search for: residue

Dear list, I would like to know how the function residuals.lm calculates the partial residuals from an lm object with more than one predictor variable. In other words what is residuals.lm(...,type="partial") doing behind the scenes? According to the help file for residuals.lm (?residuals.lm), "The partial residuals are a matrix with each column formed by omitting a term from

Hello, I am using the Ljung Box test in R to compute if the resiudals of my fitted model is random or not. I am not sure though what the results mean, I have looked at various sources on the internet and have come up with contrasting explanations (mainly because these info deal with different program languages, like SAS, SPSS, etc). I know that my residuals should appropriate white noise( is

Dear all, I am running a logistic regression and this is the output: glm(formula = educationUniv ~ brncntr, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max # ???? ????? ?? ???????? -0.8825 -0.7684 -0.7684 1.5044 1.6516 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.06869 0.01155 -92.487 <2e-16 *** brncntrNo

On Thu, Jun 19, 2014 at 03:30:22PM +0400, lvqcl wrote: > BTW, what can you say about the following place in stream_decoder.c > in read_subframe_lpc_() function: > > /*@@@@@@ technically not pessimistic enough, should be more like > if( (FLAC__uint64)order * ((((FLAC__uint64)1)<<bps)-1) * ((1<<subframe->qlp_coeff_precision)-1) < (((FLAC__uint64)-1)

I am interested in a model diagnostic for logistic regression which is normally distributed (much like the residuals in linear regression with are ~ N(0,variance unknown). My understanding is that most (all?) of the residuals returned by residuals.lrm {design} either don't have a well defined distribution or are distributed as Chi-Square. Have I overlooked a residual measure or would it be

Miroslav Lichvar wrote: > I think it would be interesting to know how common are such streams. I > patched flac to print a warning on decoding or testing when this is > detected, but didn't find any files with this problem in my (small) > music collection. > > If someone has a large collection and some cycles to spare, can you please > consider compiling flac from git

...14] ? ?? ?rrf = residual[(j-5):j,15] ? ?? ?rex = rx-rrf ? ?? ?rerm = rrm-rrf ? ?? ?#Berechnung ? ?? ?res <- sum(rex-(reglist[[i]][2]*rerm+reglist[[i]][3]*rsmb+reglist[[i]][4]*rhml))/sd(rex-(reglist[[i]][2]*rerm+reglist[[i]][3]*rsmb+reglist[[i]][4]*rhml)) ? ?? ?reglist[[i]] <- res }? ? ? ? #Residuen auf alle Aktien ? ?alist[[a]] <- reglist } ? ? #Matrix mit Residuen ? ?matrixres <- do.call(cbind,alist) #Spaltennamen/Zeilennamen s<- names(residual)[2:11] colnames(matrixres)<-s #RenditeMatrix matrixr <- do.call(cbind,residual[60:243,2:11]) Now I want to combines the two matr...

Dear list, does anybody know whether there is a way to easily retrieve the so called "partial residuals" of a gam fit with package mgcv? The partial residuals are the residuals you would get if you would "leave out" a particular predictor and are the dots in the plots created by plot(gam.object,residuals=TRUE) residuals.gam() gives me whole model residuals and

Dear all, I'm writing a package and I would like to reuse the residuals() function. When I use a function which calls the redefined residuals (for my custom class) I get an error (see below). It looks like the wrong method is used. The strange this is, that when it execute the code manually it get no error. Any suggestions? Best regards, Thierry The entire source code is at

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

Dear R Users, I suppose this is a school boy question, but here it is anyway. I'm trying to re-create the residuals for a poisson GLM with simulated data; x<-rpois(1000,5) model<-glm(x~1,poisson) my.resids<-(log(x)- summary(model)$coefficients[1]) plot(my.resids,residuals(model)) This shows that my calculated residuals (my.resids) are not the same as residuals(model). p 65 of

I am using the lm function in R to fit several linear models to a fair-sized dataset (~160 collections of ~1000 data points each). My data have intrinsic, systematic uncertainty much greater than the measurement errors on any individual point. My thought is to use the residuals of my linear fits to quantify this intrinsic uncertainty, but I am puzzled over the correct interpretation of R's

hi: sorry to bother you all again. I am running a simple lm(y~x+z) regression, in which some of the observations are missing. Unfortunately, the residuals vector from the lm object omits all the missing values, which means that I cannot simply do residual diagnostics (e.g., plot(y,x)). Would it not make more sense to have the residuals propagate the missing values, so that the residuals

Hi there, I hope I can get advice regarding the calculation of leverage values or studentized residual values of a non-linear regression model. It seems like rstudent() does not work on a nls object. Many thanks in advance! Best regards, Xingli

Dear list members, I want to save residuals above or less than a certain value to an R object. I have performed a multiple linear regression, 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

On Thu, Jun 19, 2014 at 03:30:06PM +0200, Miroslav Lichvar wrote: > But, as we have seen with unusual data the residual signal can be > wider than bps. The FLAC format specification doesn't seem to mention > this. Should it be treated as a valid FLAC stream? I think it would be interesting to know how common are such streams. I patched flac to print a warning on decoding or testing

Dear R-List users, Can anyone explain exactly the difference between Weights options in lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call:

I am trying to evaluate a model by using the commands durbin.watson and cr.plot. However, I keep getting errors that I can't figure out. A description follows. Does anyone have a hint as to what may be wrong? 1)The Durbin Watson Test. In running the command I kept getting the message "residuals include missing values" when actually this was NOT the case. Example:

Dear all, I am trying to make a series of waffle plot-like figures for my data to visualize the ratios of amino acid residues at each position. For each one of 37 positions, there may be one to four different amino acid residues. So the data consist of the positions, what residues are there, and the ratios of residues. The ratios of residues at a position add up to 100, or close to 100 (more on this soon)*. I am hoping t...