bdsm
The goal of bdsm is to provide tools to model panel data.
Installation
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")Basic Usage
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 0.0697345816191463Troubleshooting
- Cannot install required packages / setup renv environment
Make 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.
Advanced Usage: parallel computing
# 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)