hetero.test: The heterogeneity test for the subtype-specific effet Testing...
In joolee0918/AIPWsubtype: An augmented inverse weighted semiparametric cause-specific competing risks model with partially missing cause of failure
hetero.test R Documentation
The heterogeneity test for the subtype-specific effet
Testing whether the exposures are not associated with the specific subtypes of disease.
Description
The heterogeneity test for the subtype-specific effet
Testing whether the exposures are not associated with the specific subtypes of disease.
Usage
hetero.test(fit, fit2 = NULL, data = NULL)
Arguments
fit
an object of class subtype, IPWsubtype, or AIPWsubtype. If a single object fit was specified, it tests whether all unconstrained variables, so called, exposures are not associated with disease subtypes.
fit2
an object of the same class as fit to compare the heterogeneity effect.
data
a data frame which was used in fit. It must be specified if a single object fit is specified.
Details
Log-likelihood tests are conducted to test whether the heterogeneity effects are significant. To test the heterogeneity effects, marker_rr can be used to drop out the marker-specific effects of exposures.
Value
a data frame: names of variables which are compared, test statistics, the degree of freedom, and p-value.
joolee0918/AIPWsubtype documentation built on Feb. 29, 2024, 5:03 p.m.
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| hetero.test | R Documentation |
The heterogeneity test for the subtype-specific effet Testing whether the exposures are not associated with the specific subtypes of disease.
Description
The heterogeneity test for the subtype-specific effet Testing whether the exposures are not associated with the specific subtypes of disease.
Usage
hetero.test(fit, fit2 = NULL, data = NULL)
Arguments
fit |
an object of class |
fit2 |
an object of the same class as |
data |
a data frame which was used in |
Details
Log-likelihood tests are conducted to test whether the heterogeneity effects are significant. To test the heterogeneity effects, marker_rr can be used to drop out the marker-specific effects of exposures.
Value
a data frame: names of variables which are compared, test statistics, the degree of freedom, and p-value.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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