gtWAS (version 1.1.0)

ModelFit: Compute model fit statistics

Description

Compute model fit statistics based on a given criteria for linear model function

Usage

ModelFit(criteria, lmresult, nObs, sigma_sqr)

Arguments

criteria

The class of criteria including Akaike information criterion(AIC), the corrected form of Akaike information criterion(AICc), Bayesian information criterion(BIC), Schwarz criterion(SBC) and significant levels(SL)

lmresult

Result of linear model function

nObs

Number of observation

sigma_sqr

The estimation of pure error variance for the full model in regression

Value

A numeric of model fit statistics

References

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

Judge, & GeorgeG. (1985). The Theory and practice of econometrics /-2nd ed. The Theory and practice of econometrics /. Wiley.

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

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

Sawa, T. (1978). Information criteria for discriminating among alternative regression models. Econometrica, 46(6), 1273-1291.

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

Examples

# NOT RUN {
set.seed(4)
YX <- matrix(rnorm(200,20,4),20,10)
YX <- as.data.frame(YX)
colnames(YX) <- c("Y1","Y2",paste("X",c(1:8),sep=""))
lm_formula <- as.formula("Y1~X1+X2+X3+X4+X5")
lmresult <- lm(lm_formula,data=YX)
ModelFit("SBC", lmresult, nrow(YX), 0)
# }