You have two options:
1. Run predictions using tiling
(https://github.com/Envirometrix/BigSpatialDataR#dem-analysis-using-tiling-and-parallelization)
2. Buy more RAM.
I suggest using option 1 since option 2 can propagate to infinity.
PS: I am working on a new package
(https://github.com/Envirometrix/landmap/) that should give more
flexibility to users and maybe even incorporate tiling of large objects
by default.
On 9/13/19 5:38 PM, Manuel Spínola wrote:
Dear list members,
I am fitting a model with the GSIF package, but I ran into a problem of
vector allocation. Is there any way to solve this problem? See code and
error message below.
I am using:
R 3.6.1
GSIF 0.5-5
Mac with 16 GB of RAM
rk_rf_ac <- fit.gstatModel(variables_todos_sp["ac"], ac_formulaString_correlacion, covar_finales_sp, method
= "quantregForest")Fitting a Quantile Regression Forest model...Shapiro-Wilk normality test and
Anderson-Darling normality test report probability of < .05 indicating lack of normal distribution for
residualsFitting a 2D variogram...Saving an object of class 'gstatModel'...> rk_rf_ac_pred <-
GSIF::predict(rk_rf_ac, covar_finales_sp, predict.method = "KED")Error: cannot allocate vector of size 19.4 Gb
Thank you very much,
Manuel
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