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.

khb(X, y, z)

Arguments

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.

References

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.

Examples

## 1. load results from Klein (2015a) data(klein15a) ## 2. apply KHB method with(klein15a$variables, khb(X=X, y=Y, z="eta"))
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> #> Karlson-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