similar to: survreg & gompertz

Dear list: I am attempting to use what I thought would be a pretty straightforward practical application of Cox regression. I figure users of the survival package must have come across this problem before, so I would like to ask you how you dealt with it. I have set up an illustrative example and included it at the end of this post. I took a sample of 100 data points from each of two populations

Hello, all. I have co-opted a number of functions that can be used to plot the hazard/survival functions and associated density distribution for a Gompertz mortality model, given known parameters. The Gompertz hazard model has been shown to fit relatively well to the human adult lifespan. For example, if I wanted to plot the hazard (i.e., mortality) functions: pop1 <- function (t) {

Hi, all. I'm working with published paleodemographic data (counts of skeletons that have been assigned into an age-range category, based upon observed morphological characteristics). For example, the following is the age distribution from a prehistoric cemetery in Egypt: naga <-

Dear All, I would like to fit some parametric survival models using left truncated, right censored data in R. However I am having problems finding a function to fit parametric survival models which can handle left truncated data. I have tested both the survreg function in package survival: fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1) and the psm function in package

Dear all! I tried to fit Gompertz growth models to describe cummulative germination rates using nls. I used the following code: germ.model<-nls(percent.germ~a*exp(-b*exp(-k*day)),data=tab,start=list(a=100,b=10,k=0.5)) My problem is that I want that the fitted model goes through the origin, since germination cannot start before the experiment was started, and y-max should be 100. Does anyone

Hello. I am trying to write an R function to plot the survival function (and associated hazard and density) for a Siler competing hazards model. This model is similar to the Gompertz-Makeham, with the addition of a juvenile component that includes two parameters---one that describes the initial infant mortality rate, and a negative exponential that describes typical mortality decline over the

I haven't used r in quite a while but would like to get back into it. I have a problem that I would like to solve with r. I have some multinomial data that looks to follow an asymmetric sigmoidal growth pattern. Solving a multinomial gompertz partial differential equation in r is what I’m after. Would anyone be able to provide me the code and packages to do something like this? Regards,

Dear all, I am fitting a parametric regression model to survival data using the flexsurvreg function from the flexsurv package. I am using a Gompertz distribution (a 2-parameter distribution) to describe the hazard function and I want to compare two groups. The model is very simple: flexsurvreg(formula = Surv(day, censored) ~ group, dist = "gompertz") and the output is also quite

List gurus, I'm trying to code a Gompertz growth curve function as part of a larger project and have run across a problem due to my ignorance of environments. Some sample data and the function are as follows: growth <- data.frame(age = c(1.92, 3, 5.83, 3.17, 15.5, 1.17, 5.58, 13.33, 14.29, 5.83, 13.79, 6.33, 13.75, 16.83, 13, 11.67, 0.25, 1.73, 9.46, 5.67), length = c(157, 165, 179,

Hi, r-help members. I have a question about summing two density distributions. I have two samples from which I've estimated hazard parameters for a Gompertz mortality model. With those parameters, I can calculate the PDF (survival function times hazard function) of ages-at-death in a birth cohort subject to the hazard function at each age. I'd like to combine these two density

Dear Sir/ Madam, I'm having trouble de-bugging the following - which works perfectly well with optim or optimx - but not with mle2. I'd be really grateful if someone could show me what is wrong. Many thanks in advance. JSC: gompertz<- function (x,t=data) { a3<-x[1] b3<-x[2] shift<-data[1] h.t<-a3*exp(b3*(t-shift))

Hi- I'm a biologist trying to figure out the growth rate of salamanders in different ponds. I collected individuals from various populations at different dates, and using the size and date collected, I want to figure out the growth curve of each population. My question is: How do I fit my data to a Gompertz function in R? Thank you so much! Sarah -- View this message in context:

I feel like a complete dolt, as I know this question has been asked by others on a fairly regular basis, but I'm going in circles trying to get the following to work: id.prob<-function (tt) { library(mvtnorm) #============================ Makeham<-function(tt) { a2=0.030386513 a3=0.006688287 b3=0.039047537 t<-tt-20 h.t<-a2+a3*exp(b3*t) S.t<-exp(-a2*t+a3/b3*(1-exp(b3*t)))

Dear all, This is a rather lengthy message, but I don't know what I made wrong in my real example since the simple code works. I have two variables a, b and a function f for which I would like to calculate all possible combinations of the values of a and b. If f is multiplication, I would simply do: a <- 1:5 b <- 1:5 outer(a,b) ## A bit more complicated is this: f <-

Dear all, This is a rather lengthy message, but I don't know what I made wrong in my real example since the simple code works. I have two variables a, b and a function f for which I would like to calculate all possible combinations of the values of a and b. If f is multiplication, I would simply do: a <- 1:5 b <- 1:5 outer(a,b) ## A bit more complicated is this: f <-

Hi, I build a graph taking into account the times: 1, 2,4,6,8,10,12,15,18,21,24,28,32 and 48. Be that the scale of the X axis does not look right. It seems equidistant. (graph attached) What changes have I to do in the following commands so that the scale be correct? interaction.plot(Tempo, Trat, Valor, ylim=c(0, 2), las=1, lty=c(1,2,3,4), lwd=3, bty='l',

Hi, Someone released a succefull interconnection in H323 with WTL equipement ? I'm trying to do that with an IPNx. But get dead air. With chan_oh323 it's fine, all works. With chan_h323 => dead air. The configuration is GW to GW. This is my configuration from h323.conf: [general] port=1720 bindaddr=my.ipaddr dtmfmode=rfc2833

I am trying to fit a curve to a cumulative mortality curve (logistic) where y is the cumulative proportion of mortalities, and t is the time in hours (see below). Asym. at 0 and 1 > y [1] 0.00000000 0.04853859 0.08303777 0.15201970 0.40995074 0.46444992 0.62862069 0.95885057 1.00000000 [10] 1.00000000 1.00000000 > t [1] 0 13 20 24 37 42 48 61 72 86 90 I tried to find starting values for

Dear R-users, I would like to show you a simple example that gives an overview of one of my current issue. Although my working setting implies a different parametric model (which cannot be framed in the glm), I guess that what I'll get from the following example it would help for the next steps. Anyway here it is. Firstly I simulated from a series of exposures, a series of deaths (given a

Hello, I want to fit a Gompertz model for tree diameter growth that depends on a 4 levels edaphic factor (?Drain?) and I don?t manage to introduce the factor variable in the formula. Dinc is the annual diameter increment and D is the Diameter. >treestab > Dinc D Drain [1,] 0.03 26.10 2 [2,] 0.04 13.05 1 [3,] 0.00 24.83 1 [4,] 0.00 15.92 4