se_method = "robust" reports the sandwich the weighted outcome model
computes for itself, which treats the estimated weights as known. Printing
such a result writes what print() writes for any ipw()
result and then one line naming the method, so that the number beside each
estimate is not read as one accounting for the propensity score model. See
Standard errors as a diagnostic in ipw() for what the diagnostic does
and does not cover.
Usage
# S3 method for class 'ipw_diagnostic_se'
print(x, ...)
# S3 method for class 'ipw_diagnostic_se'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
# S3 method for class 'ipw_diagnostic_se'
tidy(x, ...)Value
x, invisibly.
as.data.frame() returns what it returns for any ipw() result,
carrying an ipw_se_diagnostic attribute naming the method.
tidy() returns what it returns for any ipw() result, carrying an
ipw_se_diagnostic attribute naming the method.
See also
ipw() for the estimator and the standard error methods.
Examples
set.seed(2)
n <- 300
x <- rnorm(n)
z <- rbinom(n, 1, plogis(0.4 * x))
y <- rbinom(n, 1, plogis(-0.3 + 0.7 * z + 0.5 * x))
dat <- data.frame(x, z, y)
ps_mod <- glm(z ~ x, data = dat, family = binomial())
wts <- wt_ate(ps_mod)
#> ℹ Using exposure variable "z" from the propensity score model
#> ℹ Treating `.exposure` as binary
outcome_mod <- glm(y ~ z, data = dat, family = quasibinomial(), weights = wts)
print(ipw(ps_mod, outcome_mod, se_method = "robust"))
#> Inverse Probability Weight Estimator
#> Estimand: ATE
#> Effects: marginal (population-averaged)
#>
#> Weight Estimator:
#> Call: glm(formula = z ~ x, family = binomial(), data = dat)
#>
#> Outcome Model:
#> Call: glm(formula = y ~ z, family = quasibinomial(), data = dat, weights = wts)
#>
#> Marginal estimates:
#> estimate std.err z ci.lower ci.upper conf.level p.value
#> mean 0 0.398460 0.041088 9.6978 0.317929 0.47899 0.95 < 2.2e-16
#> mean 1 0.585255 0.042736 13.6947 0.501494 0.66902 0.95 < 2.2e-16
#> rd 1 vs 0 0.186795 0.059284 3.1509 0.070601 0.30299 0.95 0.001628
#> log(rr) 1 vs 0 0.384441 0.126353 3.0426 0.136794 0.63209 0.95 0.002345
#> log(or) 1 vs 0 0.756270 0.245729 3.0777 0.274649 1.23789 0.95 0.002086
#>
#> mean 0 ***
#> mean 1 ***
#> rd 1 vs 0 **
#> log(rr) 1 vs 0 **
#> log(or) 1 vs 0 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Standard errors: robust, a diagnostic that treats the weights as known