similar to: Testing for normality of residuals in a regression model

Dear Federico, see: ? shapiro.test(stats) Shapiro-Wilk Normality Test and ? jarque.bera.test(tseries) Jarque-Bera Test They are the most common tests used for normality testing. Ciao Vito Federico Gherardini wrote on Fri Oct 15 14:44:18 CEST 2004: Hi all, Is it possible to have a test value for assessing the normality of residuals from a linear regression model,

Dear list, I'm trying to fit a glm model using family=poisson(link = "identity"). The problem is that the glm function fits a model with a negative intercept, which sounds like a nonsense to me, being the response a Poisson variable. >From a previous discussion on this list I've understood that the glm function uses IRLS for the fitting without any constraint so it is

Hi, you can use this simple function: add.col<-function(df, new.col) {n.row<-dim(df)[1] length(new.col)<-n.row cbind(df, new.col) } see this example: > x<-cbind(c(1,2,3),c(4,5,6)) > x [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 > y<-c(7,8) > y [1] 7 8 > add.col<-function(df, new.col)

Hi all, I'm trying to read a 1220 * 20000 table in R but I'm having lot of problems. Basically what it happens is that R.bin starts eating all my memory until it gets about 90%. At that point it locks itself in a uninterruptible sleep status (at least that's what top says) where it just sits there barely using the cpu at all but keeping its tons of memory. I've tried with

Hi all, I've used the split argument to print four lattice plots on a single page. The problem now is that I need to reduce the amount of white space between the plots. I've read other mails in this list about the new trellis parameters layout.heights and layout.widhts but I haven't been able to use them properly. I've tried to input values between 0 and 1 as the padding value

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Hi all I'm trying to use the function glm from the MASS package to do the following fit. fit <- glm(FP ~ rand, data = tab, family = poisson(link = "identity"), subset = rand >= 1) (FP is >= 0) but I get the following error Error: no valid set of coefficients has been found:please supply starting values In addition: Warning message: NaNs produced in: log(x) in contrast

On Wed, 28 Jul 2004 09:53:08 +0200 Uwe Ligges <ligges at statistik.uni-dortmund.de> wrote: > > If your data is numeric, you will need roughly > > 1220 * 20000 * 8 / 1024 / 1024 ~~ 200 MB > > just to store one copy in memory. If you need more than two copies, your > machine with its 512MB will start to use swap space ..... > Hence either use a machine with more

Full_Name: Federico Gherardini Version: 1.8.1 OS: Gentoo linux Submission from: (NULL) (81.208.36.89) When I use fix() to edit a matrix R immediately quits with a segmentation fault if I use the copy button in the graphical device window. Clicking on the name of the variables completely messes up the screen too. In this case you have to close the grahpic device and call fix() again. I've

Hi, I'm dealing with time series. I usually use stl() to estimate trend, stagionality and residuals. I test for normality of residuals using shapiro.test(), but I can't test for autocorrelation and heteroskedasticity. Is there a way to perform Durbin-Watson test and Breusch-Pagan test (or other simalar tests) for time series? I find dwtest() and bptest() in the package lmtest, but it

Dear R community, In the excellent nlme package the default diagnostic plot graphs the innermost residuals against innermost fitted values. I recently fit a mixed-effects model in which there was a very clear positive linear trend in this plot. I inferred that this trend occurred because my fixed effect was a two-level factor, and my random effect was a 12-level factor. The negative residuals

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

I think it's baloney mainly because i can't find a mention of it anywhere. Is there REALLY a limit on the number of physical network interfaces in the Kernel?

Dear All, I would like to ask how to get the Sum of Squares from fitted lme model. I appreciate that lme maximises the likelihood (or REML) and uses likelihood ratio tests, but I just fail why I could not get the SS if I want them. I could use lm to calculate them, but it looks quite pointless to fit a second (and wrong in a way) model for something so trivial. I know that the issue has been

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

Hi, I have a coxph model like coxph(Surv(start, stop, censor) ~ x + y, mydata) I would like to calculate the Schoenfeld residuals for the null, i.e the same model where the beta hat vector (in practical terms, the coeff vector spat out by summary()) is constrained to be all 0s --all lese stays the same. I could calculate it by hand, but I was wondering if there is a way of doing it with

Dear all I am using lars package to do lasso in R. I dont undesrtand what max.steps do?and how I can understand from the outputs to obtain the last steps in this packagethanks for your helpbest [[alternative HTML version deleted]]

Dear all, I would like to know which is the right way to check the normality assumption for performing ANOVA. How do you check normality for the following example? I did an experiment where people had to evaluate on a 7 point scale, the degree of realism of some stimuli presented in 2 conditions. The problem is that if I check normality with the Shapiro test I get that the data are not

Hello Helpers, I have some problems with fitting the model for my data... -->my Literatur says (crawley testbook)= Non-normality of errors-->I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90

Hi, I am looking for a normality test in R to see if a vector of data I have can be assumed to be normally distributed and hence used in a linear regression. > help.search("normality test") suggests the Shapiro test, ?shapiro.test. Now maybe I am interpreting things incorrectly (as is usually the case), am I right in assuming that this is a composite test for normality, and hence a