pscore | R Documentation |
Propensity scores estimation, using an algorithm that checks the balancing hypothesis using strata and enable the estimation of the treatment effect using stratification methods
pscore(formula, data, maxiter = 4, tol = 0.005, link = c("logit", "probit"))
## S3 method for class 'pscore'
summary(object, ...)
## S3 method for class 'pscore'
print(
x,
...,
digits = getOption("digits"),
var_equal = c("none", "strata", "group", "both")
)
## S3 method for class 'summary.pscore'
print(
x,
...,
digits = getOption("digits"),
format = c("rst", "pipe", "simple"),
step = c("all", "strata", "covariates", "atet")
)
## S3 method for class 'pscore'
nobs(object, ..., smpl = c("total", "cs"))
## S3 method for class 'summary.pscore'
nobs(object, ..., smpl = c("total", "cs"))
rg(object, ...)
## S3 method for class 'pscore'
rg(object, ..., smpl = c("total", "cs"))
## S3 method for class 'summary.pscore'
rg(object, ..., smpl = c("total", "cs"))
stdev(object, ...)
## S3 method for class 'pscore'
mean(x, ..., var_equal = c("none", "strat", "group", "both"))
## S3 method for class 'summary.pscore'
mean(x, ...)
## S3 method for class 'pscore'
stdev(object, ..., var_equal = c("none", "strata", "group", "both"))
## S3 method for class 'summary.pscore'
stdev(object, ..., var_equal = c("none", "strata", "group", "both"))
formula |
a Formula object; the left-hand side should contain
two variables ( |
data |
a data frame |
maxiter |
the maximum number of iterations |
tol |
stratas are cut in halves as long as the hypothesis of
equal means is rejected at the |
link |
the link for the binomial glm estimation, either
|
... |
further arguments |
x , object |
a |
digits |
number of digits for the |
var_equal |
to compute the variance of the ATET, variances can
be computed at the class/group level ( |
format |
one of |
step |
for the |
smpl |
the sample to use, either the whole sample ( |
an object of class "pscore"
, with the following elements:
strata
: a tibble containing the stratas, the frequencies, the
means and the variances of the propensity scores for treated and
controled observations
cov_balance
: a tibble containing the results of the balancing
tests for every covariate; the results for the class with the
lowest p-value is reported
unchecked_cov
: a character vector containing the names of the
covariates for which the balancing test could be computed
model
: a tibble containing the original data, with
supplementary columns: gp_
for the groups, resp_
for the
outcome and cls_
for the stratas
pscore
: the glm model fitted to compute the propensity scores
DEHE:WAHB:02micsr
\insertRefBECK:ICHI:02micsr
data_tuscany <- dplyr::mutate(dplyr::filter(twa, region == "Tuscany"),
dist2 = dist ^ 2, livselfemp = I((city == "livorno") * (occup == "selfemp")),
perm = ifelse(outcome == "perm", 1, 0))
formula_tuscany <- group | perm ~ city + sex + marital + age +
loc + children + educ + pvoto + training +
empstat + occup + sector + wage + hour + feduc + femp + fbluecol +
dist + dist2 + livselfemp
pscore(formula_tuscany, data_tuscany)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.