Scenario: I have data comparing the number of tree stems in 30 forest plots between two sampling years (1992 and 2012). Each plot received hurricane damage between these 2 sampling years -- this damage was coded as being 0-100% of trees felled/damaged.
I ran a linear regression using lm() in R including a centered year term, hurricane damage, and an interaction term between them.
I get the following output:
Call:
lm(formula = Count.Ha ~ I(Year - 1992) * HurrDam, data = dataset, ])
Residuals:
Min 1Q Median 3Q Max
-368.84 -69.79 -23.01 81.30 413.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 147.3300 50.7297 2.904 0.00529 **
I(Year - 1992) -17.2595 3.4007 -5.075 4.73e-06 ***
HurrDam -1.4680 1.6764 -0.876 0.38503
I(Year - 1992):HurrDam 0.7634 0.1128 6.766 9.11e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 138.1 on 55 degrees of freedom
Multiple R-squared: 0.5886, Adjusted R-squared: 0.5662
F-statistic: 26.23 on 3 and 55 DF, p-value: 1.15e-10
As you can see, Year is significant as is the interaction term, but HurrDam is not.
How do I interpret this??
I've seen a number of posts discussing intepretation when discreet variables or even continuous non-bounded variables are involved, but I'm not sure how my inclusion of a time variable and a bounded percentage as a variable impact the way one would interpret these results.
Note: my ultimate hypothesis I'm trying to investigate is that the number of stems did not increase with time except in plots with greatest hurricane damage.
HurrDamchanges. Also, with a count outcome variable you perhaps should be using a count-based generalized linear model (Poisson, negative binomial) instead, and in either case a mixed model or robust coefficient (co)variance estimates if you are evaluating the same plots over time. $\endgroup$