similar to: Mixed effects model

Hello. I have some trouble with mixed effects in R, similar to problems that other people had with not nested models and lme, as I understand from the mailing list archive. Unfortunately, I could not understand the solutions that were proposed... I have a data set with response variable (y) and two explanatory variables x1 and x2 (x1 - fixed factor, x2 - random factor). Fixed factor x1 is

Dear all, I run a linear model with three significant explanatory variabels x1: a factor with 4 levels x2 and x3: factors with two levels each x4: continuous model <- lm(y ~ x1 + x2 * x3 + x4) <> The data is not perfectly balanced between the different factor-combinations and I use treatment contrasts. <> With an aov.object, I assume I could have used model.tables(aov.object,

Dear all, I have a question on the dose-response estimation with clustered/ correlated binary data. I would like to estimate the hit rate for a certain test at various concentration levels. The test is used on 5 subjects, and each subject is tested 20 times. If we assume that the 100 samples are independent, the hit rate estimate is unbiased, but the variance is under-estimated. The other

Take a look at lmekin() in the coxme package. The motivating data set for my development of coxme was the Minnesota Family Breast Cancer project: 24050 subjects in 462 families. The random effect is an intercept per subject with sigma^2 K as its variance where K is the kinship matrix (1 for self-self, .5 for parent-child or sib-sib, .25 for uncle-neice, etc). lmekin is a linear models front

I am trying to replicate the first example from stepAIC from the MASS package with my own dataset but am running into error. If someone can point where I have gone wrong, I would appreciate it very much. Here is an example : set.seed(1) df <- data.frame( x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000) ) df$y <- 0.5*df$x1 + rnorm(1000, mean=8, sd=0.5) # pairs(df); head(df) lo <-

Hello all R-users _question 1_ I need to make a statistical model and respective ANOVA table but I get distinct results for the T-test (in summary(lm.object) function) and the F-test (in anova(lm.object) ) shouldn't this two approach give me the same result, i.e to indicate the same significants terms in both tests??????? obs. The system has two restrictions: 1) sum( x_i ) = 1 2) sum(

Full_Name: Jerome Asselin Version: 1.3.1 OS: MacOS 9.2 Submission from: (NULL) (142.103.173.46) The step() function attempts to calculate the deviance of fitted models even if does not really need it. As a consequence, the step() function gives an error when it is used with coxph(). (There is currently no method to calculate the deviance of coxph() fits.) The code below gives an example of how

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

I'm new using R im trying to do a tukey test, but when i see the results the p value results in NA im guessing its because i have missing values but im not sure how to fix it AnovaModel.2 <- aov(area ~ trat, data=apilados) > summary(AnovaModel.2) Df Sum Sq Mean Sq F value Pr(>F) trat 4 11847 2961.76 9.9905 1.500e-06 *** Residuals 76 22531 296.46

Hello, I am having trouble doing a split plot analysis of variance with lme. The study design is ponds split in two, one side of each pond was a control and the other was one of three chemical treatments. Each pond side was sampled three times, response variable is the mean of twenty animals. So we have sample time within side within pond. Pond is random effects. There are three or four

I'm creating an ANOVA model. Is there a way to pull in consecutive columns of variables for the test? Example: aov1<-aov(y~x1+x2+REG[,2:num], data=REG) I'm not looking for interaction effects, I just want to create a model for the first few columns of variables (exact number and names will vary) and a few other predetermined variables (in the example I named them x1 and x2). Thanks

Dear all, I would like to Boostrap the stepAIC() procedure from package MASS for variety of model objects, i.e., fn <- function(object, data, B = 2){ n <- nrow(data) res <- vector(mode = "list", length = B) index <- sample(n, n * B, replace = TRUE) dim(index) <- c(n, B) for (i in 1:B) { up.obj <- update(object, data = data[index[, i], ])

Hi, Thanks in advance for the help. I have a few vectors and I need to get all the combinations of the vector values. Normally I would use a few for-loops to do that. e.g x1_c(1,3,5) x2_c(2,5,6) for (i in x1) { for (j in x2) { ... }} My problem is that I don't know how many vectors there are ahead of time, so I don't know how many for-loops I need. Any

Dear list I need your help: Execuse me for my limited R knowledge. #example data set set.seed (134) lm=c(1:4) block = c(rep(lm,6)) gen <- c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4),rep(5, 4),rep(6, 4)) X1 = c( rnorm (4, 10, 4), rnorm (4, 12, 6), rnorm (4, 10, 7),rnorm (4, 5, 2), rnorm (4, 8, 4), rnorm (4,7, 2)) X2 = X1 + rnorm(length(X1), 0,3) yvar <- c(X1, X2) X <- c(rep( 1,

Dear all, I am unsure about how to specify a model in R and I thought of asking some advice to the list. I have two groups ("Group"= A, B) of subjects, with each subject undertaking a test before and after a certain treatment ("Time"= pre, post). Additionally, I want to enter the age of the subject as a covariate (the performance on the test is affected by age),

Does anyone have any suggestions for how to test for violation of the sphericity assumption and apply the Huynh-Feldt correction to a repeated-measure model? My model follows the format below: Rep.anova=aov(Y~X1*X2+Error(Subject/(X1*X2),data=DataFile)) I've already looked at the explanations aov() and Error() in Baron's “Notes on the use of R for psychology experiments and

if I do: yyy=aov(Y~A*B*C); it seems that the three way ANOVA is based on all fixed-effects. There is no way to signal to "aov" the A and B are random effects and C is fixed effect; or A is random and B and C are fixed? Moreover, I guess I will need the Expected Mean Squares in order to do the F-test, where can I obtain these Expected Mean Squares in R? And is there a command that

Dear R colleagues, a friend and I are trying to develop a modest workflow for the problem of decomposing tests of higher-order terms into interpretable sets of tests of lower order terms with conditioning. For example, if the interaction between A (3 levels) and C (2 levels) is significant, it may be of interest to ask whether or not A is significant at level 1 of C and level 2 of C. The

hi all - i'm having trouble using lme to specify a mixed effects model. i'm pretty sure this is quite easy for the experienced anova-er, which i unfortunately am not. i have a data frame with the following columns: col 1 : "Score1" (this is a continuous numeric measure between 0 and 1) col 2 : "Score2" (another continuous numeric measure, this time bounded between 0

Dear R-list members, I've been struggling with the proper setup for analysing my data. I've performed a route choice experiment, in which participants had to make a choice at each junction for the next road. During the experiment they received traffic information, but also encountered two different accidents. They also made trips without accidents. What I'm interested in is to