Dear R-user:
I have a left censored longitudinally measured data set with 4 variables such as
sub (which is id), x (only covariate), y (repeatedly measured response) and w
(weights) (note, ?-5? indicates the left censored value in the attached data
set). I am using following R codes (?survival? library and ?survreg? package)
for fitting a random effect tobit model for the left censored longitudinal data:
library(splines)
library(survival)
setwd('C:/data')
data=read.table('C:/data/Ruser4897.csv', sep=",")
names(data)=c("sub", "x", "y", "w")
data[1:100,]
x=data[,2]
y=data[,3]
w=data[,4]
survreg(Surv(y, y>=0, type='left')~x, dist='gaussian',
weight=w)
The output is as follows:
Call:
survreg(formula = Surv(y, y >= 0, type = "left") ~ x, weights = w,
dist = "gaussian")
Coefficients:
(Intercept) x
-18.1990038 0.1749655
Scale= 9.831055
Loglik(model)= -23508.9 Loglik(intercept only)= -23947.1
Chisq= 876.48 on 1 degrees of freedom, p= 0
n=4840 (57 observations deleted due to missingness)
I am not seeing any estimated variance component in the output. Could you please
help me in finding the appropriate argument so that I can get the estimated
robust variance component in the output please? FYI, if I put ?sub? using
?cluster(sub)? in the model to get the variance component estimation, then
following error message is giving:
> survreg(Surv(y, y>=0, type='left')~x+cluster(sub),
dist='gaussian', weight=w)
Error in model.frame(formula, rownames, variables, varnames, extras, extranames,
:
invalid type (closure) for variable 'cluster(sub)'
I have another related question please. If it is possible, how may I fit the
marginal (or population average) model for this data either by modifying
following function or any other function?
survreg(Surv(y, y>=0, type='left')~x, dist='gaussian',
weight=w)
Your suggestion or help could save me from breaking up this endeavor. I would
really appreciate you if you could help me in figuring out the error in the
approach.
Sincerely,
Sattar
__________________________________________________
Do You
http://mail.yahoo.com
Dear R-user:
I have a left censored longitudinally measured data set with 4 variables such as
sub (which is id), x (only covariate), y (repeatedly measured outcome variable)
and w (weights) (note, ?-5? indicates the left censored value in the attached
data set). I am using following R codes (?survival? library and ?survreg?
package) for fitting a random effect tobit model for the left censored
longitudinal data:
library(splines)
library(survival)
setwd('C:/data')
data=read.table('C:/data/Ruser4897.csv', sep=",")
names(data)=c("sub", "x", "y", "w")
data[1:100,]
x=data[,2]
y=data[,3]
w=data[,4]
survreg(Surv(y, y>=0, type='left')~x, dist='gaussian',
weight=w)
The output is as follows:
Call:
survreg(formula = Surv(y, y >= 0, type = "left") ~ x, weights = w,
dist = "gaussian")
Coefficients:
(Intercept) x
-18.1990038 0.1749655
Scale= 9.831055
Loglik(model)= -23508.9 Loglik(intercept only)= -23947.1
Chisq= 876.48 on 1 degrees of freedom, p= 0
n=4840 (57 observations deleted due to missingness)
I am not seeing any estimated variance component in the output. Could you please
help me in finding the appropriate argument in survreg function so that I can
get the estimated variance component in the output please? FYI, if I put ?sub?
using ?cluster(sub)? in the model to get the variance component estimation, then
following error message is giving:
> survreg(Surv(y, y>=0, type='left')~x+cluster(sub),
dist='gaussian', weight=w)
Error in model.frame(formula, rownames, variables, varnames, extras, extranames,
:
invalid type (closure) for variable 'cluster(sub)'
I have another related question please. If it is possible, how may I fit the
marginal (or population average) model for this data either by modifying
following function or any other function?
survreg(Surv(y, y>=0, type='left')~x, dist='gaussian',
weight=w)
Your suggestion or help could save me from breaking up this endeavor. I would
really appreciate you if you could help me in figuring out the error in my
coding.
Sincerely,
Sattar
_________________________________________
protection around
http://mail.yahoo.com