similar to: correlation structure

I am having trouble figuring out the right form for the nlme arguments. I do have examples in Modern and Applied Statistics with S and from other sources, but I still can't figure it out. I am trying to estimate species richness (sr) in streams across minnesota. My predictor variables are depth (d), habitat diversity (habdiv), drainage area (da) and an indicator variable representing the

Dear R Friends, I need help with an error associated with corCompSymm in an lme fit. I am using a mixed effects model to analyze a split-plot with repeated measures and would like to fit with the compound symmetry correlation structure. This problem doesn't occur when using corAR1 or any of the other structures. I would greatly appreciate help on how to solve this issue. Here's my

Dear group, I am trying to use gamm() in mgcv. Here's the scenario. The data frame has approx. 110K observations with information on paediatric readmission binary outcome (Y/N) and total volume of their most responsible physician as the covariate. Since any physician can have multiple patients, the data contains clustering structure which I am trying to account for. My original formula is

Hi folks I have some data to which I've been fitting linear mixed effects models. I am currently using a lme model in the nlme package, with terms for random effects due to repeated measures on individuals and the corCAR1 serial correlation structure. However, there is some suggestion in the data (and from theory) that a breakpoint (change point) model may be more appropriate. Scott, Norman

Dear listers, As an exercise, I am trying to fit a logistic model with nlme. Blue tit pulli (youngs) were weighted occasionnally (for field reasons) along time in 17 nestboxes. Individuals where not idenfied but their age was known. This means that for a given age several measurements were done but individuals could not be identified from a time to the other. This makes repeated values for

Dear List, After running a compound symmetric model using gls, I realized that the default contrasts were not the ones that made the most sense given the biological relationships among the factor levels. When I either changed the factor levels to re-arrange the order they occur in the gls model (not shown below) OR specifically change the contrasts I get the exact same estimates for the

Dear all, I am trying to define my own corStruct which is different from the classical one available in nlme. The structure of this correlation is given below. I am wondering to know how to continue with this structure by using specific functions (corMatrix, getCovariate, Initialize,...) in order to get a structure like corAR1, corSymm which will be working for my data. Thanks in advance.

Dear R-users, I hope you will have time to read me and I will try to be brief. I am also sorry for my poor english. I used gls function from the package nlme to correct two types of bias in my database. At first, because my replicates are spatially aggregated, I would like to fit a corStruct function like corLin, corSpher, corRatio, corExp or corGaus in my gls model, and simultaneously,

I am trying to distribute the entries of a data frame (dat) to a vector (water) based on the values of two other vectors (region and year). region is also the columns and year the rows of the data frame (dat). I can write a bunch of ifelse statements or I created the for statement below - but there must be a simpler way?? The for statement probably won't work if I am distributing a non

Goal: use GEE or GLMM to analyze repeated measures data in R GEE problem: can?t find a way to do GEE with negative binomial family in R GLMM problem: not sure if I?m specifying random effect correctly Study question: Does the interaction of director and recipient group affect rates of a behavior? Data: Animals (n = 38) in one of 3 groups (life stages): B or C. Some individuals (~5)

Hi folks, I am working with repeated measures data and I ran into issues where the paired t-test results did not match those obtained by employing glht() contrasts on a lme model. While the lme model itself appears to be fine, there seems to be some discrepancy with using glht() on the lme model (unless I am missing something here). I was wondering if someone could help identify the issue. On

Full_Name: daniel t. O'Shea Version: 2.2.1 OS: xp Submission from: (NULL) (156.98.28.4) using the cor command in the base package. cor(x,y) x is a matrix (15 rows and 1000 columns). I did not specify a column and R crashed - shut down. error signature AppName:rgui.exe AppVer 2.21.51220.0 ModName: r.dll ModVer: 2.21.51220.0

I have a data frame and one separate vector that is a grouping variable for the data frame. I would like to take all rows of the data frame belonging to each group and then sum the columns with out using a for statement. Something like: take all rows of group 1 then apply(group1.data,1,sum), but do this without having to do it separately for each group. Any ideas? thank you. Dan

Dear R-devel members, While writing a new correlation structure similar to 'corCompSymm' and intended to be used with 'gnls', I got puzzled with the 'Initialize' method. Using 'Initialize' before 'gnls' may be regarded as a mean to set an initial value for the corStruct parameter. However 'gnls' does not work properly with a

I am trying to refer to a variable name in a lm regression where the variable name is in another variable, but it does seem to work. Here is an example: y<-rnorm(10) dat<-data.frame(x1=rnorm(10),x2=rnorm(10),x3=rnorm(10)) nam<-c('x1','x2','x3') library(gtools) com<-combinations(3,2,1:3) mod<-lm(y~nam[com[1,1]],data=dat) #error in model frame....:variable

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

Dear R-listers, I am fitting bivariate mixed models for cost-effectiveness data of cluster randomized trials using lme in R. So I have individuals nested within clusters. My response variable is a vector with bivariate response (individual level costs and effects) stacked into a single column. The covariates in my models are a constant and a treatment term. They are response-specific, e.g. a

Dear all, I'm using R function "glmmPQL" in "MASS" package for generalized linear mixed model considering the temporal correlations in random effect. There are 1825 observations in my data, in which the random effect is called "Date", and there are five levels in "Date", each repeats 365 times. When I tried

We are trying to use the corCompSymm function in nlme The example from the help pages for the corAR1 function gives the following > corAR1(0.2, form = ~ 1 | Mare) Correlation structure of class corAR1 representing Phi 0.2 We are expecting a somewhat similar correlation specification with the help page example for corCompSymm, but just get an error instead > corCompSymm(0.5, form = ~ 1 |

Hello all, I am trying to use lme to examine how a response variable (Chla) changes over time in different treatments (2 Temp & 2 Light levels). Within each treatment combination, there are two replicate tanks (each with unique TankID) with coral fragments in them. All tanks are subject to the same environment until Time=0, when treatments are imposed, and Chla is measured for each