STAR (version 0.3-7)

df4counts: Generates a Data Frame from a repeatedTrain Object After Time Binning

Description

Generates a data.frame object out of a repeatedTrain object after time binning in order to study trials stationarity with a glm fit.

Usage

df4counts(repeatedTrain, breaks = length(repeatedTrain))

Arguments

repeatedTrain

a repeatedTrain object or a list which can be coerced to such an object.

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.

Value

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 factor.

Trial

the trial index (a factor.

Rate

the count divided by the length of the corresponding bin.

Time

the time of the midpoints of the bins.

Details

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.

See Also

as.repeatedTrain, psth

Examples

## Not run: 
# ## 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")
# ## End(Not run)