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I've got a completely randomized block design with three treatments and four replications. Biodiversity was measured in four successive years.

I figured that a mixed model with repeated measures as random terms should be appropriate to analyse this design.

My hypothesis is that considering all years, biodiversity is different between the treatments.

This is my analysis:

library(nlme)
library(multcomp)
# Made-up random dataset
mydata <- data.frame(
  Treatment=rep(c("Control", "Irrigation", "Fertailization"), 16), 
  Block=rep(1:4, 12), 
  Year=rep(2000:2003, 12), 
  Value=runif(48, 0.5, 1.5)
)
# Model Treatment is a fixed effect, Year is a random effect
fit <- lme(Value ~ Treatment,  random = ~1|Year, data = mydata)
# Post-hoc comparison
summary(glht(fit,linfct=mcp(Treatment="Tukey")))

My questions:

1. Is my model correct?

2. Is the post-hoc comparison appropriate?

3. How could I include the bock effect?

I've got a completely randomized block design with three treatments and four replications. Biodiversity was measured in four successive years.

I figured that a mixed model with repeated measures as random terms should be appropriate to analyse this design.

My hypothesis is that considering all years, biodiversity is different between the treatments.

This is my analysis:

library(nlme)
library(multcomp)
# Made-up random dataset
mydata <- data.frame(
  Treatment=rep(c("Control", "Irrigation", "Fertailization"), 16), 
  Block=rep(1:4, 12), 
  Year=rep(2000:2003, 12), 
  Value=runif(48, 0.5, 1.5)
)
# Model Treatment is a fixed effect, Year is a random effect
fit <- lme(Value ~ Treatment,  random = ~1|Year, data = mydata)
# Post-hoc comparison
summary(glht(fit,linfct=mcp(Treatment="Tukey")))

My questions:

1. Is my model correct?

2. Is the post-hoc comparison appropriate?

3. How could I include the bock effect?

If I understood you right, "Year" should be nested in "Block" - so the correct model would be coded like this:

fit <- lme(Value ~ Treatment,  random = ~1|Block/Year, data = mydata)

There seems to be a linear temporal trend in the data. How could I account for this in the model?

I've got a completly randomized block design with three treatments and four replications. Biodiversity was measured in four successive years.

I figured that a mixed model with repeated measures as random terms should be appropriate to analyse this design.

My hypothesis is that considering all years, biodiversity is different between the treatments.

This is my analysis:

library(nlme)
library(multcomp)
# Made-up random dataset
mydata <- data.frame(
  Treatment=rep(c("Control", "Irrigation", "Fertailization"), 16), 
  Block=rep(1:4, 12), 
  Year=rep(2000:2003, 12), 
  Value=runif(48, 0.5, 1.5)
)
# Model Treatment is a fixed effect, Year is a random effect
fit <- lme(Value ~ Treatment,  random = ~1|Year, data = mydata)
# Post-hoc comparison
summary(glht(fit,linfct=mcp(Treatment="Tukey")))

My questions:

1. Is my model correct?

2. Is the post-hoc comparison appropriate?

3. How could I include the bock effect?

I've got a completely randomized block design with three treatments and four replications. Biodiversity was measured in four successive years.

I figured that a mixed model with repeated measures as random terms should be appropriate to analyse this design.

My hypothesis is that considering all years, biodiversity is different between the treatments.

This is my analysis:

library(nlme)
library(multcomp)
# Made-up random dataset
mydata <- data.frame(
  Treatment=rep(c("Control", "Irrigation", "Fertailization"), 16), 
  Block=rep(1:4, 12), 
  Year=rep(2000:2003, 12), 
  Value=runif(48, 0.5, 1.5)
)
# Model Treatment is a fixed effect, Year is a random effect
fit <- lme(Value ~ Treatment,  random = ~1|Year, data = mydata)
# Post-hoc comparison
summary(glht(fit,linfct=mcp(Treatment="Tukey")))

My questions:

1. Is my model correct?

2. Is the post-hoc comparison appropriate?

3. How could I include the bock effect?

I've got a completely randomized block design with three treatments and four replications. Biodiversity was measured in four successive years. I figured that a mixed model with repeated measures as random terms should be appropriate to analyse this design. My hypothesis is that considering all years, biodiversity is different between the treatments.

This is my analysis:

library(nlme)
library(multcomp)
# Made-up random dataset
mydata <- data.frame(Treatment=rep(c("Control","Irrigation","Fertailization"), 16), 
                     Block=rep(1:4,12), Year=rep(2000:2003, 12), Value=runif(48,0.5,1.5))

# Model Treatment is a fixed effect, Year is a random effect
fit <- lme(Value ~ Treatment,  random = ~1|Year, data = mydata)
 
# Post-hoc comparison
summary(glht(fit,linfct=mcp(Treatment="Tukey")))

• Is my model correct?
• Is the post-hoc comparison appropriate?
• How could I include the bock effect?

I've got a completly randomized block design with three treatments and four replications. Biodiversity was measured in four successive years. 

I figured that a mixed model with repeated measures as random terms should be appropriate to analyse this design. 

My hypothesis is that considering all years, biodiversity is different between the treatments.

This is my analysis:

library(nlme)
library(multcomp)
# Made-up random dataset
mydata <- data.frame(
  Treatment=rep(c("Control", "Irrigation", "Fertailization"), 16), 
  Block=rep(1:4, 12), 
  Year=rep(2000:2003, 12), 
  Value=runif(48, 0.5, 1.5)
)
# Model Treatment is a fixed effect, Year is a random effect
fit <- lme(Value ~ Treatment,  random = ~1|Year, data = mydata)
# Post-hoc comparison
summary(glht(fit,linfct=mcp(Treatment="Tukey")))

My questions:

1. Is my model correct?

2. Is the post-hoc comparison appropriate?

3. How could I include the bock effect?