khb | R Documentation |
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)
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 |
khb
returns for all model coefficients the p-value for the null hypothesis that the change in coefficients is not attributable to confounding by z.
Thilo Klein
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"))
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