On Tue, 6 Nov 2007, ONKELINX, Thierry wrote: > > Maybe spautolm() rescales the coordinates before calculating the model > parameters. In that case maybe het units mod1$fit are in the new scale > and the units of summary() in the original scale.
No, no rescaling or other tricks. The code is quite clear, the returned matrix is not premultiplied by s^2 - see my other reply. Roger > > HTH, > > Thierry > > ------------------------------------------------------------------------ > ---- > ir. Thierry Onkelinx > Instituut voor natuur- en bosonderzoek / Research Institute for Nature > and Forest > Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, > methodology and quality assurance > Gaverstraat 4 > 9500 Geraardsbergen > Belgium > tel. + 32 54/436 185 > [EMAIL PROTECTED] > www.inbo.be > > Do not put your faith in what statistics say until you have carefully > considered what they do not say. ~William W. Watt > A statistical analysis, properly conducted, is a delicate dissection of > uncertainties, a surgery of suppositions. ~M.J.Moroney > > -----Oorspronkelijk bericht----- > Van: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] Namens Sam Field > Verzonden: maandag 5 november 2007 23:19 > Aan: [EMAIL PROTECTED] > CC: r-sig-geo@stat.math.ethz.ch > Onderwerp: Re: [R-sig-Geo] spautolm - standard errors of regression > paramters > > thanks Roger! > > > In my haste, I mistyped. I meant to write: > > > sqrt(diag(mod1$fit[["imat"]])) > > > which should be equivalent to > > > summary(mod1)$Coef[,2], > > the standard errors of the regression coefficients. > > > In any case, I have managed to replicate the case where the two commands > produce different results. Using the "columbus data"... > > > library(spdep) > data(columbus) > > #defining W > > columbus_poly <- readShapePoly(system.file("etc/shapes/columbus.shp", > package="spdep")[1]) > columbus_nb <- poly2nb(columbus_poly) > columbus_listw <- nb2listw(columbus_nb) > > #running spautolm() > > mod1 <- spautolm(CRIME ~ HOVAL + DISCBD,listw=columbus_listw,data = > columbus,family="SAR") > > > sqrt(diag(mod1$fit[["imat"]])) > > summary(mod1)$Coef[,2] > > > > and the result: > > >> sqrt(diag(mod1$fit[["imat"]])) > (Intercept) HOVAL DISCBD > 0.468345807 0.009013047 0.146973631 > > >> summary(mod1)$Coef[,2] > (Intercept) HOVAL DISCBD > 4.68573639 0.09017431 1.47045128 > > > looks like the decimal place is shifted over one place. If you add more > variables to the model, the results differ by more then a decimal place > in this case (in my case the results are very different). For example, > > > mod2 <- spautolm(CRIME ~ HOVAL + DISCBD + INC + > PLUMB,listw=columbus_listw,data = columbus,family="SAR") > > sqrt(diag(mod2$fit[["imat"]])) > > summary(mod2)$Coef[,2] > > > results in: > >> sqrt(diag(mod2$fit[["imat"]])) > (Intercept) HOVAL DISCBD INC PLUMB > 0.52884967 0.01002545 0.17186710 0.03379109 0.04940024 >> >> summary(mod2)$Coef[,2] > (Intercept) HOVAL DISCBD INC PLUMB > 4.86800598 0.09228322 1.58201870 0.31104348 0.45472408 > > > Initially, I just wanted the standard errors so that I could write them > out in a text file and put them in a table for a MSWord document. > However, I will also need the covariances of the parameters and, thus, > need the off diagonal elements > of the variance covariance matrix. Am I reading this matrix > incorrectly? > > > thanks for all of your help! > > Sam > > > > > > > > > > Quoting Roger Bivand <[EMAIL PROTECTED]>: > >> On Mon, 5 Nov 2007, Sam Field wrote: >> >>> List, >>> >>> I would like to grab the standard errors of the regression >>> parameters from >> an >>> spautolm object. Currently I am using... >>> >>> mod1 <- spautolm(y~var1 + var2,....) >>> >>> mod1_sd <- (diag(mod1$fit[["imat"]])^2 >> >> As with most model fitting functions, you use the summary method, so >> >> summary(mod1)$Coef >> >> is a four-column matrix, and >> >> summary(mod1)$Coef[,2] >> >> is the column you want. >> >> Roger >> >> PS. Reading summary.spautolm shows that the diagonal values of the >> matrix you refer to are the squares of the SE values. >> >>> >>> >>> This does produce a vector of the diagonal elements of a matrix that > >>> look >> like a >>> variance covariance matrix (correct dimensions and row and column >>> labels), >> but >>> the values I get do not agree with what the summary() function >>> displays -- >> they >>> also seem implausibly small. >>> >>> any hints? >>> >>> Thanks! >>> >>> Sam >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >> >> -- >> Roger Bivand >> Economic Geography Section, Department of Economics, Norwegian School >> of Economics and Business Administration, Helleveien 30, N-5045 >> Bergen, Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43 >> e-mail: [EMAIL PROTECTED] >> >> > > > -- > ********Note the new contact information******* > > Samuel H. Field, Ph.D. > Senior Research Investigator > CHERP/Division of Internal Medicine - University of Pennsylvania > Philadelphia VA Medical Center 3900 Woodland Ave (9 East) Philadelphia, > PA 19104 > (215) 823-5800 EXT. 6155 (Office) > (215) 823-6330 (Fax) > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > -- Roger Bivand Economic Geography Section, Department of Economics, Norwegian School of Economics and Business Administration, Helleveien 30, N-5045 Bergen, Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43 e-mail: [EMAIL PROTECTED] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
