# summary.nls

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

`summary`

method for class `"nls"`

.

- Keywords
- models, regression

##### Usage

```
## S3 method for class 'nls':
summary(object, correlation = FALSE, symbolic.cor = FALSE, \dots)
```## 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"), ...)

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

.coefficients a $p \times 4$ matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. sigma 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. df degrees of freedom, a 2-vector $(p, n-p)$. (Here and elsewhere $n$ omits observations with zero weights.) cov.unscaled a $p \times p$ matrix of (unscaled) covariances of the parameter estimates. correlation 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.symbolic.cor (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 3.3, License: Part of R 3.3*