similar to: survreg with measurement uncertainties

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 [[alternative HTML version deleted]]
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'"