R Dataset / Package robustbase / milk

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-milk.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-96899.csv 3.56 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 milk data set which pertains to Daudin's Milk Composition Data. The milk data set is found in the robustbase R package. Try to load the milk data set in R by issuing the following command at the console data("milk"). This may load the data into a variable called milk. If R says the milk 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("milk"). 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 milk 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 milk R data set. The size of this file is about 3,647 bytes.


Daudin's Milk Composition Data

Description

Daudin et al.(1988) give 8 readings on the composition of 86 containers of milk. They speak about 85 observations, but this can be explained with the fact that observations 63 and 64 are identical (as noted by Rocke (1996)).

The data set was used for analysing the stability of principal component analysis by the bootstrap method. In the same context, but using high breakdown point robust PCA, these data were analysed by Todorov et al. (1994). Atkinson (1994) used these data for ilustration of the forward search algorithm for identifying of multiple outliers.

Usage

data(milk)

Format

A data frame with 86 observations on the following 8 variables, all but the first measure units in grams / liter.

X1

density

X2

fat content

X3

protein content

X4

casein content

X5

cheese dry substance measured in the factory

X6

cheese dry substance measured in the laboratory

X7

milk dry substance

X8

cheese product

Source

Daudin, J.J. Duby, C. and Trecourt, P. (1988) Stability of Principal Component Analysis Studied by the Bootstrap Method; Statistics 19, 241–258.

References

Todorov, V., Neyko, N., Neytchev, P. (1994) Stability of High Breakdown Point Robust PCA, in Short Communications, COMPSTAT'94; Physica Verlag, Heidelberg.

Atkinson, A.C. (1994) Fast Very Robust Methods for the Detection of Multiple Outliers. J. Amer. Statist. Assoc. 89 1329–1339.

Rocke, D. M. and Woodruff, D. L. (1996) Identification of Outliers in Multivariate Data; J. Amer. Statist. Assoc. 91 (435), 1047–1061.

Examples

data(milk)
(c.milk <- covMcd(milk))
summarizeRobWeights(c.milk $ mcd.wt)# 19..20 outliers
umilk <- unique(milk) # dropping obs.64 (== obs.63)
summary(cumilk <- covMcd(umilk, nsamp = "deterministic")) # 20 outliers
--

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