staticGP: Performs a data analysis for data with non-adaptive treatment(s).

Description

Performs Bayesian's Gaussian process regression or Bayesian additive regression tree for data with non-adaptive treatment(s).

Usage

staticGP(
  datafile = NULL,
  dataref = NULL,
  method = "BART",
  outcome,
  outcome.type = "Continuous",
  outcome.bound_censor = "neither",
  outcome.lb = NULL,
  outcome.ub = NULL,
  outcome.censor.yn = NULL,
  outcome.censor.lv = NULL,
  outcome.censor.uv = NULL,
  outcome.link = "identity",
  treatment,
  x.explanatory = NULL,
  x.confounding = NULL,
  tr.type = "Discrete",
  tr.values = NULL,
  c.margin = NULL,
  tr.hte = NULL,
  time,
  time.value = NULL,
  burn.num = 500,
  mcmc.num = 500,
  x.categorical = NULL,
  mi.datafile = NULL,
  mi.dataref = NULL,
  sheet = NULL,
  mi.sheet = NULL,
  seed = 5000,
  token = NULL,
  use.cache = NULL
)

Value

jobid

Arguments

datafile: File to upload (.csv or .xls)
dataref: Reference to already uploaded file.
method: The method to be used. "GP" for GP method and "BART" for BART method. The default value is "BART".
outcome: The name of the outcome variable.
outcome.type: Outcome type ("Continuous" or "Discrete"). The default value is "Continuous".
outcome.bound_censor: The default value is "neither". "neither" if the outcome is not bounded or censored. "bounded" if the outcome is bounded. "censored" if the outcome is censored.
outcome.lb: Putting a lower bound if the outcome is bounded.
outcome.ub: Putting a upper bound if the outcome is bounded.
outcome.censor.yn: Censoring variable if outcome is censored.
outcome.censor.lv: lower variable of censored interval if outcome is censored.
outcome.censor.uv: upper variable of censored interval if outcome is censored.
outcome.link: function for outcome; the default value is "identity". "identity" if no transformation needed. "log" for log transformation. "logit" for logit transformation.
treatment: The vector of the name of the treatment variables. Users can input at most two treatment variables.
x.explanatory: The vector of the name of the explanatory variables.
x.confounding: The vector of the name of the confounding variables.
tr.type: The type of the first treatment. "Continuous" for continuous treatment and "Discrete" for categorical treatment. The default value is "Discrete".
tr.values: user-defined values for the calculation of ATE if the first treatment variable is continuous
c.margin: An optional vector of user-defined values of c for PrTE.
tr.hte: An optional vector specifying variables which may have heterogeneous treatment effect with the first treatment variable
time: Time variable.
time.value: Pre-specified time exposure.
burn.num: numeric; the number of MCMC 'burn-in' samples, i.e. number of MCMC to be discarded. The default value is 500.
mcmc.num: numeric; the number of MCMC samples after 'burn-in'. The default value is 500.
x.categorical: A vector of the name of categorical variables in data.
mi.datafile: File to upload (.csv or .xls) that contains the imputed data in the model.
mi.dataref: Reference to already uploaded file that contains the imputed data in the model.
sheet: If datafile or dataref points to an Excel file this variable specifies which sheet to load.
mi.sheet: If mi.datafile or mi.dataurl points to an Excel file this variable specifies which sheet to load.
seed: Sets the seed. The default value is 5000.
token: Authentication token.
use.cache: Use cached results (default True).