I'm pretty new to this, so please bear with me.

I am trying to analyse complicated dataset of microbiome data and I can't figure out what I'm doing...

I have three conditions:

1. Treatment: None, Chronic Stress
2. Status: PNS, Control
3. Time.Point: Before, After

I'm trying to see if there is a main effect or interaction of any of these conditions, and then do pairwise comparisons.

Because it's a phyloseq object I first convert it to a DESeq Data Set

dsq <- phyloseq_to_deseq2(subset2, ~Status+Treatment+Time.Point)

Then I turn the columns into factors

dsq$Status <- factor(dsq$Status, levels = c("Control","PNS"))
dsq$Treatment <- factor(dsq$Treatment, levels = c("None","Chronic Stress"))
dsq$Time.Point <- factor(dsq$Time.Point, levels = c("Before","After"))

I then run DESeq

dsq <- DESeq(dsq)

And get the names

resultsNames(dsq)

[1] "Intercept"                        "Status_PNS_vs_Control"            "Treatment_Chronic.Stress_vs_None"
[4] "Time.Point_After_vs_Before"  

Then the reults

res2 <- results(dsq)

log2 fold change (MLE): Time.Point After vs Before 
LRT p-value: '~ Status + Treatment + Time.Point' vs '~ 1' 
DataFrame with 39751 rows and 6 columns

etc.

How do I see if there is a main effect of any of the conditions?

I have tried:

dsq <- phyloseq_to_deseq2(subset2, ~Status+Treatment+Time.Point + Status:Treatment:Time.Point)

But get an error

Error in checkFullRank(modelMatrix) : 
  the model matrix is not full rank, so the model cannot be fit as specified.
  Levels or combinations of levels without any samples have resulted in
  column(s) of zeros in the model matrix.

Unfortunately, I have to focus my time on the support site to software related questions. For statistical consultation on how to set up the design and interpret contrasts, I'd recommend to work with a local statistician or someone familiar with linear modeling in R.