summary method for class "ptsr".
# S3 method for ptsr
summary(object, ...)# S3 method for summary.ptsr
print(x, digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"), ...)
object of class "ptsr".
further arguments passed to or from other methods.
an object of class "summary.ptsr",
usually, a result of a call to summary.ptsr.
minimal number of significant digits, see
print.default.
logical. If TRUE,
‘significance stars’ are printed for each coefficient.
The function summary.ptsr computes and returns a list
of summary statistics of the fitted model given in object.
Returns a list of class summary.ptsr, which contains the
following components:
the residuals of the model.
a \(k \times 4\) matrix with columns for the estimated coefficient, its standard error, z-statistic and corresponding (two-sided) p-value.
the square root of the estimated variance of the random
error $$\hat\sigma^2 = \frac{1}{n-k}\sum_i{r_i^2},$$
where \(r_i\) is the \(i\)-th residual, residuals[i].
degrees of freedom, a 3-vector \((k, n-k, k*)\), the first being the number of non-aliased coefficients, the last being the total number of coefficients.
a \(k \times k\) matrix of (unscaled) covariances. The inverse ov the information matrix.
the sum of the log-likelihood values
the AIC value. \(AIC = -2*loglik+2*k\).
the BIC value. \(BIC = -2*loglik + log(n)*k\).
the HQC value. \(HQC = -2*loglik + log(log(n))*k\).
print.summary.btsr tries to be smart about formatting the
coefficients, standard errors, etc. and additionally provides
‘significance stars’.