# How to make the linear model work

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
---
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.

Thank you.

• Are you sure your variables, Precip and Rad are okay? They look NA in the model. – HelloWorld Nov 21 '15 at 5:40
• Yes, you have to average your climate data in a meaningful way so that you end up with a single value for each growing season. The NAs in the model most likely came up because there is insufficient data to estimate the model as it is specified since there are only three data points (i.e. years in which height was measured) and four independent variables. But it would be best if you would provide some data with that output. – Stefan Nov 21 '15 at 6:10