PseudoR2: Pseudo-R2 Statistics
In BaylorEdPsych: R Package for Baylor University Educational Psychology Quantitative Courses

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Returns various pseudo-R^2 statistics (as well as Akaike's [corrected] information criterion) from a glm object. Should mimic those returend from a logistic/probit regression in Stata when using fitstat

1	PseudoR2(glmModel)

glmModel

Object from a glm model, preferably logsitstic regression,
e.g., family=binomial(link="logit")

None

`McFadden`	McFadden Pseudo-R^2
`Adj.McFadden`	McFadden Adjusted Pseudo-R^2
`Cox.Snell`	Cox and Snell Pseudo-R^2 (also known as ML Pseudo-R^2)
`Nagelkerke`	Nagelkerke PseudoR^2
`McKelvey.Zavoina`	McKelvey and Zavoina Pseudo-R^2
`Effron`	Effron Pseudo-R^2
`Count`	Count Pseudo-R^2, number of correctly classified cases, uisng \hat{π}> .50 as the cutoff
`Adj.Count`	Adjusted Count Pseudo-R^2
`AIC`	Akaike's information criterion
`Corrected.AIC`	Corrected Akaike information criterion

There are many documented problems with using pseudo-R2 values (e.g., Long, 1997). Use the values judiciously.

A. Alexander Beaujean

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Springer-Verlag.

Efron, B. (1978). Regression and ANOVA with zero-one data: Measures of residual variation. Journal of the American Statistical Association, 73(361), 113–121.

Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Hoboke, NJ: Wiley.

Long, J. S.(1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA:Sage.

McFadden, D. (1979). Quantitative methods for analysing travel behavior of individuals: Some recent developments. In D. A. Hensher & P. R. Stopher (Eds.), Behavioural travel modelling (pp. 279–318). London: Croom Helm.

McKelvey, R. D., & Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent variables. The Journal of Mathematical Sociology, 4(1), 103–120

Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691–692.

glm

data(MLBOffense2011)
MLBOffense2011$NL<-ifelse(MLBOffense2011$Lg=="NL", 1,0)
#predict MLB league membership from RBI and slugging
model1<-glm(NL~RBI + SLG, data=MLBOffense2011, family=binomial(link="logit"))  
PseudoR2(model1)

        McFadden     Adj.McFadden        Cox.Snell       Nagelkerke 
       0.1928445        0.1830248        0.2041590        0.2941805 
McKelvey.Zavoina           Effron            Count        Adj.Count 
       0.3345218        0.1928930        0.7209302        0.0000000 
             AIC    Corrected.AIC 
     663.5830719      663.6181596