search for: residu

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...

...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 random) since a check of its ACF shows it to be so (signifant correlation only at lag 1, decays very quickly to zero). But I am not sure how to interpret the ljung-box result given by R. To check for randomness of residuals, should the p-value be small or lar...

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 0.32654 0.03742 8.726 <2e-16 **...

...esn't make much sense to me. With that it can rewritten in log2 as bps + subframe->qlp_coeff_precision + FLAC__bitmath_ilog2(order - 1) < 32 This is indeed more pessimistic that the currently used check (< 32 vs <= 32), but I think both make a mistake that the qlp coefficients and residuals are unsigned integers and are actually more pessimistic than they would need to be if residuals were at most bps wide. With signed multiplication I think the correct check would actually be bps + subframe->qlp_coeff_precision + FLAC__bitmath_ilog2(order) - 1 <= 32 But, as we have seen wi...

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 possible to tr...

...rge collection and some cycles to spare, can you please > consider compiling flac from git with the attached patch and see if > you have any files that fail with "flac -t" ? > > With the known problem file (snippet6.wav) encoded by 1.3.0 it prints > this: > > WARNING: residual -11025151 wider than bps 24 > WARNING: residual 41873263 wider than bps 24 > WARNING: residual -67175215 wider than bps 24 > WARNING: residual 69950995 wider than bps 24 > WARNING: residual -67108864 wider than bps 24 > ... > WARNING: residual 11227392 wider than bps 24 > WAR...

...post this in the wrong section. I have problem concerning ma two matrizes. After a regressione and so on, I got two matrizes Matrixres contains the results of ma calculation. Matrixr contains my detiene, which where Aldo used for the regression. Please ser the following code: #Datei einlesen residual = read.csv2("E:***Input-R_Renditen.csv",header=TRUE, sep=";") #Aktientitel alist <- list() for (a in 2:11){ #Regression ? ?#L?nge Gesamtzeit ? ?t <- 243 ? ?tx <- t-59 ? ?#L?nge Regression ? ?reglist <- list() ? ?for (i in 1:tx){ ? ?j <- i+59 ? ?#Regression...

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 predict.gam(...,type...

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 sourc...

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 t...

..., 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, p-value = 0.805, merv have unit root than diff(merv,1) is stationary. Than I made Breushch-Pagan test to test if residuals are identically distributed: library(lmtest) bptest(merv[2:1730]~-1+merv[1:1729],~merv[1:1729]+I(merv[1:1729])^2) BP = 81.3443, df = 2, p-value = < 2.2e-16 So merv.reg$resid aren't identically distributed. Than merv is heteroscedastik. Finally I made Box-Ljung test to test if residuals...

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 Annette Dobson's b...

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 output. I have attached plots of the fit and the residuals to one of my sub-groups, for illustration. By eye, the overwhelming majority of the residuals are within +- 0.4, and...

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 are guaranteed to have the same length as the variables?...

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 de...

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 when this is detected...

...0660 Here ys= y*sqrt(W), Xs<- sqrt(W)*cbind(1,x) So we can see weights here for lm means the scale for X and y. But for glm and gls I try > glm1 Call: glm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual Null Deviance: 1049000 Residual Deviance: 28210 AIC: 7414 > glm2 Call: glm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.1955 7.3053 Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual Null Deviance: 1548000 Residual Deviance: 448...

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: >durbin.watson(hw8LM0) Error in durbin.watson.lm(hw8LM0) : residuals include missing values (2)Partial Residual Plots: Here I kept getting that the variables I choose are not in the model, i.e. "Time is not in...

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...