summary.nls: Summarizing Non-Linear Least-Squares Model Fits

Description Usage Arguments Details Value See Also

Description

`summary` method for class `"nls"`.

Usage

 ```1 2 3 4 5 6 7``` ```## S3 method for class 'nls' summary(object, correlation = FALSE, symbolic.cor = FALSE, ...) ## 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 ‘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

 `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, t-statistic and corresponding (two-sided) p-value. `sigma` the square root of the estimated variance of the random error σ^2 = 1/(n-p) Sum(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 x 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.

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