Description Usage Arguments Value Examples

View source: R/BayesKnockdown.r

Calculates posterior probabilities for an ExpressionSet object by defining one feature as the predictor. Each other feature in the ExpressionSet is is then used as a response variable and posterior probabilities are calculated, incorporating prior probabilities potentially unique to each response variable.

1 | ```
BayesKnockdown.es(es, predFeature, prior = 0.5, g = sqrt(dims(es)[2,1]))
``` |

`es` |
An ExpressionSet object with |

`predFeature` |
The name of the feature to use as the predictor. |

`prior` |
Prior probabilities for the outcome variables. Defaults to 0.5 for all variables. |

`g` |
The value to use for Zellner's |

A vector of `p-1`

posterior probabilities indicating
the probability of a relationship between the predictor variable
and each outcome variable.

1 2 3 4 5 | ```
library(Biobase);
data(sample.ExpressionSet);
subset <- sample.ExpressionSet[1:10,];
BayesKnockdown.es(subset, "AFFX-MurIL10_at");
``` |

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