similar to: Games-Howell function for post-hoc multiple comparisons

Hello, I read a book about statistics in psychology. The authors use SPSS. They talk about post hoc tests after ANOVA finds significant effects: - Gabriel's procedure (for equal or slightly different sample sizes) - Hochberg's GT2 (for different sample sizes) - Games-Howell procedure (for populations with unequal variances) I could not find them in R. Do they not exist in R

Hello everybody, I use the sweetpotato database included in R package: data(sweetpotato) This dataset contains two variables: yield(continous variable) and virus(factor variable). Due to Levene test is significant I cannot assume homogeneity of variances and I apply Welch test in R instead of one-way ANOVA followed by Tukey posthoc. Nevertheless, the problems come from when I apply posthoc

Dear listusers, I don't know whether my problem is statistical or computational, but I hope I could recieve some help in either case. I'm currently working on a MC-simulation in which I would like to control the skewness of a heteroscedastic dependent variable defined as: y=d*z+sqrt(.5+.5*x^2)*e (eq.1) where d is a parameter and, z, x, and e are gamma r.vs. The variables x

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the

Hello, I have some patient data for my masters thesis with three groups (n=16, 19 & 20) I have completed compiling the results of 7 tests, for which one of these tests the variances are unequal. I wish to perform an ANOVA between the three groups but for the one test with unequal variance (<0.001 by both bartlett and levene's test) I am not sure what to do. I thought i would run

When going through my earlier post I find a mistake in the example that I provided. The correct version is provided below. I also start to suspect that my problem is that although the cumulant of a sum of independent variable is the sum of the cumulants, the moments of a sum is not the sum of the moments. But that might not be the only flaw in my application. Regards, Karl-Oskar #An

Hello, I am trying to determine the most appropriate way to run post-hoc comparisons on my lme model. I had originally planned to use Tukey HSD method as I am interested in all possible comparisons between my treatment levels. TukeyHSD, however, does not work with lme. The only other code that I was able to find, and which also seems to be widely used, is glht specified with Tukey:

Full_Name: Peter Ehlers Version: 1.6.0 OS: Windows 2000 Pro Submission from: (NULL) (136.159.61.178) In: p.adjust package:base R Documentation In the paragraph: Hochberg's and Hommel's methods are valid when the hypothesis tests are independent or when they are non-negatively associated (Sarkar, 1998; Sarker and Chang, 1997). Hommel's method is

Dear R users, I 'm working on a dataset consisting of 4 different dataframes with tree, leaf, fruit and seed measurements made on 300 trees, coming from 10 provenances (30 trees per provenance, 10 leaves/fruits/seeds per tree). Provenances are fixed effects (they were not randomly chosen), but trees within provenances and leaves/fruits/seeds within trees were randomly assigned. I wanted to

Dear list, I?m wondering if there is something analogous to the TukeyHSD function that could be used for parametric terms in a GAM. I?m using the mgcv package to fit models that have some continuous predictors (modeled as smooth terms) and a single categorical predictor. I would like to do post hoc test on the categorical predictor in the models where it is significant. Any suggestions?

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

Hi All, I am struggling for 2 weeks solving authentication problem in dovecot. logs from /etc/mail/maillog Oct 15 18:00:35 localhost dovecot: auth: Debug: Loading modules from directory: /usr/lib64/dovecot/auth Oct 15 18:00:35 localhost dovecot: auth: Debug: Module loaded: /usr/lib64/dovecot/auth/libauthdb_ldap.so Oct 15 18:00:35 localhost dovecot: auth: Debug: Module loaded:

Dear All, I am using the function gls (in the nlme package) and I would like to fit a heteroscedastic model, with different variances for each of the levels of two stratification variables. In p. 210 of Pinheiro & Bates ("Mixed effects models in S and S-Plus", 2000, Springer), the authors show the use of the "*" operator. However, that is not what I want, because it

# Using Moving/Rolling Windows, here we do an OLS Regression with corrections for #Heteroscedasticity and Autocorrelations (HAC) using Newey West Method. This code is a #extension of Ajay Shah?s code for moving windows simple OLS regression. # The easiest way to adjust for Autocorrelations and Heteroscedasticity in the OLS residuals is to #use the coeftest function that is included in the

Re: Heteroscedasticity and mgcv. (Collin Lynch) The GAMLSS package can model heterogeneity in the scale parameter (e.g. standard deviastion) [and also heterogeity in skewness and kurtosis parameters].of the response variable distribution. For parametric models a generalized likelihood ratio test can be used to test whether the heterogeity is needed. Alternatively a generalized Akaike

If you don't get a response it is because you did not read the Posting Guide which indicates that the R-sig-ME mailing list is where this question would have been on-topic. -- Sent from my phone. Please excuse my brevity. On August 16, 2017 6:17:03 AM PDT, b88207001 at ntu.edu.tw wrote: >Hello dear uesRs, > >I am working on modeling both level one and level two

A better place for this post would be on R's mixed models list: r-sig-mixed-models . Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Aug 16, 2017 at 6:17 AM, <b88207001 at ntu.edu.tw> wrote: > Hello dear

Hi, I am currently trying to do a GLMM on a dataset with percent cover of seagrass (dep. var) and a suite of explanatory variables including algal (AC) and epiphyte cover (EC), rainfall, temperature and sunshine hours. M2=glmer(SG~AC+EC+TP+SS+RF+(1|Location/fSi/fTr), family=binomial,data=data,nAGQ=1) As the dependent variable is percent cover, I used a binomial error structure. I also have a

Hello dear uesRs, I am working on modeling both level one and level two heteroscedasticity in HLM. In my model, both error variance and variance of random intercept / random slope are affected by some level two variables. I found that nlme is able to model heteroscedasticity. I learned how to use it for level one heteroscedasticity but don't know how to use it to model the level

Dear all, Sorry if this is too obvious. I am trying to fit my multiple regression model using lm() Before starting model simplification using step() I checked whether the model presented heteroscedasticity with ncv.test() from the CAR package. It presents it. I want to correct for it, I used hccm() from the CAR package as well and got the Heteroscedasticity-Corrected Covariance Matrix. I am not