Predictions 1. Multiple linear regression Kenneth D. Harris April 29, 2015.

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Presentation transcript:

Predictions 1. Multiple linear regression Kenneth D. Harris April 29, 2015

Predictions in neurophysiology Predict neuronal activity from sensory stimulus/behaviour “encoding model” Predict stimulus/behaviour from neuronal activity “decoding model” In principle, these are equivalent. In practice, very different.

Different types of prediction What are you predicting? Data type Dimensionality What are you predicting it from? Data type Dimensionality How many data points do you have? What sort of prediction do you need? What sort of relationship can you assume?

Different types of prediction What are you predicting? Data typeContinuous, binary, count, nominal Dimensionality What are you predicting it from? Data typeContinuous, binary, count, nominal Dimensionality How many data points do you have? What sort of prediction do you need? What sort of relationship can you assume?

Different types of prediction What are you predicting? Data typeContinuous, binary, count, nominal Dimensionality What are you predicting it from? Data typeContinuous, binary, count, nominal Dimensionality How many data points do you have? What sort of prediction do you need?Probability distribution, or single best guess? What sort of relationship can you assume?

Different types of prediction What are you predicting? Data typeContinuous, binary, count, nominal Dimensionality What are you predicting it from? Data typeContinuous, binary, count, nominal Dimensionality How many data points do you have?N What sort of prediction do you need?Probability distribution, or single best guess? What sort of relationship can you assume?

Different types of prediction What are you predicting? Data typeContinuous, binary, count, nominal Dimensionality What are you predicting it from? Data typeContinuous, binary, count, nominal Dimensionalityp How many data points do you have?N What sort of prediction do you need?Probability distribution, or single best guess? What sort of relationship can you assume?

Different types of prediction What are you predicting? Data typeContinuous, binary, count, nominal DimensionalityOften irrelevant (assume independent predictions) What are you predicting it from? Data typeContinuous, binary, count, nominal Dimensionalityp How many data points do you have?N What sort of prediction do you need?Probability distribution, or single best guess? What sort of relationship can you assume?

Different types of prediction What are you predicting? Data typeContinuous, binary, count, nominal DimensionalityOften irrelevant (assume independent predictions) What are you predicting it from? Data typeContinuous, binary, count, nominal Dimensionalityp How many data points do you have?N What sort of prediction do you need?Probability distribution, or single best guess? What sort of relationship can you assume?Lots of choices…

Linear regression, no offset What are you predicting? Data typeContinuous Dimensionality1 What are you predicting it from? Data typeContinuous Dimensionality1 How many data points do you have?Enough What sort of prediction do you need?Single best guess What sort of relationship can you assume?Linear

Linear regression, no offset

Geometric picture Circle of equal error

Geometric picture

Multiple linear regression, no offset What are you predicting? Data typeContinuous Dimensionality1 What are you predicting it from? Data typeContinuous Dimensionalityp How many data points do you have?Enough What sort of prediction do you need?Single best guess What sort of relationship can you assume?Linear

Geometric picture

Multiple linear regression

Things to remember

What about offsets?