R Dataset / Package robustbase / epilepsy

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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Ratio

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Regression

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Stem and Leaf Plots

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Visual Summaries

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Submitted by pmagunia on March 9, 2018 - 1:06 PM
<iframe src="https://embed.picostat.com/r-dataset-package-robustbase-epilepsy.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-29158.csv 6.53 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the epilepsy data set which pertains to Epilepsy Attacks Data Set. The epilepsy data set is found in the robustbase R package. Try to load the epilepsy data set in R by issuing the following command at the console data("epilepsy"). This may load the data into a variable called epilepsy. If R says the epilepsy data set is not found, you can try installing the package by issuing this command install.packages("robustbase") and then attempt to reload the data with library("robustbase") followed by data("epilepsy"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing epilepsy at the command-line which should display the entire dataset.

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 epilepsy R data set. The size of this file is about 6,683 bytes.


Epilepsy Attacks Data Set

Description

Data from a clinical trial of 59 patients with epilepsy (Breslow, 1996) in order to illustrate diagnostic techniques in Poisson regression.

Usage

data(epilepsy)

Format

A data frame with 59 observations on the following 11 variables.

ID

Patient identification number

Y1

Number of epilepsy attacks patients have during the first follow-up period

Y2

Number of epilepsy attacks patients have during the second follow-up period

Y3

Number of epilepsy attacks patients have during the third follow-up period

Y4

Number of epilepsy attacks patients have during the forth follow-up period

Base

Number of epileptic attacks recorded during 8 week period prior to randomization

Age

Age of the patients

Trt

a factor with levels placebo progabide indicating whether the anti-epilepsy drug Progabide has been applied or not

Ysum

Total number of epilepsy attacks patients have during the four follow-up periods

Age10

Age of the patients devided by 10

Base4

Variable Base devided by 4

Details

Thall and Vail reported data from a clinical trial of 59 patients with epilepsy, 31 of whom were randomized to receive the anti-epilepsy drug Progabide and 28 of whom received a placebo. Baseline data consisted of the patient's age and the number of epileptic seizures recorded during 8 week period prior to randomization. The response consisted of counts of seizures occuring during the four consecutive follow-up periods of two weeks each.

Source

Thall, P.F. and Vail S.C. (1990) Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657–671.

References

Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994) Analysis of Longitudinal Data; Clarendon Press.

Breslow N. E. (1996) Generalized linear models: Checking assumptions and strengthening conclusions. Statistica Applicata 8, 23–41.

Examples

data(epilepsy)
str(epilepsy)
pairs(epilepsy[,c("Ysum","Base4","Trt","Age10")])Efit1 <- glm(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy)
summary(Efit1)## Robust Fit :
Efit2 <- glmrob(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy,
                method = "Mqle",
                tcc=1.2, maxit=100)
summary(Efit2)

--

Dataset imported from https://www.r-project.org.
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R Output Date
Picostat Output - Simple Linear Regression Jun 25, 2019
Picostat Output - Simple Linear Regression Jun 25, 2019
Picostat Output - Simple Linear Regression Jun 25, 2019
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