similar to: significant difference between Gompertz hazard parameters?

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

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 <-

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

Hi, all. I've written a function that returns the survival function for a Gompertz mortality model. I've specified the two model parameters. Using a simple integration, I can calculate the life expectancy at any age. Is there a way I can use a loop with the integration that will quickly return life expectancy over a range of ages, say 0 to 80, so that I don't have to manually type

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

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

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,

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,

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 want to make a barplot with the following datasets: I have a file as following: name chr position A1 A2 pop1 pop1 pop2 pop2 I have calculated a measure using all the values in columns "pops", the values are saved in a vector. Now I want to make a barplot using the values in this vector as the y axis and the values in the column "position" in the x-axis. thank you

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:

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

Hello, I'm trying to build a frequency table for a vector, broken down by the combination of factors. For further analyses, I need to have the result arranged in a new data frame, with the upper limit of the histogram's breaks, the per bin count, and the factors identifying each record. My problem is including the latter: ---<---------------cut

summary: Specifically, how does one do stack/FIFO operations in R? Generally, how does one code functions with side effects in R? details: I have been a coder for years, mostly using C-like semantics (e.g., Java). I am now trying to become a scientist, and to use R, but I don't yet have the sense of "good R" and R idiom (i.e., expressions that are to R what (e.g.) the Schwartzian

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 All, I am in the annoying position of having to present some data to someone who seems to be somewhat less than numerate. I need to label the y-axes of a multhist with the y-axis labeled not as counts but as percentage of a population. Plotting the standard histogram is in a way fine, all I need is to: -- have a left-handside y-axis labels for pop 1 and a right-handside y-axis labels for pop2

Dear R user: I am studying the allele data of two populations. the following is the data: a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 pop1 0.0217 0.0000 0.0109 0.0435 0.0435 0.0000 0.0109 0.0543 0.1739 0.0761 0.1413 0.1522 0.1087 0.0870 0.0435 0.0217 0.0109 pop2 0.0213 0.0213 0.0000 0.0000 0.0000 0.0426 0.1702 0.2128 0.1596 0.1809 0.0957 0.0745 0.0106

Hi, I'm doing a function that describe two populations in competition. that's the function that i wrote: exclusao<-function(n10, n20, k1, k2, alfa, beta, t){ n1<-k1-(alfa*n20) n2<-k2-(beta*n10) if(t==0){plot(t, n10, type='b', xlim=range(c(1:t),c (1:t)), ylim=range(n10, n20), xlab='tempo', ylab='tamanho populacional') points(t, n20, type='b',

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