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The goal of bdsm is to provide tools to model panel data.
You can install the released version of bdsm from CRAN with:
install.packages("bdsm")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("mateuszwyszynski/bdsm")library(magrittr)
devtools::load_all()
#> ℹ Loading bdsm
set.seed(20)
# STEP 1
# Prepare data
#
# Features are scaled and centralized around the mean.
# Then they are centralized around the mean within cross-sections
data_prepared <- bdsm::economic_growth[,1:7] %>%
feature_standardization(timestamp_col = year, entity_col = gdp) %>%
feature_standardization(timestamp_col = year, entity_col = country,
cross_sectional = TRUE, scale = FALSE)
# If needed track computation time
library(tictoc)
tic()
# STEP 2
# Find optimal model space
#
# Parameters for each model are initialized with init_value. Then MLE for each
# model is searched numerically
model_space <-
optimal_model_space(df = data_prepared, dep_var_col = gdp,
timestamp_col = year, entity_col = country,
init_value = 0.5)
#> initial value 700.805202
#> iter 100 value 63.489964
#> final value 62.413224
#> converged
#> initial value 755.227924
#> iter 100 value -7.081746
#> final value -9.945489
#> converged
#> initial value 731.149545
#> iter 100 value -5.728710
#> final value -7.336652
#> converged
#> initial value 865.987070
#> iter 100 value -75.528524
#> final value -81.347491
#> converged
#> initial value 821.652572
#> iter 100 value 12.593978
#> final value 9.478839
#> converged
#> initial value 788.836005
#> iter 100 value -61.377699
#> final value -62.826938
#> converged
#> initial value 747.497621
#> iter 100 value -56.890341
#> final value -58.756416
#> converged
#> initial value 792.979857
#> iter 100 value -127.201548
#> final value -132.333776
#> converged
#> initial value 665.161630
#> iter 100 value -242.698752
#> final value -256.663454
#> converged
#> initial value 708.042561
#> iter 100 value -326.609584
#> final value -328.729156
#> converged
#> initial value 678.350819
#> iter 100 value -315.110317
#> final value -323.599046
#> converged
#> initial value 802.194508
#> iter 100 value -392.089550
#> final value -397.592119
#> converged
#> initial value 750.297273
#> iter 100 value -296.583891
#> final value -309.577948
#> converged
#> initial value 709.806113
#> iter 100 value -375.455174
#> final value -381.498968
#> converged
#> initial value 661.102743
#> iter 100 value -365.114246
#> final value -375.096037
#> converged
#> initial value 701.509591
#> iter 100 value -436.936673
#> final value -448.914323
#> converged
print(paste("Computation Time:", toc()))
#> 50.095 sec elapsed
#> [1] "Computation Time: c(elapsed = 4.228)"
#> [2] "Computation Time: c(elapsed = 54.323)"
#> [3] "Computation Time: logical(0)"
#> [4] "Computation Time: 50.095 sec elapsed"
tic()
# STEP 3
# Compute intermediate BMA results
bma_result <- bma_summary(df = data_prepared, dep_var_col = gdp,
timestamp_col = year, entity_col = country,
model_space = model_space)
#> [1] "Prior Mean Model Size: 2"
#> [1] "Prior Inclusion Probability: 0.5"
print(paste("Computation Time:", toc()))
#> 15.783 sec elapsed
#> [1] "Computation Time: c(elapsed = 54.323)"
#> [2] "Computation Time: c(elapsed = 70.106)"
#> [3] "Computation Time: logical(0)"
#> [4] "Computation Time: 15.783 sec elapsed"
# STEP 4
# Summary for parameters of interest
regressors <- bdsm:::regressor_names(data_prepared, year, country, gdp)
bma_params_summary <- bdsm:::parameters_summary(
regressors = regressors, bet = bma_result$bet, pvarh = bma_result$pvarh,
pvarr = bma_result$pvarr, fy = bma_result$fy, fyt = bma_result$fyt,
ppmsize = bma_result$ppmsize, cout = bma_result$cout, nts = bma_result$nts,
pts = bma_result$pts, variables_n = bma_result$variables_n
)
#> [1] "Posterior Mean Model Size: 4.08027534311768"
bma_params_summary
#> varname postprob pmean std
#> alpha 1 1.00832558392276 0.110742334576895
#> V1 ish 0.724215389851411 0.1218982030087 0.0316376449005969
#> V2 sed 0.70775503500249 -0.000673106583091735 0.0654581209209995
#> V3 pgrw 0.661624194249673 -0.0278472442338177 0.0404467740791176
#> V4 pop 0.986680724014111 0.142000377827331 0.0514636442549736
#> stdR unc_pmean unc_std
#> 0.167230503443864 1.00832558392276 0.110742334576895
#> V1 0.0781287204805418 0.0882805546141323 0.0607674354710891
#> V2 0.107215420410677 -0.000476394573276497 0.0550695754101354
#> V3 0.091135707929527 -0.0184244105282735 0.0354399302399243
#> V4 0.0682640855066145 0.140109035604948 0.0536490878147803
#> unc_stdR
#> 0.167230503443864
#> V1 0.0859561842307829
#> V2 0.0901988993098509
#> V3 0.0752919228484835
#> V4 0.0697345816191463Make sure to go through the displayed errors. The problem might be connected to your OS environment. E.g. you might see an information like the following:
Configuration failed to find one of freetype2 libpng libtiff-4 libjpeg. Try installing:
* deb: libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev (Debian, Ubuntu, etc)
* rpm: freetype-devel libpng-devel libtiff-devel libjpeg-devel (Fedora, CentOS, RHEL)
* csw: libfreetype_dev libpng16_dev libtiff_dev libjpeg_dev (Solaris)In such case you should first try installing the recommended packages. With properly configured system environment everything should work fine.
# To find the optimal model space with parallel computations
# replace the STEP 2 with:
library(parallel)
cl <- makeCluster(detectCores(), 'FORK')
setDefaultCluster(cl)
model_space <-
optimal_model_space(df = data_prepared, dep_var_col = gdp,
timestamp_col = year, entity_col = country,
init_value = 0.5,
run_parallel = TRUE)
# and STEP 3 with:
bma_result <- bma_summary(df = data_prepared, dep_var_col = gdp,
timestamp_col = year, entity_col = country,
model_space = model_space,
run_parallel = TRUE)
#> [1] "Prior Mean Model Size: 2"
#> [1] "Prior Inclusion Probability: 0.5"
stopCluster(cl = NULL)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.