The *car() functions include checks to ensure that all
data is set up in a way that the model can use. If the checks notice
something incompatible with the model, an error will be displayed in the
console. Here, we list each possible error for the MSTCAR model,
including its reasoning and ways to check and fix your inputs.
All checks that are run on the data check for general information, such as extraneous list objects which cannot be used by the model. In these cases, RSTr will not use these parts of the dataset. It is recommended to remove these datasets from the model to prevent unnecessary clutter.
dataOne or more objects missing from list 'data': The
data object requires a list with two objects: an array
Y for mortality counts and an array n for
population counts. These are needed to perform parameter updates inside
of the MSTCAR model. Check names(data) to see a vector of
names that exists inside of your array and fix names
accordingly.
Data not same dimensions. Ensure dim(Y) == dim(n):
This error occurs when the arrays fed into the data list
are not of the same size. Use the dim() function to check
that the dimensions of your two arrays do line up. You can use the
aperm() function to rearrange arrays if they have the same
set of dimensions on different margins.
Invalid Y/n values. Check that all Y's/n's are at least 0 and finite:
While NA and NULL are accepted values in
Y, both Y and n must be comprised
of positive finite values. Check your data with summary()
for a quick look at your minimum values.
sp_dataAdjacency different length than data. Ensure length(adjacency) == dim(Y)[1]:
Adjacency information must be present for all regions. Look at the
length of your adjacency information: it should line up with the number
of rows in your Y and n arrays.
Some regions have no neighbors. Ensure all regions have at least one neighbor:
If a region does not have a neighbor, the model will not run. If working
with list adjacency information, you can first check which
regions have no neighbors by running
which(lapply(adjacency, \(x) all(x == 0))) and then fixing
the regions accordingly. For assistance with fixing the adjacency
information, read vignette("RSTr-adjacency").
inits and priorsFor troubleshooting inputs with inits and
priors, reference
vignette("RSTr-initialvalues") and
vignette("RSTr-priors"), respectively.
ignore_checksIf you have input data that you are certain is correct, yet are
receiving error messages anyway, set the ignore_checks
argument to TRUE when setting up your model to skip data
checks. This is not recommended as it is easy to break RSTr
when data isn’t properly checked, and should only be used as a last
resort when using RSTr.