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.

• Documentation is available here.

• For compiling errors on Windows, see the relevant guide.

Basic working example

Load Example Data

• binary: a data frame containing all the data for FLPS. It is used in runFLPS function.
• This data will be converted to a list of data for rstan package.
• 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 for rstan, subsequently executing FLPS.

• To avoid re-compiling the Stan code each time, pre-compile it using modelBuilder(), which stores the stanmodel object in the flps directory, 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 the compiled_stan argument in runFLPS. 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 rstan and StanHeaders packages.

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")