similar to: Announcement: obsSens Package

Hi, I have the following conceptual / interpretative question regarding random effects: A mixed effects model was fit on biological data, with observations coming from different species. There is a clear overall effect of certain predictors (entering the model as fixed effect), but as different species react slightly differently, the predictor also enters the model as random effect and with

Dear R gurus I am analysing data from a study of behaviour and shade utilization of chimpanzees. I am using GLMs in R (version 2.13.0) to test whether shade/sun utilization is predicted by behaviour observed. I am thus interested in whether an interaction of behaviour (as a predictor) and presence in the sun/shade (also predictor) predicts the counts I have for the respective categories. I have

PhD studentship in Medical Statistics (full time, 3 years) - University of Exeter Medical School Applications are invited for a PhD studentship at the University of Exeter Medical School (UEMS). We are seeking to attract a PhD candidate of outstanding ability to join a rapidly expanding programme of internationally rated research. The successful applicant will pursue a project investigating new

Hello, I am having a problem figuring out how to model a continuous outcome (y) given a continuous predictor (x1) and two levels of nested categorical predictors (x3 nested in x2). The data are observational, not from a designed experiment. There are about 15 levels of x2 and between 3 and 14 levels of x3 nested within each level of x2. There are between 6 and 50 x1,y observations for each unique

A student brought this question to me and I can't find any articles or examples that are directly on point. Suppose there are 2 ordinal logistic regression models, and one wants to set them into a simultaneous equation framework. Y1 might be a 4 category scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America.

Hi all, I'm new (post #1!) and I hope you'll forgive me if I'm acting like an idiot... I have been asked for some power analyses for some mixed-effects models I'm running using lmer. My studies nearly always contain mixes of repeated-measures and between-subjects predictor variables. As an example, suppose I want to see if men or women show a stronger word frequency effect. I

I am interested in knowing whether and how I can test the significance of the relationship between my continuous predictor variable (a covariate) and my binary response variable according to two different groups, my categorical predictor variable, in a logistic regression model (glm). Specifically, can I determine whether the relationships are identical (the hypothesis of coincidence), or whether

I've fit a linear model to my data set using the <lm> function. One of the outputs of that function is a vector of the residuals. I would like to do a residual plot of this data versus a predictor variable, but the length of the residual vector is shorter than the length of the predictor variable vector. This is because when <lm> computes the residual vector, it deletes entries

Hi all, Following up on Holger's email last week: Does anyone know if there exists a library that implements the sensitivity tests for hidden bias for matched pairs and unmatched groups as proposed in Rosenbaum's Observational Studies (2002: ch.4)? Thanks. Best, jens

R-packages leaps and subselect implement various methods of selecting best or good subsets of predictor variables for linear regression models, but they do not seem to be applicable to logistic regression models. Does anyone know of software for finding good subsets of predictor variables for linear regression models? Thanks. -Ben p.s., The leaps package references "Subset Selection

Hi, I am using mgcv:gam and have developed a model with 5 smoothed predictors and one factor. gam1 <- gam(log.sp~ s(Spr.precip,bs="ts") + s(Win.precip,bs="ts") + s( Spr.Tmin,bs="ts") + s(P.sum.Tmin,bs="ts") + s( Win.Tmax,bs="ts") +factor(site),data=dat3) The total deviance explained = 70.4%. I would like to extract the variance explained

Hi David: If your variables are in a dataframe called DF and the dependent variable is in the first column , you can do below but you probably are well aware of this anyway. lmresults<-lapply(names(DF[,-1],function(.name) { lm(DF[,1] ~ DF[,.name], data=DF) }) This will run through each of the variables in the dataframe and regress the first column on each variable individually. On

My question is related to plot( ) in the mgcv package. Before modelling the data, a few predictors were transformed to normalize them. Therefore, the x-axes in the plots show transformed predictor values. How do I back-transform the predictors so that the plots are easier to interpret? Thanks in advance, Suzan -- Suzan Pool Oregon State University Cooperative Institute for Marine

Hi dear R community, I have up to 12 measures of a protein for each of 6 patients, taken every two or three days. The pattern of the protein looks periodic, but the height of the peaks is highly variable. I'm testing for periodicity using a Monte Carlo simulation envelope approach applied to a cumulative periodogram. Now I want to predict the location of the peaks in time. Of course, the

Hi all, I am trying to fit an ANOVA model in R using the aov/lm commands. I have a set of observational (i.e. no fixed experimental effects) data, in which I have identified high and low clusters of the response variable. The design is unbalanced, with 773 high cluster observations, and 523 low cluster observations. I would like to test a set of 7 correlates to see if there are significant

Hi all, does anyone know if there exists some library that implements the sensitivity tests for matched pairs and unmatched groups proposed in Rosenbaum's Observational Studies (2002: ch.4)? Cheers, Holger

Hi there, I have a question regarding model comparisons that seems simple enough but to which I cannot find an answer. I am interested in developing a predictive model relating some measure of a tree's stem to the total leaf area (TLA) of the tree. Predictor variables might include, for example, the total cross-sectional area of the tree (commonly referred to as basal area) or the amount

Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not sure how to simulate

Hi, Assume I have a variable Y having two discrete values and two predictor variables x1 and x2. I then do a logistic regression model fit as: fit<-glm(Y~x1+x2,family=binomial). Are there functions in R than calculate the Sensitivity, Specificity , and Agreement of the model "fit"? Thanks Meir ******************************************** Meir Preiszler - Research Engineer I t