Generates a `data.frame`

object out of a
`repeatedTrain`

object after time binning in order to study
trials stationarity with a `glm`

fit.

1 |

`repeatedTrain` |
a |

`breaks` |
a numeric. A single number is interpreted has the number of bins; a vector is interpreted as the position of the "breaks" between bins. |

The bins are placed between the `floor`

of the smallest
spike time and the `ceiling`

of the largest one when
`breaks`

is a scalar. After time binning the number of spikes of
each trial falling in each bin is counted (in the same way as the
`counts`

component of a `psth`

list is
obtained). This matrix of count is then formatted as a data frame.

A `data.frame`

with the following variables:

`Count ` |
a count (number of spikes in a given bin at a given trial). |

`Bin ` |
the bin index (a |

`Trial ` |
the trial index (a |

`Rate ` |
the count divided by the length of the corresponding bin. |

`Time ` |
the time of the midpoints of the bins. |

When a `glm`

of the poisson family is used for subsequent
analysis the important implicit hypothesis of an inhomogenous Poisson
train is of course made.

Christophe Pouzat christophe.pouzat@gmail.com

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
## Load the Vanillin responses of the first
## cockroach data set
data(CAL1V)
## convert them into repeatedTrain objects
## The stimulus command is on between 4.49 s and 4.99s
CAL1V <- lapply(CAL1V,as.repeatedTrain)
## Generate raster plot for neuron 1
raster(CAL1V[["neuron 1"]],c(4.49,4.99))
## make a smooth PSTH of these data
psth(CAL1V[["neuron 1"]],stimTimeCourse=c(4.49,4.99),breaks=c(bw=0.5,step=0.05),colCI=2,xlim=c(0,10))
## add a grid to the plot
grid()
## The response starts after 4.5 s and is mostly over after 6 s: create
## breaks accordingly
myBreaks <- c(0,2.25,4.5,seq(4.75,6.25,0.25),seq(6.5,11,0.5))
## get a count data frame
CAL1Vn1DF <- df4counts(CAL1V[["neuron 1"]],myBreaks)
## use a box plot to look at the result
boxplot(Rate ~ Time, data=CAL1Vn1DF)
## watch out here the time scale is distorted because of our
## choice of unequal bins
## Fit a glm of the Poisson family taking both Bin and Trial effects
CAL1Vn1DFglm <- glm(Count ~ Bin + Trial,family=poisson,data=CAL1Vn1DF)
## use an anova to see that both the Bin effect and the trial effect are
## highly significant
anova(CAL1Vn1DFglm, test="Chisq")
``` |

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