# OR: Causal odds ratio of a binary/continuous/discrete endogenous... In JRM: Joint Regression Modelling

## Description

`OR` can be used to calculate the causal odds ratio of a binary/continuous/discrete endogenous predictor/treatment, with corresponding interval obtained using posterior simulation.

## Usage

 ```1 2 3 4 5``` ```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", ...) ```

## Arguments

 `x` A fitted `SemiParBIV`/`copulaReg` object. `nm.end` Name of the endogenous variable. `E` If `TRUE` then `OR` calculates the sample OR. If `FALSE` then it calculates the sample OR for the treated individuals only. `treat` If `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`. `type` This argument can take three values: `"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` Binary logical variable. It can be used to calculate the OR for a subset of the data. Note that it does not make sense to use `ind` when some observations are excluded from the OR calculation (e.g., when using `E = FALSE`). `n.sim` Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used when `delta = FALSE`. It may be increased if more precision is required. `prob.lev` Overall probability of the left and right tails of the OR distribution used for interval calculations. `length.out` Ddesired length of the sequence to be used when calculating the effect that a continuous/discrete treatment has on a binary outcome. `hd.plot` If `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. `or.plot` For the case of continuous/discrete endogenous variable and binary outcome, if `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. `main` Title for the plot. `xlab` Title for the x axis. `...` Other graphics parameters to pass on to plotting commands. These are used only when `hd.plot = TRUE`.

## Details

OR calculates the causal odds ratio for a binary/continuous/discrete treatment. Posterior simulation is used to obtain a confidence/credible interval.

## Value

 `prob.lev` Probability level used. `sim.OR` It returns a vector containing simulated values of the average OR. This is used to calculate intervals. `Ratios` For the case of continuous/discrete endogenous treatment and binary outcome, it returns a matrix made up of three columns containing the odds ratios for each incremental value in the endogenous variable and respective intervals.

## Author(s)

Maintainer: Giampiero Marra [email protected]

`JRM-package`, `SemiParBIV`, `copulaReg`
 `1` ```## see examples for SemiParBIV and copulaReg ```