summary.surerob {robustsur} | R Documentation |
Summary of surerob estimation
Description
These functions create and print summary results of the estimated equation system.
Usage
## S3 method for class 'surerob'
summary(object, residCov=TRUE, equations=TRUE, ...)
## S3 method for class 'summary.surerob'
print(x, digits=max(3, getOption("digits")-1),
residCov=x$printResidCov, equations=x$printEquations, ...)
Arguments
object |
an object of class |
x |
an object of class |
residCov |
logical. If |
equations |
logical. If |
digits |
number of digits to print. |
... |
not yet used. |
Value
Applying summary
on an object of class surerob
returns a list of class summary.surerob
.
An object of class summary.surerob
contains all results that belong to the whole system.
This list contains one special object: eq
.
This is a list and contains objects of class
summary.lmrob
.
These objects contain the results that belong to each of the estimated equations.
The objects of classes summary.surerob
have the following components
method |
estimation method. |
residuals |
residuals. |
residCovEst |
residual covariance matrix used for estimation. |
residCov |
estimated residual covariance matrix. |
residCor |
correlation matrix of the residuals. |
detResidCov |
determinant of |
rweights |
matrix of robust weights. |
eq |
a list containing the summary from function
|
df |
degrees of freedom, a 2-vector, where the first element is the number of coefficients and the second element is the number of observations minus the number of coefficients. |
coefficients |
a matrix with columns for the estimated coefficients, their standard errors, t-statistic and corresponding (two-sided) p-values. |
ssr_weighted |
weighted residual sum of squares. |
r.squared |
|
adj.r.squared |
adjusted |
coefCov |
estimated covariance matrix of the coefficients. |
printResidCov |
argument |
printEquations |
argument |
control |
list of control parameters used for the estimation. |
call |
the matched call of |
Author(s)
Claudio Agostinelli and Giovanni Saraceno
References
Giovanni Saraceno, Fatemah Alqallaf and Claudio Agostinelli (2021?) A Robust Seemingly Unrelated Regressions For Row-Wise And Cell-Wise Contamination, submitted
See Also
Examples
library(systemfit)
data("Kmenta")
eqDemand <- consump~price+income
eqSupply <- consump~price+farmPrice+trend
system <- list(demand=eqDemand, supply=eqSupply)
## Robust estimation
fitrob <- surerob(system, data=Kmenta)
summary(fitrob)