On Fri, Nov 12, 2010 at 5:28 PM, David Winsemius - dwinsem...@comcast.net <+cran+miller_2555+c0e7477398.dwinsemius#comcast....@spamgourmet.com> wrote:
> > On Nov 12, 2010, at 5:07 PM, David Winsemius wrote: > > >> On Nov 12, 2010, at 4:22 PM, cran.30.miller_2...@spamgourmet.com wrote: >> >> Hi - >>> >>> I have a dataframe of (x,y) values. I'd like to fit an exponential >>> curve to the data for further statistical analysis (pretty much the same >>> functionality provided by Excel's LOGEST worksheet array function). Can >>> someone point me to the (set of) functions/ package that is best suited >>> to >>> provide this functionality? Admittedly, I am a novice in the use of R >>> statistical functions, so a brief example of how to compute a correlation >>> coefficient off a fitted exponential curve would be greatly appreciated >>> (though I could probably work through it over time if I knew the proper R >>> tools). >>> >>> >> Probably (not seeing a clear description of the LOGEST function): >> >> ?exp >> ?log >> ?lm >> ?cor >> >> > I set up a OO.org Calc spreadsheet which has a lot of Excel work-alike > functions and does have a LOGEST. Giving an argument of x=1:26 and y=exp(x) > to the first two arguments of LOGEST, I get 1 and e. The OO.org help page > says > "FunctionType (optional). If Function_Type = 0, functions in the form y = > m^x will be calculated. Otherwise, y = b*m^x functions will be calculated." > > This might be the equivalent R operation: > > > x<-1:26 > > y<-exp(x) > > lm(log(y) ~ x) > > Call: > lm(formula = log(y) ~ x) > > Coefficients: > (Intercept) x > 0 1 > > > exp(coef(lm(log(y) ~ x))) > (Intercept) x > 1.000000 2.718282 > > Note this is not a correlation coefficient but rather an (exponentiated) > regression coefficient. > > -- > David Winsemius, MD > West Hartford, CT > > > Thanks, but I'm looking to fit an exponential curve (not a linear model). However, I was able to identify the `nls()` function that works well (adapted from John Fox's contribution "Nonlinear Regression and Nonlinear Least Squares<http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-nonlinear-regression.pdf>" [Jan 2002] ref: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-nonlinear-regression.pdf). For those interested, the following short script highlights my simple test case (though a little sloppy): mydf <- as.data.frame(cbind(1:6,rev(c(48.0000, 24.0000, 12.0000, 6.0000, 3.0000, 1.5000)),rev(c( 51.4943, 12.4048, 12.9587, 3.7707, 2.4253, 2.0400)))); colnames(mydf) <- c("X","Y","Y2"); my.mod <- nls(Y2 ~ a*exp(b*X), data=mydf, start=list(a=3.00,b=2.00), trace=T) plot(mydf[,"X"],residuals(my.mod)) plot(mydf[,"X"],mydf[,"Y2"], lwd=1) lines(mydf[,"X"],fitted.values(my.mod), lwd=2) [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
