I'm looking at effects of tree mortality (using "Biomass loss") on forest growth patterns. I incorporate loss into a mixed effects model like so (using
lmer in R):
lmer(GrowthRate ~ Year + BiomassLoss + (1 + Year | Plot), data = dat, REML = F)
BiomassLoss is negative (i.e., the range of the actual values is
-50 to 0).
So when I examine my model estimates, how do I interpret the estimate for
95% Confidence Intervals Estimate Lower Upper (Intercept) 7.97955622 6.44676081 9.51907782 Year 0.02984233 0.01092181 0.04854870 BiomassLoss 0.09893282 0.06394322 0.13372741
Normally (i.e., for a predictor on a positive scale), the estimate can be interpreted as:
For every unit increase of the predictor, the
interecptwill increase by the value of the predictor's estimate.
But how does this work for a negatively-scaled predictor?
Does "every unit increase" of a negatively-scaled predictor mean:
as it becomes less negative (i.e., as it increases in value) or
as it becomes more negative (i.e., as it increases in magnitude) ?
In other words, do I interpret my results as:
for every unit less of biomass lost my intercept increases by
for every unit more (i.e., every unit lost) my intercept increases by
This is important so I can understand if mortality is having a positive or negative impact on the trend...