R Dataset / Package boot / neuro

How To Create a Barplot

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How To Create a Stacked Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

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Pie Chart

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Plot

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Regression

Stem and Leaf Plots

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Visual Summaries

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<iframe src="https://embed.picostat.com/r-dataset-package-boot-neuro.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-79459.csv 13.55 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the neuro data set which pertains to Neurophysiological Point Process Data. The neuro data set is found in the boot R package. You can load the neuro data set in R by issuing the following command at the console data("neuro"). This will load the data into a variable called neuro. If R says the neuro data set is not found, you can try installing the package by issuing this command install.packages("boot") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the neuro R data set. The size of this file is about 13,879 bytes.

Documentation

Neurophysiological Point Process Data

Description

neuro is a matrix containing times of observed firing of a neuron in windows of 250ms either side of the application of a stimulus to a human subject. Each row of the matrix is a replication of the experiment and there were a total of 469 replicates.

Note

There are a lot of missing values in the matrix as different numbers of firings were observed in different replicates. The number of firings observed varied from 2 to 6.

Source

The data were collected and kindly made available by Dr. S.J. Boniface of the Neurophysiology Unit at the Radcliffe Infirmary, Oxford.

References

Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.

Ventura, V., Davison, A.C. and Boniface, S.J. (1997) A stochastic model for the effect of magnetic brain stimulation on a motorneurone. To appear in Applied Statistics.

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Dataset imported from https://www.r-project.org.