flps
Fully-Latent Principal Stratification
Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023).
Fully Latent Principal Stratification (FLPS)[^1]
Fully Latent Principal Stratification (FLPS) is an extension of principal stratification.
Installation
Install the latest release from CRAN or git repository:
devtools::install_github("sooyongl/flps")
install.packages("flps")
library(flps)
## Version: 1.0.0
##
## It is a demo.
## Acknowledgements. It is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.
Basic working example
Load Example Data
binary: a data frame containing all the data for FLPS. It is used inrunFLPSfunction.- This data will be converted to a list of data for
rstanpackage. - For latent variable models, Rasch, 2PL, GRM, SEM (one-factor CFA), and mixture models (LCA and LPA) are available.
- Multilevel structure will be supported soon.
data(binary)
# Input data frame
data.table::data.table(binary)
## schid id sex race pretest stdscore cm_sex cm_race cm_pretest
## <int> <int> <int> <int> <int> <num> <num> <num> <num>
## 1: 1 2383 0 1 20 -0.3296 0.4406780 0.9322034 14.81356
## 2: 1 2384 1 0 8 1.1597 0.4406780 0.9322034 14.81356
## 3: 1 2385 0 1 14 -0.7385 0.4406780 0.9322034 14.81356
## 4: 1 2387 0 1 12 -1.3518 0.4406780 0.9322034 14.81356
## 5: 1 2388 0 1 6 -1.2057 0.4406780 0.9322034 14.81356
## ---
## 4762: 63 4761 0 1 16 0.6457 0.4860558 0.3944223 17.03187
## 4763: 63 4763 0 0 11 0.3168 0.4860558 0.3944223 17.03187
## 4764: 63 4764 0 0 18 -0.4114 0.4860558 0.3944223 17.03187
## 4765: 63 4765 1 0 31 2.2116 0.4860558 0.3944223 17.03187
## 4766: 63 4766 1 1 13 -0.0668 0.4860558 0.3944223 17.03187
## cm_stdscore trt Y q1 q2 q3 q4 q5 q6 q7 q8
## <num> <int> <num> <int> <int> <int> <int> <int> <int> <int> <int>
## 1: -0.4068119 1 -0.487 0 NA 1 1 1 1 1 NA
## 2: -0.4068119 1 0.487 1 NA 1 1 1 1 1 1
## 3: -0.4068119 1 -1.073 0 NA 1 1 0 NA NA NA
## 4: -0.4068119 1 -1.087 0 NA 1 1 1 NA NA NA
## 5: -0.4068119 1 -1.171 0 NA 1 1 NA NA NA NA
## ---
## 4762: 0.0997502 0 -0.114 0 NA NA NA NA NA NA NA
## 4763: 0.0997502 0 -0.438 0 NA NA NA NA NA NA NA
## 4764: 0.0997502 0 -1.047 0 NA NA NA NA NA NA NA
## 4765: 0.0997502 0 0.765 0 NA NA NA NA NA NA NA
## 4766: 0.0997502 0 0.539 0 NA NA NA NA NA NA NA
## q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20
## <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1: NA NA NA NA NA NA NA NA NA NA NA NA
## 2: 1 1 1 1 NA NA NA NA NA NA NA NA
## 3: NA NA NA NA NA NA NA NA NA NA NA NA
## 4: NA NA NA NA NA NA NA NA NA NA NA NA
## 5: NA NA NA NA NA NA NA NA NA NA NA NA
## ---
## 4762: NA NA NA NA NA NA NA NA NA NA NA NA
## 4763: NA NA NA NA NA NA NA NA NA NA NA NA
## 4764: NA NA NA NA NA NA NA NA NA NA NA NA
## 4765: NA NA NA NA NA NA NA NA NA NA NA NA
## 4766: NA NA NA NA NA NA NA NA NA NA NA NA
Model Fitting with FLPS
-
runFLPS()internally transforms binary data into the format suitable forrstan, subsequently executing FLPS. -
To avoid re-compiling the Stan code each time, pre-compile it using
modelBuilder(), which stores the stanmodel object in theflpsdirectory, accelerating subsequent analyses. -
Once the Stan model is compiled, use
importModel()to bring in the compiled Stan code. This code can then be provided to thecompiled_stanargument inrunFLPS.If this step is omitted,runFLPS()will compile the Stan code during each execution of FLPS.
modelBuilder(type = "rasch")
complied_stan <- importModel(type = "rasch")
- In case of errors, try the latest
rstanandStanHeaderspackages.
remove.packages(c("rstan", "StanHeaders"))
install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
Now, execute your FLPS model. Given the time-intensive nature of the process, chains and iterations have been initially limited to 1 and 5000, respectively. It is advisable to increase these values for your specific research needs.
# Subset of data: 1000 students
binary <- binary[c(sample(which(binary$trt == 1), 250),
sample(which(binary$trt == 0), 250)),]
res <- runFLPS(
inp_data = binary,
# complied_stan = complied # if necessary
outcome = "Y",
trt = "trt",
covariate = c("sex","race","pretest","stdscore"),
lv_type = "rasch",
lv_model = "F =~ q1 + q2 + q3 + q4 + q5 + q6 + q7 + q8 + q9 + q10",
stan_options = list(iter = 5000, cores = 1, chains = 2)
)
Results
Retrieve summaries and visualize results with the following:
summary(res)
The flps_plot() shows the plot related to FLPS models
flps_plot(res, type = "causal")
flps_plot(res, type = "latent")
[^1]: Acknowledgements. This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.