Plot a profile.nls Object With Profile Traces

Displays a series of plots of the profile t function and the likelihood profile traces for the parameters in a nonlinear regression model that has been fitted with nls and profiled with profile.nls.

p.profileTraces(x, cex = 1,
                subtitle = paste("t-Profiles and traces of ",
                       deparse(attr(x,"summary")$formula)))

Arguments

x

an object of class "profile.nls", typically resulting from profile(nls(.)), see profile.nls.

cex

character expansion, see par(cex =).

subtitle

a subtitle to set for the plot. The default now includes the nls() formula used.

Author

Andreas Ruckstuhl, R port by Isabelle Flückiger and Marcel Wolbers

Note

the stats-internal stats:::plot.profile.nls plot method just does “the diagonals”.

See also

profile, and nls (which has unexported profile and stats:::plot.profile.nls methods).

Examples

require(stats)
data(Puromycin)
Treat <- Puromycin[Puromycin$state == "treated", ]
fm <- nls(rate ~ T1*conc/(T2+conc), data=Treat,
          start = list(T1=207,T2=0.06))
(pr <- profile(fm)) # quite a few things..
#> $T1
#>       tau par.vals.T1 par.vals.T2
#> 1  -3.209    190.8634      0.0455
#> 2  -2.563    195.0840      0.0488
#> 3  -1.916    199.3911      0.0523
#> 4  -1.269    203.7889      0.0560
#> 5  -0.621    208.2802      0.0600
#> 6   0.000    212.6837      0.0641
#> 7   0.609    217.0872      0.0684
#> 8   1.229    221.6741      0.0730
#> 9   1.848    226.3617      0.0780
#> 10  2.468    231.1568      0.0833
#> 11  3.086    236.0657      0.0890
#> 12  3.704    241.0959      0.0951
#> 
#> $T2
#>       tau par.vals.T1 par.vals.T2
#> 1  -3.356    195.0005      0.0397
#> 2  -2.672    198.4424      0.0440
#> 3  -1.988    201.9612      0.0486
#> 4  -1.305    205.5617      0.0535
#> 5  -0.624    209.2376      0.0589
#> 6   0.000    212.6837      0.0641
#> 7   0.583    215.9820      0.0694
#> 8   1.177    219.4187      0.0751
#> 9   1.770    222.9382      0.0812
#> 10  2.363    226.5483      0.0877
#> 11  2.955    230.2558      0.0946
#> 12  3.547    234.0683      0.1021
#> 
#> attr(,"original.fit")
#> Nonlinear regression model
#>   model: rate ~ T1 * conc/(T2 + conc)
#>    data: Treat
#>       T1       T2 
#> 212.6837   0.0641 
#>  residual sum-of-squares: 1195
#> 
#> Number of iterations to convergence: 5 
#> Achieved convergence tolerance: 1.38e-06
#> attr(,"summary")
#> 
#> Formula: rate ~ T1 * conc/(T2 + conc)
#> 
#> Parameters:
#>    Estimate Std. Error t value Pr(>|t|)    
#> T1 2.13e+02   6.95e+00   30.61  3.2e-11 ***
#> T2 6.41e-02   8.28e-03    7.74  1.6e-05 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 10.9 on 10 degrees of freedom
#> 
#> Number of iterations to convergence: 5 
#> Achieved convergence tolerance: 1.38e-06
#> 
#> attr(,"class")
#> [1] "profile.nls" "profile"    
op <- par(mfcol=1:2)
plot(pr) # -> 2 'standard' plots

par(op)
## ours:
p.profileTraces(pr)