The
summary.nls method 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.
USAGE:
summary(object, pool)
REQUIRED ARGUMENTS:
object
an object inheriting from class
nlsList,
representing a list of
nls fitted objects.
OPTIONAL ARGUMENTS:
pool
an optional logical value indicating whether a pooled
estimate of the residual standard error should be used. Default is
attr(object, "pool").
VALUE:
a list with summary statistics obtained by applying
summary.nls
to the elements of
object, inheriting from class
summary.nlsList
. The components of
value are:
call
a list containing an image of the
nlsList call that
produced
object.
parameters
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.
correlation
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.
cov.unscaled
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.
df
an array with the number of degrees of freedom for the model
and for residuals, for each
nls component.
df.residual
the total number of degrees of freedom for
residuals, corresponding to the sum of residuals df of all
nls
components.
pool
the value of the
pool argument to the function.
RSE
the pooled estimate of the residual standard error.
sigma
a vector with the residual standard error estimates for
the individual
lm fits.