similar to: Weibull and survival

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)

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,

Hello, I'm quite new to R and want to make a Weibull-regression with the survival package. I know how to build my "Surv"-object and how to make a standard-weibull regression with "survreg". However, I want to fit a translated or 3-parametric weibull dist to account for a failure-free time. I think I would need a new object in survreg.distributions, but I don't know how

Dear R-users I would like to fit weibull parameters using "Method of moments" in order to provide the inital values of the parameter to de function 'fitdistr' . I don`t have much experience with maths and I don't know how to do it. Can anyone please put me in the rigth direction? Borja [[alternative HTML version deleted]]

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

Dear R experts, How should lambda and gamma (with std.errors) be calculated for a weibull model with age as an independent predictor? I have assumed that this can be done with survreg with e. g. (summary(survreg(Surv(time, status) ~ age, dist = 'weibull')) ) and predict.survreg with e.g. (predict(model, se.fit = T, newdata = data.frame(age = seq(50, 80, 5)) but unfortunately I'm

Hi I am using R to fit a survival function to my data (with a weibull distribution). Data: Survival of individuals in relation to 4 treatments ('a','b','c','g') syntax: ---- > survreg(Surv(date2)~males2, dist='weibull') But I have some problems interpreting the outcome and getting the parameters for each curve. --------- Value Std.

Dear R-users I have some datasets, all left-censoring, and I would like to fit distributions to (weibull,exponential, etc..). I read one solution using the function survreg in the survival package. i.e survreg(Surv(...)~1, dist="weibull") but it returns only the scale parameter. Does anyone know how to successfully fit the exponential, weibull etc... distributions to left-censoring

I read through Harrington and Fleming (1982) but it is beyond my statistical comprehension. I have survival data for insects that have a very finite expiration date. I'm trying to test for differences in survival distributions between different groups. I understand that the medical field is most often dealing with censored data and that survival analysis, at least in the package survival,

Dear R users, Please can you help me with a relatively straightforward problem that I am struggling with? I am simply trying to plot a baseline survivor and hazard function for a simple data set of lung cancer survival where `futime' is follow up time in months and status is 1=dead and 0=alive. Using the survival package: lung.wbs <- survreg( Surv(futime, status)~ 1, data=lung,

I am trying to generate predictions from a weibull survival curve but it seems that the predictions assume that the shape(scale for survfit) parameter is one(Exponential but with a strange rate estimate?). Here is an examle of the problem, the smaller the shape is the worse the discrepancy. ### Set Parameters scale<-10 shape<-.85 ### Find Mean scale*gamma(1 + 1/shape) ### Simulate Data

Hi all I try to make a weibull survival analysis on R. I know make this on GLIM, and now I try to make the GLIM exercice GLEX8 on R to learning and compare the test. The variables are: time censor group bodymass In GLIM I make: $calc %s=1 $ to fit weibull rather than exponential $input %pcl weibull $ $macro model group*bodymass $endmac$ $use weibull t w %s $ Then, GLIM estimate an alpha for the

Hello! I have a matrix with data and a column indicating whether it is censored or not. Is there a way to apply weibull and exponential maximum likelihood estimation directly on the censored data, like in the paper: Backtesting Value-at-Risk: A Duration-Based Approach, P Chrisoffersen and D Pelletier (October 2003) page 8? The problem is that if I type out the code as below the likelihood

Hi all, I'm trying to generate a Weibull distribution including four covariates in the model. Here is the code I used: T = rweibull(200, shape=1.3, scale=0.004*exp(-(-2.5*b1+2.5*b2+0.9*x1-1.3*x2)/1.3)) C = rweibull(n, shape=1.5, scale=0.008) #censoring time time = pmin(T,C) #observed time is min of censored and true event = time==T # set to 1 if event is observed

Hi all, Please help on this. We will be teaching epidemiology using opensource software. What are R built-in functions or functions in available packages that are capable of doing these: a) Logistic regression (glm?) b) Conditional logistic regression c) Logistic regression with random effects d) Beta-binomial regression e) Poisson regression f) Weibull regression (eha?) g) Exponential

OK this is bound to be something silly as I'm completely new to R - having started using it yesterday. However I am already warming to its lack of 'proper' GUI... I like being able to rerun a command by editing one parameter easily... try and do that in a Excel Chart Wizzard! I eventually want to use it to analyse some chemotherapy response / survival data. That data will not be

Dear R-users! I would like to fit exponential, Weibull and log-logistic via glm() like functions. Does anyone know a way to do this? Bellow is a bit longer description of my problem. Hm, could family() be adjusted/improved/added to allow for these distributions? SAS procedure GENMOD alows to specify deviance and variance functions to help in such cases. I have not tried that option and I do not

Hi, I am trying to fit a non-linear model for a parasite dataset. Initially, I tried log-transforming the data and conducting a 2-way ANCOVA, and found that the equal variance of populations and normality assumptions were violated. Gaba et al. (2005) suggests that the Weibull Distribution is best for highly aggregated parasite distributions, and performs better (lower type 1 and 2 error rates)

Dear R users, I'm trying to fit a parametric survival model using the survreg function with a Weibull distribution. I'm studying the time to death of individuals from different families and I would like to fit different shape parameters (ie 1/scale in R) for each of the families. I looked it up in the help pdf and on the internet, but I couldn't find anything. Would it be possible to

Dear all, I'm trying to fit a curve to some 1 year failure-time data, so that I can extrapolate and predict failure rates up to 3 years. The data is in the general form: Treatment Time Status Treatment A 28 0 Treatment B 28 0 Treatment B 28 0 Treatment A 28