Michael: You appear to be laboring under the illusion that a single numeric summary (**any summary**)is a useful measure of model adequacy. It is not; for details about why not, consult any applied statistics text (e.g. on regression) and/or post on a statistics site, like stats.stackexchange.com. Better yet, consult a local statistician.
Incidentally, this is even more the case for NON-linear models. Again, consult appropriate statistical resources. Even googling on "R^2 inadequate for nonlinear models" brought up some interesting resources, among them: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2892436/ Cheers, Bert Bert Gunter "Data is not information. Information is not knowledge. And knowledge is certainly not wisdom." -- Clifford Stoll On Sat, Sep 26, 2015 at 8:56 AM, peter dalgaard <pda...@gmail.com> wrote: > >> On 26 Sep 2015, at 16:46 , Michael Eisenring <michael.eisenr...@gmx.ch> >> wrote: >> >> Dear Peter, >> Thank you for your answer. >> If I look at my summary I see there a Residual standard error: 1394 on 53 >> degrees of freedom. >> This number is very high (the fit of the curve is pretty bad I know but >> still...). Are you sure the residual standard error given in the summary is >> the same as the one described on this page: >> http://onlinestatbook.com/2/regression/accuracy.html > > Sure I'm sure (& I did check!)... But notice that unlike R^2, Residual SE is > not dimensionless. Switch from millimeters to meters in your response measure > and the Residual SE instantly turns into 1.394. It's basically saying that > your model claims to be able to predict Y within +/-2800 units. How good or > bad that is, is your judgment. > >> I am basically just looking for a value that describes the goodness of fit >> for my non-linear regression model. >> >> >> This is probably a pretty obvious question, but I am not a statistician and >> as you said the terminology is sometimes pretty confusing. >> Thanks mike >> >> -----Ursprüngliche Nachricht----- >> Von: peter dalgaard [mailto:pda...@gmail.com] >> Gesendet: Samstag, 26. September 2015 01:43 >> An: Michael Eisenring <michael.eisenr...@gmx.ch> >> Cc: r-help@r-project.org >> Betreff: Re: [R] How to calculate standard error of estimate (S) for my >> non-linear regression model? >> >> This is one area in which terminology in (computational) statistics has gone >> a bit crazy. The thing some call "standard error of estimate" is actually >> the residual standard deviation in the regression model, not to be confused >> with the standard errors that are associated with parameter estimates. In >> summary(nls(...)) (and summary(lm()) for that matter), you'll find it as >> "residual standard error", and even that is a bit of a misnomer. >> >> -pd >> >>> On 26 Sep 2015, at 07:08 , Michael Eisenring <michael.eisenr...@gmx.ch> >> wrote: >>> >>> Hi all, >>> >>> I am looking for something that indicates the goodness of fit for my >>> non linear regression model (since R2 is not very reliable). >>> >>> I read that the standard error of estimate (also known as standard >>> error of the regression) is a good alternative. >>> >>> >>> >>> The standard error of estimate is described on this page (including >>> the >>> formula) http://onlinestatbook.com/2/regression/accuracy.html >>> <https://3c.gmx.net/mail/client/dereferrer?redirectUrl=http%3A%2F%2Fon >>> linest atbook.com%2F2%2Fregression%2Faccuracy.html> >>> >>> Unfortunately however, I have no clue how to programm it in R. Does >>> anyone know and could help me? >>> >>> Thank you very much. >>> >>> >>> >>> I added an example of my model and a dput() of my data >>> >>> #CODE >>> >>> dta<-read.csv("Regression_exp2.csv",header=T, sep = ",") >>> attach(dta) # tells R to do the following analyses on this dataset >>> head(dta) >>> >>> >>> >>> # loading packages: analysis of mixed effect models >>> library(nls2)#model >>> >>> #Aim: fit equation to data: y~yo+a*(1-b^x) : Two parameter exp. single >>> rise to the maximum # y =Gossypol (from my data set) x= Damage_cm >>> (from my data set) #The other 3 parameters are unknown: yo=Intercept, >>> a= assymptote ans b=slope >>> >>> plot(Gossypol~Damage_cm, dta) >>> # Looking at the plot, 0 is a plausible estimate for y0: >>> # a+y0 is the asymptote, so estimate about 4000; # b is between 0 and >>> 1, so estimate .5 dta.nls <- nls(Gossypol~y0+a*(1-b^Damage_cm), dta, >>> start=list(y0=0, a=4000, b=.5)) >>> >>> xval <- seq(0, 10, 0.1) >>> lines(xval, predict(dta.nls, data.frame(Damage_cm=xval))) >>> profile(dta.nls, alpha= .05) >>> >>> >>> summary(dta.nls) >>> >>> >>> >>> >>> >>> >>> >>> #INPUT >>> >>> structure(list(Gossypol = c(948.2418407, 1180.171957, 3589.187889, >>> 450.7205451, 349.0864019, 592.3403778, 723.885643, 2005.919344, >>> 720.9785449, 1247.806111, 1079.846532, 1500.863038, 4198.569251, >>> 3618.448997, 4140.242559, 1036.331811, 1013.807628, 2547.326207, >>> 2508.417927, 2874.651764, 1120.955, 1782.864308, 1517.045807, >>> 2287.228752, 4171.427741, 3130.376482, 1504.491931, 6132.876396, >>> 3350.203452, 5113.942098, 1989.576826, 3470.09352, 4576.787021, >>> 4854.985845, 1414.161257, 2608.716056, 910.8879471, 2228.522959, >>> 2952.931863, 5909.068158, 1247.806111, 6982.035521, 2867.610671, >>> 5629.979049, 6039.995102, 3747.076592, 3743.331903, 4274.324792, >>> 3378.151945, 3736.144027, 5654.858696, 5972.926124, 3723.629772, >>> 3322.115942, 3575.043632, 2818.419785), Treatment = structure(c(5L, >>> 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >>> 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, >>> 3L, 3L, 3L, 2L, 2L, 2L, 4L, 2L, 4L, 4L, 2L, 4L, 2L, 2L, 4L, 4L, 4L, >>> 4L, 4L, 4L, 2L), .Label = c("1c_2d", "1c_7d", "3c_2d", "9c_2d", "C"), >>> class = "factor"), Damage_cm = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, >>> 0.142, 0.4035, 0.4435, 0.491, 0.4955, 0.578, 0.5895, 0.6925, 0.6965, >>> 0.756, 0.8295, 1.0475, 1.313, 1.516, 1.573, 1.62, 1.8115, 1.8185, >>> 1.8595, 1.989, 2.129, 2.171, 2.3035, 2.411, 2.559, 2.966, 2.974, >>> 3.211, 3.2665, 3.474, 3.51, 3.547, 4.023, 4.409, 4.516, 4.7245, 4.809, >>> 4.9835, 5.568, 5.681, 5.683, 7.272, 8.043, 9.437, 9.7455), >>> Damage_groups = c(0.278, 1.616, 2.501, 3.401, 4.577, 5.644, 7.272, >>> 8.043, 9.591, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), >>> Gossypol_Averaged = c(1783.211, 3244.129, 2866.307, 3991.809, >>> 4468.809, 5121.309, 3723.629772, 3322.115942, 3196.731, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Groups = c(42006L, 42038L, >>> 42067L, 42099L, 42130L, 42162L, 42193L, 42225L, 42257L, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, >>> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("Gossypol", >>> "Treatment", "Damage_cm", "Damage_groups", "Gossypol_Averaged", >>> "Groups"), class = "data.frame", row.names = c(NA, -56L)) >>> >>> >>> >>> >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >>> 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. >> >> -- >> Peter Dalgaard, Professor, >> Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 >> Frederiksberg, Denmark >> Phone: (+45)38153501 >> Email: pd....@cbs.dk Priv: pda...@gmail.com >> >> >> >> >> >> >> >> > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Email: pd....@cbs.dk Priv: pda...@gmail.com > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
