perturb.n {multiColl} | R Documentation |
Perturbation and estimation in a multiple linear model
Description
The function quantifies the variations in the estimations of the coefficients of a multiple linear regression when a perturbation is introduced in the quantitative data set.
Usage
perturb.n(data, n, mu, dv, tol = 0.01, pos = NULL)
Arguments
data |
Data set |
n |
Number of times that perturbation is performed. |
mu |
Any real number. |
dv |
Any real positive number. |
tol |
A value between 0 and 1. By default |
pos |
A numeric vector that indicates the position of the independent variables to disturb once you eliminate in |
Value
tols |
A vector presenting the percentage of disturbance induced in the variables indicated in each iteration. |
norms |
A vector presenting the percentage of variation in the estimations of the coefficients in each iteration. |
Note
tols
must be a constant vector equal to tol
. It is obtained to check if data have been correctly perturbed.
Author(s)
R. Salmerón (romansg@ugr.es) and C. García (cbgarcia@ugr.es).
References
D. Belsley (1982). Assessing the presence of harmfull collinearity and other forms of weak data throught a test for signal-to-noise. Journal of Econometrics, 20, 211-253.
L. R. Klein and A.S. Goldberger (1964). An economic model of the United States, 1929-1952. North Holland Publishing Company, Amsterdan.
H. Theil (1971). Principles of Econometrics. John Wiley & Sons, New York.
See Also
Examples
tol = 0.01
mu = 10
dv = 10
# Henri Theil's textile consumption data modified
data(theil)
head(theil)
cte = array(1,length(theil[,2]))
theil.y.X = cbind(theil[,2], cte, theil[,-(1:2)])
head(theil.y.X)
iterations = 5
perturb.n.T = perturb.n(theil.y.X, iterations, mu, dv, tol, pos = c(1,2))
perturb.n.T
mean(perturb.n.T[,1])
mean(perturb.n.T[,2])
c(min(perturb.n.T[,2]), max(perturb.n.T[,2]))
# Klein and Goldberger data on consumption and wage income
data(KG)
head(KG)
cte = array(1,length(KG[,1]))
KG.y.X = cbind(KG[,1], cte, KG[,-1])
head(KG.y.X)
iterations = 1000
perturb.n.KG = perturb.n(KG.y.X, iterations, mu, dv, tol, pos = c(1,2,3))
mean(perturb.n.KG[,1])
mean(perturb.n.KG[,2])
c(min(perturb.n.KG[,2]), max(perturb.n.KG[,2]))