Displaying 20 results from an estimated 100 matches similar to: "the problem about sample size"
2012 Oct 05
0
jointModel error messages
I contacted the package developer and that lead to me removing events at time
0 (or subjects with only 1 longitudinal measurement). I then still had the
error message "Can't fit a Cox model with 0 failures" which I have managed
to avoid by adding 1.8*10^(-15) to all my survival times, any number greater
than this also works but nothing smaller! Any explanation of this would
help!
2005 Dec 12
2
convergence error (lme) which depends on the version of nlme (?)
Dear list members,
the following hlm was constructed:
hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I)
the grouped data object is located at and can be downloaded:
www.anicca-vijja.de/lg/hlm_example.Rdata
The following works:
library(nlme)
summary( fitlme <- lme(hlm) )
with output:
...
AIC BIC logLik
425.3768 465.6087 -197.6884
Random effects:
2006 Sep 03
2
Running cox models
Hi,
I'm reading van Belle et al "Biostatistics" and trying to run a cox test using
a dataset from:
http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html
(Primary Biliary Cirrhosis data link at top of the page),
I'm using the following code:
--------------- start of code
library(survival)
liver <-
2007 Nov 07
1
Aggregate with non-scalar function
R-Helpers,
I'm sorry to have to ask this -- I've not used R very much in the last
8 or 10 months, and I've gotten rusty.
I have the following (ff2 is a subset of a much, much larger dataset):
> ff2
hostName user sys idle obsTime
10142 fred 0.4 0.5 98.0 2007-11-01 02:02:18
16886 barney 0.5 0.2 94.6 2007-10-25 19:12:12
8795 fred 0.0 0.1 99.8
2006 Jan 11
3
how to obtain "par(ask=TRUE)" with trellis-plots
Dear alltogether,
how can a delay like possible with par(ask=TRUE) be attained while using
trellis-plots within a loop or something like that?
the following draws each plot without waiting for a signal
(mouse-klick), so par() does not work for that:
library(nlme)
for(i in 1:3)
{
fitlme <- lme(Orthodont)
par(ask=TRUE) # does not work with trellis....
print(
2011 Nov 22
0
plotting output from LME with natural cubic spline
I have used LME to fit a mixed effects model on my data. The data has
274 subjects with 1 to 6 observations per subject. Time is not linearly
associated with the outcome, so I used ns to fit a natural cubic spline
with 3 auto knots. Subject and the natural cubic time of spline are both
treated as random effects. This model has run without any problem, but
now I would like to plot trajectories for
2003 Mar 12
1
simulating 'non-standard' survival data
Dear all,
I'm looking for someone that help me to write an R function to simulate
survival data under complex situations, namely time-varying hazard ratio,
marginal distribution of survival times and covariates. The algorithm is
described in the reference below and it should be not very difficult to
implement it. However I tried but without success....;-(
Below there the code that I used; it
2018 Mar 15
0
jointModel error messages
Dear Graham.
Any updates regarding your message about JointModel error messages? I am encountering similar errors.
Thank you.
Havi
[[alternative HTML version deleted]]
2006 Nov 09
1
Extracting the full coefficient matrix from a gls summary?
Hi,
I am trying to extract the coefficients matrix from a gls summary.
Contrary to the lm function, the command fit$coefficients returns
only the estimates of the model, not the whole matrix including the
std errors, the t and the p values.
example:
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <-
2006 Sep 08
4
Any Rails Developers in Montana?
Any Rails Developers in Montana? Especially Helena or Bozeman?
I don''t expect there''s enough of us to form a user group, but it might
be nice to talk shop ''offline'' every once and a while.
Eric
(in Helena)
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2010 Mar 11
2
about IRT simulation
hello R:
we have a two-parameter IRT simulation code. The goal is to generate a
response matrix.But the "for" part doesn't run. we don't know what is wrong
with it.
Thanks so much~~~
I <- 10
J <- 5
response <- matrix(0, 10, 5)
pij <- function(a,b,theta)
{
a <- rnorm(J, 0.8, 0.04)
a
b <- rnorm(J, 0, 1)
b
theta <- rnorm(I, 0,1)
theta
for( i in 1:I ) {
for(
2010 Dec 15
0
package JM -- version 0.8-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint modeling of longitudinal and
time-to-event data using shared parameter models. These models are
applicable in mainly two settings. First, when focus is in the survival
outcome and we wish to account for the effect of a time-dependent
covariate measured with error.
2010 Dec 15
0
package JM -- version 0.8-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint modeling of longitudinal and
time-to-event data using shared parameter models. These models are
applicable in mainly two settings. First, when focus is in the survival
outcome and we wish to account for the effect of a time-dependent
covariate measured with error.
2008 Aug 22
0
Censored Poisson Data
Dear list,
I am wondering whether R allows to perform a Poisson regression on
counting data which include censored observations, that is, observations of the form
"2 events or more" or "less than 2 events" or even "1 or 2 events".
Can anyone give me a hint whether this is already possible and how to do it?
Data of the form (obstime is the observation time,
2007 Sep 12
0
constructing an lm() formula in a function
I'm working on some functions for generalized canonical discriminant
analysis in conjunction with the heplots package. I've written a
candisc.mlm function that takes an mlm object and computes a
candisc object containing canonical scores, coeficients, etc.
But I'm stumped on how to construct a mlm for the canonical scores,
in a function using the *same* right-hand-side of the model
2006 Aug 08
1
Fitting data with optim or nls--different time scales
Hi,
I have a system of ODE's I can solve with lsoda.
Model=function(t,x,parms)
{
#parameter definitions
lambda=parms[1]; beta=parms[2];
d = parms[3]; delta = parms[4];
p=parms[5]; c=parms[6]
xdot[1] = lambda - (d*x[1])- (beta*x[3]*x[1])
xdot[2] = (beta*x[3]*x[1]) - (delta*x[2])
xdot[3] = (p*x[2]) - (c*x[3])
return(list(xdot))
}
I want
2008 Feb 20
0
New Package 'JM' for the Joint Modelling of Longitudinal and Survival Data
Dear R-users,
I'd like to announce the release of the new package JM (JM_0.1-0
available from CRAN) for the joint modelling of longitudinal and
time-to-event data.
The package has a single model-fitting function called jointModel(),
which accepts as main arguments a linear mixed effects object fit
returned by function lme() of package nlme, and a survival object fit
returned by either
2008 Feb 20
0
New Package 'JM' for the Joint Modelling of Longitudinal and Survival Data
Dear R-users,
I'd like to announce the release of the new package JM (JM_0.1-0
available from CRAN) for the joint modelling of longitudinal and
time-to-event data.
The package has a single model-fitting function called jointModel(),
which accepts as main arguments a linear mixed effects object fit
returned by function lme() of package nlme, and a survival object fit
returned by either
2011 Sep 28
0
package JM -- version 0.9-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint modeling of longitudinal and
time-to-event data using shared parameter models. These models are
applicable in mainly two settings. First, when focus is in the survival
outcome and we wish to account for the effect of an endogenous (aka
internal) time-dependent
2011 Sep 28
0
package JM -- version 0.9-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint modeling of longitudinal and
time-to-event data using shared parameter models. These models are
applicable in mainly two settings. First, when focus is in the survival
outcome and we wish to account for the effect of an endogenous (aka
internal) time-dependent