I'd like to analyse data of 11 plots with 15 plant individuals on each plot.
A variable was measured on each plant in 9 different years.
So 11 plots with 15 plants on each plot and measurements in 9 years = 1485 observations.
year id plot val 1 2000 A_01 A 0.70 2 2000 A_02 A 0.90 3 2000 A_03 A 0.79 4 2000 A_04 A 1.04 5 2000 A_05 A 0.84 6 2000 A_06 A 0.84 ... year id plot val 1480 2008 N_10 N 0.35 1481 2008 N_11 N 0.72 1482 2008 N_12 N 0.36 1483 2008 N_13 N 0.20 1484 2008 N_14 N 0.41 1485 2008 N_15 N 0.51
My goal is to find differences on each plot between years as well as differences in each year between the plots.
Thus my analysis in R looks like this so far:
library(lme4) mod <- lmer(val ~ year * plot + (1|id), data = dat) > summary(mod) Linear mixed model fit by REML ['lmerMod'] Formula: val ~ year * plot + (1 | id) Data: dat REML criterion at convergence: 299.6 Scaled residuals: Min 1Q Median 3Q Max -3.0167 -0.5385 -0.0967 0.4548 14.6926 Random effects: Groups Name Variance Std.Dev. id (Intercept) 5.596e-17 7.481e-09 Residual 5.992e-02 2.448e-01
As I want to calculate p-values to find significant differences between years/plots I think I need to make sure I have normally distributed residuals of the random effects (?).
So I'm looking at the residuals ..
mod.res <- ranef(mod)$id$`(Intercept)`
.. and find that they are right-skewed:
So I found that there is the package
robustlmm that - I think - can take care of that.
rmod <- rlmer(val ~ year * plot + (1|id), data = dat)
However, now the variance of the random effect is 0
Robust linear mixed model fit by DAStau Formula: val ~ year * plot + (1 | id) Data: dat Scaled residuals: Min 1Q Median 3Q Max -3.5596 -0.6193 -0.0762 0.6120 19.8078 Random effects: Groups Name Variance Std.Dev. id (Intercept) 0.00000 0.000 Residual 0.03766 0.194
I am stuck now. What does that mean? How can I proceed?