similar to: logistic Regression with SSlogis but y == 0 ?!

>>>>> "millanti" == millanti <millanti at libero.it> writes: millanti> Hi, I'm an R user and I have to answer a question millanti> about a problem I'm not able to solve by R code: millanti> the command nlm(....) to fit a non linear model millanti> requires an initial estimation of the model's millanti> unknown

Hello, I am doing several self-starting growth functions for using with nls(). When I list the self-starting functions included in nls library, for instance, SSlogis, there is: > SSlogis function (input, Asym, xmid, scal) ... <environment: 03476D20> attr(,"class") [1] "selfStart" What is this <environment: 03476D20> instruction? By using deriv() and then

Hi, I think this is a problem solved but I would be interested to know if there is some good reason why SSlogis() behaves like this (apologies if this has been noticed before- I'm not confident my archive searches were effective): I have been fitting large numbers of regressions using nls with a self-starting 3 parameter logistic model (SSlogis()). I got a series of unexpected errors of the

Hello, I am trying to fit a nlme (non linear mixed effect). I am using the SelfStart function SSlogis. However the data in my hand contains few observations per subject (4 or less), so the nlsList doesn't work... In this case I should fixe initial parameter estimates. I remark that values of initial estimates have a greater effect on the model fit (i.e. loglikelihood, AIC and also on

Full_Name: Hendrik Weisser Version: 2.6.1 OS: Linux Submission from: (NULL) (139.19.102.218) The following computation never finishes and locks R up: > values <- list(x=10:30, y=c(23.85, 28.805, 28.195, 26.23, 25.005, 20.475, 17.33, 14.97, 11.765, 8.857, 5.3725, 5.16, 4.2105, 2.929, 2.174, 1.25, 1.0255, 0.612, 0.556, 0.4025, 0.173)) > y.max <- max(values$y) > model <- nls(y ~

I'm wondering whether AIC scores extracted from nls() objects using AIC() are based on the correct number of estimated parameters. Using the example under nls() documentation: > data( DNase ) > DNase1 <- DNase[ DNase$Run == 1, ] > ## using a selfStart model > fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) Using AIC() function: >

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),

Hello, I am wondering if someone can help me. I have the following function that I derived using nls() SSlogis. I would like to find its derivative. I thought I had done this using deriv(), but for some reason this isn't working out for me. Here is the function: asym <- 84.951 xmid <- 66.90742 scal <- -6.3 x.seq <- seq(1, 153,, 153) nls.fn <- asym/((1+exp((xmid-x.seq)/scal)))

Hi everyone, Does the selfstart function SSlogis of the "nlme" library allows the introduction of time varying covariates ? For example how can I interpret the xmid parameter (reperesenting the age at which we reach the half of the asymptote) if I want to explain it by a some time varying covariate? Thanks in adavance, Abderrahim Abderrahim Oulhaj, Phd in Statistics Oxford

Thanks Adaikalavan, however the problem remains. Considering AIC() as applied to the linear model in AIC() help documentation: > data(swiss) > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1) [1] 326.0716 Clearly this includes the estimation of the residual standard error as an estimated parameter, as this gives the correct score: > -2*logLik(lm1) + 2*(length(coef(lm1))+1)

Hi R help, I am trying to determine how nls() generates a function based on the self-starting SSlogis and what the formula for the function would be. I've scoured the help site, and other literature to try and figure this out but I still am unsure if I am correct in what I am coming up with. ************************************************************************** dat <-

Peter Dalgaard reminded me to be more specific about my computing platform; it's Debian 4.1.1-19 on a 32-bit Pentium 4 machine (Dell Optiplex GX620). The problem I described (nls not returning) also occurs with different data at other values of the scal parameter. Regards Hendrik Weisser --

Hello, I'm having problems creating and using a selfStart model with nlme. Briefly, I've defined the model, a selfStart object, and then combined them to make a selfStart.default model. If I apply getInitial to the selfStart model, I get results. However, if I try usint it with nls or nlsList, these routines complain about a lack of initial conditions. If someone could point out

Hello, I'm trying to do some non-linear regression with 2 cell types and 4 tissue type treatments using selfStart models Following Ritz and Streibig (2009), I wrote the following routines: ##Selfstart expDecayAndConstantInflowModel <- function(Tb0, time, aL, aN, T0){ exp(-time*aL)*(T0*aL+(-1+exp(time * aL))*Tb0 * aN)/aL } expDecayAndConstantInflowModelInit <- function(mCall, LHS,

Hello, nls library provides 6 self-starting models, among them: SSfp, a four parameters logistic function. Its self-starting procedure involves several steps. One of these steps is: pars <- as.vector(coef(nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xydata, start = list(lscal = 0), algorithm = "plinear"))) which assumes an initial value of lscal equal to 0. If lscal

Hi Everyone, I'd omitted the non-optional 'parameters' argument to selfStart. Making this change to SSbatch gives the same (successful) result from the two calls to nls. SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate coefficients as mean of each batch xy <- sortedXyData(mCall[["Batch"]],

Motivated by the discovery of 'loglet analysis' (http://phe.rockefeller.edu/LogletLab/) that allows one to decompose growth curves into a series of logistic equations, I attempted to do the same thing in R. SIMULATED DATA Time <- 1:200 pop.size <- SSlogis(Time,10,20,5) + SSlogis(Time,20,100,20) + rnorm(length(Time)) MY ANALYSIS results <- nls(size ~ SSlogis(Time, Asym1, xmid1,

Dear list, Apologies, I have sent this message before but received no replies so I'm trying again just in case... Motivated by the discovery of 'loglet analysis' (http://phe.rockefeller.edu/LogletLab/) that allows decomposition of growth curves into a series of logistic equations, I attempted to do the same thing in R. #SIMULATED DATA Time <- 1:200 pop.size <-

Hi All, Another nls related problem (for background, I'm migrating a complicated modelling package from S-plus to R). Below I've reduced this to the minimum necessary to demonstrate my problem (I think); the real situation is more complicated. Two similar selfStart functions, ssA and ssB. The 'initial' function for ssB modifies its arguments a little and then calls getInital

Hi Everyone, I seek guidance to avoid wasting a lot of time and doing things badly. Several times I've solved my problems, only to find that my solutions were clumsy and not robust. (see "nested" getInitial calls; variable scoping problems: Solved?? http://finzi.psych.upenn.edu/R/Rhelp02a/archive/139943.html for one truly horrible approach). I'm sure that I'm not the