# How can I interpret the output of lme

I am having some problems to interpret the output of lme on my model. This is my thesis work and I am having a hard time to work on the model. Here is the model;

model2 <- lme(Kfs~Density+Treat+Year, data=inf, random=~1|SiteNo/Plot, na.action=na.omit).

Kfs is Field- saturated hydraulic connectivity and Density is Bulk Density and these values measured $2$ times in $2$ years, so they are repeated measurements. These were measured on $5$ or $6$ different treatment types, so Treat means Treatments. I had $3$ different sites and many plots in each site. I used plot and sites as random part because we did not know where to make these measurements, so they were randomly selected.

Here is another question, Does R recognize the repeated measurements from the model or should I need to add more things?

I also added the output of the model to get help interpreting it.

Any help will be very useful for me. I do not know how to add data here. If you need to see the data, please inform me.

Thanks, Iskender

Thanks for the answer! Can you give me more details because they look all have different intercept and the slope? My purpose is to understand what is the statistical difference between Treat types especially between L, LSC, and MP.

I looked at some more details and I found how they make R recognize the repeated measurements. Here is the new code; see the new outputs Thanks, Iskender

Output of lme from the last model.

This is output lsmeans. I want to interpret this but There is no p-value, I need help how to add P-value and I also gave an example that is what I want to get from output lsmeans.

Thanks!

That is what I want to get if it is possible to get from lsmeans.

• To test for differences in those treatment groups you'd need to set up the dummy variables so, for example, L is the reference level, in which case the coefficient for LSC would quantify the difference between those two groups (and you can look at the corresponding p-value). To test for overall heterogeneity between the treatment groups, compare this model with the one that excludes the treatment variable complete (e.g. using a likelihood ratio test). – gammer Jan 30 '17 at 5:13