similar to: Should data for the linear mixed model analysis meet the three assumptions of ANOVA?

Dear Colleagues,   I am running a three-way mixed-design ANOVA, with one manipulated IV, a group membership IV, and pre-/post- within subject factor. I am interested in the three-way interaction effect. The regular ANVOA is problematic, because (a) the sample sizes are very unbalanced, due to the group IV, (b) the homogeneity of variance is violated, and (c) the homogeneity of covariance is also

I have two questions I would appreciate assistance with: (1) I believe "by" is the command used to split a file. In the following example, "mydat" is the dataframe , "group" the variable I want to split the analysis by & "WK1FREQ,WK2FREQ" the variables attach(mydat) by (GROUP) cor.test (WK1FREQ,WK2FREQ) I have also tried: (by (GROUP) cor.test

Hi, I'm a new user of R. My background is Electrical Engineering, so please bear with me if this is a silly question. I'm trying to assess whether the results of an experiment satisfy the hypothesis of homoscedasticity (my ultimate goal is to use ANOVA). The result of the experiment is mean delay (dT), which depends on three factors, topology, drift, and lambda. The first two factors are

I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear

Dear r-helpers, Prior to analysis of variance, I ran the Boxcox function (MASS library) to find the best power transformation of my data. However, reading the Boxcox help file, I cannot figure out if this function (through its associated log-likelihood function) corrects for * normality only * or if it also induces * homogeneity of variances *. I found in Biometry (Sokal and Rohlf, p. 419)

Dear all, I'm a beginner of R and I need to carry out some three-way mixed ANOVAs. Following examples at http://personality-project.org/r/r.anova.html, I managed to get the ANOVA part, but I don't know how can I check data normality and homogeneity of variance in R (since they're the required assumptions of ANOVA analysis). Are there any special command/packages? Could anyone give me

Hi All, I am making the switch to R and uncertain which of the several packages for mixed models is appropriate for my analysis. I am waiting for Pinheiro and Bates' book to arrive via inter-library loan, but it will be a week or more before it arrives. I am trying to fit a generalized linear mixed model of survival data (successes/trials) as a function of several categorical fixed and

Hi all, I would like to test the homoegeneity of variances between several linear model for some analysis of covariance. It seems that the Bartlett's test is a good test to use but I am having problem using with linear model and I cannot find any examples on the internet. There are some examples for comparisons of variances but not linear models. If I take the hellung data set, which is

Dear colleagues, I have a data from a repeated measures design that I'm analysing through a mixed model. Nine independent sampling units (flasks with culture medium with algae) were randomly divided into 3 groups ("c", "t1", "t2"). There is no need for inclusion of the random effect of the intercept, because the nine sample units are homogeneous among each other

Hello,   Anyone familiar with robust ANOVA in R? Please help.   I am conducting a between-between-within (2 X 2 X 2) ANOVA, with the focus on the significance of the three-way interaction. Because of very uneven sample sizes, and the violation of covariance homogeneity, I need to use some sort of robust ANOVA method, and I prefer bootstrap. I am aware of Rand Wilcox's robust package that can

I am testing the homogeneity of variances via bartlett.test and fligner.test. Using the following example, how should I interpret the p-value in order to accept or reject the null hypothesis ? set.seed(5) x <- rnorm(20) bartlett.test(x, rep(1:5, each=4)) Bartlett test of homogeneity of variances data: x and rep(1:5, each = 4) Bartlett's K-squared = 1.7709, df = 4, p-value =

The syntax below works, and gives the expected results, in R version 2.0.0, provided that I have loaded packages Matrix, latticeExtra and lme4. However, I cannot get it to work in version 2.2.1. Can anyone tell me how to fit this model in the more recent version? Thanks in anticipation, Nick Galwey barleyprogeny.model1lme <- lme(yield_g_m2 ~ 1, data =

Hello everybody, This is my firth question in this mailing list. The question is about the how to deal with a model with both random and fixed factors. I know to do the analysis using SPSS. The command line wit be as follows: UNIANOVA alpha.ug.mg BY Line Year Rep /RANDOM=Year Rep /METHOD=SSTYPE(4) /INTERCEPT=EXCLUDE /PRINT=HOMOGENEITY /CRITERIA=ALPHA(0.05) /DESIGN= Year Rep*Year Line

Breslow-Day test A statistical test for the homogeneity of odds ratios. Homogeneity In <javascript:void(0);> systematic reviews homogeneity refers to the degree to which the results of studies included in a review are similar. "Clinical homogeneity" means that, in studies included in a review, the participants, interventions and outcome measures are similar or comparable.

Hi all, I want to know how to perform the test Breslow-Day test for homogeneity of odds ratios (OR) stratified for svytable. This test is obtained with the following code: epi.2by2 (dat = daty, method = "case.control" conf.level = 0.95, units = 100, homogeneity = "breslow.day", verbose = TRUE) where "daty" is the object type table svytable consider it, but

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not

Hi, All! To calculate Levene's Test for Homogeneity of Variance I use R Commander, and this is the output: > levene.test(Dataset$age, Dataset$sex) Levene's Test for Homogeneity of Variance Df F value Pr(>F) group 1 0.8739 0.3567 33 I am not sure what means "Pr(>F)"? Can anyone explain/translate this? Regards, Iurie Malai Department of Psychology and

Full_Name: Karel Zvara Version: 1.8.1 OS: MS Winows 2000 Submission from: (NULL) (195.113.30.163) The test statistics of the fligner.test (ctest package) depends on the order of cases: > fligner.test(count~spray,data=InsectSprays) Fligner-Killeen test for homogeneity of variances data: count by spray Fligner-Killeen:med chi-squared = 14.4828, df = 5, p-value = 0.01282 >

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Dear All: I want to do association study based on mixed linear model, My model not only includes serval fixed effects and random effects but also incorporates some covariates such as "birth weight". Otherwise, the size of the data are about 180 individuals and 12 variables and 60000 Fixed effect estimates As asreml-R is not free ,is there any packages for my study? I heard nlme or