Exponential regression in R -

Exponential regression in R -

i have points logarithmic curve. curve i'm trying obtain like: y = * exp(-b*x) + c

my code:

x <- c(1.564379666,1.924250092,2.041559879,2.198696382,2.541267447,2.666400433,2.922534874,2.965726615,3.009969443,3.248480245,3.32927682,3.371404563,3.423759668,3.713001284,3.841419166,3.847632349,3.947993339,4.024541136,4.030779671,4.118849343,4.154008445,4.284232251,4.491359108,4.585182188,4.643299476,4.643299476,4.643299476,4.684369939,4.84424144,4.867973977,5.144490521,5.324298915,5.324298915,5.988637637,6.146599422,6.674937463) y <- c(25600,23800,11990,14900,15400,19000,9850,7500,10000,12500,11400,8950,10900,3600,11500,9990,4000,3500,4000,3000,8000,5500,6000,7900,2800,2800,2800,2950,4990,4999,3500,6001,6000,1100,1200,6000) df <- data.frame(x, y) m <- nls(y ~ i(a*exp(-b*x)+c), data=df, start=list(a=max(y), b=0, c=10), trace=t)

the output:

error en nlsmodel(formula, mf, start, wts) : singular gradient matrix @ initial parameter estimates

what doing wrong?

i think b = 0, nls can't calculate gradient respect , c since b=0 removes x equation. start different value of b (oops, see comment above). here's example...

m <- nls(y ~ i(a*exp(-b*x)+c), data=df, start=list(a=max(y), b=1, c=10), trace=t) y_est<-predict(m,df$x) plot(x,y) lines(x,y_est) summary(m) formula: y ~ i(a * exp(-b * x) + c) parameters: estimate std. error t value pr(>|t|) 6.519e+04 1.761e+04 3.702 0.000776 *** b 6.646e-01 1.682e-01 3.952 0.000385 *** c 1.896e+03 1.834e+03 1.034 0.308688 --- signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 residual standard error: 2863 on 33 degrees of freedom number of iterations convergence: 5 achieved convergence tolerance: 5.832e-06

r regression exponential-distribution