search for: nonnorm

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, homoscedasticity, independence, and normally-distributed residuals. Absent t...

Hi everybody, somehow i dont get the shapiro wilk test for normality. i just can?t find what the H0 is . i tried : shapiro.test(rnorm(5000)) Shapiro-Wilk normality test data: rnorm(5000) W = 0.9997, p-value = 0.6205 If normality is the H0, the test says it?s probably not normal, doesn ?t it ? 5000 is the biggest n allowed by the test... are there any other test ? ( i know qqnorm

...equality of variances" (editet by Journal of the American Statistical Association Vol. 69, pp.: 364-367) "...the common F-ratio and Bartlett??s test are very sensitive to the assumption that the underlying populations are from a Gaussian distribution. When the underlying distributions are nonnormal, these tests can have an actual size several times larger than their nominal level of significance...." Peter Armitage in Statistical Methods in Medical Research ( Blackwell Scientific Publication, 1971, page. 212) "...Bartlett's test maybe is less useful than it seems; motif are...

...;m thinking of applying a censored regression model to cross-sectional data, using either the tobit (package survival) or the censReg function (package censReg). The dependent variable is left and right-censored. My hopefully not too silly question is this: I understand that heteroskedasticity 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 applic...

...hypothesis: two.sided Warning message: cannot compute correct p-values with ties in: ks.test(Year5.lm$residuals, pnorm) I am wondering if anybody can tell me what this error message means. Also, could anybody clarify how I could have a regression model with a high Rsquared, rouglhy .67, but with nonnormal residuals? Does this take away from the validity of my model? jdr

...actors are binary and the dependent measure is a frequency of successes between 0 and 4. Treating this as a normally distributed variable, I would perform a repeated-measures ANOVA as follows: > aov(y ~ A*B*C + Error(subj/(A+B+C))) but since the distribution of the dependent measure is clearly nonnormal, I would like to fit an analoguous model which is appropriate and I believe this would be a GLMM with a logit link and a random intercept for subjects. I have fitted this model using 'glmmPQL' function in MASS as: > glmmPQL(cbind(y,4-y) ~ A*B*C, random = ~ 1|subj, family=binomial(),da...

Does someone out there have an example of R-code for a split-split plot ANOVA using aov or another function? The design is not balanced. I never set up one in R before and it would be nice to see an example before I tackle a very complex design I have to model. Thanks, Mike Mike Saunders Research Assistant Forest Ecosystem Research Program Department of Forest Ecosystem Sciences University of

Dear all, Can garch function fit also t-innovations or only Gaussian innovations? -- With kind regards -- Lepo pozdravljeni -- Gr??e (Gr?ezi) -- Gorazd Brumen ------------------------------- Mail 1: gbrumen at student.ethz.ch Mail 2: gorazd.brumen at fmf.uni-lj.si Tel.: +41 (0)1 63 34906 Homepage: valjhun.fmf.uni-lj.si/~brumen

Dear all, I have data that contain more than 30 variables and 600 observations, it?s a longitudinal data,data contains a lot of non normal data (despite trying to do some transformation i hav still nonnormal variables ) i have a lot of missing data, i want to impute these missing data , i wonder if There is some specifications and some manner to impute missing data for a data where we have longitudinal data After some resarch i found AMELIA II package, and the funcion aregImpute(Hmisc) My quest...

To whom it may concern: I recently used lmer (for non-normally distributed data and mixed effects, using the Laplace method). All 3 of my fixed effects were categorical, including two ordered factors and one unordered factor. In my tables, I currently report the number of observations for the response variable, and both the degrees of freedom and Chi Square values from tests of reduced

Well fellow R users, I throw myself on your mercy. Help me, the unworthy, satisfy my employer, the ungrateful. My feeble ramblings follow... I've searched R-Help, the R Website and done a GOOGLE without success for a one way ANOVA procedure to analyse data that are both non-normal in nature and which exhibit unequal variances and unequal sample sizes across the 4 treatment levels. My

I am getting the following error message using the lmer function for mixed models with method="Laplace": "nlminb returned message false convergence (8) in: LMEopt(x=mer,value=cv)" Could anyone explain what this means, and how I might overcome (or track down) the problem? Bill Shipley [[alternative HTML version deleted]]

Hi everybody: I?m trying to rewrite some routines originally written for SAS?s PROC NLMIXED into LME4's glmer. These examples came from a paper by Nelson et al. (Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Models with Nonnormal Random Effects - 2006). Firstly the authors fit a Poisson model with canonical link and a single normal random effect bi ~ N(0;Sigma^2).The SAS routine was: log_s =log(SURVT) cens=1 proc nlmixed data=liver; parms logsig2 = 0 b0 = -2.8 btrt = -0.54 bhrt =0.18; xb= log_s + b0 + btrt * treat + bhrt...

On 07/02/2015 05:43 PM, David Keaton wrote: > On 07/02/2015 05:30 PM, Philip Reames wrote: >> >> >> On 07/02/2015 04:44 PM, David Keaton wrote: >>> On 07/02/2015 03:17 AM, Kuperstein, Michael M wrote: >>>> You want to redefine ["won't break the program"], by specifying a new >>>> abstract machine, which is >>>> more

...http://crantastic.org/packages/coxme Cox proportional hazards models containing Gaussian random effects, also known as frailty models. * csampling (1.1-3) Alessandra R. Brazzale http://crantastic.org/packages/csampling Monte Carlo conditional inference for the parameters of a linear nonnormal regression model * desire (1.0.5) Olaf Mersmann http://crantastic.org/packages/desire Harrington and Derringer-Suich type desirability functions * difR (1.0) Sebastien Beland http://crantastic.org/packages/difR The difR package contains several traditional methods to detect DIF in...

Dear R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package

...t; He quantified these two problems in the context of roughly three dozen > published examples, if my memory is correct, and found that in not > quite all cases, the parameter effects were at least an order of > magnitude greater than the intrinsic curvature. > > 2. In nonnormal situations, maximum likelihood is subject > to more approximation error -- intrinsic curvature -- than "simple" > nonlinear least squares. However, I would expect this comparison to > still be fairly accurate, even if the differences may not be quite as stark. > >...

The normality of the residuals is important in the inference procedures for the classical linear regression model, and normality is very important in correlation analysis (second moment)... Washington S. Silva > Thank you all for your replies.... they have been more useful... well > in my case I have chosen to do some parametric tests (more precisely > correlation and linear regressions

Dear All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

If I have data that I feed into shapio.test and jarque.bera.test yet they seem to disagree. What do I use for a decision? For my data set I have p.value of 0.05496421 returned from the shapiro.test and 0.882027 returned from the jarque.bera.test. I have included the data set below. Thank you. Kevin "Category","Period","Residual" "CHILD HATS, WIGS &