similar to: random effects mixed model, different regressors

To make a long story short I was doing some in-sample testing in which some dynamically created regressors would end up either all true or all false based on the validation portion. In my case a new mainframe configuration (this is a crappy way to handle a level shift but I do what I can.) So here is the code snippet that finally let me pre-check my regressors and drop any of them that were all

The model matrix for the code at the end the email is shown below. Since the model matrix doesn't have -1, I think that it is made of dummy regressors rather than deviation regressors. I'm wondering how to make a model matrix using deviation regressors. Could somebody let me know? > model.matrix(aaov) (Intercept) A2 B2 B3 A2:B2 A2:B3 1 1 0 0 0 0 0 2

Hello everyone, Hope you all are doing great! I have been fitting arima models and performing forecasts pretty straightforwardly in R. However, I wanted to add a couple of regressors to the arima model to see if it could improve the accuracy of the forecasts but have had a hard time trying to do so. I used the following R function: arima(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0,

Hello, I need to perform a robust regression on data which contains errors in BOTH observations and regressors. Right now I am using rlm from the MASS package with 'method="MM"' and get visually very nice results. MASS is quite clear, however, that the described methodologies are only applicable to observation-error only data (p. 157, 4th Ed.). So here's the questions now:

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Hi all, I've experienced this problem using step() and stepAIC() when a name of a regressors has blanks in between (R:R1.7.0, os: w2ksp4). Please look at the following code: "x" <- c(14.122739306734, 14.4831100207131, 14.5556459667089, 14.5777151911177, 14.5285815352327, 14.0217803203846, 14.0732571632964, 14.7801310180502, 14.7839362960477, 14.7862217992577)

Hi, Using arima on data that are in a data frame, especially when adding xreg, would be much easier if the arima function contained 1) a "data=" argument 2) the possibility to include the covariate(s) in a formula style. Ideally the call could be something like > arima(symptome, order=c(1,0,0), xreg=~trait01*mesure0, data=anxiete) ( or arima(symptome~trait01*mesure0,

Dear R-users, consider a 2-level linear mixed effects model (LME) with random intercept AND random slope for level 1 AND 2. Does anybody know how to calculate Intraclass-coefficient (ICC) for highest (innermost) level 2 ??? In the literature, I did not find an example for these kind of komplex models. For 1-level Random-Intercept models it would be easy: ICC = variance due to the clustering

Hi, All. I have a logistic regression model that I have run. The question came up: which of these regressors is more important than another? (I'm using Design) Logistic Regression Model lrm(formula = iconicgesture ~ ST + SSP + magnitude + Condition + Expertise, data = d) Coef S.E. Wald Z P Intercept -3.2688 0.2854 -11.45 0.0000 ST 2.0871 0.2730 7.64

dear r-experts: there is probably a very easy way to do it, but it eludes me right now. I have a large data frame with, say, 26 columns named "a" through "z". I would like to define "sets of regressors" from this data frame. something like myregressors=c("b", "j", "x") lm( l ~ myregressors, data=... ) is the best way to create new

Does anyone know if there is a way to specify regressors dynamically rather than explicitly? More specifically, I have a data set in "long format" that details a number of individuals and their responses to a question (which can be positive, negative, or no answer). Each individual answers as many questions as they want, so there are a different number of rows per individual. For each

##### BEGIN CODE ###### #!/usr/bin/perl ###### # # --start, -s = The date you would like to start generating regressors #--end, -e = When to stop generating holiday regressros # --scope, -c = D, W for Daily or Weekly respectively (e.g. Does this week have a particular holiday) # --file, -f = Ummm where to write the output silly! # # **NOTE** The EOM holiday is "End of Month" for

Hi everyone, this should be pretty basic but I need asking for help as I got stuck. I am running simple linear regression models on R with k regressors where k > 1. In order to automate my code I packed all the regressors in a matrix X so that lm(y~X) will always produce the results I want regardless of the variables in X. I am new to R but I found this advice somewhere so I guess it is

I would like to be able to use lme to fit random effect models In which some but not all of the random effects are constrained to be independent. It seems as thought the pdMat options in lme are a promising avenue. However, none of the existing pdMat classes seem to allow what I want. As a specific example, I would like to fit a random intercept/slope mixed model to longitudinal observations in

Hello, I'm having trouble correctly specifying the random effects for a nlme model. The general summary of what I'm trying to do is that I've got a data set that has multiple individuals and multiple machines that took measurements from those individuals. At least one of the machines has drift during the day causing a visually linear decrease in the readout during the day, so I have

Hello all, I using lmer to develop a mixed effects model. I start with an overly parameterized model (as suggested in Zuur et al. Mixed Effects Models and Extension in Ecology with R) that looks something like this: m1 <- lmer( Y ~ aS + bS + c + d + e + (c|SpeciesId) + (d|SpeciesId) + (e|SpeciesId)) aS and bS are species level predictors an so do not vary within a SpeciesId. However, c, d, and

Dear R-Users, I have fitted a cross-classified generalized linear mixed model using the lmer package with the following code. Mod<-lmer(y~x+(1|a)+(1|b)+ (1|c), family=binomial) In this case, only including a covariate (x) as a fixed effect. The fitted values, using fitted(mod), correspond to the raw data nicely, and the mean of the fitted values is equal to the mean of the raw data. In

Hello. I am running a multivariate multilevel mixed effects model, and am trying to understand what the interaction term tells me. A very simplified version of the model looks like this: model <- lmer (phq ~ -1 + as.factor(index_phq) * Neuro + ( -1 + as.factor(index_phq)|UserID), data=data) The phq variable is a categorical depression score on 9 depression items (classified by the variable

Hello R users, I have a quick question I was hoping to get your input on. I am new to R and the smooth statistical regression world, and am trying to wrap my mind around the issues concerning using splines for mixed effect modeling. My question is the following: in the ?gamm? function, generalized additive mixed models can be estimated by including random components. These can be explicitly

Hello fellow R users, I am having a problem finding the estimates for some overall treatment effects for my mixed models using 'lme' (package nlme). I hope someone can help. Firstly then, the model: The data: Plant biomass (log transformed) Fixed Factors: Treatment(x3 Dry, Wet, Control) Year(x8 2002-2009) Random Factors: 5 plots per treatment, 5 quadrats per plot (N=594 (3*5*5*8)