Error with argument alpha.upper=0
jjsolano opened this issue · comments
Hi,
I was trying to fit a straight BM model with l1ou
package implementing alpha=0 in the estimate_shift_configuration
function. To do that, I used the argument alpha.upper=0
, but I got the following error message:
eModel <- estimate_shift_configuration(tree = lizard$tree,
lizard$Y, criterion="AICc",
alpha.upper = 0,
nCores = 3)
Error in estimate_shift_configuration(tree = lizard$tree, lizard$Y, criterion = "AICc", :
alpha.upper must be strictly positive.
Follow to read this error message, I fit the alpha.upper=0.001
but I got an similar error:
eModel <- estimate_shift_configuration(tree = lizard$tree,
lizard$Y, criterion="AICc",
alpha.upper = 0.001,
nCores = 3)
Starting first LASSO (alpha=0) to find a list of candidate configurations.
Error in fit_OU_model(tree, Y, result$shift.configuration, opt) :
model score is NA in fit_OU_model function!
This should not happen. Please set quietly to false to see the reason.
Thanks,
Jaiber
Thank you Jaiber for posting your example 👍
What kind of error message do you get if you add the option quietly=FALSE
in the estimate_shift_configuration
command?
Hi Cécile,
If I try using alpha.upper=0
the error it's the same, but if I try with alpha.upper=0.001
the error message is:
Starting first LASSO (alpha=0) to find a list of candidate configurations.
Error in phylolm(Y ~ preds - 1, phy = tree, model = opt$root.model, upper.bound = opt$alpha.upper.bound) :
The starting value is not within the bounds of the parameter.
Error in fit_OU_model(tree, Y, result$shift.configuration, opt) :
model score is NA in fit_OU_model function!
This should not happen. Please set quietly to false to see the reason.
In addition: Warning message:
In my_phylolm_interface(tr, y, s.c, opt) :
phylolm returned error with a shift configuration
of size 0. You may
want to change alpha.upper/alpha.lower!
thank you this is useful. Could you try to add the following options: alpha.starting.value=0.0005
and perhaps also alpha.lower=0.0
?
Thanks Cécile,
I tried with the parameters that you indicate me and it work very well. I will run my datasets with these parameters.
eModel <- estimate_shift_configuration(lizard$tree, lizard$Y,
criterion="AICc",
max.nShifts = 0, nCores = 3, quietly=FALSE,
alpha.upper = 0.001, alpha.lower=0.0, alpha.starting.value=0.0005
)