Full_Name: Carsten Urbach
Version: 2.1.1 (2005-06-20)
OS: Linux
Submission from: (NULL) (141.34.5.241)
I observed one case where nls failed to return the correlation matrix, while the
parameter estimates were computed correctly. In the follwing I include all the
commands leading to this problem. R was started with 'R --vanilla':
version
_
platform i686-pc-linux-gnu
arch i686
os linux-gnu
system i686, linux-gnu
status
major 2
minor 1.1
year 2005
month 06
day 20
language R
x y
1 -0.3209 0.80709
2 -0.1364 0.71202
3 0.0219 0.67844
4 0.1628 0.65797
5 0.2885 0.64604
6 0.4956 0.63047
fit4 <- nls(y ~ a+b*x+c*x^2+d*x^3+e*x^4, data = df, start = list(a = 1., b =
-1., c = 1., d=-1., e=1.))
summary(fit4)
Formula: y ~ a + b * x + c * x^2 + d * x^3 + e * x^4
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 0.680936 0.001327 513.002 0.00124 **
b -0.168167 0.010660 -15.775 0.04030 *
c 0.325593 0.047433 6.864 0.09210 .
d -0.860767 0.117710 -7.313 0.08652 .
e 0.957177 0.319101 3.000 0.20486
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Residual standard error: 0.001722 on 1 degrees of freedom
Correlation of Parameter Estimates:
a b c d
b 1
c , , 1
d * , 1
e . , * *
attr(,"legend")
[1] 0 ? ? 0.3 ?.? 0.6 ?,? 0.8 ?+? 0.9 ?*? 0.95 ?B? 1
fit4
Nonlinear regression model
model: y ~ a + b * x + c * x^2 + d * x^3 + e * x^4
data: df
a b c d e
0.6809363 -0.1681674 0.3255929 -0.8607670 0.9571768
residual sum-of-squares: 2.966569e-06