[R-sig-eco] Can I weight LS means estimates by elevation in lme4/lmerTest packages?
Chris Howden
chris at trickysolutions.com.au
Mon Dec 1 07:44:09 CET 2014
Hi Mathew,
Are you sure you want to 'weight' by elevation? That would imply higher elevations have a greater weight on the analysis?
I think you actually want to know if elevation changes the effect of your other predictors? In which case you need to include it as an interaction with those other predictors. That said you may need to convert elevation into a categorical predictor first. This would basically give you different predictor parameters for different elevations, you could then look to see if they differ.
Chris Howden
Founding Partner
Tricky Solutions
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> On 29 Nov 2014, at 12:35, Matthew Van Scoyoc <scoyoc at gmail.com> wrote:
>
> Good afternoon r-sig-ecology,
>
> I'm running linear mixed models using the lme4 and lmerTest packages to
> examine ecosystem structure in grasslands and shrublands. The grasslands
> are located at lower elevations than the shrublands, and I would like to
> weight the estimates from LS means to reflect the differences in
> elevations. My colleague says there is an easy way to do it in SAS, but I
> haven't found a way to do it in R.
>
> There is an example dataset and workflow. Here I would be examining grass
> cover differences between the two ecosystems, the quality of the
> ecosystems, and examining the interaction between ecosystem and quality. I
> am also interested in systematic changes throughout the study area (not
> represented in this example) so I don't want to run separate analyses on
> for each ecosystem. I just want to adjust the LS means estimates to reflect
> the differences in elevation and not a mean elevation of the sampled plots.
>
>> library("lme4")
>> library("lmerTest")
>>
>> df = data.frame(PlotID = rep(c(paste0("G", 1:30), paste0("S", 1:30)), 2),
> + SamplePeriod = as.factor(c(rep(2012, 30), rep(2014,
> 30))),
> + Ecosystem = rep(c(rep("Grassland", 30), rep("Shrubland",
> 30)), 2),
> + Quality = rep(rep(c(rep("Good", 10), rep("Moderate", 10),
> rep("Poor", 10)), 2), 2),
> + GrassCover = c(runif(10, min = 0.50, max = 0.85), # 2012
> Grassland Good
> + runif(10, min = 0.50, max = 0.60), # 2012
> Grassland Moderate
> + runif(10, min = 0.30, max = 0.40), # 2012
> Grassland Poor
> + runif(10, min = 0.25, max = 0.60), # 2012
> Shrubland Good
> + runif(10, min = 0.20, max = 0.45), # 2012
> Shrubland Moderate
> + runif(10, min = 0.05, max = 0.25), # 2012
> Shrubland Poor
> + runif(10, min = 0.50, max = 0.90), # 2014
> Grassland Good
> + runif(10, min = 0.50, max = 0.55), # 2014
> Grassland Moderate
> + runif(10, min = 0.30, max = 0.30), # 2014
> Grassland Poor
> + runif(10, min = 0.25, max = 0.60), # 2014
> Shrubland Good
> + runif(10, min = 0.20, max = 0.30), # 2014
> Shrubland Moderate
> + runif(10, min = 0.05, max = 0.15))) # 2014
> Shrubland Poor
>> Elevation = c(sample(1200:1700, size = 30, replace = T),
> sample(1800:3000, size = 30,
>> replace = T))
>> df$Elevation = c(Elevation, Elevation); rm(Elevation)
>>
>> lmm = lmer(GrassCover ~ Ecosystem*Quality + Elevation + (1|PlotID), data
> = df, REML = T)
>> anova(lmm, ddf = "Satterthwaite", type = 3, method.grad = "Richardson")
> Analysis of Variance Table of type 3 with Satterthwaite
> approximation for degrees of freedom
> Sum Sq Mean Sq NumDF DenDF
> F.value Pr(>F)
> Ecosystem 1.68172 1.68172 1 113
> 51.239 8.79e-11 ***
> Quality 1.97119 0.98560 2 113
> 152.611 < 2.2e-16 ***
> Elevation 0.00311 0.00311 1 113
> 0.016 0.89978
> Ecosystem:Quality 0.04201 0.02101 2 113 3.604
> 0.03039 *
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>> lsms = lsmeans(lmm, method.grad = "Richardson")
>> estimates = lsms[[1]]$Estimate
>
> At this point I would be plotting the estimates of the significant response
> variables and interactions to look at the differences. As I understand, the
> ecosystem effect and the interaction between ecosystem and quality are with
> an average elevation for all plots, and this could exaggerate LS means
> estimate. Correcting for elevation would provide more accurate
> estimations. Right?
>
> Okay, thanks for you help.
> Cheers,
> MVS
> =====
> Matthew Van Scoyoc
>
> <https://mail.google.com/mail/?view=cm&fs=1&tf=1&to=mvanscoyoc@aggiemail.usu.edu>
> https://sites.google.com/site/scoyoc/
> =====
> Think SNOW!
>
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>
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