I have produced a logistic regression model in R using the logistf function from the logistf package due to quasi-complete separation. I get the error message:
Error in solve.default(object$var[2:(object$df + 1), 2:(object$df + 1)]) : system is computationally singular: reciprocal condition number = 3.39158e-17
The data is structured as shown below, though a lot of the data has been cut here. Numbers represent levels (i.e 1 = very low, 5 = very high) not count data. Variables OrdA to OrdH are ordered factors. The variable Binary is a factor.
OrdA OrdB OrdC OrdE OrdF OrdG OrdH Binary
1 3 4 1 1 2 1 1
2 3 4 5 1 3 1 1
1 3 2 5 2 4 1 0
1 1 1 1 3 1 2 0
3 2 2 2 1 1 1 0
I have read here that this can be caused by multicollinearity, but have tested this and it is not the problem.
VIFModel <- lm(Binary ~ OrdA + OrdB + OrdC + OrdD + OrdE +
OrdF + OrdG + OrdH, data = VIFdata)
vif(VIFModel)
GVIF Df GVIF^(1/(2*Df))
OrdA 6.09 3 1.35
OrdB 3.50 2 1.37
OrdC 7.09 3 1.38
OrdD 6.07 2 1.57
OrdE 5.48 4 1.23
OrdF 3.05 2 1.32
OrdG 5.41 4 1.23
OrdH 3.03 2 1.31
The post also indicates that the problem can be caused by having "more variables than observations." However, I have 8 independent variables and 82 observations.
For context each independent variable is ordinal with 5 levels, and the binary dependent variable has 30% of the observations with "successes." I'm not sure if this could be associated with the issue. How do I fix this issue?
X <- model.matrix(Binary ~ OrdA+OrdB+OrdC+OrdD+OrdE+OrdF+OrdG+OrdH,
Data3, family = "binomial"); dim(X); Matrix::rankMatrix(X)
[1] 82 24
[1] 23
attr(,"method")
[1] "tolNorm2"
attr(,"useGrad")
[1] FALSE
attr(,"tol")
[1] 1.820766e-14