khb: Karlson-Holm-Breen method for comparing probit coefficients

Description Usage Arguments Author(s) References Examples

View source: R/khb.R

Description

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.

Usage

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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.

Author(s)

Thilo Klein

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

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## 1. load results from Klein (2015a)
 data(klein15a)

## 2. apply KHB method
with(klein15a$variables, khb(X=X, y=Y, z="eta"))

Example output

OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed

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
Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 
Warning message:
system call failed: Cannot allocate memory 

matchingMarkets documentation built on May 31, 2017, 1:33 a.m.