Displaying 5 results from an estimated 5 matches for "recidiv".
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recidive
2008 Dec 28
1
cox regression warning/error messages
...d rather than the R generated dummy variables. Is it
the case that plots should be run for each dummy variable, or is it
correct simply to run the analysis on the original variable?
The code employed to generate the analysis follows:
Any assistance is appreciated,
Bob
cox.NV <- coxph(Surv(recidivism$intDaysUntilFNVPO,
recidivism$Event_nv) ~ recidivism$strGender +
recidivism$intAgeAtMHCIndex + recidivism$PRE.nv + group +
recidivism$MHC.nv + recidivism$SNFP , data = recidivism)
> summary(cox.NV)
Call:
coxph(formula = Surv(recidivism$intDaysUntilFNVPO, recidivism$Event_nv) ~
recid...
2008 Nov 29
0
Error in check(itp) : object does not represent a K sample problem with censored data
Hello,
I have two questions regarding a survival analysis I have been
working on. Below is the code to date.
The variables:
1) recidivism$intDaysUntilFVPO are the number of days before an
violent offence was committed - if no offence was committed than the
days between court hearing and end of data collection was recorded.
2) recidivism$intDaysUntilFNVPO are the number of days before a
nonviolent offence was committed - if no...
2012 Nov 29
5
bootstrapped cox regression (rms package)
...ome success with the bootcov function in the rms
package, which at least generates confidence intervals similar to what is
observed in SPSS. However, the p-values associated with each predictor in
the model are not really close in many instances.
Here is the code I am using:
formula=Surv(months, recidivate) ~ fac1 + fac2 + fac3 + fac4 + fac5 + fac6
+ fac7 + fac8
fit=cph(formula, data=temp, x=T, y=T)
validate(fit, method="boot", B=9999, bw=F, type="residual", sls=0.05,
aics=0,force=NULL, estimates=TRUE, pr=FALSE)
out=bootcov(fit, B=9999, pr=F, coef.reps=T, loglik=F)
for (i in 1:...
2012 Nov 29
0
bootstrapped cox regression in rms package (non html!)
...ome success with the bootcov function in
the rms package, which at least generates confidence intervals similar
to what is observed in SPSS. However, the p-values associated with
each predictor in the model are not really close in many instances.
Here is the code I am using:
formula=Surv(months, recidivate) ~ fac1 + fac2 + fac3 + fac4 + fac5 +
fac6 + fac7 + fac8
fit=cph(formula, data=temp, x=T, y=T)
validate(fit, method="boot", B=9999, bw=F, type="residual", sls=0.05,
aics=0,force=NULL, estimates=TRUE, pr=FALSE)
out=bootcov(fit, B=9999, pr=F, coef.reps=T, loglik=F)
for (i in 1:...