From df71467290fe6e0761645a1347d160ede143428d Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Wed, 19 Jun 2024 02:20:14 +0000 Subject: [PATCH] build based on 60bbc53 --- dev/.documenter-siteinfo.json | 2 +- dev/index.html | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 78d3f0d..ad3c5ca 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.6.7","generation_timestamp":"2024-06-18T02:19:39","documenter_version":"1.4.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.6.7","generation_timestamp":"2024-06-19T02:20:09","documenter_version":"1.4.1"}} \ No newline at end of file diff --git a/dev/index.html b/dev/index.html index 22263fe..4c6e988 100644 --- a/dev/index.html +++ b/dev/index.html @@ -183,4 +183,4 @@ # calculate p-value polrtest(house_null, cont; test=:score)
0.0001648743597587817

Step 3: Now suppose we want to test significance of another predictor, z1. We just need to call polrtest with z1 and the same fiited null model. No model fitting is needed.

For demonstration purpose, we generate z1 randomly. The score test p-value of z1 is, not suprisingly, large.

z1 = randn(nobs(house_null))
 polrtest(house_null, z1)
0.1673512522966108

Step 4: We can also test a set of precitors or a factor.

z3 = randn(nobs(house_null), 3)
-polrtest(house_null, z3)
6.709335149358069e-10
+polrtest(house_null, z3)
6.709335149358069e-10