Dear all,
I would like to clear up some confusion regarding the SE argument within the
geiger package.
Namely, there can be 3 scenarios:
Scenario 1: SE = NA (no measurement error modeled/ignore SE)
By setting SE = NA, we tell the function to ignore any potential measurement
error in the trait data. The model assumes the trait values are measured
perfectly, meaning all observed variation in trait values is due to
evolutionary processes.
Scenario 2: no SE argument (implicitly assuming SE = 0)
When we omit the SE argument, the function assumes that there is no uncertainty
explicitly stated for the traits. However, the model still treats the trait
data as exact but does not impose the same assumption of perfect measurement as
SE = NA. Essentially, it acts as if the measurement error is not considered,
which may implicitly include the idea that some noise might still exist,
although it isn't modeled.
Scenario 3: SE = real calculation
When we provide a vector of standard error, the function adjusts the likelihood
to account for measurement uncertainty in the trait data. The trait values are
considered as estimates of true trait values with some noise due to measurement
error.
In practice, not providing the SE argument can result in model fits that are
somewhat similar to those obtained by including estimated SEs, particularly
because both scenarios lead to the model being less simplistic than when using
SE = NA.
My question is: what is the difference between Scenario 1 and Scenario 2? The
model does exactly the same in Scenario 1 and 2, yet the outcomes are
different, just because in scenario 1 we are making a clear decision to ignore
errors and in the scenario 2 the model makes that assumption on its own because
no errors were mentioned?
My understanding from the literature (Silvestri 10.1111/2041-210X.12337) is
that the whole reason for considering the error is because more complex models
may by mistake be preferred if the error is not accounted for.
Can someone explain, please.
Best wishes,
Jovana
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