diagnostic: Collinearity diagnostics for femlm objects

In FENmlm: Fixed Effects Nonlinear Maximum Likelihood Models

Collinearity diagnostics for femlm objects

Description

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 variable is problematic.

Usage

diagnostic(x)

Arguments

x	A femlm object obtained from function femlm.

Details

This function tests: 1) collinearity with the cluster variables, 2) perfect multi-collinearity between the variables, and 3) identification issues when there are non-linear in parameters parts.

Value

It returns a text message with the identified diagnostics.

Examples

# Creating an example data base:
cluster_1 = sample(3, 100, TRUE)
cluster_2 = sample(20, 100, TRUE)
x = rnorm(100, cluster_1)**2
y = rnorm(100, cluster_2)**2
z = rnorm(100, 3)**2
dep = rpois(100, x*y*z)
base = data.frame(cluster_1, cluster_2, x, y, z, dep)

# creating collinearity problems:
base$v1 = base$v2 = base$v3 = base$v4 = 0
base$v1[base$cluster_1 == 1] = 1
base$v2[base$cluster_1 == 2] = 1
base$v3[base$cluster_1 == 3] = 1
base$v4[base$cluster_2 == 1] = 1

# Estimations:

# Collinearity with the cluster variables:
res_1 = femlm(dep ~ log(x) + v1 + v2 + v4 | cluster_1 + cluster_2, base)
diagnostic(res_1)
# => collinearity with cluster identified, we drop v1 and v2
res_1bis = femlm(dep ~ log(x) + v4 | cluster_1 + cluster_2, base)
diagnostic(res_1bis)

# Multi-Collinearity:
res_2 =  femlm(dep ~ log(x) + v1 + v2 + v3 + v4, base)
diagnostic(res_2)

# In non-linear part:
res_3 = femlm(dep ~ log(z), base, NL.fml = ~log(a*x + b*y),
              NL.start = list(a=1, b=1), lower = list(a=0, b=0))
diagnostic(res_3)

FENmlm documentation built on Aug. 22, 2023, 5:11 p.m.