search for: linf

...ctions, wich were entered by me. Unfortunately for no it was arrived at the convergence, several error messages appeared. I am going to display the growth functions so as they were entered and a "nlme" call example: # differents growth function #vonBertlalanffy vonBert<- function(x, Linf, K, t0) Linf*(1-exp(-K*(x-t0))) size ~ vonBert(age, Linf, K,t0) vonBert <-deriv(~ Linf*(1-exp(-K*(x-t0))), c("Linf","K","t0"),function(x,Linf,K,t0){}) vonBertInit <- function(mCall, LHS, data) { xy <- sortedXyData(mCall[["x"]], LHS, data...

Hi, I' m having a hard time understanding the computation of degrees of freedom when runing nlme() on the following model: > formula(my data.gd) dLt ~ Lt | ID TasavB<- function(Lt, Linf, K) (K*(Linf-Lt)) my model.nlme <- nlme (dLt ~ TasavB(Lt, Linf, K), data = my data.gd, fixed = list(Linf ~ 1, K ~ 1), start = list(fixed = c(70, 0.4)), na.action= na.include, naPattern = ~!is.na(dLt)) > summary(my model.nlme) Nonlinear mixed-effects model fit by maximum l...

Dear R-list members, Some months ago I wrote a message on the usage of gnls (nlme library) and here I come again. Let me give an example: I have a 10 year length-at-age data set of 10 fishes (see growth.dat at the end of this message) and I want to fit a von Bertalanffy growth model, Li= Linf*(1-exp(-k*(ti-t0))) where Li = length at age i, Linf= asymptotic length, k= curvature parameter, ti= age i, and t0= age of length 0. I'd solved the problem using nls but the points are not independent. The length-at-age data are correlated for each fish because I'm following the growth his...

Dear All., I've created R-code for which a user will be asked to choose between 2 analyses. I've written one function for each type of analysis. Within each function, the users is prompted to enter information. An example is: cat("Enter value for lower Linf :\n") L1<-scan(n=1) cat("Enter value for upper Linf :\n") L2<-scan(n=1) cat("Enter Linf interval :\n") int_L<-scan(n=1) cat("Enter value for lower K :\n") K1<-scan(n=1) cat("Enter value for upper K :\n"...

...attach(BLG) names(BLG) dput(BLG,"") $ Von Bertalanffy Growth Model$ source("http://www.rforge.net/NCStats/InstallNCStats.R") source("http://www.rforge.net/FSA/InstallFSA.R") library(FSA) library(NCStats) library(nlstools) # provide starting values svCom <- list(Linf=199.8132, K=0.5631745, t0=-1.960147) svCom <- vbStarts(Length~Age,data=BLG) # repeat the starting values for each Cohort svGen <- lapply(svCom, rep, length(unique(Cohort))) # define a new variable, uniquely numbered for each Cohort Cohortf <- as.factor(Cohort) # fit a single model with...

I'm having hard time understanding the computation of degrees of freedom when runing nlme () on the following model: > formula(my data.gd) dLt ~ Lt | ID TasavB<- function(Lt, Linf, K) (K*(Linf-Lt)) my model.nlme <- nlme (dLt ~ TasavB(Lt, Linf, K), data = my data.gd, fixed = list(Linf ~ 1, K ~ 1), start = list(fixed = c(70, 0.4)), na.action= na.include, naPattern = ~!is.na(dLt)) > summary(my model.nlme) Nonlinear mixed-effects model fit by maximum l...

...19 obs. of 5 variables: $ age: num 1.27 2.25 2.25 2.25 2.25 ... $ tl : num 226 208 226 226 234 ... $ sex: Factor w/ 2 levels "female","male": 1 1 1 1 1 1 1 1 1 1 ... $ Gf : num 1 1 1 1 1 1 1 1 1 1 ... $ Gm : num 0 0 0 0 0 0 0 0 0 0 ... # starting values > sLinf <- 405 > sK <- 0.11 > sto <- -5.2 > svb <- list(Linf=sLinf,K=sK,to=sto) # Fit the additive error structure model to both groups combined > vbla <- nls(tl~Linf*(1-exp(-K*(age-to))),start=svb,data=fwd) > summary(vbl1) Formula: tl ~ Linf * (1 - exp(-K * (age...

I'm tyring to fit a relatively simple nls model to some data, but keep coming up against the same error (code follows): Oto=nls(Otolith ~ Linf*(1-exp(-k(AGE-to))), data = ages, start = list(Linf=1000, k=0.1, to=0.1), trace = TRUE) The error message I keep getting is "Error in eval(expr, envir, enclos) : could not find function "k"". I've used this line of code for other nls models (with different...

I have data that looks like O.lengthO.age 176 1 179 1 182 1 ... 493 5 494 5 514 5 606 5 462 6 491 6 537 6 553 6 432 7 522 7 625 8 661 8 687 10 704 10 615 12 (truncated) with a simple VonB growth model from within nls(): plot(O.length~O.age, data=OS) Oto = nls(O.length~Linf*(1-exp(-k*(O.age-t0))), data=OS, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) mod <- seq(0, 12) mod=seq(1,12, by=0.001) lines(mod, predict(Oto, list(O.age = mod))) I'm trying to put 95% confidence intervals...

All - I have data: TL age 388 4 418 4 438 4 428 5 539 10 432 4 444 7 421 4 438 4 419 4 463 6 423 4 ... [truncated] and I'm trying to fit a simple Von Bertalanffy growth curve with program: #Creates a Von Bertalanffy growth model VonB=nls(TL~Linf*(1-exp(-k*(age-t0))), data=box5.4, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) #Scatterplot of the data plot(TL~age, data=box5.4, pch=19, xlab="Age (yr)", ylab="Total length (mm)") #Incorporates the model VonB onto the called scatterplot mod1a=seq(2, 11, by=0.001) line...

...growth model into the three new datasets to estimate growth parameters. Since there are now three new datasets I want to get three new estimates of the growth parameters, essentially one from each new dataset. I am using the von Bertalanffy growth model. Here is my code: par<-list(Linf=700, K=0.20, to=-0.1) #these are the starting values for parameters vb<-nls(Length~Linf*(1-exp(-K*(Age-to))), start=par, data=???) #this is the growth model with the parameters estimated with non linear least squares (nls) I tried placing my resampling function the data part of the...

This works fine: fit42<-nls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) But this, identical except using gnls, doesn't converge: fit43<-gnls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs

...rstly: > barplot(rev(sort(table(lin)))) which gives me a Pareto chart which I think is related to the power law distribution. However, I specifically want a log-log chart of the in-degree against frequency with a fitted line whose slope is the exponent. Thus, I devised the following: > linf <- as.data.frame.table((table(lin)),mode="numeric") > linx = as.vector(linf$lin, mode="numeric") > liny = as.vector(linf$Freq, mode="numeric") > plot(linx, liny, pch=3, log="xy",main="Log-log", xlab="Indegree", ylab="Frequ...

Dear Dr. Bates, Thank you very much for your response. I had consulted the algorithm described in Pinheiro and Bates. However, what I don't understand (among other things) is why my two parameters appear to be estimated at different grouping levels (based on the DF values). Affect this different values of DF at the estimates parameters? The estimates fixed effects were get at the same level of

Dear colleagues, Von Bertalanffy model is commonly adjust to data on fish length (TL) and age (AGE) TL= Linf*(1-exp(-K*(AGE-t0)). Linf, K and t0 are parameters of the model. One main goal of the growth study is the comparison of growth parameter estimates between sexes of the same species, or estimates from different populations. The realibility statistical tests normally applied are highly dependent on...

...model to it. Thanks for all your help. Everything worked out well, but I am having a difficult time getting the new parameter values. I bootstrapped the data 300 times and I want to get the 300 sets of parameter estimates and plot them in Excel. Here is my code: par<-list(Linf=700, K=0.3, to=-0.1) #starting values for parameter estimates vb<-nls(length~Linf*(1-exp(-K*(age-to))), start=par, data=small) #von Bertalanffy growth mode boo<- nlsBoot(vb, niter=300) #This bootstraps my data 300 times and...

..., class = "data.frame", row.names = c(NA, -37L)) ? In case it's helpful here is all my code before this point: str(LMB) MMi=as.integer(MM) Yearf=as.factor(Year) Agei=as.integer(Age) ( svCom <- vbStarts(MMi~Agei,data=LMB)) ( svGen <- lapply(svCom,rep,2) ) vbGen <- MMi~Linf[Yearf]*(1-exp(-K[Yearf]*(Age-t0[Yearf]))+error) fitGen <- nls(vbGen,data=LMB,start=svGen) ? Thank you, April

...bles I read in above. For example, 3.5 would be cell (i,j) in the Poultry MEAN density table; 0.108 would be cell (i,j) in the Poultry STDEV table; and 47 the single estimate for cell (i,j) of the Wild bird density table. Poultry <- mcstoc(rnorm, type="U", 3.5, 0.108, rtrunc=TRUE, linf=0) Wild <- mcstoc(rtriang, type="U", min=0, mode=47, max=75) Risk <- Poultry * Wild #this is the risk function the MC is applied to Questions: 1) How can I edit the Poultry and Wild variables above to read the data values directly from the 3 input tables (i.e., replacing 3...

Hi all, I need to calculate a multidimensional integration on R. I am using the command "adapt" (from library adapt), although sometimes I get the following error message: Ifail=2, lenwrk was too small. -- fix adapt() ! Check the returned relerr! in: adapt(3, linf, lsup, functn = Integrando1) I guess it happens because the domain of integration is too small, although I tried a change of variables to avoid this problem and it didn?t help. The command adapt calls a fortran routine, but I don?t know fortran enough to fix the problem. Can someone help me? Thank...

R-help, I 'm trying to optimize a model to data using log-likelihoods but I encounter the following error message: > l= c(49.4, 57.7,64.8,70.9,78.7,86.6,88.3,91.6,99,115) > t=3:12 > fn <- function(params, l=l, t=t) { Linf <- params[1] k <- params[2] t0 <- params[3] sigma <- params[4] lhat <- params[1]*(1-exp(-params[2]*(t-params[3]))) logL <- -sum(dnorm(log(l),log(lhat),sqrt(sigma),TRUE)) return(logL)...