# summary.nls

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##### 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),

##### 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
• residualsthe weighted residuals, the usual residuals rescaled by the square root of the weights specified in the call to nls.
• coefficientsa $p \times 4$ matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.
• sigmathe 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.
• dfdegrees of freedom, a 2-vector $(p, n-p)$. (Here and elsewhere $n$ omits observations with zero weights.)
• cov.unscaleda $p \times p$ matrix of (unscaled) covariances of the parameter estimates.
• correlationthe 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.

The model fitting function nls, summary.
Function coef will extract the matrix of coefficients with standard errors, t-statistics and p-values.