qesci.ls {rqlm}

Calculating confidence interval for modified least-squares regression based on the quasi-score test

Description

Recent studies revealed the robust standard error estimates of the modified least-squares regression analysis are generally biased under small or moderate sample settings. To adjust the bias and to provide more accurate confidence intervals, confidence interval and P-value of the test for risk difference by modified least-squares regression are calculated based on the quasi-score test of Noma and Gosho (2024).

Usage

qesci.ls(formula, data, x.name=NULL, cl=0.95, C0=10^-5, digits=4)

Arguments

formula

An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.

data

A data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.

x.name

The variable name that the confidence interval is calculated for the regression coefficient; should be involved in formula as an explanatory variable. Specify as a character object.

cl

Confidence level for calculating confidence intervals (default: 0.95)

C0

A tuning parameter to control the precisions of numerical computations of confidence limits (default: 10^-5).

digits

Number of decimal places in the output (default: 4).

Value

Results of the modified least-squares analyses are presented. Three objects are provided: Results of the modified least-squares regression with the Wald-type approximation by rqlm, quasi-score confidence interval for the corresponding covariate, and P-value for the quasi-score test of RD=0.

References

Noma, H. and Gosho, M. (2024). Bootstrap confidence intervals based on quasi-likelihood estimating functions for the modified Poisson and least-squares regressions for binary outcomes. Forthcoming.

Examples

data(exdata01)

qesci.ls(y ~ x1 + x2 + x3 + x4, data=exdata01, "x3")