search for: rstandard

I contacted John Fox about this first, because parts of the file are attributed to him. He says that he didn't write rstandard.glm(), and suggests asking r-devel. As it stands, rstandard.glm() has summary(model)$dispersion outside the sqrt(), while in rstandard.lm(), the sd is already sqrt()ed. This seems to follow stdres() in VR/MASS/R/stdres.R. Of course for the c("poisson", "binomial") families,...

Full_Name: Heather Turner Version: 2.2.0 OS: Windows XP Submission from: (NULL) (137.205.240.44) Standardized residuals as calculated by rstandard.lm, rstandard.glm and plot.lm are Inf/NaN rather than zero when the un-standardized residuals are zero. This causes plot.lm to break when calculating 'ylim' for any of the plots of standardized residuals. Example: "occupationalStatus" <- structure(as.integer(c(50, 16, 12,...

Dear all, the application of the function rstandard() in the base package to a glm object does not produce residuals standardized to have variance one: the reason is that the deviance residuals are divided by the dispersion estimate and not by the square root of the estimate for the dispersion. Should the function not be changed to produce res...

...So I think we need to return NaN=20 when hat is within rounding error of 1. My take is that plot.lm should handle this: you will see most but not = all=20 cases have na.rm=3DTRUE in calculating ylim, but as Inf is theoretically impossible it has not been considered. Note that plot.lm does not use rstandard and so needs a separate fix. Thanks for the report On Tue, 6 Dec 2005 Heather.Turner at warwick.ac.uk wrote: > Full_Name: Heather Turner > Version: 2.2.0 > OS: Windows XP > Submission from: (NULL) (137.205.240.44) > > > Standardized residuals as calculated by rstandard.lm, r...

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'...

Hi all, I'm trying to apply loess regression to my data and then use the fitted model to get the *standardized/studentized residuals. I understood that for linear regression (lm) there are functions to do that:* * * fit1 = lm(y~x) stdres.fit1 = rstandard(fit1) studres.fit1 = rstudent(fit1) I was wondering if there is an equally simple way to get the standardized/studentized residuals for a loess model? BTW my apologies if there is something here that I'm missing. All the best, * * *Oliver * [[alternative HTML version deleted]]

...mber of observations N <- 100 ## binomial sample size d <- data.frame(r=rbinom(n,size=N,prob=0.5), num=N) ## fit with proportion/weights syntax g1 <- glm(r/N~1,family=binomial,weights=num,data=d) ``` The scale of the standardized residuals looks appropriate: ```{r} range(rstandard(g1)) ``` Showing the results of `plot.lm()`, `qqnorm()` on standardized residuals, and th enhanced Q-Q plot from the `mgcv` package: ```{r fig.width=10,fig.height=5} par(mfrow=c(1,3),las=1,bty="l") plot(g1,which=2,main="plot.lm()") qqnorm(rstandard(g1),main="qqnorm()"...

...pectively, of a group of cats. With the following code, I am making two plots, both to be viewed in the same plot window in R: library(MASS) maleData <- subset(cats, Sex == "M") linreg0 <- lm(maleData$Hwt ~ maleData$Bwt) par(mfrow=c(1,2)) plot(maleData$Hwt ~ maleData$Bwt) plot(rstandard(linreg0) ~ fitted(linreg0)) My problem is that the two plots end up all oblong and squashed, because the plot window doesn't have a size suited for having two plots next to one another. Can I tell R to adjust the plot window? Also, I would like to do something along the lines of: newData &lt...

...n, and now I want to find out which cases have a residual of above + 2.5 and ? 2.5. Below I provide the R commands I have used. Reg<-lm(a~b+c+d+e+f) # perform multiple regression with a as the dependent variable. Residuals<-residuals(reg) # store residuals to an R object. stdresiduals<-rstandard(reg) #save the standardized residuals. #now I want to type a command that will save the residuals of certain range to an object. I realize that by plotting the data I can quickly see the residuals outside a certain boundtry, however, I am totally blind, so visually inspecting the plot is not...

Hi, I must be doing something silly here, because I can't get the studentised and standardised residuals from r output of a linear model to agree with what I think they should be from equation form. Thanks in advance, Jennifer x = seq(1,10) y = x + rnorm(10) mod = lm(y~x) rstandard(mod) residuals(mod)/(summary(mod)$sigma) rstudent(mod) residuals(mod)/(summary(mod)$sigma*sqrt(1-lm.influence(mod)$hat)) -- View this message in context: http://r.789695.n4.nabble.com/studentized-and-standarized-residuals-tp3732997p3732997.html Sent from the R help mailing list archive at Nabble.c...

...oskedasticity and nonnormal errors are even more serious problems in a censored regression than in an ols-regression. But I'm not sure how to test for these assumptions in R? Is there a way to get to the residuals of censored regression models (given that corresponding functions for lm, such as rstandard, are not applicable)? (Or perhaps I'm on the wrong track in a more fundamental sense and shouldn't be Iooking for equivalences to lm?) Many thanks for any help. EH

Dear community, I'm having this silly problem. I've a linear model. After fixing it, I wanted to know which data had studentized residuals larger than 3, so i tried this: d1 <- cooks.distance(lmmodel) r <- sqrt(abs(rstandard(lmmodel))) rstu <- abs(rstudent(lmmodel)) a <- cbind( mydata, d1, r,rstu) alargerthan3 <- a[rstu >3, ] And suddenly a[rstu >3, ] has 17 rows, 7 of them are "new rows", where all the entries are NA's, even its rownames. Because of this I'm not sure of the di...

...) dat.diff$resp <- 1 # response variable is all 1's # Fit on differences and 1's, remove intercept fit.diff <- glm(resp ~ -1 + x1.diff, family = binomial, data = dat.diff) summary(fit.diff) summary(fit.clogit)$coefficients # GOF: delta chi-squared plot(fit.diff$fitted.values, rstandard(fit.diff)^2) round(sort(rstandard(fit.diff)^2), 4) Thanks! Joe [[alternative HTML version deleted]]

...ich cases have a residual of above + 2.5 and ? 2.5. > >Below I provide the R commands I have used. > >Reg<-lm(a~b+c+d+e+f) # perform multiple regression with a as the >dependent variable. > >Residuals<-residuals(reg) # store residuals to an R object. >stdresiduals<-rstandard(reg) #save the standardized residuals. >#now I want to type a command that will save the residuals of >certain range to an object. > >I realize that by plotting the data I can quickly see the residuals >outside a certain boundtry, however, I am totally blind, so visually >ins...

...tAIC.lm* family.lm* formula.lm* hatvalues.lm [17] influence.lm* kappa.lm labels.lm* logLik.lm* [21] model.frame.lm model.matrix.lm nobs.lm* plot.lm [25] predict.lm print.lm proj.lm* qr.lm* [29] residuals.lm rstandard.lm rstudent.lm simulate.lm* [33] summary.lm variable.names.lm* vcov.lm* Non-visible functions are asterisked > > library(gdata) gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED. gdata: read.xls support for 'XLSX' (Excel 2007+) files ENA...

....e. Null); 2 Residual Null Deviance: 695.9 Residual Deviance: 0.8535 AIC: 41.25 No problems, errors, or any signs of potential problems. In the changes to R 2.3.0 (in the NEWS file, eg http://mirror.aarnet.edu.au/pub/CRAN/src/base/NEWS) I find this: o Influence measures such as rstandard() and cooks.distance() could return infinite values rather than NaN for a case which was fitted exactly. Similarly, plot.lm() could fail on such examples. plot.lm(which = 5) had to be modified to only plot cases with hat < 1. (PR#8367) lm.influence() was incorrectly reporting 'coef...

...at overall standard deviation (sd) is of length one, but for "mlm" models we have a multivariate response with a multicolumn residual matrix. The functions also get correctly the sd vector corresponding to the columns, but it is not applied to these, but recycled for rows. This influences rstandard.lm and cooks.distance.lm. For instance, in cooks.distance.lm we have ((res/(sd * (1 - hat)))^2 * hat)/p, where res is a n x m matrix, sd is a m-vector and hat is a n-vector). Both of these functions are very easily fixed. 2) Another problem is that several functions are based on lm.influence func...

Hello, Are there any packages/functions available for testing the assumptions underlying assumptions for a good GLM model? Like linktest in STATA and smilar. If not, could somebody please describe their work process when they check the validity of a logit/probit model? Regards, Jay

Am I asking for too much: for any object that a stat proc returns ( y <- lm( y~x) , etc ) ) , is there a super convenient function like give_all_extractors( y ) that lists all extractor functions , the datatype returned , and a text descriptor field ("pairwisepval" "lsmean" etc) That would just be so convenient. What are my options for querying an object so that I can

...gth one x and POSIXlt table gave a segfault (PR#17459). * Fork clusters could hang due to a race condition in cluster initialization (makeCluster()). * nextn(n) now also works for larger n and no longer loops infinitely for e.g, n <- 214e7. * cooks.distance() and rstandard() now work correctly for multiple linear models ("mlm"). * polym() and corresponding lm() prediction now also work for a boundary "vector" case fixing PR#17474, reported by Alexandre Courtiol. * With a very large number of variables terms() could segf...