khb(X, y, z)

- X
- data frame comprising independent variables including confounding variable.
- y
- vector of dependent variable.
- z
- character string giving the name of the confounding variable in
`X`

.

Significance test for confounding; that is, the difference between regression coefficients from same-sample nested logit and probit models. The test procedure follows Karlson et al (2012), Section 3.4.

Karlson, K.B., A. Holm and R. Breen (2012). Comparing regression coefficients between same-sample nested models using logit and probit: A new method. *Sociological Methodology*, 42(1):286--313.

## 1. load results from Klein (2015a) data(klein15a) ## 2. apply KHB method with(klein15a$variables, khb(X=X, y=Y, z="eta"))Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning message: glm.fit: fitted probabilities numerically 0 or 1 occurredKarlson-Holm-Breen method Null hypothesis: Change in coefficient is not attributable to confounding by z.p.value pi.inv 0.5581 wst.ieq 0.0480 loan_size.add 0.9034 loan_size2.add 0.0442 lngroup_agei.add 0.0412 `0` 0.9535 `1` 0.5665 `2` 0.2517 `3` 0.4388 `4` 0.3487 `5` 0.5749 `6` 0.7760 `7` 0.3651 `8` 0.3345 `9` 0.6832 `10` 0.4322 `11` 0.4884 `12` 0.6036 `13` 0.6391