Dear All,

I am struggling to interpret the result of the hypothesis function in an analysis I’ve run, and would appreciate any guidance. I’m not sure whether I have done something wrong or am just failing to understand the output.

I’ve run the following model to analyse a between-subjects single-factor experiment (with a continuous dependent variable and 18 items):

m1 <- brm(DV ~ Condition + (Condition|Item) + (1|ID), data = Data, iter = 4000, chains = 3, refresh = 0, prior = c(prior(normal(0, 100), class = “Intercept”), prior(normal(0, 100), class = “b”), prior(cauchy(0, 5), class = “sigma”)), sample_prior = TRUE)

The treatment-coded variable ‘Condition’ has the levels ‘Null’, ‘Double’, ‘PropAtt’, and ‘Unhedged’. The model produces the following estimates for the population-level effects.

`Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat Intercept 77.04 2.76 71.48 82.46 798 1.00 ConditionDouble -27.70 3.58 -34.67 -20.32 767 1.01 ConditionPropAtt -27.98 3.53 -34.87 -21.05 859 1.01 ConditionUnhedged -25.23 3.53 -32.16 -18.19 879 1.01`

I’d like to test some point hypotheses with the ‘hypothesis’ function. This works well for most hypotheses. For instance:

hypothesis(m1, “ConditionUnhedged = ConditionPropAtt”)

produces

Hypothesis Tests for class b:

Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio

1 (ConditionUnhedge… = 0 2.75 3.44 -4.14 9.5 30.42

Post.Prob Star

1 0.97

But comparing the null (control) condition with the others produces a result I don’t understand. For instance:

hypothesis(m1, “Intercept = ConditionUnhedged”)

Hypothesis Tests for class b:

Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio

1 (Intercept)-(Cond… = 0 102.26 5.24 91.77 112.62 0

Post.Prob Star

1 0 *

This estimate doesn’t seem to correspond to the difference between the “Intercept” and “ConditionUnhedged” estimates for the population-level effects. And yet it seems like it should, according to examples in various online tutorials. If I compare the intercept versus specific values, however, it once again makes sense, i.e. testing whether the intercept, above, is equal to 75 would return the estimate 2.04.

Any help interpreting/correcting this would be very much appreciated. I’m sorry if it’s a rather basic question - I’m a newcomer to the package. I’m happy to provide any more information.

Many thanks!

- Operating System: macOS High Sierra
- brms Version: 2.40