DNase

Boxplot

Submitted by pmagunia on April 22, 2018 - 3:07 PM

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Correlation Coefficient

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Cumulative Frequency Histogram

Submitted by pmagunia on April 22, 2018 - 3:09 PM

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Dotplot

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Hollow Histogram

Submitted by pmagunia on April 22, 2018 - 3:10 PM

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Mean

Submitted by pmagunia on April 22, 2018 - 3:11 PM

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Pie Chart

Submitted by pmagunia on April 22, 2018 - 3:11 PM

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Plot

Submitted by pmagunia on April 22, 2018 - 3:07 PM

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Regression

Submitted by pmagunia on April 22, 2018 - 3:12 PM

Select any two columns for a simple regression analysis. The first column selected will be the independent variable.

Stem and Leaf Plots

Submitted by pmagunia on April 22, 2018 - 3:12 PM

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Summary

Submitted by pmagunia on April 22, 2018 - 2:51 PM

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Visual Summaries

Submitted by pmagunia on April 22, 2018 - 3:13 PM

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Submitted by pmagunia on February 26, 2017 - 11:28 AM
Attachment Size
dataset-31941.csv 2.72 KB
Documentation

Elisa assay of DNase

The DNase data frame has 176 rows and 3 columns of data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

Usage

DNase

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

Run

an ordered factor with levels 10 < ... < 3 indicating the assay run.

conc

a numeric vector giving the known concentration of the protein.

density

a numeric vector giving the measured optical density (dimensionless) in the assay. Duplicate optical density measurements were obtained.

Details

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Davidian, M. and Giltinan, D. M. (1995) Nonlinear Models for Repeated Measurement Data, Chapman & Hall (section 5.2.4, p. 134)

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer.

Examples

require(stats); require(graphics)

coplot(density ~ conc | Run, data = DNase,
       show.given = FALSE, type = "b")
coplot(density ~ log(conc) | Run, data = DNase,
       show.given = FALSE, type = "b")
## fit a representative run
fm1 <- nls(density ~ SSlogis( log(conc), Asym, xmid, scal ),
    data = DNase, subset = Run == 1)
## compare with a four-parameter logistic
fm2 <- nls(density ~ SSfpl( log(conc), A, B, xmid, scal ),
    data = DNase, subset = Run == 1)
summary(fm2)
anova(fm1, fm2)

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