The summary function is applied to each nls component of
object to produce summary information on the individual fits,
which is organized into a list of summary statistics. The returned
object is suitable for printing with the print.summary.nlsList
method.
# S3 method for nlsList
summary(object, …)an object inheriting from class "nlsList",
representing a list of nls fitted objects.
optional arguments to the summary.lmList method.
One such optional argument is pool, a logical value indicating
whether a pooled estimate of the residual standard error should be
used. Default is attr(object, "pool").
a list with summary statistics obtained by applying summary
to the elements of object, inheriting from class
summary.nlsList. The components of value are:
a list containing an image of the nlsList call that
produced object.
a three dimensional array with summary information
on the nls coefficients. The first dimension corresponds to
the names of the object components, the second dimension is
given by "Value", "Std. Error", "t value",
and "Pr(>|t|)", corresponding, respectively, to the
coefficient estimates and their associated standard errors,
t-values, and p-values. The third dimension is given by the
coefficients names.
a three dimensional array with the
correlations between the individual nls coefficient
estimates. The first dimension corresponds to the names of the
object components. The third dimension is given by the
coefficients names. For each coefficient, the rows of the associated
array give the correlations between that coefficient and the
remaining coefficients, by nls component.
a three dimensional array with the unscaled
variances/covariances for the individual lm coefficient
estimates (giving the estimated variance/covariance for the
coefficients, when multiplied by the estimated residual errors). The
first dimension corresponds to the names of the object
components. The third dimension is given by the
coefficients names. For each coefficient, the rows of the associated
array give the unscaled covariances between that coefficient and the
remaining coefficients, by nls component.
an array with the number of degrees of freedom for the model
and for residuals, for each nls component.
the total number of degrees of freedom for
residuals, corresponding to the sum of residuals df of all nls
components.
the value of the pool argument to the function.
the pooled estimate of the residual standard error.
a vector with the residual standard error estimates for
the individual lm fits.
# NOT RUN {
fm1 <- nlsList(SSasymp, Loblolly)
summary(fm1)
# }
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