admin管理员组文章数量:1414861
I'm conducting a multinomial logistic regression model using proc logistic
in SAS with around 3.6 million observations, an outcome with 5 levels, and dozens of categorical predictors. I had no issue running both univariate and multivariate models when setting param = ref
.
However, once I tried param = glm
, it started giving the warning message of "The information matrix is singular and thus the convergence is questionable. specifying a larger SINGULAR= value." in multivariate models. After doing some research, I found this message suggesting a multicollinearity issue in the model. I then tried to use only 2 predictors and it still gave the message while the correlation matrix showed no correlation between the two predictors.
As far as I know, the only difference of param = ref
and param = glm
is that param = glm
uses less-than-full-rank reference coding, meaning that it will create k-1
dummy variables given k
levels in the categorical predictor. These two parametrization methods should generate the same log-likelihood and estimates given the same reference level. To confirm this, I also compared the result of the two models using only 2 predictors. While param = glm
throwing a warning, the result is identical to param = ref
(Except a bunch of zeros in the estimates of reference levels for each predictor in param = glm
, is it the cause?).
My question is, why did the param = glm
model throwing a warning while param = ref
did not. And more importantly, in this situation, should I trust the result of the param = ref
even though no warning was displayed.
I appreciate any advice and suggestions. Thank you in advance.
本文标签: sasquotparamglmquot gave a singular matrix warning while quotparamrefquot did notStack Overflow
版权声明:本文标题:sas - "param = glm" gave a singular matrix warning while "param = ref" did not - Stack Overf 内容由网友自发贡献,该文观点仅代表作者本人, 转载请联系作者并注明出处:http://www.betaflare.com/web/1745201178a2647380.html, 本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容,一经查实,本站将立刻删除。
发表评论