search for: covariate1

...differ in mean level of height as well as the relationship between SPI and height. Thus, I also want to a) account for mean level differences in height and b) check whether the relationship between height and SPI is different between the groups. I hope this is sufficient information. height, SPI, covariate1, and covariate2 are numeric. population is a factor with 5 levels. Here are the steps I took: summary(mod1 <- lme(height ~ SPI + covariate1 + covariate2, random = ~ SPI | population)) summary(mod2 <- lme(height ~ SPI + covariate1 + covariate2, random = ~ 1 | population)) anova(mod1,mod2) #...

...n 12 12 zCANTAB_sqrt_SWM_Total 13 13 zSS_WJ_PC 14 14 zSS_WJ_CALC 15 15 zSS_WJ_LW 16 16 zSS_WJ_AP 17 17 zSS_WJ_MF What I'm trying to do is varnames[1,2] -> outcome lm(outcome ~ income + covariate1 + coviarate2, data=my.data) -> model I get the following error message: "Error in model.frame.default(formula = outcome ~ Hollings_Enroll + Child_sex + : variable lengths differ (found for 'Hollings_Enroll')" But when I run the model: lm(zCANTAB_log_IED_totaltrials ~ inco...

Dear R-helpers, I'd like to use glmmPQL to predict binary responses based on a data.frame data1 containing N entries (N<1000): target covariate1 covariate2 covariate3 ... covariateM cluster 134131 1 -0.30031885 0 0 -2.886870e-07 1 38370 1 -0.04883229 0 1 -1.105720e-07 1 19315 1 -0.11084267 0 0 6.362602e-07 1 33806 1 -...

...inomial model (It does work when I use GLM Poisson). The GLM Negative Binomial gives the following error statement: Error in get.models(NBModel, subset = delta < 4) : object has no 'calls' attribute Here is the unsuccessful Negative Binomial code. > > BirdNegBin <-glm(B~Covariate1 + Covariate2 + Covariate3 + Covariate4 + Covariate5 + Covariate6 >+ Covariate7, data = SquareSumLowland,family = negative.binomial(theta = 1)) > > NegBinDredge <- dredge(BirdNegBin) > confset.d4 <- get.models(BirdNegBin, subset = delta < 4) > model.avg(confset.d4) > confs...

...a large dataset with several variables, one of which is a state variable, coded 1-50 for each state. I'd like to run a regression of 28 select variables on the remaining 27 variables of the dataset (there are 55 variables total), and specific for each state, ie run a regression of variable1 on covariate1, covariate2, ..., covariate27 for observations where state==1. I'd then like to repeat this for variable1 for states 2-50, and the repeat the whole process for variable2, variable3,..., variable28. I think I've written the correct R code to do this, but the next thing I'd like to do is...

...rmally distributed (with ~50 levels). One aim of the analysis is to assess whether the measures are related to certain covariates, and I have tried the generalised estimating equation function geeglm (library geepack) with the 'gaussian' family details like so: geeout <- geeglm(outcome ~ covariate1 + covariate2, id=familyID, family=gaussian, data=dat, corstr="unstructured") But I'm thinking that the limited range of the outcome violates the assumption of normality and that the results could be off. Q: Is there a way in R, either in geeglm or another appropriate function, to tak...

...Date: Sat, 21 Oct 2006 12:08:13 -0400 From: Larry Schaeffer <lrs at uoguelph.ca> Reply-To: lrs at uoguelph.ca To: Animal Geneticist's Discussion <agdg-list at colostate.edu> For those that are interested only: R Computer Programming Challenge Given: y = Factor A + Factor B + b1(Covariate1) + b2(Covariate2) + ... + bp(Covariate p) + animal + e where y is a single trait - vector of observations, Factors A and B are fixed with ma and mb levels respectively, Covariates 1 to p are fixed regressions and p is a general number but less than 20. Heritability is a variable and should be reque...

...lly x = 3 objects. In the next time step, t1, the number of total objects is Poisson-distributed, subject to a function of x at t0, covariates, and parameters. So x_t1 ~ Pois, with E(x_t1) = f(x_t0, covariates, parameters). Let's choose a very simple function for f, say just f = x * par1 * Covariate1. Now let this process be repeated for say 6 times, always with the number of objects obtained in a previous step as input (x) for the next step. The problem is, at all time steps the total number of objects remains unobservable, because they are only detected with a certain, low probability (it...

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >