### install.packages("baggr")
library(baggr)
## Warning: package 'baggr' was built under R version 3.6.2
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 3.6.2
## This is baggr v0.1; see vignette('baggr') for tutorial, ?baggr for basic help.
## For execution on a local, multicore CPU with excess RAM call:
## options(mc.cores = parallel::detectCores())
data(schools)
head(schools)
## group tau se
## 1 School A 28 15
## 2 School B 8 10
## 3 School C -3 16
## 4 School D 7 11
## 5 School E -1 9
## 6 School F 1 11
data(microcredit_simplified)
head(microcredit_simplified)
## group consumerdurables treatment
## 1 1 14.488149 0
## 2 1 1.061632 0
## 3 1 9.523460 0
## 4 1 2.266896 0
## 5 1 1.679876 0
## 6 1 8.805297 0
summary(microcredit_simplified)
## group consumerdurables treatment
## Min. :1.000 Min. : 0.00 Min. :0.0000
## 1st Qu.:3.000 1st Qu.: 0.00 1st Qu.:0.0000
## Median :3.000 Median : 1.81 Median :1.0000
## Mean :3.181 Mean : 96.31 Mean :0.5286
## 3rd Qu.:4.000 3rd Qu.: 10.85 3rd Qu.:1.0000
## Max. :4.000 Max. :52204.48 Max. :1.0000
data(microcredit)
head(microcredit)
## group consumerdurables consumption expenditures profit revenues
## 1 angelucci NA 0 0 0 0
## 2 angelucci NA 0 0 0 0
## 3 angelucci NA 0 0 0 0
## 4 angelucci NA 0 0 0 0
## 5 angelucci NA 0 0 0 0
## 6 angelucci NA 0 0 0 0
## temptation treatment
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
microcredit_summary_data <- prepare_ma(microcredit_simplified,
outcome = "consumerdurables")
head(microcredit_summary_data)
## group mu tau se.mu se.tau
## 1 1 5.369612 1.074862 0.3226702 0.425339
## 2 2 1156.243056 -6.503679 146.2217092 191.120776
## 3 3 23.868992 4.440776 1.2379155 2.207257
## 4 4 6.516936 1.377748 1.0043951 2.271460
baggr(microcredit_summary_data, model = "mutau",
pooling = "partial", prior_hypercor = lkj(1),
prior_hypersd = normal(0,10),
prior_hypermean = multinormal(c(0,0),matrix(c(10,3,3,10),2,2)))
##
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## Warning: There were 371 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#bulk-ess
## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#tail-ess
## Model type: Aggregate data (with control group)
## Pooling of effects: partial
##
## Aggregate treatment effect:
## Hypermean (tau) = 1.2 with 95% interval -2.3 to 4.4
## Hyper-SD (sigma_tau) = 2.73 with 95% interval 0.35 to 9.27
##
## Treatment effects on mean:
## mean sd pooling
## 1 1.1 0.41 0.11
## 2 2.0 4.01 1.00
## 3 3.2 1.93 0.53
## 4 1.4 1.50 0.54
# Most basic comparison between no, partial and full pooling
# (This will run the models)
baggr_compare(schools)
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
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## Attempting to infer the correct model for data.
## Chosen model rubin
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## * sigma_tau ~ Uniform(0, 104)
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
## Attempting to infer the correct model for data.
## Chosen model rubin
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## * tau ~ Normal(0, (10*28)^2)
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## There is no treatment effect estimated when pooling = 'none'.
## There is no treatment effect estimated when pooling = 'none'.
## Mean treatment effects:
## 2.5% mean 97.5%
## none NA NA NA
## partial -2.0831530 7.991202 17.81778
## full -0.2333689 7.449649 15.15602
##
## SD for treatment effects:
## 2.5% mean 97.5%
## none NA NA NA
## partial 0.258201 6.456836 20.48731
## full 0.000000 0.000000 0.00000
## $plot
##
## $models
## $models$none
## Model type: Aggregate data (Rubin)
## Pooling of effects: none
##
## Aggregate treatment effect:
## No treatment effect estimated as pooling = 'none'.
##
##
## Treatment effects on mean:
## mean sd pooling
## School A 28.0 15.0 0
## School B 8.1 10.2 0
## School C -3.0 15.7 0
## School D 6.9 11.3 0
## School E -1.1 9.1 0
## School F 1.0 11.0 0
## School G 18.0 10.2 0
## School H 12.0 17.6 0
##
##
## $models$partial
## Model type: Aggregate data (Rubin)
## Pooling of effects: partial
##
## Aggregate treatment effect:
## Hypermean (tau) = 8.0 with 95% interval -2.1 to 17.8
## Hyper-SD (sigma_tau) = 6.46 with 95% interval 0.26 to 20.49
##
## Treatment effects on mean:
## mean sd pooling
## School A 11.4 8.3 0.83
## School B 8.0 6.2 0.73
## School C 6.2 7.8 0.84
## School D 7.7 6.4 0.75
## School E 5.3 6.4 0.70
## School F 6.2 6.7 0.75
## School G 10.7 6.6 0.73
## School H 8.5 7.5 0.87
##
##
## $models$full
## Model type: Aggregate data (Rubin)
## Pooling of effects: full
##
## Aggregate treatment effect:
## Hypermean (tau) = 7.45 with 95% interval -0.23 to 15.16
## (SD(tau) undefined.)
# Compare prior vs posterior
baggr_compare(schools, what = "prior")
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## Warning: There were 3150 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#bulk-ess
## Mean treatment effects:
## 2.5% mean 97.5%
## Prior -2.045565 7.853905 17.60816
## Posterior -554.662950 8.328897 575.78301
##
## SD for treatment effects:
## 2.5% mean 97.5%
## Prior 0.2153587 6.389943 20.30441
## Posterior 2.7500022 53.409876 102.77522
## $plot
##
## $models
## $models$Prior
## Model type: Aggregate data (Rubin)
## Pooling of effects: partial
##
## Aggregate treatment effect:
## Hypermean (tau) = 7.9 with 95% interval -2.0 to 17.6
## Hyper-SD (sigma_tau) = 6.39 with 95% interval 0.22 to 20.30
##
## Treatment effects on mean:
## mean sd pooling
## School A 11.2 8.4 0.83
## School B 7.9 6.2 0.73
## School C 6.2 7.5 0.84
## School D 7.6 6.6 0.76
## School E 5.1 6.5 0.70
## School F 6.2 6.7 0.76
## School G 10.5 6.8 0.73
## School H 8.3 7.7 0.87
##
##
## $models$Posterior
## Model type: Prior predictive draws for Aggregate data (Rubin)
##
## Aggregate treatment effect:
## Hypermean (tau) = 8.3 with 95% interval -554.7 to 575.8
## Hyper-SD (sigma_tau) = 53.4 with 95% interval 2.8 to 102.8
# Compare existing models:
bg1 <- baggr(schools, pooling = "partial")
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
bg2 <- baggr(schools, pooling = "full")
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
##
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baggr_compare("Partial pooling model" = bg1, "Full pooling" = bg2,
arrange = "grid")
## Mean treatment effects:
## 2.5% mean 97.5%
## Partial pooling model -2.5203424 7.859432 18.31094
## Full pooling -0.1178683 7.844937 15.60672
##
## SD for treatment effects:
## 2.5% mean 97.5%
## Partial pooling model 0.2409968 6.607161 20.59997
## Full pooling 0.0000000 0.000000 0.00000
## $`Partial pooling model`
##
## $`Full pooling`
#' ...or simply draw prior predictive dist (note ppd=T)
bg1 <- baggr(schools, ppd=T)
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## Warning: There were 3101 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#bulk-ess
## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#tail-ess
bg2 <- baggr(schools, prior_hypermean = normal(0, 5), ppd=T)
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## Warning: There were 2943 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#bulk-ess
baggr_compare("Prior A, p.p.d."=bg1,
"Prior B p.p.d."=bg2,
compare = "effects")
## Mean treatment effects:
## 2.5% mean 97.5%
## Prior A, p.p.d. -530.877592 5.4442342 569.87563
## Prior B p.p.d. -9.396979 0.4198681 10.50479
##
## SD for treatment effects:
## 2.5% mean 97.5%
## Prior A, p.p.d. 2.518297 49.74917 102.0917
## Prior B p.p.d. 3.972368 53.41951 102.0184
# Compare posterior effects as a function of priors (note ppd=F)
bg1 <- baggr(schools, prior_hypersd = uniform(0, 20))
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## Warning: There were 689 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
bg2 <- baggr(schools, prior_hypersd = normal(0, 5))
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## Warning: There were 1 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
baggr_compare("Uniform prior on SD"=bg1,
"Normal prior on SD"=bg2,
compare = "effects")
## Mean treatment effects:
## 2.5% mean 97.5%
## Uniform prior on SD -1.538942 7.932796 17.60041
## Normal prior on SD -0.880077 7.758003 16.30916
##
## SD for treatment effects:
## 2.5% mean 97.5%
## Uniform prior on SD 0.2232261 5.872433 16.636104
## Normal prior on SD 0.1421595 3.367299 9.463626
# You can also compare different subsets of input data
bg1_small <- baggr(schools[1:6,], pooling = "partial")
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (113)
## * sigma_tau ~ Uniform(0, 113)
##
## SAMPLING FOR MODEL 'rubin' NOW (CHAIN 1).
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## Warning: Examine the pairs() plot to diagnose sampling problems
baggr_compare("8 schools model" = bg1, "First 6 schools" = bg1_small)
## Mean treatment effects:
## 2.5% mean 97.5%
## 8 schools model -1.538942 7.932796 17.60041
## First 6 schools -6.342079 5.595427 18.46612
##
## SD for treatment effects:
## 2.5% mean 97.5%
## 8 schools model 0.2232261 5.872433 16.63610
## First 6 schools 0.2989367 8.021108 26.59948
# A single effects plot
bg1 <- baggr(schools, prior_hypersd = uniform(0, 20))
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
## Automatically naming models; please use named arguments to override.
# Compare posterior effects as a function of priors (note ppd=F)
bg2 <- baggr(schools, prior_hypersd = normal(0, 5))
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
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effect_plot("Uniform prior on SD"=bg1,
"Normal prior on SD"=bg2)
# even simple examples may take a while
cv <- loocv(schools, pooling = "partial")
## Attempting to infer the correct model for data.
## Chosen model rubin
## Set hypermean prior according to max effect:
## * tau ~ Normal(0, (10*28)^2)
## Set hyper-SD prior using 10 times the naive SD across sites (104)
## * sigma_tau ~ Uniform(0, 104)
## Warning: There were 6 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning: Examine the pairs() plot to diagnose sampling problems
## (Prior distributions taken from the model with all data. See $prior.)
## Repeating baggr() for 8 separate models
##
##
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##
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
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##
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## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
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print(cv) # returns the lpd value
## Based on 8-fold cross-validation
##
## Estimate Standard Error
## elpd -32 0.98
## looic 64 -1.96
attributes(cv) # more information is included in the object
## $names
## [1] "se" "elpd" "looic" "df" "full_model"
## [6] "prior" "K"
##
## $class
## [1] "baggr_cv"