I ran a linear model using 2 variables which I "log+1" transformed prior to analysis. I included a fixed effect of month of sample collection (factor with 3 levels - September, October and December). My model formula was as follows: ww_log1 ~ int_log1 + month, data=perla, where ww_log1 is log+1 transformed values for "wet weight (g)" and int_log1 is log+1 transformed values for "intraocular distance (mm)". I am unsure of my interpretation of the resulting output as I am not so familiar with interpreting fixed effects (like the differences between months here). Is someone able to provide their insight? It would be greatly appreciated.
Model output:
lm(formula = ww_log1 ~ int_log1 + month, data = perla)
Residuals:
Min 1Q Median 3Q Max
-0.059159 -0.022567 -0.003213 0.016212 0.169219
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.183241 0.004996 -36.675 < 2e-16 ***
int_log1 0.249509 0.004651 53.646 < 2e-16 ***
monthB - October -0.009703 0.003853 -2.518 0.01203 *
monthC - December -0.008314 0.002742 -3.032 0.00253 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0318 on 643 degrees of freedom
Multiple R-squared: 0.8251, Adjusted R-squared: 0.8243
F-statistic: 1011 on 3 and 643 DF, p-value: < 2.2e-16