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:

setwd("/Users/mc1499/Documents/Thesis Measurements/2017 & 2018 Fall/GDD Rye")

dat<-read.csv("GDD Rye CSV.csv")

head(dat)

block<-as.factor(dat$Rep)

trt<-as.factor(dat$Treat)

seeddate<-as.factor(dat$SeedingDate)

yr<-as.factor(dat$year)

str(dat)

DIA0WAS<-lme(CalDays ~ Treat*SeedingDate,random = ~1|year/Rep,data=dat)

anova(DIA0WAS)

predictmeans(DIA0WAS,"Treat:SeedingDate",pairwise=TRUE)

I am trying to graph my data and the programs needs either % critical value or standard deviation, what code can I use to get that from my data?