CN: Condition Number
In multiColl: Collinearity detection in a multiple linear regression model

Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function returns the Condition Number (CN) of the independent variables in a multiple linear regression.

CN(X)

`X`	A numeric design matrix that should contain more than one regressor (intercept included).

Due to the CN takes into account the intercept, it allows to detect not only the essential but also the non-essential collinearity. It also allows to consider non-quantitative independent variables.

Its calculation is obtained from the function lu, contrary to the function kappa.

The condition number of a matrix, that is, the maximum condition index.

Values of CN between 20 and 30 indicate near moderate multicollinearity while values higher than 30 indicate near worrying collinearity.

R. Salmeron (romansg@ugr.es) and C. Garcia (cbgarcia@ugr.es).

D. A. Belsley (1991). Conditioning diagnostics: collinearity and weak dara in regression. John Wiley & Sons, New York.

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.

lu, kappa, CNs.

# Henri Theil's textile consumption data modified
data(theil)
head(theil)
cte = array(1,length(theil[,2]))
theil.X = cbind(cte,theil[,-(1:2)])
CN(theil.X)

# Klein and Goldberger data on consumption and wage income
data(KG)
head(KG)
cte = array(1,length(KG[,1]))
KG.X = cbind(cte,KG[,-1])
CN(KG.X)

      obs consume income relprice twentys
[1,] 1923    99.2   96.7    101.0       1
[2,] 1924    99.0   98.1    100.1       1
[3,] 1925   100.0  100.0    100.0       1
[4,] 1926   111.6  104.9     90.6       1
[5,] 1927   122.2  104.9     86.5       1
[6,] 1928   117.6  109.5     89.7       1
[1] 53.39671
  consumption wage.income non.farm.income farm.income
1        62.8       43.41           17.10        3.96
2        65.0       46.44           18.65        5.48
3        63.9       44.35           17.09        4.37
4        67.5       47.82           19.28        4.51
5        71.3       51.02           23.24        4.88
6        76.6       58.71           28.11        6.37
[1] 35.88644