# summary.nls

##### Summarizing Non-Linear Least-Squares Model Fits

`summary`

method for class `"nls"`

.

- Keywords
- models, regression

##### Usage

```
# S3 method for nls
summary(object, correlation = FALSE, symbolic.cor = FALSE, …)
```# S3 method for summary.nls
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 to`summary.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 (see`symnum`

) 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 `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

the *weighted* residuals, the usual residuals
rescaled by the square root of the weights specified in the call to
`nls`

.

a \(p \times 4\) matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.

the square root of the estimated variance of the random error $$\hat\sigma^2 = \frac{1}{n-p}\sum_i{R_i^2},$$ where \(R_i\) is the \(i\)-th weighted residual.

degrees of freedom, a 2-vector \((p, n-p)\). (Here and elsewhere \(n\) omits observations with zero weights.)

a \(p \times p\) matrix of (unscaled) covariances of the parameter estimates.

the correlation matrix corresponding to the above
`cov.unscaled`

, if `correlation = TRUE`

is specified and
there are a non-zero number of residual degrees of freedom.

(only if `correlation`

is true.) The value
of the argument `symbolic.cor`

.

##### See Also

The model fitting function `nls`

, `summary`

.

Function `coef`

will extract the matrix of coefficients
with standard errors, t-statistics and p-values.

*Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0*