Summarizing Non-Linear Least-Squares Model Fits

summary.nls {stats}

R Documentation

Summarizing Non-Linear Least-Squares Model Fits

Description

summary method for class "nls".

Usage

## 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`.