KD-STAT0035-GMupdate: Generalised Joint Regression Modelling

Description Usage Arguments Details Value Author(s) See Also

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.

OR(x, nm.end, E = TRUE, treat = TRUE, type = "joint", 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", ...)

`x`	A fitted `gjrm` 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 `"joint"` (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`.

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

`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.