Elisa assay of DNase

Description

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(datasets)
> png(filename="/home/ddbj/snapshot/RGM3/R_rel/result/datasets/DNase.Rd_%03d_medium.png", width=480, height=480)
> ### Name: DNase
> ### Title: Elisa assay of DNase
> ### Aliases: DNase
> ### Keywords: datasets
> 
> ### ** Examples
> 
> require(stats); require(graphics)
> ## Don't show: 
> options(show.nls.convergence=FALSE)
> ## End(Don't show)
> 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)

Formula: density ~ SSfpl(log(conc), A, B, xmid, scal)

Parameters:
      Estimate Std. Error t value Pr(>|t|)    
A    -0.007897   0.017200  -0.459    0.654    
B     2.377239   0.109516  21.707 5.35e-11 ***
xmid  1.507403   0.102080  14.767 4.65e-09 ***
scal  1.062579   0.056996  18.643 3.16e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.01981 on 12 degrees of freedom

> anova(fm1, fm2)
Analysis of Variance Table

Model 1: density ~ SSlogis(log(conc), Asym, xmid, scal)
Model 2: density ~ SSfpl(log(conc), A, B, xmid, scal)
  Res.Df Res.Sum Sq Df     Sum Sq F value Pr(>F)
1     13  0.0047896                             
2     12  0.0047073  1 8.2314e-05  0.2098 0.6551
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>