search for: model0

1. The function "KalmanLike" seems to change its inputs AND PREVIOUSLY MADE copies of the inputs. Consider the following (using R 2.1.0 patched under Windows XP): > Fig2.1 <- StructTS(x=Nile, type="level") > unlist(Fig2.1$model0[2:3]) a P 1120 286379470 > tst2 <- tst <- Fig2.1$model0 > tst23 <- tst[2:3] > tst23u <- unlist(tst23) > nile.KL <- KalmanLike(nile, tst2) > unlist(tst[2:3]) a P 798.3682 4032.1469 > unlist(tst2[2:3]) a...

Hi, I am running R2.8.1 under Linux, and I am having trouble using the variance functions in nlme My basic model was something like: model0 <- lme( log(growth) ~ light * species.group , data=data, random=~light|species ) # with 20 odd species divided in 2 groups Following the methods in Pinheiro&Bates I tried to put a variance function in the model: model1 <- update(model0, weights=varIdent(form = ~1|species.group) ) I...

Hi all: My data has 3 variables: age(3levels : <30y=1 30-50y=2, >50y=3) gender(Male=0, Female=1) CD4 cell count(raw lab measurement) y(1:death 0:alive) I perform logistic regression to find out the factors that influence y. result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial) >From the result,I can get OR(Odds Ratio) of gender via exp(Estimate of Female,

...point. > > > Program > #Read data > data.dir<-system.file("dta",package="mitools") > files.imp<-imputationList(lapply(list.files(data.dir, > pattern="imp.\\.dta", full=TRUE), read.dta)) > > #estimate model over each imputed dataset > model0<-with(files.imp,lmer( erq2tnc ~1+trt2+nash+wash+male+coh2+coh3+(1 | > sitebeth))) > #extract betas and standard errors > betas<-MIextract(model0,fun=coef) > vars<-MIextract(model0,fun=vcov) > #Combine the results > summary(MIcombine(betas,vars)) > > Error in cbar +...

...rning logistic regressions. When I add a quadratic term to my linear model, I cannot draw the line through my scatterplot anymore, which is no problem without the quadratic term. In this example my binary response variable is "incidence", the explanatory variable is "sun": > model0<-glm(incidence~1,binomial) > model1<-glm(incidence~sun,binomial) > anova(model0,model1,test="Chi") Analysis of Deviance Table Model 1: incidence ~ 1 Model 2: incidence ~ sun Resid. Df Resid. Dev Df Deviance P(>|Chi|) 1 299 332.94 2...

Is it possible to create a database in MySQL via RMySQL? Also, is the format for the authorization field 'userName/password at databasename'? I saw an example like this somewhere in the documenation, but I haven't found the actual specification. Thanks, Barnet Wagman

Hi, how can I change the significance level in test F to select variable in step command? I used step(model0, ~x1+x2+x3+x4, direction=c("forward"), test='F', alpha=.05) but it does't work. -------------------------------------- Silvano Cesar da Costa Departamento de Estat?stica Universidade Estadual de Londrina Fone: 3371-4346

Dear all, In a greenhouse experiment we tested performance of 4 different species (B,H,P,R) under 3 different water levels in 10 replications. As response variable e.g. the number of emerging sprouts were measured on three dates. A simple Anova considering every measurement date separately shows a higly significant effect of species and moisture (and partly the interaction of both). The

Hi all, Here is my problem: I performed bioassays using a unique insecticide on 9 different genotypes and got their mortality depending on the dose of insecticide used. Now, I want to see wether some genotypes are different or not in their responses to insecticide. My problem is that I have up to four replicates for some genotypes, but only one for other... Due to this unbalanced design, I

I am struggling to generate p values for comparisons of levels (post-hoc tests) in a glm with a negative binomial distribution I am trying to compare cell counts on different days as grown on different media (e.g. types of cryogel) so I have 2 explanatory variables (Day and Cryogel), which are both factors, and an over-dispersed count variable (number of cells) as the response. I know that both

...uot;) valideta <- function(eta) TRUE link <- paste("logexp(", days, ")", sep="") structure(list(linkfun = linkfun, linkinv = linkinv, mu.eta = mu.eta, valideta = valideta, name = link), class = "link-glm") } model0 <- glm(survive~1, family=binomial(logexp(days=expos))) ##Call to glm with custom link --------------------- Best, Dan Barton -- Daniel C. Barton PhD Candidate USGS Montana Cooperative Wildlife Research Unit & Program in Organismal Biology and Ecology University of Montana 205 Natural Sci...

hi, people How can we compare two probit models brought out from the same data? Let me use the example used in "An Introduction to R". "Consider a small, artificial example, from Silvey (1970). On the Aegean island of Kalythos the male inhabitants suffer from a congenital eye disease, the effects of which become more marked with increasing age. Samples of islander males

Hi, I am running a Cox Mixed Effects Hazard model using the library coxme. I am trying to model time to onset (age_sym1) of thought problems (e.g. hearing voices) (sym1). As I have siblings in my dataset, I have decided to account for this by including a random effect for family (famid). My covariate of interest is Mother's diagnosis where a 0 is bipolar, 1 is control, and 2 is major