# summary.nls

0th

Percentile

##### 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),

##### 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.

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