similar to: test the significances of two regression lines

Hi, 1. I've written a function to obtain significances of correlations (there is probably already a function for this, but I'm still not very familar with all the functions). This function works just fine, but there are some differences with e.g. SPSS, not large though. Maybe some floating point calculation issues [1]? Maybe someone has time to have a look, I've uploaded a working

Hi friends, If someone can find out some time to go through my problem would be really grateful. I have a dataset(dataset1) as shown below:-- recmeanC1 recmeanC2 recmeanC3 recmeanC4 i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 1 NA 1 1.00 1.800000 NA 1 NA 1 1 NA 2 2 2 NA 2 2 2 2 1.00

This concerns whether p-values from lmer can be trusted. From simulations, it seems that lmer can produce very small, and probably spurious, p-values. I realize that lmer is not yet a finished product. Is it likely that the problem will be fixed in a future release of the lme4 package? Using simulated data for a quite standard mixed-model anova (a balanced two-way design; see code for the

Hello all! I have a problem that calls for a better understanding, than mine, of how lme() uses the random part of the call. The dataset consists of eleven field trials (Trial) with three replicates (Block) and four fertiliser treatments (Treat). Analysing for example yield with lme() is easy: m1 <- lme(Yield ~ Treat, data=data, random =~1| Trial/Block) giving estimates of

two ARIMA models, both have several bars signicant in ACF and PACF plots of their residuals, but when run Ljung Box tests, both don't show any significant correlations... however, one model has p-value that is larger than the other model, based on the p-values, can I say the model with larger p-values should be better than the model with smaller p-values? [[alternative HTML version

Hello, I need nemenyi-test or any other post-hoc test for kruskal-wallis, but I just can't find out how to implement this in R. My data set is nitrite concentrations in four different groups which I intend to compare. The kruskal-wallis-test showed significance, but I still don't know between which groups these significances are. Can anybody help me with this? Thanks

Dear list, I have been using multivariate multiple regression (MMR) in the form lm(Y~X) where Y and X are matrices of response and predictor variables. I know that summary(mlm.object) would give the usual lm statistics for each response variable separately and that anova.mlm(mlm.object) will give the analysis of variance table of the mlm object. However, anova.mlm (also manova(mlm.object))

Hello all! Consider a dataset with a grouping structure, Group (factor) Several treatments, Treat (factor) Some sort of yield, Yield (numeric) Something, possibly important, measured for each group; GroupCov (numeric) To look for fixed effects from Treat on Yield, a first attempt could be: m1 <- lm(Yield ~ Treat) which gives, in a symmetric situation, the same estimated fixed effects as:

Hello; I am having a problems with the interpretation of models using ordered or unordered predictors. I am running models in lmer but I will try to give a simplified example data set using lm. Both in the example and in my real data set I use a predictor variable referring to 3 consecutive days of an experiment. It is a factor, and I thought it would be more correct to consider it ordered. Below

Dear R people, I have a couple of questions about post-doc analyses for 2 by 2 within subjects ANOVA. I conducted a psycholinguistic study that combined a 2 by 2 design and a latin square design. Specifically, I had 32 items each of which generated 4 conditions. Participants saw each of the 32 items only once: 8 in Condition A, 8 in B, 8 in C, and 8 in D. The table below serves as an example.

This is more of a statistics question with implementation implications. I have used R to calculate logistic regressions for various characterstics. I would now like to verify that the difference between a particular subset is significantly different from the logistic regression of the entire set. Example. I have a logistic regression containing every running back drafted between 1980-2000. I

I posted this a week ago on r-devel but to no avail and hope this not considered cross-posting: ===============================cut=============================== hi everybody, which each release I hope that the section "weights: an optional numeric vector of (fixed) weights. When present, the objective function is weighted least squares. _not yet

Hello Everyone! It's been a while since I last posted a question! Hope everyone has been doing well! ~~~ CONTEXT ~~~ I have recently entered a beginner-level competition on kaggle. The goal of the competition is to build a model that predicts who did/did not survive on the Titanic. I decided to use random forests as I have been wanting to learn the algorithm and the competition

Hi all, I have been trying to calculate Type III SS in R for an unbalanced two-way anova. However, the Type III SS are lower for the first factor compared to type I but higher for the second factor (see below). I have the impression that Type III are always lower than Type I - is that right? And a clarification about how to fit Type III SS. Fitting model<-aov(y~a*b) in the base package and

My question concerns the logic behind hypothesis tests for fixed-effect terms in models fitted with lme. Suppose the levels of Subj indicate a grouping structure (k subjects) and Trt is a two-level factor (two treatments) for which there are several (n) responses y from each treatment and subject combination. If one suspects a subject by treatment interaction, either of the following models seem

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Please forgive a straight stats question, and the informal notation. let us say we wish to perform a liner regression: y=b0 + b1*x + b2*z There are two ways this can be done, the usual way, as a single regression, fit1<-lm(y~x+z) or by doing two regressions. In the first regression we could have y as the dependent variable and x as the independent variable fit2<-lm(y~x). The second

#is there a way to get NA in the table of descriptive statistics instead of the function stopping Thank you in advance #data x.f <- structure(list(Site = structure(c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("BC", "HC", "RM119", "RM148", "RM179", "RM185",

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

I've upgraded one of my client boxes to Windows 64 bit edition but now it cannot login to the Samba PDC, instead it gives a RPC error after entering the password. The Win32 boxes can still login fine. I can get accounts without admin rights to login but those with the rights are given a message stating "The system cannot log you on due to the following error: A remote procedure call