search for: plinear

...t;- list(Ca=4, Cb=3, Cc=3, Cd=3) In these idealised circumstances nls converges using the default algorithm. # nls, default algorithm nls(y ~ Ca + Cb*x + Cc*x^Cd, data=aDF, start=startL) Unfortunately, in reality it often fails to converge. This model is linear in a, b and c so I've used the plinear algorithm. # nls, plinear algorithm, explicit RHS nls(y ~ cbind(Ca=1,Cb=x, Cc=x^Cd), data=aDF, start=startL["Cd"], algorithm="plinear") This converges much more often but sometimes crashes with the error message "Error in numericDeriv(form[[3]], names(ind), env) : Missi...

Hi All. I've run into a problem with the plinear algorithm in nls that is confusing me. Assume the following reaction time data over 15 trials for a single unit. Trials are coded from 0-14 so that the intercept represents reaction time in the first trial. trl RT 0 1132.0 1 630.5 2 1371.5 3 704.0 4 488.5 5 575.5...

...and c are variables: y~1/(a*x^2+b*x+c) With the standardalgorithm "Newton-Gauss" the fitted curve contain an peak near the second x,y-point. This peak is not correct for my purpose. The fitted curve should descend from the maximum y to the minimum y given in my data. The algorithm "plinear" give me following error: phi function(x,y) { k.nls<-nls(y~1/(a*(x^2)+b*x+c),start=c(a=0.0005,b=0.02,c=1.5),alg="plinear") coef(k.nls) } phi(k[,1],k[,2]) Error in qr.solve(QR.B, cc) : singular matrix `a' in solve I have found in the mailinglist &quot...

Dear sirs It seems like there is a bug in `profile.nls` with `algorithm = "plinear"` when a matrix is supplied on the right hand side. Here is the bug and a potential fix ##### # example where profile.nls does not work with `plinear` but does with # `default` require(graphics) set.seed(1) DNase1 <- subset(DNase, Run == 1) x <- rnorm(nrow(DNase1)) f1 <- nls(density...

hello all i havnt had a chance to read through the references provided for the "nls" function (since the libraries are closed now). can anyone shed some light on how the "plinear" algorithm works? also, how are the fitted values obtained? also, WHAT DOES THE ".lin" below REPRESENT? thanking you in advance ###################################### i have a quick example: (data below) f1=nls(r~242*(p+exp(-a1*p)/a1-1/a1)*(1-exp(-a2*o))/( (100+exp(-a1*100)/a1-1/...

...n R-devel, not R-patched. a synopsis of the problems with confint(): with a 1-parameter model (is confint not appropriate for 1-parameter models? it doesn't say so in the docs [by the way, "normality" is misspelled as "nornality" in ?confint]): algorithm=default or plinear: get a complaint from qr.qty ('qr' and 'y' must have the same number of rows) port: "cannot allocate vector of size [large]" [caused by C code looking for dims when they aren't there] 2-parameter models: default OK port "cannot allocate vector&quo...

I want to fit a nonlinear model of the form: Y=A+B*X1+C*X2+log(X3/(X3+D)) I think that the best way is to use the plinear algorithm, but I don't know how to specify the formula in the nls function. I've tried: Y~cbind(rep(1,times=length(Y)),X1,X2,log(X3/(X3+D))) But this fits the model: Y=A+B*X1+C*X2+D1*log(X3/(X3+D)) How can I specify the formula correctly? Thanks, M

I was wondering if there is way to place constraints upon the "plinear" algorithm of nls, or rather is there a manner in which this can be achieved because nls does not allow this to be done. I only want to place constraints on one of the nonlinear parameters, a, such that it is between 0 and 1. I have attempted to use a=pnorm(a*) , but then the fitting proced...

Hi everyone, I'm trying to perform a bi exponential Fit with the package NLS. the plinear algorithm seems to be a good choice see: p<-3000 q<-1000 a<--0.03 b<--0.02 t<-seq(0:144);t y<-p*exp(a*t) + q*exp(b*t)+rnorm(t,sd=0.3*(p* exp(a*t) + q*exp(b*t))) fittA <- nls(y~cbind(exp(a*t), exp(b*t)), algorithm="plinear",start=list(a=-.1, b=-0.2), data=list(y=y, t...

...~ (((age-d)^(b-1))/((gamma(b))*(a^b)))* exp(-((age-d)/a))) gamma.asfr1 <- nls(gamma.asfr, data= asfr.aus, start = list(b = 28, a = 1, d= 0.5), trace = TRUE) error: Error in numericDeriv(form[[3L]], names(ind), env) : Missing value or an infinity produced when evaluating the model when I use plinear algoritm, and run this gamma.asfr1 <- nls(gamma.asfr, data= asfr.aus, start = list(b = 28, a = 1, d= 0.5), trace = TRUE, algorithm="plinear") error: number of iterations exceeded maximum of 50 then i fix the iteration and minFactor even then its can't work gamma.asfr1 <- nls...

...n numericDeriv(form[[3]], names(ind), env) : Missing value or an Infinity produced when evaluating the model df <- read.table("data.txt", header=T); df1 = na.omit(df[, 1:2]) library(nls); fm = nls(y ~ (x+d)^(-exp(lb)), data = df1, start=c(lb = 0, d = 0),alg = 'plinear', trace = TRUE); I would be glad if someone can help me. Thanks & Regards, Sai Charan Komanduru >To: Komanduru Sai C <sck2348 at cacs.louisiana.edu> >Cc: r-help at stat.math.ethz.ch >Subject: Re: [R] nls >From: Douglas Bates <bates at stat.wisc.edu> >Date:...

...o some data points, with reasonable starting value estimates (I think). I keep getting "singular matrix 'a' in solve". This is the code: ox <- c(-600,-300,-200,1,100,200) ir <- c(1,2.5,4,9,14,20) model <- nls(ir ~ k*l^(m*ox),start=list(k=10,l=3,m=0.004),algorithm="plinear") summary(model) plot(ox,ir) testox <- seq(-600,200,length=100) k <- 10 l <- 3 m <- 0.004 testir <- k*l^(m*testox) lines(testox,testir) Any thoughts? Thanks! -- This message was sent on behalf of pieterprovoost at gmail.com at openSubscriber.com http://www.opensubscriber.com/m...

...m = 1,to = 12, by = 1) ## Models Bass.Model <- adoption ~ ((p + q)^2/p) * (exp(-(p + q) * time)/((q / p) * exp(-(p + q) * time) + 1)^2) ## Starting Parameters Bass.Params <- list(p = 0.1, q = 0.1) ## Model fitting Bass.Fit <- nls(formula = Bass.Model, start = Bass.Params, algorithm = "plinear", trace = TRUE) Chris Hulme-Lowe University of Minnesota Department of Psychology Quant. Methods and Psychometrics [[alternative HTML version deleted]]

...l,myday) # data object ### fitting model equation in "nls" function ### when i assigned initial value for tt = 0.6,   CASE-I:   > mytest <- nls(rl ~ (c/r)*log(1+exp(r*(myday-tt))), data = mydata, + na.action = na.omit, + start = list(c = 2.0, r = 0.05, tt = 0.6),algorithm = "plinear") Error in numericDeriv(form[[3L]], names(ind), env) :   Missing value or an infinity produced when evaluating the model   CASE - II: When i assigned initial value for tt = 1:   > mytest <- nls(rl ~ (c/r)*log(1+exp(r*(myday-tt))), data = mydata, + na.action = na.omit, + start = list(c...

The documentation for nls says the following about the starting values: start: a named list or named numeric vector of starting estimates. Since R 2.4.0, when 'start' is missing, a very cheap guess for 'start' is tried (if 'algorithm != "plinear"'). It may be a good idea to document that when algorithm = "port", if start is a named list, the elements of the list must be numeric vectors of length 1. Ie, start = list(a=1,b=2,c=3) is ok, but start = list(a=c(1,2), b=3) is not. This is not the case when algorithm is &quot...

...frame(rl,myday) # data object # fitting model equation in "nls" function, when i assigned initial value for tt = 0.6, CASE-I: > mytest <- nls(rl ~ (c/r)*log(1+exp(r*(myday-tt))), data = mydata, + na.action = na.omit, + start = list(c = 2.0, r = 0.05, tt = 0.6),algorithm = "plinear") Error in numericDeriv(form[[3L]], names(ind), env) :   Missing value or an infinity produced when evaluating the model   CASE - II: When i assigned initial value for tt = 1:   > mytest <- nls(rl ~ (c/r)*log(1+exp(r*(myday-tt))), data = mydata, + na.action = na.omit, + start = list(c...

Hi When I type: > ?nls I come across this section: algorithm: character string specifying the algorithm to use. The default algorithm is a Gauss-Newton algorithm. Other possible values are '"plinear"' for the Golub-Pereyra algorithm for partially linear least-squares models and '"port"' for the 'nl2sol' algorithm from the Port package. The simple question is: where's the Port package? I can't find it on cran. Thanks, Jos

...mf<-evalq(mf,data); n<-nrow(mf) mf<-as.list(mf); wts <- if (!mWeights) model.weights(mf) else rep(1, n) if (any(wts < 0 | is.na(wts))) stop("missing or negative weights not allowed") m <- switch(algorithm, plinear = nlsModel.plinear(formula, mf, start, wts), port = nlsModel(formula, mf, start, wts, upper), nlsModel(formula, mf, start, wts)); I am struggling with the environment issues associated with performing these operations. I did not include the data because it is 9000 observations of...

Hello. I am trying to do a non-linear fit using the 'nls' command. The data that I'm using is as follows pH k 1 3.79 34.21 2 4.14 25.85 3 4.38 20.45 4 4.57 15.61 5 4.74 12.42 6 4.92 9.64 7 5.11 7.30 8 5.35 5.15 9 5.67 3.24 with a transformation of pH to H <- 10^-pH When using the nls command for a set of parameters - a, b and c, I receive two sets of errors: >

...elt.prob.kum) ## looks a little like an "S" gamma <- rep(0.5, 10) nls.dataframe <- data.frame(p.kum,felt.prob.kum, gamma) nls.kurve <- nls( formula = felt.prob.kum ~ p.kum^gamma/(p.kum^gamma+(1-p.kum)^gamma)^(1/gamma), data=nls.dataframe, start=c(gamma=gamma), algorithm="plinear" ) summary(nls.kurve) gives: Error in La.chol2inv(x, size) : 'size' cannot exceed nrow(x) = 10 If I go with the Gauss-Newton algorithm I get an singular gradient matrix error, so I tried the Golub-Pereyra algorithm for partially linear least-squares. It also seems the n...