PStrata  R Documentation 
Perform pincipal stratification analysis when there are confounding variables after randomization
PStrata(
PSobject = NULL,
S.formula,
Y.formula,
Y.family,
data = NULL,
strata = NULL,
ER = NULL,
prior_intercept = prior_flat(),
prior_coefficient = prior_normal(),
prior_sigma = prior_inv_gamma(),
prior_alpha = prior_inv_gamma(),
prior_lambda = prior_inv_gamma(),
prior_theta = prior_normal(),
survival.time.points = 50,
filename = NULL,
...
)
PSobject 
an object of class 
S.formula , Y.formula 
an object of class " 
Y.family 
an object of class " 
data 
(optional) a data frame object. This is required when either

strata , ER 
arguments to define the principal strata. See Alternatively, one can pass an object of class 
prior_intercept , prior_coefficient , prior_sigma , prior_alpha , prior_lambda , prior_theta 
prior distribution for corresponding parameters in the model. 
survival.time.points 
a vector of time points at which the estimated survival probability is evaluated (only used when the type of outcome is survival), or an integer specifying the number of time points to be chosen. By default, the time points are chosen with equal distance from 0 to the 90% quantile of the observed outcome. 
filename 
(optional) string. If not 
... 
additional parameters to be passed into 
An object of class PStrata
or PStrata_survival
,
which is a list containing
PSobject 
An object of 
post_samples 
An object of class 
require(abind)
PSobj < PSObject(
S.formula = Z + D ~ 1,
Y.formula = Y ~ 1,
Y.family = gaussian("identity"),
data = sim_data_normal,
strata = c(n = "00*", c = "01", a = "11*")
)
PStrata(PSobj, cores = 2, chains = 2, iter = 200)
# Another example for survival data
PSobj < PSObject(
S.formula = Z + D ~ 1,
Y.formula = Y + delta ~ 1,
Y.family = survival("Cox"),
data = sim_data_Cox,
strata = c(`nevertaker` = "00*", complier = "01", `alwaystaker` = "11*")
)
PStrata(PSobj, cores = 2, chains = 2, iter = 200)
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