similar to: Problems running a 4-parameter Weibull function with nls

Hi Keep your messages in the list, you increase your chance to get some answer. I changed your data to groupedData object (see below), but I did not find any problem in it. plot(wlg) gives reasonable picture and I am not such expert to see any problem with data. Seems to me, that something has to be wrong with nlsList function. > wheat.list <- nlsList(Prop ~ SSlogis(end,Asym, xmid,

Hi all, I am trying to use nonlinear regression (nls) to analyze some seed germination data, but am having problems with error codes. The data that I have closely matches the germination dataset included in the drc package. Here is the head of the data temp species start end germinated TotSeeds TotGerminated Prop 1 10 wheat 0 1 0 20 0 0.0 2 10 wheat

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

Hi All, I am trying to do a survivorship analysis with library(survival)from a data set that looks like this: I followed a bunch of naturally germinated seedlings of an annual plant from germination to death (none made it to reproduce, and died in a period of ~60 days after germination.) I also know the size of the seed of every individual censused. So I am trying to analyze seedling survival as

Does someone out there have an example of R-code for a split-split plot ANOVA using aov or another function? The design is not balanced. I never set up one in R before and it would be nice to see an example before I tackle a very complex design I have to model. Thanks, Mike Mike Saunders Research Assistant Forest Ecosystem Research Program Department of Forest Ecosystem Sciences University of

Hi R-people, I performed controlled experiments to evaluated the seeds germination of two palms under four levels of water treatments. I conducted a generalized linear model (GLM) with a Poisson distribution to verify whether there were significant differences in the number of seed germination (NS-count variable) between treatments and species (explanatory variables). Thus, my model and output

Hi there,? I've been battling with an extension of this in my own data: getting appropriate error bars once data is stacked in a bar graph.? (original question: http://r.789695.n4.nabble.com/ggplot2-se-variable-in-geom-errorbar-s-limits- td3311176.html). It wouldn't let me reply to that thread. A modification of the earlier answer:? data(diamonds)? ?diamonds_df <- ddply(diamonds,

Hi, I have a data set on germination and plant growth with the following variables: dataset=fm mass (response) sub (fixed effect) moist (fixed effect) pop (fixed effect) mum (random effect nested within population) iheight (covariate) plot (random effect- whole plot factor for split-plot design). I want to see if moist or sub interacts with mum for any of the pops, but I am getting an error

Does anyone have advice on how to instruct R CMD check to use a non-standard set of libraries? Here's the situation: I'm trying to do some automated checking on package dependencies of a package I maintain. In order to do that I've written code that takes the list of the dependent packages and for each package (1) downloads the most recent/available .tar.gz file; (2) installs the

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Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Hello, I'm trying to adjust a non linear model in which the biological response variable (ratio of germinated fungus spores) is dependent on 2 covariates (temperature and time). The response to temperature is modeled by a kind of beta function with 2 parameters (optimal and maximum temperatures) and the time function is a 2-parameter Weibull. Adjustments with nls or gnls work, but I need to

I'm getting errors when running what seems to be a simple Weibull distribution function: This works: x <- c(23,19,37,38,40,36,172,48,113,90,54,104,90,54,157,51,77,78,144,34,29,45,16,15,37,218,170,44,121) rate <- c(.01,.02,.04,.05,.1,.2,.3,.4,.5,.8,.9) year <- c(100,50,25,20,10,5,3.3,2.5,2,1.2,1.1) library(MASS) x <- sort(x) tryCatch( f<-fitdistr(x, 'weibull'), error

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

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 What is the appropriate method to test if a given distribution is a weibull thank you meriema email meriema.belaidouni at int-evry.fr

Hello, is there a quick way of estimating Weibull parameters for some data points that are assumed to be Weibull-distributed? I guess I'm just too lazy to set up a Maximum-Likelihood estimation... ...but maybe there is a simpler way? Thanks for any hint (and yes, I've read help(Weibull) ;) Kaspar Pflugshaupt -- Kaspar Pflugshaupt Geobotanical Institute ETH Zurich, Switzerland

Dear list, dear Frank, I try to fit a Weibull survival regression model with package Design: sclear <- psm(sobj~V1+V2,dist="weibull") sobj is a one-dimensional survival object (no event indicators), V1 and V2 are factors. I get the following result: Parametric Survival Model: Weibull Distribution psm(formula = sobj ~ V1 + V2, dist = "weibull") Obs Events

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.

Hello, How can I do a test of two weibull distributions? I have two weibull distribution sets from two wind datasets in order to check whether they are same. I thought 2 sample t-test would be applicable but I couldn't find any ways to do that on the Internet. Does anyone know what type of test is applicable to my purpose? and what R function can you recommend? Plus, if it turned out that