Any help with my problem could be appreciated. So I'm trying to run a linear model comparing four sets of climate data (daily temperature, daily relative humidity, daily precipitation, and daily solar radiation) to two sets of growth data for willow trees (diameter and height). The climate data was recorded on a daily basis over the course of the growing season (5 months) over 3 years, while the growth data was recorded at the end of each of these 3 years when each growing season ended and the growth for that year has ended.
Now I'm trying to run a linear model comparing how the climate variables may be affecting these growth factors, but I can't seem to make it work. Do I need to ensure that the time frames for both climate and growth variables are the same? Meaning, should I average the climate variables to get averages I can compare against with the yearly records of the growth variables? And if so, how should I go about doing it correctly? Considering I keep getting results like this if I average the climate data and compare it against the yearly growth data:
Call: lm(formula = Maxht ~ temp + RH + Precip + Rad, data = g1) Residuals: Min 1Q Median 3Q Max -373.06 -26.60 4.57 32.57 158.20 Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 17674.190 1460.144 12.10 <2e-16 *** temp -768.808 67.886 -11.32 <2e-16 *** RH -58.125 4.272 -13.61 <2e-16 *** Precip NA NA NA NA Rad NA NA NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 51.05 on 357 degrees of freedom Multiple R-squared: 0.4514, Adjusted R-squared: 0.4483 F-statistic: 146.9 on 2 and 357 DF, p-value: < 2.2e-16
Any help would be appreciated and I'd be happy to explain as best as I can to clarify if needed.