[R] Visualizing binary response data?
Frank E Harrell Jr
f.harrell at Vanderbilt.Edu
Wed May 5 04:17:53 CEST 2010
On 05/04/2010 09:12 PM, Thomas Stewart wrote:
> For binary w.r.t. continuous, how about a smoothing spline? As in,
> OR how about a more parametric approach, logistic regression? As in,
> FOR binary w.r.t. categorical it depends. Are the categories ordinal (is
> there a natural ordering?) or are the categories nominal (no ordering)? For
> nominal categories, the data is essentially a contingency table, and
> "strength of the predictor" is a test of independence. You can still do a
> graphical exploration: maybe plotting the proportion of Y=1 for each
> category of X. As in,
> If your goal is to find strong predictors of Y, you may want to consider
> graphical measures that look at the predictors jointly. Maybe with a
> generalized additive model (gam)?
> There is probably a lot more you can do. Be creative.
And you have to decide why you would look to a graph to select
predictors. This can badly distort later inferences (confidence
intervals, P-values, biased regression coefficients, biased R^2, etc.).
> On Tue, May 4, 2010 at 9:04 PM, Kim Jung Hwa<kimhwamaillist at gmail.com>wrote:
>> Hi All,
>> I'm dealing with binary response data for the first time, and I'm confused
>> about what kind of graphics I could explore in order to pick relevant
>> predictors and their relation with response variable.
>> I have 8-10 continuous predictors and 4-5 categorical predictors. Can
>> suggest what kind of graphics I can explore to see how predictors behave
>> w.r.t. response variable...
>> Any help would be greatly appreciated, thanks,
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
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