# ModelFit: Compute model fit statistics In gtWAS: Genome and Transcriptome Wide Association Study

## Description

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

## Usage

 `1` ```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

JunhuiLi

## 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

 ```1 2 3 4 5 6 7``` ```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) ```

gtWAS documentation built on June 1, 2019, 5:02 p.m.