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?