| summary.nls | R Documentation |
summary method for class "nls".
## 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$symbolic.cor,
signif.stars = getOption("show.signif.stars"), ...)
object |
an object of class |
x |
an object of class |
correlation |
logical; if |
digits |
the number of significant digits to use when printing. |
symbolic.cor |
logical. If |
signif.stars |
logical. If |
... |
further arguments passed to or from other methods. |
The distribution theory used to find the distribution of the standard errors and of the residual standard error (for t ratios) is based on linearization and is approximate, maybe very approximate.
print.summary.nls tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
‘significance stars’ if signif.stars is TRUE.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$correlation
directly.
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
|
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
|
symbolic.cor |
(only if |
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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