A more descriptive explanation of WOE/IV may help you better understand those alternatives to the logistic regression approach. I haven't used them, so can only comment on logistic regression diagnostics.
To build a model for determining the binary treatment, response or dependent variable, conventionally designated Y, as a function of the `covariates or dependent variable or variables, conventionally designated X_i ... X_n, there are alternative approaches:
- Purposeful selection of covariates
- Stepwise additive
- Stepwise subtractive
Depending on the number of covariates, I prefer to begin with a fully saturated model, using the stepwise subtractive approach
fit <- glm(Y ~ x1 + x2 + x3 ...)
summary(fit)
The summary fit identifies candidate covariates for deletion
- Those that have a
p-value less than the pre-selected $\alpha$
- Those that have NA
p-values due to missingness
- Those that are collinear
For the surviving covariates, I'd usually then check for interactions
fit2 <- glm(Y ~ x1 + x2 + x1*x2 ...)
and make the same assessment. The end result is the main effects model.
To calculate the odds ratio I use this function
odr <- function(x) {
exp(cbind(OR = coef(x), confint(x)))
}
where x is a fitted glm model.
The next step is to assess goodness of fit. The Hosmer-Lemeshow goodness of fit test has a null hypothesis H_0, that the fit is poor; accordingly a high p-value is evidence of a good fit. The results are based on dividing the probabilities for Y into deciles and then to examine the expected and actual results against their estimates.
The odds ratio for an individual is simple: it is either 1 or 0, because the OR is calculated from a population, not an individual.