modelFitStat: Fit Model Statistics

View source: R/modelFitStat.R

modelFitStatR Documentation

Fit Model Statistics

Description

Fit Model Statistics with least square or likelihood method to return an information criteria value

Usage

modelFitStat(ic, fit, method = c("LeastSquare", "Likelihood"), cox = FALSE)

Arguments

ic

Information criteria, including AIC, AICc, BIC, CP, HQ, HQc, Rsq, adjRsq and SBC

fit

Object of linear model or general linear model

method

Method to calculate information criteria value, including 'LeastSquare' and 'Likelihood'

cox

Compute model fit statistics for cox regression or not, where partial likelihood value will be used instead of the ordinary.

Author(s)

Junhui Li

References

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Harold Hotelling. (1992). The Generalization of Student's Ratio. Breakthroughs in Statistics. Springer New York.

Hocking, R. R. (1976). A biometrics invited paper. the analysis and selection of variables in linear regression. Biometrics, 32(1), 1-49.

Hurvich, C. M., & Tsai, C. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307.

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Mcquarrie, A. D. R., & Tsai, C. L. (1998). Regression and Time Series Model Selection. Regression and time series model selection /. World Scientific.

Pillai, K. C. S. (2006). Pillai's Trace. Encyclopedia of Statistical Sciences. John Wiley & Sons, Inc.

R.S. Sparks, W. Zucchini, & D. Coutsourides. (1985). On variable selection in multivariate regression. Communication in Statistics- Theory and Methods, 14(7), 1569-1587.

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Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), pags. 15-18.

Examples

data(mtcars)
fit <- lm(mpg~wt+qsec+vs+am+gear+carb,data=mtcars)
modelFitStat("AIC",fit,"LeastSquare")


StepReg documentation built on Dec. 28, 2022, 1:07 a.m.