s********9
发帖数: 74 | 1 outcome: A
independet variables: B C D E F G H
univariable logistic regression: B C D E F G H all have significant
influence on A.
multivariable logistic regression: Only B has significant influence on A.
Is factor B the only factor should be considered as A's influence factor. |
b*****o
发帖数: 482 | 2 如果B C D E F G H两两highly correlated的话 只放一个进去
具体放哪个进去 最好看哪个variable最好解释model |
s********9
发帖数: 74 | 3 If there are independet variables like income, food cost, house renting/
payment, education cost ...... They are correlated, but each of them is
important. If each of them significant in univariable analysis, but only
income is significant in multivariable analysis. What could be the
conclusion for the impact factors for the outcome? |
s********9
发帖数: 74 | 4 Thank you!
Statistically, they are all correlated, but each of them is important.
【在 b*****o 的大作中提到】
: 如果B C D E F G H两两highly correlated的话 只放一个进去
: 具体放哪个进去 最好看哪个variable最好解释model
|
D******n
发帖数: 2836 | 5 who told u they are important?
and why must "important" variables be included in ur model?
model is a model.
【在 s********9 的大作中提到】
: Thank you!
: Statistically, they are all correlated, but each of them is important.
|
d*******o
发帖数: 493 | 6 1. Dimension reduction: run PCA/FA before the logistic regression.
2. Heuristic method: include all variables in the logistic model. Most
procedure has subset selection algorithm (backward/forward/stepwise),which
can decide the most significant variables. |
b*****o
发帖数: 482 | 7 什么叫"important"? 有什么理由让你一定要放其他variable进去?
一般来讲, 一个model越简单越好. 能用最简单的数据做prediction才是最牛的. model
越复杂,
意义越小. 如果income已经足够predict你的outcome, 你就不用去看其他的东西.
only
【在 s********9 的大作中提到】
: If there are independet variables like income, food cost, house renting/
: payment, education cost ...... They are correlated, but each of them is
: important. If each of them significant in univariable analysis, but only
: income is significant in multivariable analysis. What could be the
: conclusion for the impact factors for the outcome?
|
a****m
发帖数: 693 | |
