summary.nls
Summarizing NonLinear LeastSquares Model Fits
summary
method for class "nls"
.
 Keywords
 models, regression
Usage
"summary"(object, correlation = FALSE, symbolic.cor = FALSE, ...)
"print"(x, digits = max(3, getOption("digits")  3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)
Arguments
 object
 an object of class
"nls"
.  x
 an object of class
"summary.nls"
, usually the result of a call tosummary.nls
.  correlation
 logical; if
TRUE
, the correlation matrix of the estimated parameters is returned and printed.  digits
 the number of significant digits to use when printing.
 symbolic.cor
 logical. If
TRUE
, print the correlations in a symbolic form (seesymnum
) rather than as numbers.  signif.stars
 logical. If
TRUE
, ‘significance stars’ are printed for each coefficient.  ...
 further arguments passed to or from other methods.
Details
The distribution theory used to find the distribution of the standard errors and of the residual standard error (for t ratios) is based on linearization and is approximate, maybe very approximate.
print.summary.nls
tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
‘significance stars’ if signif.stars
is TRUE
.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$correlation
directly.
Value

The function
 residuals
 the weighted residuals, the usual residuals
rescaled by the square root of the weights specified in the call to
nls
.  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 $$\hat\sigma^2 = \frac{1}{np}\sum_i{R_i^2},$$ where $R[i]$ is the $i$th 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
cov.unscaled
, ifcorrelation = TRUE
is specified and there are a nonzero number of residual degrees of freedom.  symbolic.cor
 (only if
correlation
is true.) The value of the argumentsymbolic.cor
.
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
See Also
The model fitting function nls
, summary
.
Function coef
will extract the matrix of coefficients
with standard errors, tstatistics and pvalues.
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