similar to: Basic population dynamics

Please pardon the simplicity of this question of biological nature. I'm trying to calculate a statistic, px, the proportion of a cohort that survives through the interval x:x+1. I have the vector from which the calc is to be made but I can't figure out how to tell R to take the current value and divide it by the next value. The formula is P0=L1/LO The following is an example of the

Dear all, I'm trying reproduce an example of bioassays Yielding Concentration-Mortality Data particularly control - adjustment model from book Bioassay of Entomopathogenic Microbes and Nematodes chapter 7 with R. I used glm with family=binomial and link=probit, but I do not know how to implement parameter gamma (control mortality - mortality of the untreated control insect in this exaple)

I'm having trouble figuring out how to format Date variables when used as axis labels in graphs. The particular case here is an attempt to re-create Nightingale's coxcomb graph with ggplot2, where I'd like the months to be labeled as "Mar 1885", "Apr 1885", using a date format of "%b %Y" applied to label the dates, or really anything other than

I have 11 vectors representing insect survival probabilities in response to different levels of toxins at 10 concentrations lx100=c(1,1,1,.8,.5,.4,.2,0) day100=c(0,1,2,3,4,5,6,7,8) lx90=c(1,1,1,1,.9,.8,.6,.4,.2,.1,0) day90=c(0,1,2,3,4,5,6,7,8,9,10) #...and so on10% and a zero (control) series lx0=c(1,1,1,1,1,1,.9,.9,.8,.8,.6,.5,.4,.3,.2,.1,.1,0)

Hi I am trying to carry out some maximum likelihood estimation and I'm not making much headway, and I'm hoping that someone will be able to point me in the right direction. I am modelling mortality statistics. One way to do this is to model the mortality rate (or, more accurately, log of the mortality rate, log_m) as (say) a constant plus a proportion of age, plus time, so: r_1 <-

Hi i am doing an environmental research The equation is as follow: gam(y1 ~ x1 + s(x2) + s(x3) + s(x4), family = gaussian, fit = true) I would like to obtain the beta coefficient and 95CI of x4 (or s(x4)), what should I do? Thanks, Lung -- View this message in context: http://r.789695.n4.nabble.com/mgcv-beta-coefficient-and-95-CI-tp3320491p3320491.html Sent from the R help mailing list

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

e1 <- function(x,b,t){ d<-(x)*(b^t) plot(d) } e1(2, 2,seq(from=0, to=6, by=1)) Is there a way to do this with a change in time. I would like to use differential equations. I am trying to model a population with an initial value, fecundity per time step, and a death rate. The above simply shows an exponential growth rate. I would like to model species response like the famous

I would like to build a model in R to simulate the seed dispersal by one plant. The plant produced 5 seeds and the probability of falling inside the eight closest space was 0.8 and in the next space 0.2 and in the rest space 0: 0 0 0 0 0 0 0.2 0.2 0.2 0.2 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.8 1 0.8 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.2 0.2 0.2 0.2 0 0 0 0 0 0 0

I am interning in a computer science lab and I'm very new to R. The language basics are clear, but this particular problem isn't: I have a very large dataframe called "data" which holds data from Halo matches. I'm trying to analyze a certain window such that data$deaths>20 and data$deaths<=27. When I enter the line kills = data$kills[data$deaths>20] or any single

Dear all, How can I analyze a life table (e.g. for a cohort of insects) in R? I have 20 insects in 200 cages with two different treatments, whose survival is followed over time, such that, e.g., in one treatment, the number of animals surviving is c(20,18,16,12,10,8,4,0), while in the other treatment the survival is c(20,20,18,18,16,15,15,14) at 8 subsequent time intervals. I would very

I am a relative newbie to survival analysis and R in general, but would like to use the coxme package to analyse some data I currently have. The data is relative to survival times of drosophila melanogaster populations to infection with pathogens, and has the variables: Time, Status, Treatment (4 treatments + 2 controls) Population Replicate ?and I'm currently using the following call

I am trying to apply this technique (M.J Anderson 2001) to a dataset of aquatic insect abundances. There is a sample in the unrestored and restored segement of a stream for every time period. I would like to compare the centroids of the distance matrices for the treatments up (unrestored) and dn (restored) to see if there is a difference in insect communities between the treatments. I will not

Hi, I am new to R. I am trying to plot the decay of a population over time (0-50yrs). I have the initial population value (5000) and the mortality rate (0.26/yr) and I can't figure out how to apply this so I get a remaining population value each year. In excel (ignoring headings) I would put 5000 in A1, in B2 I would enter the formula A1*0.26, and then in A2 (the next years population) I

Hello R Community, I have the following design question. I have a data set that looks like this (shortened for the sake of example). Gender Age M 70 F 65 M 70 Each row represents a person with an age/gender combination. We could put this data into a data frame. Now, I would like to do some actuarial analysis on this data set. To do so, I need to create and store

Dear friends: I'm interested to make a stacked plot of cumulative incidence. that's, the cuminc model is fitted [fit=cuminc(time, relapse)] and cumulative incidence is in place. I'd like to stack the cuminc plots (relapse of luekemia and death free from leukemia, for example) , then the constituent ratio of leukemia relapse and treatment related mortality is very clear. Can

Dear List, Is there an automated way to use the survival package to generate survival rate estimates and their standard errors? To be clear, *not *the survivorship estimates (which are cumulative), but the survival *rate * estimates... Thank you in advance for any help. Best, Brian [[alternative HTML version deleted]]

Hello, I'm working on using the LLVM JIT for a little project of mine, amazing work first off! I have a question about optimization passes. I initially have this function I've created, in python it looks like this: OS_end = 50OS_start = 0OS_timestep = 1birth_rate = .3population = 30.0for time in range(OS_start, OS_end, OS_timestep): births = birth_rate * population deaths = 0.1

Hello, I am new to R and have tried to search similar questions but could not find exactly what I am looking for, but I apologize if the question was already asked. I have 10 different treatments and want to know whether they affect the sex ratios of insect emergence. After running the glms I got this table: Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL

Dear list, I am trying to set up a propensity-weighted regression using the survey package. Most of my population is sampled with a sampling probability of one (that is, I have the full population). However, for a subset of the data I have only a 50% sample of the full population. In previous work on the data, I analyzed these data using SAS and STATA. In those packages I used a propensity weight