# Distribution Fitting

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## Distribution Fitting

 Dear All, I am experiencing some problems in fitting a trimodal distribution (which should be thought as a sum of three Gaussian distributions) using the nls function for nonlinear fittings. As a test, just consider the very simple code: rm(list=ls()); mydata<-rnorm(10000,0,4); mydens<-density(mydata,kernel="gaussian"); y1<-mydens\$y; x1<-mydens\$x; myfit<-nls(y1~A*exp(-x1^2/sig)); which I use to get the empirical density (as I would from real experimental data) and test it against a Gaussian ansatz. Well, either R always crashes for a segmentation fault and I have to restart it manually or I get this output: Error in match.call(definition, call, expand.dots) : '.Primitivï¿½iï¿½dï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½...' is not a function Am I missing the obvious or is there some bug in my R build? Many thanks Lorenzo         [[alternative HTML version deleted]] _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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## Re: Distribution Fitting

 On 24 April 2006 at 00:10, Lorenzo Isella wrote: |  Dear All, | I am experiencing some problems in fitting a trimodal distribution (which | should be thought as a sum of three Gaussian distributions) using the nls | function for nonlinear fittings. | As a test, just consider the very simple code: | | rm(list=ls()); | mydata<-rnorm(10000,0,4); | mydens<-density(mydata,kernel="gaussian"); | y1<-mydens\$y; | x1<-mydens\$x; | myfit<-nls(y1~A*exp(-x1^2/sig)); | | | which I use to get the empirical density (as I would from real experimental | data) and test it against a Gaussian ansatz. | Well, either R always crashes for a segmentation fault and I have to restart | it manually or I get this output: | | Error in match.call(definition, call, expand.dots) : | '.Primitivï¿½iï¿½dï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½...' is not a function | | Am I missing the obvious or is there some bug in my R build? Are you aware of fitdistr() in MASS? > library(MASS) > mydata<-rnorm(10000,0,4) > fitdistr(mydata, "normal")         mean                  sd               -0.0055755191185632    3.9956609772436904  ( 0.0399566097724369) ( 0.0282535897233148) > fitdistr() fits a bunch of distribution. I can't recall whether I used this, or one of the mixed distribution packages from CRAN when I was doing some work on mixtures for fat tails. Dirk -- Hell, there are no rules here - we're trying to accomplish something.                                                   -- Thomas A. Edison _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance