I'm running generate_pseudo_pop() on a random subset of my full dataset and get the following two errors when (a) I use a large subset of the data or (b) when I use less trimming in the "trim_quantiles" parameter. Would appreciate any advice!
Error in xgboost::xgb.DMatrix(data = X, label = Y, weight = obsWeights) :
[16:20:52] amalgamation/../src/data/data.cc:1163: Check failed: valid: Input data contains inf or nan
In addition: Warning message:
In FUN(X[[i]], ...) : Error in algorithm m_xgboost_internal
The Algorithm will be removed from the Super Learner (i.e. given weight 0)
I'm running generate_pseudo_pop() on a random subset of my full dataset and get the following two errors when (a) I use a large subset of the data or (b) when I use less trimming in the "trim_quantiles" parameter. Would appreciate any advice!
Error in xgboost::xgb.DMatrix(data = X, label = Y, weight = obsWeights) : [16:20:52] amalgamation/../src/data/data.cc:1163: Check failed: valid: Input data contains
inf
ornan
In addition: Warning message: In FUN(X[[i]], ...) : Error in algorithm m_xgboost_internal The Algorithm will be removed from the Super Learner (i.e. given weight 0)
My code is the following:
Originally posted by @m-qin in https://github.com/fasrc/CausalGPS/discussions/143