search for: ratec

...a R code   ## FUNCTION NO. 3   library(reshape) no_rate = 3   combi_3 = function(n, N, rateA, rate_name1, rateA_rf1, rateA_rf2, rateA_rf3, rateAprob1, rateAprob2, rateAprob3,                            rateB, rate_name2, rateB_rf1, rateB_rf2, rateB_rf3, rateBprob1, rateBprob2, rateBprob3,          rateC, rate_name3, rateC_rf1, rateC_rf2, rateC_rf3, rateCprob1, rateCprob2, rateCprob3)   {   rateA_prob1 = rateAprob3/2 rateA_prob2 = rateAprob2/2 rateA_prob3 = rateAprob1 rateA_prob4 = rateA_prob2 rateA_prob5 = rateA_prob1   rateA_ran1_min = rateA-rateA_rf3     rateA_ran1_max = rateA-rateA_rf2 rateA_r...

...y parfm struggles with low event rates? Or could someone please run my code to see if they get the same issue? Full reproducible code is presented below. Many thanks for any help, Alex CODE: ### Create function to generate data simulWeib <- function(N, lambda, rho, beta1, beta2, beta3, beta4, rateC, sigma) { # covariate --> N Bernoulli trials x1 <- sample(x=c(0, 1), size=N, replace=TRUE, prob=c(0.5, 0.5)) # Now create random effect stuff # Create one vector of length N, all drawn from same normal distribution rand.effect <- rnorm(N,0,sigma) # Weibull latent event times...

...h may be helpful. Reproducible example here (using coxme): setwd("/mnt/ja01-home01/mbrxsap3/phd_risk/R/p4_run_analysis/") library(coxme) library(survival) ### Create data with a group level random effect simulWeib.group <- function(N, lambda, rho, beta1, beta2, beta3, beta4, rateC, sigma, M) { # covariate --> N Bernoulli trials x1 <- sample(x=c(0, 1), size=N, replace=TRUE, prob=c(0.5, 0.5)) x2 <- sample(x=c(0, 1), size=N, replace=TRUE, prob=c(0.5, 0.5)) x3 <- sample(x=c(0, 1), size=N, replace=TRUE, prob=c(0.5, 0.5)) x4 <- sample(x=c(0, 1)...

...have some questions how to apply and interpret the coxph and related functions: I have time-dependent covariates with several measurements per subject with constant delta t. The covariates change in each time step. I fitted the following model: fit <- coxph(Surv(start, stop, event) ~ ratePO + rateC + BLamp + BLP80 + cluster(subjectID), data=dat) and get n= 1081, number of events= 10 coef exp(coef) se(coef) robust se z Pr(>|z|) ratePO -0.50189 0.60539 0.25195 0.17696 -2.836 0.004565 ** BLamp -0.05340 0.94800 0.02877 0.01470 -3.632 0.000281 *** rat...

.../65 ; Primoz exten => 66,hint,SIP/66 ; Tibor exten => 67,hint,SIP/67 ; Gregor exten => 68,hint,SIP/68 ; Bostjan exten => 69,hint,SIP/69 ; Oskar exten => 70,hint,SIP/70 ; Jan exten => 71,hint,SIP/71 ; Sinisa exten => 73,hint,SIP/73 ; Tomaz Doma exten => 78,hint,SIP/78 ; Tomaz Ratece exten => 80,hint,SIP/80 ; Bostjan doma exten => 82,hint,SIP/82 ; Tomaz exten => 92,hint,SIP/92 ; Tomaz exten => 95,hint,SIP/95 ; Test This is it. Kind regards, Jan Prunk -- Jan Prunk <janprunk AT SPAMFREE gmail DOT com> Website: http://www.prunk.si PGP key: 00E80E86 Fingerpri...