In some occasions, the optimization algorithm of
femlm may fail to converge, or the variance-covariance matrix may not be available. The most common reason of why this happens is colllinearity among variables. This function helps to find out which set of variables is problematic.
An integer. If higher than or equal to 1, then a note is prompted at each step of the algorithm. By default
This function tests: 1) collinearity with the fixed-effect variables, 2) perfect multi-collinearity between the variables, 4) perfect multi-collinearity between several variables and the fixed-effects, and 4) identification issues when there are non-linear in parameters parts.
It returns a text message with the identified diagnostics.
# Creating an example data base: set.seed(1) fe_1 = sample(3, 100, TRUE) fe_2 = sample(20, 100, TRUE) x = rnorm(100, fe_1)**2 y = rnorm(100, fe_2)**2 z = rnorm(100, 3)**2 dep = rpois(100, x*y*z) base = data.frame(fe_1, fe_2, x, y, z, dep) # creating collinearity problems: base$v1 = base$v2 = base$v3 = base$v4 = 0 base$v1[base$fe_1 == 1] = 1 base$v2[base$fe_1 == 2] = 1 base$v3[base$fe_1 == 3] = 1 base$v4[base$fe_2 == 1] = 1 # Estimations: # Collinearity with the fixed-effects: res_1 = femlm(dep ~ log(x) + v1 + v2 + v4 | fe_1 + fe_2, base) collinearity(res_1) # => collinearity with the first fixed-effect identified, we drop v1 and v2 res_1bis = femlm(dep ~ log(x) + v4 | fe_1 + fe_2, base) collinearity(res_1bis) # Multi-Collinearity: res_2 = femlm(dep ~ log(x) + v1 + v2 + v3 + v4, base) collinearity(res_2)
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