Set p-value threshold for stepwiseglm() function?

1 次查看（过去 30 天）

显示更早的评论

Justin 2014-9-17

1
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/155223-set-p-value-threshold-for-stepwiseglm-function

评论： Justin 2014-10-8

采纳的回答： the cyclist

Hi, I am using

stepwiseglm(D(1:2719,2:end),D(1:2719,1),'constant','upper','linear','Distribution','binomial','Link','logit')

in a process to explore available parameters for a logistic regression and decide on which ones to use and which ones to ignore.

The function adds the parameter if p-value of marginal value is <.05, discards parameter if >.10

Since my purpose is exploratory, I'd be interested in having a look at parameters with a p-value of say, up to .25

I am new to matlab and having trouble finding if I can adjust the p-value thresh-hold.

Anyone know? Thanks-- Justin

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

采纳的回答

the cyclist 2014-9-18

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/155223-set-p-value-threshold-for-stepwiseglm-function#answer_152062

编辑：the cyclist 2014-9-18

Use the Criterion name-value pair to specify which criterion is measured to determine terms to remove, and the PRemove name-value pair to specify the value.

See the "Name-Value Pair Arguments" section of the documentation for stepwiseglm for details.

8 个评论
显示 6更早的评论隐藏 6更早的评论

the cyclist 2014-10-4

在 MATLAB Online 中打开

I finally found some time to explore. Here is a contrived example in which I construct three different models -- "strict","loose", and "very loose" -- off the same data.

rng(1)
N = 50;
X = randn(N,2);
noise = 10;
Y = 2 + 3*X(:,1) + 3.6*X(:,2) + noise*randn(N,1);
model_strict = stepwiseglm(X,Y,'constant','upper','linear')
model_loose = stepwiseglm(X,Y,'constant','upper','linear','Criterion','Deviance','PEnter',0.25,'PRemove',1)
model_very_loose = stepwiseglm(X,Y,'constant','upper','linear','Criterion','Deviance','PEnter',0.90,'PRemove',1)

"model_strict" uses default MATLAB settings for adding and removing terms. Notice that this model does not add either X(:,1) or X(:,2).

"model_loose" adds terms if p-value is less than 0.25, and only removes if p-value greater than 1. (In other words, it never removes terms.) This model adds X(:,2), but not X(:,1).

"model_very_loose" adds terms if p-value is less than 0.90, and only removes if p-value greater than 1. (In other words, it never removes terms.) This model adds both X(:,2) and X(:,1).

I think "model_loose" is effectively to what you are trying to achieve.

I hope this helps. The "Algorithm" section of this documentation page describes the algorithm MATLAB uses to add and remove terms.

Justin 2014-10-8