pleio (version 1.9)

pleio.glm.test: Single test of the number of traits associated with genotype

Description

Perform single test of the number of traits associated with a genotype, by allowing a specified number of regression beta's to be unconstrained.

Usage

pleio.glm.test(obj.pleio.glm.fit, count.nonzero.coef = 0)

Value

A list containing:

stat

global test statistic

df

degrees of freedom of the statistic

pval

p-value for the test

index.nonzero.coef

index of the non-zero coefficients that provide the minimum tk goodness of fit statistic - this configuration is assumed to have coefficients for all other indices equal to zero.

tk

test testistic for the tests performed on trait combinations in vk.set

vk.set

data.frame containing the tests performed. For m traits, and k = count.nonzero.coef, there are m-choose-k tests considered in the null hypothesis. The data.frame provides the indices of the unconstrained coefficients for the corresponding tk test statistic for the configuration. Rows are the indices for each configuration, and the columns are for the different configurations tested.

Arguments

obj.pleio.glm.fit

result of pleio.glm.fit

count.nonzero.coef

Number of non-zero coefficients (betas). A non-zero beta is allowed to be unconstrained, while all other beta's are constrained to be zero.

Author

Dan Schaid and Jason Sinnwell

Details

By specifying the number of non-zero coefficients, the algorithm evaluates all possible ways of choosing unconstrained and constrained betas, and for each configuration a statistic (tk) is computed. This tk statistic can be considered a measure of fit of a model. The minimum tk over all possible configurations provides a global test of whether one of the models fits well.

References

Schaid DJ, Tong X, Larrabee B, Kennedy RB, Poland GA, Sinnwell JP. Statistical Methods for Testing Genetic Pleiotropy. Genetics. 2016 Oct;204(2):483-497.

Examples

data(pleio.demo)
obj <- pleio.glm.fit(y, geno, glm.family=c("gaussian","binomial","ordinal"))
test1 <- pleio.glm.test(obj, count.nonzero.coef = 0)
test1
test2 <- pleio.glm.test(obj, count.nonzero.coef = 1)
test2