summary.nls {stats}  R Documentation 
summary
method for class "nls"
.
## S3 method for class 'nls' summary(object, correlation = FALSE, symbolic.cor = FALSE, ...) ## S3 method for class 'summary.nls' print(x, digits = max(3, getOption("digits")  3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)
object 
an object of class 
x 
an object of class 
correlation 
logical; if 
digits 
the number of significant digits to use when printing. 
symbolic.cor 
logical. If 
signif.stars 
logical. If 
... 
further arguments passed to or from other methods. 
The distribution theory used to find the distribution of the standard errors and of the residual standard error (for t ratios) is based on linearization and is approximate, maybe very approximate.
print.summary.nls
tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
‘significance stars’ if signif.stars
is TRUE
.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$correlation
directly.
The function summary.nls
computes and returns a list of summary
statistics of the fitted model given in object
, using
the component "formula"
from its argument, plus
residuals 
the weighted residuals, the usual residuals
rescaled by the square root of the weights specified in the call to

coefficients 
a p x 4 matrix with columns for the estimated coefficient, its standard error, tstatistic and corresponding (twosided) pvalue. 
sigma 
the square root of the estimated variance of the random error σ^2 = 1/(np) Sum(R[i]^2), where R[i] is the ith weighted residual. 
df 
degrees of freedom, a 2vector (p, np). (Here and elsewhere n omits observations with zero weights.) 
cov.unscaled 
a p x p matrix of (unscaled) covariances of the parameter estimates. 
correlation 
the correlation matrix corresponding to the above

symbolic.cor 
(only if 
The model fitting function nls
, summary
.
Function coef
will extract the matrix of coefficients
with standard errors, tstatistics and pvalues.