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

View source: R/linearReg_R2stat.R

Using the classical R^2 test statistic for (linear) regression designs, this function computes the corresponding Bayes factor test.

1 | ```
linearReg.R2stat(N, p, R2, rscale = 1)
``` |

`N` |
number of observations |

`p` |
number of predictors in model, excluding intercept |

`R2` |
proportion of variance accounted for by the predictors, excluding intercept |

`rscale` |
numeric prior scale |

This function can be used to compute the Bayes factor
corresponding to a multiple regression, using the
classical R^2 (coefficient of determination) statistic.
It can be used when you don't have access to the full
data set for analysis by `lmBF`

, but you do
have the test statistic.

For details about the model, see the help for
`regressionBF`

, and the references therein.

The Bayes factor is computed via Gaussian quadrature.

a vector of length 2 containing the computed log(e) Bayes factor (against the intercept-only null), along with a proportional error estimate on the Bayes factor.

Richard D. Morey ([email protected]) and Jeffrey N. Rouder ([email protected])

Liang, F. and Paulo, R. and Molina, G. and Clyde, M. A. and Berger, J. O. (2008). Mixtures of g-priors for Bayesian Variable Selection. Journal of the American Statistical Association, 103, pp. 410-423

Rouder, J. N. and Morey, R. D. (in press, Multivariate Behavioral Research). Bayesian testing in regression.

Perception and Cognition Lab (University of Missouri): Bayes factor calculators. http://pcl.missouri.edu/bayesfactor

`integrate`

, `lm`

; see
`lmBF`

for the intended interface to this
function, using the full data set.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Use attitude data set
data(attitude)
## Scatterplot
lm1 = lm(rating~complaints,data=attitude)
plot(attitude$complaints,attitude$rating)
abline(lm1)
## Traditional analysis
## p value is highly significant
summary(lm1)
## Bayes factor
## The Bayes factor is almost 80,000;
## the data strongly favor hypothesis that
## the slope is not 0.
result = linearReg.R2stat(30,1,0.6813)
exp(result[['bf']])
``` |

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