Displaying 20 results from an estimated 500 matches similar to: "survreg with measurement uncertainties"
2009 Apr 20
4
graph with 15 combinations
Dear R helpers,
I have a data set with 4 types (W, C, E & S). Now I have values for all
types plus all possible combinations (the order is unimportant): W, C,
WC, E, WE, CE, WCE, S, WS, CS, WCS, ES, WES, CES & WCES. Ideally I would
like to represent everything in one graph and as concise as possible.
Drawing 4 circles and depicting it as overlap just gives me 13 out of
the 15
2011 Mar 07
1
null model for a single species?
Dear List members,
I would like to test whether an observed occupancy of lakes in a landscape has occurred randomly (by chance) or not.
How can I do that? The problem is that it concerns only a single species and I would like to use binary data only.
At first I thought of generating null models and test the observed occupancy against the randomly generated one. However, this needs more than one
2009 Jun 11
3
ctc package
Dear R-helpers,
I would like to export hierarchical clusters
e.g. fit <- hclust(d, method="ward") in the newick format
write(hc2Newick(fit),file='hclust.newick')
Searching for a possibility I found this solution in the package ctc
(http://www.bioconductor.org/packages/bioc/html/ctc.html). However,
neither from this site nor via CRAN it is possible to install the
package.
2008 Apr 25
3
Use of survreg.distributions
Dear R-user:
I am using survreg(Surv()) for fitting a Tobit model of left-censored longitudinal data. For logarithmic transformation of y data, I am trying use survreg.distributions in the following way:
tfit=survreg(Surv(y, y>=-5, type="left")~x + cluster(id), dist="gaussian", data=y.data, scale=0, weights=w)
my.gaussian<-survreg.distributions$gaussian
2005 May 03
2
comparing lm(), survreg( ... , dist="gaussian") and survreg( ... , dist="lognormal")
Dear R-Helpers:
I have tried everything I can think of and hope not to appear too foolish
when my error is pointed out to me.
I have some real data (18 points) that look linear on a log-log plot so I
used them for a comparison of lm() and survreg. There are no suspensions.
survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000,
979000, 17420000, 71065000, 46397000,
2009 Mar 08
2
survreg help in R
Hey all,
I am trying to use the survreg function in R to estimate the mean and
standard deviation to come up with the MLE of alpha and lambda for the
weibull distribution. I am doing the following:
times<-c(10,13,18,19,23,30,36,38,54,56,59,75,93,97,104,107,107,107)
censor<-c(1,0,0,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0)
survreg(Surv(times,censor),dist='weibull')
and I get the following
2011 Jan 10
4
Meaning of pterms in survreg object?
I am trying to model survival data with a Weibull distribution
using survreg. Units are clustered two apiece, sometimes receiving
the same treatment and sometimes opposing treatment.
2009 Nov 13
2
survreg function in survival package
Hi,
Is it normal to get intercept in the list of covariates in the output of survreg function with standard error, z, p.value etc? Does it mean that intercept was fitted with the covariates? Does Value column represent coefficients or some thing else?
Regards,
-------------------------------------------------
tmp = survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian,
2012 Nov 15
2
survreg & gompertz
Hi all,
Sorry if this has been answered already, but I couldn't find it in the
archives or general internet.
Is it possible to implement the gompertz distribution as
survreg.distribution to use with survreg of the survival library?
I haven't found anything and recent attempts from my side weren't
succefull so far.
I know that other packages like 'eha' and
2006 Jan 19
2
Tobit estimation?
Folks,
Based on
http://www.biostat.wustl.edu/archives/html/s-news/1999-06/msg00125.html
I thought I should experiment with using survreg() to estimate tobit
models.
I start by simulating a data frame with 100 observations from a tobit model
> x1 <- runif(100)
> x2 <- runif(100)*3
> ystar <- 2 + 3*x1 - 4*x2 + rnorm(100)*2
> y <- ystar
> censored <- ystar <= 0
2010 Nov 13
2
interpretation of coefficients in survreg AND obtaining the hazard function for an individual given a set of predictors
Dear R help list,
I am modeling some survival data with coxph and survreg (dist='weibull') using
package survival. I have 2 problems:
1) I do not understand how to interpret the regression coefficients in the
survreg output and it is not clear, for me, from ?survreg.objects how to.
Here is an example of the codes that points out my problem:
- data is stc1
- the factor is dichotomous
2005 Jun 09
2
Weibull survival modeling with covariate
I was wondering if someone familiar
with survival analysis can help me with
the following.
I would like to fit a Weibull curve,
that may be dependent on a covariate,
my dataframe "labdata" that has the
fields "cov", "time", and "censor". Do
I do the following?
wieb<-survreg(Surv(labdata$time,
labadata$censor)~labdata$cov,
2004 Feb 02
1
PSM function in Design package (PR#6525)
Full_Name: Oleg Raisky
Version: 1.8.1
OS: Windows 2000
Submission from: (NULL) (63.246.203.107)
This is a completely fresh R install. I'm trying to use Design package. Every
time I run the first example for psm() I'm getting an error <<couldn't find
function "survreg.fit">>. However, survreg.fit does exists in the search path.
Is there something I can do to fix
2006 Jul 07
6
parametric proportional hazard regression
Dear all,
I am trying to find a suitable R-function for
parametric proportional hazard regressions. The
package survival contains the coxph() function which
performs a Cox regression which leaves the base hazard
unspecified, i.e. it is a semi-parametric method. The
package Design contains the function pphsm() which is
good for parametric proportional hazard regressions
when the underlying base
2005 Nov 24
4
Survreg Weibull lambda and p
Hi All,
I have conducted the following survival analysis which appears to be OK
(thanks BRipley for solving my earlier problem).
> surv.mod1 <- survreg( Surv(timep1, relall6)~randgrpc, data=Dataset,
dist="weibull", scale = 1)
> summary(surv.mod1)
Call:
survreg(formula = Surv(timep1, relall6) ~ randgrpc, data = Dataset,
dist = "weibull", scale = 1)
2010 Nov 25
2
aftreg vs survreg loglogistic aft model (different intercept term)
Hi, I'm estimating a loglogistic aft (accelerated failure time) model, just a
simple plain vanilla one (without time dependent covariates), I'm comparing
the results that I obtain between aftreg (eha package) and survreg(surv
package). If I don't use any covariate the results are identical , if I add
covariates all the coefficients are the same until a precision of 10^4 or
10^-5 except
2006 Feb 28
1
ex-Gaussian survival distribution
Dear R-Helpers,
I am hoping to perform survival analyses using the "ex-Gaussian"
distribution.
I understand that the ex-Gaussian is a convolution of exponential and
Gaussian
distributions for survival data.
I checked the "survreg.distributions" help and saw that it is possible to
mix
pre-defined distributions. Am I correct to think that the following code
makes
the
2011 May 14
2
Survreg object
Hi,Just a quick one, does anyone know the command for accessing the standard errors from a survreg object? I can access the coefficients by model$coefficients, but I cant seem to find a command to access the errors. Any help would be greatly appreciated.Regards,Andre
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2010 Nov 15
1
interpretation of coefficients in survreg AND obtaining the hazard function
1. The weibull is the only distribution that can be written in both a
proportional hazazrds for and an accelerated failure time form. Survreg
uses the latter.
In an ACF model, we model the time to failure. Positive coefficients
are good (longer time to death).
In a PH model, we model the death rate. Positive coefficients are
bad (higher death rate).
You are not the first to be confused
2006 Feb 13
2
Survreg(), Surv() and interval-censored data
Can survreg() handle interval-censored data like the documentation
says? I ask because the command:
survreg(Surv(start, stop, event) ~ 1, data = heart)
fails with the error message
Invalid survival type
yet the documentation for Surv() states:
"Presently, the only methods allowing interval censored data are
the parametric models computed by 'survreg'"