staticGP {pcatsAPIclientR}

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
)

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).

Value

jobid