68e-01 1.75e+00 7.96e+00
ssct2:1 2.92e+00 3.42e-01 1.36e+00 6.27e+00
ssct2:3 9.93e-01 1.01e+00 6.49e-01 1.52e+00
I know it's wrong. What's the perfect solution? Is there anything wrong with the original data?
r
r
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
asked Mar 27 at 17:28
Lin CaijinLin Caijin
13 bronze badges
13 bronze badges
add a comment |
add a comment |
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