I feel like this is a simple and stupid question but I cannot seem to find an answer that works.
How do I calculate specific statistical results in a mixed model analysis with 'lme()'?
Data:
| year | Plot | SeedingDate | Rep | Treat | CalDays |
|---|---|---|---|---|---|
| 2017 | 11 | Sep | 1 | Controlled | 161 |
| 2017 | 12 | Sep | 1 | Deep Fraze | 35 |
| 2017 | 13 | Sep | 1 | Verticut | 161 |
| 2017 | 14 | Sep | 1 | Less Fraze | 84 |
| 2017 | 15 | Sep | 1 | Scalped | 63 |
| 2017 | 21 | Sep | 2 | Less Fraze | 63 |
| 2017 | 22 | Sep | 2 | Deep Fraze | 49 |
| 2017 | 23 | Sep | 2 | Verticut | 84 |
| 2017 | 24 | Sep | 2 | Scalped | 84 |
| 2017 | 25 | Sep | 2 | Controlled | 84 |
| 2017 | 31 | Sep | 3 | Scalped | 35 |
Code:
library(nlme)
setwd("/Users/mc1499/Documents/Thesis Measurements/2017 & 2018 Fall/GDD Rye")
dat<-read.csv("GDD Rye CSV.csv")
block<-as.factor(dat$Rep)
trt<-as.factor(dat$Treat)
seeddate<-as.factor(dat$SeedingDate)
yr<-as.factor(dat$year)
DIA0WAS<-lme(CalDays ~ Treat*SeedingDate,random = ~1|year/Rep,data=dat)
anova(DIA0WAS)
I am trying to graph my data and the program needs either % critical value or standard deviation, but anova does not give me those.
What code can I use to get that from my data?