nls

Say I some data, d , and I fit nls models to two subsets of the data. x<- seq(0,4,0.1) y1<- (x*2 / (0.2 + x)) y1<- y1+rnorm(length(y1),0,0.2) y2<- (x*3 / (0.2 + x)) y2<- y2+rnorm(length(y2),0,0.4) d<-data.frame(x,y1,y2) m.y1<-nls(y1~v*x/(k+x),start=list(v=1.9,k=0.19),data=d) m.y2<-nls(y2~v*x/(k+x),start=list(v=2.9,k=0.19),data=d) I then want to plot the fitted model regression line over data, and shade the prediction interval. I can do this with the package investr and get nice plots for each subset individually: require(investr) plotFit(m.y1,interval="prediction",ylim=c(0,3.5),pch=19,col.pred

I'm trying to plot 3 non-linear models in ggplot2. It's working in automatic scale but not in log10 scale where I get the "singular gradient error". What could be the problem(s)? The data I'm trying to fit (df1): x y 4.17 0.55 10.08 0.48 40.25 0.38 101.17 0.32 400.33 0.24 The code I tried: plot <- ggplot(df1, aes(x=x, y=y))+ stat_smooth(method="nls", formula=y~I(a*x^(-n)), data=df1, start=list(a=1,n=1), se=FALSE, colour="red")+ stat_smooth(method="nls", formula=y~m*(x+m^(1/n))^(-n), data=df1, start=list(m=0.7, n=0.17), se=FALSE, colour="blue")+ stat_smooth(method="nls", formula=y~m*(x+m^(1/n))

I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from ‘nls’): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of ‘nls’ one can see that ‘nls’ generates a vector wts <- rep(1, n) . Now for a weighted fit: fm2DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1, weights = rep(1:8, each = 2)) in which I assign increasing weights for each of the 8 concentrations with 2 replicates. Now with deviance I

I am trying to fit some data to a function which has validity limit in them. More precisely a function with different value if t<=T and t>T. Here is the code I have tried: posExpDecay <- function(t,tau,max,toff){ 1+max*(1-exp(-(t-toff)/tau)) } negExpDecay <- function(t,tau,max){ 1+max*exp(-(t)/tau) } data<-structure(list(t = c(0.67, 1, 1.33, 1.67, 2, 4, 6, 8, 10), y = c(1.02,2.33, 3.08, 3.34, 3.41,2.50, 1.86, 1.44, 1.22)), .Names = c("t", "y"), row.names = c(13L, 17L, 21L, 25L, 29L,37L, 45L, 49L, 53L), class = "data.frame") fit <- nls(y~ifelse(t<=tswitch, posExpDecay(t,tau1,max1,toff),