summary {BTSR} | R Documentation |
Summary Method of class BTSR
Description
summary
method for class "btsr"
.
Usage
## S3 method for class 'btsr'
summary(object, robust = FALSE, outer = FALSE,
full.report = TRUE, ...)
## S3 method for class 'summary.btsr'
print(x, digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"), ...)
Arguments
object |
object of class |
robust |
logical. If |
outer |
logical. If |
full.report |
logical. If |
... |
further arguments passed to or from other methods. |
x |
an object of class |
digits |
minimal number of significant digits, see print.default. |
signif.stars |
logical. If |
Details
print.summary.btsr
tries to be smart about formatting the coefficients,
standard errors, etc. and additionally provides ‘significance stars’.
Value
The function summary.btsr
computes and returns a list of summary statistics
of the fitted model given in object
. Returns a list of class
summary.btsr
, which contains the following components
-
model
: the corresponding model. -
call
: the matched call. -
residuals
: The (in-sample) residuals, that is, the observed valuesY_t
minus the fitted values\mu_t
. The same as theerror
term iferror.scale = 0
. -
coefficients
: ak \times 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 error term inresiduals
\hat\sigma^2 = \displaystyle\frac{1}{n-k}\sum_{i=1}^{n-k}{e_i^2},
where
e_i
is thei
-th residual. -
df
: degrees of freedom, a 2-vector(k, n-k)
, the first being the number of estimated coefficients. -
vcov
: ak \times k
matrix of (unscaled) covariances. The inverse ov the information matrix. -
loglik
: the sum of the log-likelihood values (L
) -
aic
: the AIC value.AIC = -2L + 2k
. -
bic
: the BIC value.BIC = -2L + k\log(n)
. -
hqc
: the HQC value.HQC = -2L + k\log(\log(n))
.