The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

flps

CRAN status 

library(flps)
#> Version: 1.1.0
#> 
#> It is a demo.
#> Acknowledgements. It is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.

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 repo:

devtools::install_github("sooyongl/flps")
install.packages("flps")

Example Usage

Data Generation and Preprocessing

set.seed(10000)
inp_data <- flps::makeInpData(
  N       = 200,  # sample size
  R2Y     = 0.2,  # r^2 of outcome
  R2eta   = 0.5,  # r^2 of eta by one covariates
  omega   = 0.2,  # the effect of eta
  tau0    = 0.23, # direct effect
  tau1    = -0.16,# interaction effect between Z and eta
  betaL   = 0.2,
  betaY   = 0.4,
  lambda  = 0.8,  # the proportion of administered items
  nitem    = 10,   # the total number of items
  nfac    = 1,    # the number of latent factors
  lvmodel = 'rasch' # tag for latent variable model; case-sensitive (use lower-case letters)
)
# Input data matrix
data.table::data.table(inp_data)
#>                Y     Z          X1        eta1    v1    v2    v3    v4    v5
#>            <num> <num>       <num>       <num> <num> <num> <num> <num> <num>
#>   1:  0.31384308     1 -0.74308228 -0.80447517     0     1     0     0     0
#>   2: -0.53385668     1  0.21197053 -0.29240295     1     0     1     1     1
#>   3: -0.01239108     1  0.34952377  0.39012726     1     1     1     0     0
#>   4:  0.05905941     1 -0.32419433  0.24014237     0     1     1     0     0
#>   5: -2.21654281     1 -1.80979412  0.52011169     0     1     0     0     0
#>  ---                                                                        
#> 196: -0.47559245     0  0.23763106 -0.27108910    NA    NA    NA    NA    NA
#> 197:  0.46877629     0 -0.03646065  1.12609970    NA    NA    NA    NA    NA
#> 198:  0.78717334     0  0.06867924  0.07008599    NA    NA    NA    NA    NA
#> 199:  0.56380180     0  0.56467755  0.34826071    NA    NA    NA    NA    NA
#> 200:  0.36809486     0  0.82158503 -0.35012492    NA    NA    NA    NA    NA
#>         v6    v7    v8    v9   v10
#>      <num> <num> <num> <num> <num>
#>   1:     0     1     0     1     0
#>   2:     1     0     0     0     1
#>   3:     1     1     1     0     1
#>   4:     1     1     0     1     1
#>   5:     1     1     1     1     0
#>  ---                              
#> 196:    NA    NA    NA    NA    NA
#> 197:    NA    NA    NA    NA    NA
#> 198:    NA    NA    NA    NA    NA
#> 199:    NA    NA    NA    NA    NA
#> 200:    NA    NA    NA    NA    NA

Model Fitting with FLPS

The runFLPS function internally converts inp_data into the appropriate data format for rstan, utilizing the provided information before executing the FLPS analysis.

In order to optimize your workflow, consider utilizing the modelBuilder() function to pre-compile the Stan code, especially if you’re working with a relevant measurement model. This function compiles the Stan code and stores the resulting stanmodel object within the flps package directory.

Note: Should you encounter an error, ensure that your Rcpp package is updated to the latest version. Additionally, refreshing your R session is recommended after executing modelBuilder() to avoid potential conflicts. Upon subsequent usage of runFLPS(), this pre-compilation ensures the function operates more swiftly and efficiently by bypassing the compilation step, providing a smoother and faster analytical process. 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 <- 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.

res <- runFLPS(
  inp_data = inp_data,
  # complied_stan = complied # if necessary
  outcome = "Y",
  trt = "Z",
  covariate = c("X1"),
  lv_type = "rasch",
  lv_model = "F =~ v1 + v2 + v3 + v4 + v5 + v6 + v7 + v8 + v9 + v10",
  stan_options = list(iter = 1000, warmup = 500, cores = 1, chains = 1)
)

Results

Retrieve summaries and visualize results with the following:

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.↩︎

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.