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.Fully Latent Principal Stratification (FLPS) is an extension of principal stratification.
Install the latest release from CRAN or git repo:
Generate simulated rectangular data and convert it to a list of
data suitable for the rstan package.
makeInpData() creates input data for running
FLPS.
inp_data: a data frame containing all the data for
FLPS. It is used in runFLPS function.
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 v6 v7 v8 v9 v10
#> 1: 0.31384308 1 -0.74308228 -0.80447517 0 1 0 0 0 0 1 0 1 0
#> 2: -0.53385668 1 0.21197053 -0.29240295 1 0 1 1 1 1 0 0 0 1
#> 3: -0.01239108 1 0.34952377 0.39012726 1 1 1 0 0 1 1 1 0 1
#> 4: 0.05905941 1 -0.32419433 0.24014237 0 1 1 0 0 1 1 0 1 1
#> 5: -2.21654281 1 -1.80979412 0.52011169 0 1 0 0 0 1 1 1 1 0
#> ---
#> 196: -0.47559245 0 0.23763106 -0.27108910 NA NA NA NA NA NA NA NA NA NA
#> 197: 0.46877629 0 -0.03646065 1.12609970 NA NA NA NA NA NA NA NA NA NA
#> 198: 0.78717334 0 0.06867924 0.07008599 NA NA NA NA NA NA NA NA NA NA
#> 199: 0.56380180 0 0.56467755 0.34826071 NA NA NA NA NA NA NA NA NA NA
#> 200: 0.36809486 0 0.82158503 -0.35012492 NA NA NA NA NA NA NA NA NA NAThe 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.
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.
Acknowledgements. This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.↩︎
