OR can be used to calculate the causal odds ratio of a binary or continuous endogenous predictor/treatment, with
corresponding interval obtained using posterior simulation.OR(x, nm.end, E = TRUE, treat = TRUE, type = "bivariate", ind = NULL, n.sim = 100, prob.lev = 0.05, length.out = NULL, hd.plot = FALSE, or.plot = FALSE, main = "Histogram and Kernel Density of Simulated Odds Ratios", xlab = "Simulated Odds Ratios", ...)SemiParBIVProbit object as produced by SemiParBIVProbit().TRUE then OR calculates the sample OR. If FALSE then it calculates the sample OR
for the treated individuals only.TRUE then OR calculates the OR using the treated only. If FALSE then it calculates the ratio using
the control group. This only makes sense if E = FALSE."naive" (the effect is calculated ignoring the presence of observed and
unobserved confounders), "univariate" (the effect is obtained from the univariate model which neglects
the presence of unobserved confounders) and "bivariate" (the effect is obtained from the bivariate model which accounts for observed and unobserved confounders).ind
when some observations are excluded from the OR calculation (e.g., when using E = FALSE).delta = FALSE. It may be increased if more precision is required.TRUE then a plot of the histogram and kernel density estimate of the simulated odds ratios is produced. This can
only be produced when binary responses are used.TRUE then a plot (on the log scale)
showing the odd ratios that the binary outcome is equal to 1 for each incremental value of the endogenous variable
and respective intervals is produced.hd.plot = TRUE.OR calculates the causal odds ratio for a binary or continuous treatment. Posterior simulation is used to obtain a confidence/credible interval.
SemiParBIVProbit-package, SemiParBIVProbit, summary.SemiParBIVProbit
## see examples for SemiParBIVProbit
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