summary: Summary Method of class PTSR

In PTSR: Positive Time Series Regression

Description

summary method for class "ptsr".

Usage

## S3 method for class 'ptsr'
summary(object, ...)

## S3 method for class 'summary.ptsr'
print(x, digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"), ...)

Arguments

object	object of class "ptsr".
...	further arguments passed to or from other methods.
x	an object of class "summary.ptsr", usually, a result of a call to summary.ptsr.
digits	minimal number of significant digits, see print.default.
signif.stars	logical. If TRUE, ‘significance stars’ are printed for each coefficient.

Details

print.summary.btsr tries to be smart about formatting the coefficients, standard errors, etc. and additionally provides ‘significance stars’.

Value

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:

residuals	the residuals of the model.
coefficients	a k x 4 matrix with columns for the estimated coefficient, its standard error, z-statistic and corresponding (two-sided) p-value.
sigma.res	the square root of the estimated variance of the random error σ^2 = \frac{1}{n-k} ∑_i r[i]^2, where r[i] is the i-th residual, residuals[i].
df	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.
vcov	a k \times k matrix of (unscaled) covariances. The inverse ov the information matrix.
loglik	the sum of the log-likelihood values
aic	the AIC value. AIC = -2*loglik+2*k.
bic	the BIC value. BIC = -2*loglik + log(n)*k.
hqc	the HQC value. HQC = -2*loglik + log(log(n))*k.

PTSR documentation built on Feb. 8, 2022, 9:06 a.m.