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I'm trying to model a logistic regression in R between two simple variables:

  • Rating: An independent ordered categorical one, ranging from 1 to 99 (1, 2, 3, 4, 5, 99 in particular, 1 is the best)
  • Result: A dependent binary variable (0-1, not accepted/accepted)

The formula I use is

glm(formula = result_dummy ~ best_rating, family = binomial(link = "logit"), 
    data = cd[1:10000, ])

result_dummy is a 0/1 numerical variable (original result column was a factor) and scaled_rating is the rating column after use the R scale function.

My thought here was to find a negative correlation (low rating -> more probability to accept) but the more samples I use the more odd results I find:

10 samples:

Coefficients:
              Estimate Std. Error z value Pr(>|z|)
(Intercept)     0.6484     0.7413   0.875    0.382
scaled_rating  -5.9403     5.8179  -1.021    0.307

100 samples:
Coefficients:
              Estimate Std. Error z value Pr(>|z|)   
(Intercept)   -0.09593    0.27492  -0.349  0.72714   
scaled_rating -5.06251    1.76645  -2.866  0.00416 **

1000 samples:

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)   -0.03539    0.09335  -0.379    0.705    
scaled_rating -6.81964    0.62003 -10.999   <2e-16 ***

10000 samples:
Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)     0.2489     0.0291   8.553   <2e-16 ***
scaled_rating  -7.2319     0.2004 -36.094   <2e-16 ***

Notes: I know that after the fit I should check residual plot, normality assumptions, etc. etc. but nonetheless I find really strange this behaviour.

I also have similar results using simply the rating column instead of the scaled one.

Edit: The rating variable is not really an ordinal one, so as pointed out by @Scortchi maybe it would be better to treat it as a categorical one. I have surely better results and model stability, obviously the model is a simple one and the residual error would be always high (because some variables as not been included in the model). Indeed, including the frequency table as requested shows that the rating variable IS NOT sufficient for having a clear separation between the result outcome.

          0      1
  1    2881  42564
  2   13878 129292
  3   36839 179500
  4   43511  97148
  5   37330  47002
  6   31801  21228
  7   19096   6034
  99  10008      3
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    $\begingroup$ Some points I think need clarifying: (1) What's the purpose of fitting the same model to progressively larger samples? The estimates bounce around a little but does that surprise you? (2) You emphasize that the predictor's ordinal, but then say you've scaled it & fitted a model with a single coefficient for it, which suggests it's interval-scale. What are you doing with it? (I'm inclined to guess '99' encodes 'not applicable' or 'missing' & you're treating it as the number 99!) $\endgroup$ Jun 21, 2016 at 16:10
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    $\begingroup$ In any case see e.g. Logistic regression and ordinal independent variables. With plenty of observations the simple approach of treating the predictor as categorical & coding it with dummy variables should also be a good approach. $\endgroup$ Jun 21, 2016 at 16:34
  • $\begingroup$ 1) The sample is taken from a larger dataset, I expect the larger the sample size the more is similar to the whole dataset (more bias, less variance). My problem is with the abnormal low p-value and an R warning about probability equal to 0 or 1. $\endgroup$ Jun 21, 2016 at 17:41
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    $\begingroup$ Sorry, I forgot to say to edit your question to add any pertinent information rather than appending it in comments. Anyway, though your model may well be a poor fit & I suspect you'd be better off treating the predictor as categorical (the '99' is rather arbitrary, & why should the relationship be linear anyway?), nothing you've said so far makes your results seem odd in the least. Why shouldn't the p-value be low, & why shouldn't some predictions be equal to or very close to zero? (For the purposes of this analysis, your data can be shown in a 2-by-6 table - you might want to show them.) $\endgroup$ Jun 22, 2016 at 9:39
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    $\begingroup$ Somewhat a similar question stats.stackexchange.com/q/195246/3277 $\endgroup$
    – ttnphns
    Jun 22, 2016 at 10:06

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Simply plotting the log odds of acceptance against rating clarifies the issue:

enter image description here

A high odds ratio for an increase of one standard deviation in rating would be expected, as would be a low p-value, given the volume of data; but setting rating to '99' when it's really not available wasn't a good idea - it makes the relationship between rating & log odds far from linear. Using a dummy variable for 'bad guarantee' would have made more sense— see here. Arguably, with plenty of data, there's no need to constrain rating to have a linear relationship with the log odds even over valid values—that the relationship appears non-monotonic is probably more surprising—& you might treat it as categorical.

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