Description Details Author(s) References See Also

Functions to simulate Poisson distributed responses relative to a baseline and compute achieved significance level and powers for tests on the simulated responses. These functions were used to perform the calculations in the paper by Steinmetz & Thorp (2013).

Package: | PsumtSim |

Version: | 0.4 |

Date: | 2012-08-26 |

License: | GPL version 3 or newer |

Built: | R 2.13.0; ; 2012-02-03 22:41:44 UTC; unix |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
Index:
calcNumRejects Calculate number of cases rejected in grouped Poisson responses.
catEffectBootAdaptor
Adaptor to use a statistic calculating function with boot
functions.
compExclusionFraction
Computes fraction of simulated cells, with Poisson responses
to background and varying rates to different categories, which
will have an effect of category but be excluded by pre-testing.
compPowerCatSelectivity
Compute number of simulated neurons with a significant effect
of category using a bootstrapped F-ratio test.
compPowerGeneralRespDetection
Perform repeated simulations of grouped responses, where all
groups differ from baseline and determine number significant.
compPowerRespDetection
Perform repeated simulations of grouped responses, where some
groups differ from baseline and determine number significant.
compRejectionFraction Compute rejection fraction for sequential tests.
simCatResp Simulate grouped Poisson responses.
simNormCatResp Simulate grouped responses which are Normally distributed.
testCatEffectBoot Test for an effect of category using bootstrapping.
``` |

This package provides a set of functions for simulating grouped responses and testing them for significant deviations from baseline. This is primarily of use for computing power of different testing methods.

The highest level functions are `compPowerGeneralRespDetection`

and
`compPowerRespDetection`

which will perform repeated simulation
and testing, determining the number of simulations which produce significant results.

The example for the `compPowerRespDetection`

shows code to generate
the data in figure 4 of Steinmetz & Thorp 2013 and the example for
`compPowerCatSelectivity`

shows code to generate figure 5.

Peter N. Steinmetz <PeterNSteinmetz@steinmetz.org>, Christopher Thorp <thorp@spacia.org>

Maintainer: Peter N. Steinmetz <PeterNSteinmetz@steinmetz.org>

Efron B, Tibshirani RJ. An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability). Chapman and Hall/CRC; 1994.

Steinmetz, P.N. & Thorp, C.K. (2013) Testing for effects of different stimuli on neuronal firing relative to background activity. Journal of Neural Engineering, Sept. 2013.

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