similar to: nonlinear regression-getting the explained variation

Hello I?m need fitt growth curve with data length-age. I want to evaluate which is the function that best predicts my data, to do so I compare the Akaikes of different models. I'm now need to evaluate if changing the initial values changes the parameters and which do not allow to estimate the model. To do this I use the function nls(); and I randomize the initial values (real positive number).

Dear list, I have encountered a problem with predict.nls (Windows XP, R.2.5.0), but I am not sure if it is a bug... On the nls man page, an example is: DNase1 <- subset(DNase, Run == 1) fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), data = DNase1, start = list(xmid = 0, scal = 1)) alg = "plinear", trace =

Hello list, I'm trying to plot nls profiles, but the plot.profile.nls function in R doesn't seem to accept any plot.default variables. Specifically, I'd like to be able to change the x-axis title and the colors to black and white. Has anyone had any luck with this? If not, is there a way to override to plotting colors, perhaps in par()? Thanks, Sam fm1 <- nls(demand ~

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Currently I have a data set looking like: License Species Year HD Quota L.R.QTA L.R.QTA Success Surplus Permit Elk 1999 101 50 87 90 151 10 Permit Deer 1999 101 50 20 10 151 8 Permit Elk 1999 101 50 87

A while ago (April 2002) there was a short thread on software for generalized linear mixed models. Since that time, has anyone written or found R code to use a glmm to analyze binomial data? I study CWD in white-tailed deer, and I'd like to do a similar analysis as Kleinschmidt et al. (2001, Am. J. Epidemiology 153: 1213-1221) used to assess control for spatial structure in malaria

Hi Eric, the following works for me. HTH, Eric library(EGRET) StartDate <- "1990-10-01" EndDate <- "2017-09-30" siteNumber <- "10310000" QParameterCd <- "00060" Daily <- readNWISDaily(siteNumber, QParameterCd, StartDate, EndDate) # Define 'center of mass' function com <- function(x) { match(TRUE, cumsum(x/sum(x)) > 0.5) -

In the following plot, the y label is missing if it is too long. x11(type="Xlib") plot(1:5, 1:5, ylab="Do, a deer, a female deer") # missing label plot(1:5, 1:5, ylab="Do") # label is present All is well for x11(type="cairo") This is true both under R devel 2017-09-01 on xubuntu (my desktop), and 3.4.1 on Centos 6.9 (department

I am trying to find a confidence band for a fitted non-linear curve. I see that the predict.nls function has an interval argument, but a previous post indicates that this argument has not been implemented. Is this still true? I have tried various ways to extract the interval information from the model object without success. My code is: Model.predict <- predict(My.nls.model,

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Hi there: I'm trying to run a large number of nonlinear regressions on a time series dataset, where the data will be formatted something to the effect of: ObservationID,time,dependentvar I'll have a number of time and dependentvars for each observation, and I want to apply a nonlinear regression to one ObservationID at a time, and I want to have a dataset that is the parameter

Hello to all R users, I use R for a year now and am dealing with geometric morphometrics of deer skulls. Yes, I am a biologist and my math skills are just beginning to brush up. To cut to the chase... I have two groups in my data (males and females) and my data is in a simple vector form. Now I need a bootstrap test for this value szc1 <- ((mean(maleCent)-mean(femaCent))^

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

Dear All I am attempting some power analyses, based on simulated data. My experimental set up is thus: Bleach: main effect, three levels (control, med, high), Fixed. Temp: main effect, two levels (cold, hot), Fixed. Main effect interactions, six levels (fixed) For each main-effect combination I have three replicates. Within each replicate I can take varying numbers of measurements (response

Eric B's response provided just the kind of quick & simple solution I was hoping for (appears as the function com below). However, I once again failed to take advantage of the power of R and have reverted back to using a for loop for the next step of the processing. The example below (which requires the library EGRET for pulling an example dataset) works, but probably can be replaced

Dear R users, Plotting question from a R beginner... When I try to plot a response through time, for example: >Date<-c("2006-08-17", "2006-08-18", "2006-08-19", "2006-08-20") >response<-c(4,4,8,12) >as.Date(Date) >plot(Date,response) The dates on the graphic appear in spanish. This I guess is the default way of plotting because my

We've just cut over to a new mail server running Dovecot 1.0.5. The underlying OS is RHEL 5. User mailboxes are stored in Unix mbox format on a local ext3 file system. The MTA on the system is the default RedHat version of Sendmail 8.13.8 with procmail for local delivery. We're using a combination of dotlock and fcntl style locking. The output of "dovecot -n" for this system

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

hello, I am looking for a step() function for GAM's. In the book Statistical Computing by Crawley and a removal of predictors has been done "by hand" model <- gam(y ~s(x1) +s(x2) + s(x3)) summary(model) model2 <- gam(y ~s(x2) + s(x3)) # removal of the unsignificant variable #then comparing these two models if an significant increase occurs. anova(model, model2,

Dear users of the R mailing list, I am a ph.d. student in biology working on red deer in Norway, who would like to conduct an analysis with random factor where the response is binomially distributed. This cannot be conducted in S-plus, and I was told by others that it may be possible in R. However, I soon got into trouble which I hope you can help me to solve. My model is on this form: