lme

  1. Luane

    Sound propagation modeling (with lme, unequal sample size, and "drop out" problems?)

    I conducted a sound propagation experiment in which 20 different recorded roar-barks (the long distance vocalization of maned wolves) were played back at different sites(x3), hours(x6: 17h,18h,23h,05h,06h,11h), and with different speaker position (x2: straight forward and inclined upward 45o)...
  2. M

    post-hoc interaction following lme with both categorical and continuous factors.

    Hello, I'm relatively new to R and have run into some problems. Hoping someone can help! I'm using lme on a model with several, both within/between, categorical/continuous and fixed/random effects. I have 1 x fixed 2-level categorical between-subjects variable "group" (dose vs. placebo), 1...
  3. H

    "No (non-missing) observations" error in jointModelBayes function

    Hi all, I'm trying to fit a joint longitudinal and time-to event model in R as shown here:http://www.r-bloggers.com/joint-models-for-longitudinal-and-survival-data/ Patients were randomized to placebo or treatment ("Treatment_Group"), had a biomarker ("CII") measured at multiple times...
  4. C

    "getVarCov" in lme

    Is "getVarCov" of lme output gives variance-covariance of estimated "Standard Deviation" of random effects or variance-covariance of estimated "variance" of random effects . library(nlme) fm1 <- lme(distance ~ age, data = Orthodont) VarCorr(fm1) getVarCov(fm1) That is , VarCorr(fm1)...
  5. C

    How is to define the lme model syntax .

    The following model assumes that the covariance between the random intercepts and random slopes across Subjects is Zero . lmer(Reaction ~ Days + (Days||Subject), sleepstudy) How can I write the same model by "lme" function under nlme pacakge assuming zero covariance between the random...
  6. L

    Cross validation for linear mixed effect model

    Hi all, I try to compare predictive performance between linear regression and linear mixed model based on the MAE value calculated from cross validation. That is an trivial task for linear regression but I do not know how to perform that test on lmm in R. How I can split the data into...
  7. S

    Estimating fixed vs. random effects with lme (Hausman?)

    I have ran two lme mixed effects models in R, both using the same fixed effects variables but each with a different random effect variable. My reviewer has said I should use the Hausman test to estimate the effect of fixed vs. random effects. On looking in to this I can only find R code for the...
  8. X

    How to report a non-significant main effect with significant contrasts (LME)

    Hi :wave:, Sorry for a lengthy first post, but I'm at my wits end :( I am using multilevel linear modelling with nlme's lme function to analyse a mixed design (2 categorical predictors, 1 a repeated measure with 5 levels, the other not repeated with 3 levels). From Andy Field's...
  9. R

    [R – lme function] Comparing curves in longitudinal data (growth curves)

    I think my problem is best solved by the use of multi-level models. However, I’d like to confirm if what I’m doing is right, and the use of the function is right too. I work with cells (biology), by doing electrophysiology. I apply to each cell a series of currents, in 20 pA steps, ranging from...
  10. P

    Trouble with random factors glmm

    Hi. I am doing a behavioural ecology study on juvinile brown trout and want to perform a glmm (glmer, package lme4) to analyse the effect of activity, size and treatment on survival. The variables i am using and are: response-survival (dead or alive), fixed effects- size (continuous), activity...
  11. A

    Advice on linear mixed model on longitudinal data

    Hello, I have a longitudinal dataset of cumulative biomass from an experiment. The subjects have been exposed of 3 different treatments (LOW, MED, HI) during 194 days, and measured repeatedly at 40 occasions. According to my plot, it looks like one of the HI treatment is starting to differ...
  12. J

    split-plot design and lme

    I’m working on a data set in order to evaluate the impact of drying on sediment microbial activities. The objective is to determine if the impact of drying varies among sediment types and/or depth within the sediment. The experimental design is as follows: The first factor Sediment...