summary.nls              package: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

                    sigma^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.

