# [R] Plotting the probability curve from a logit model with 10 predictors

Greg Snow 538280 at gmail.com
Sat Jul 7 19:50:46 CEST 2012

```Try the following:

library(TeachingDemos)
?TkPredict
fit.glm1 <- glm( Species=='virginica' ~ Sepal.Width+Sepal.Length,
data=iris, family=binomial)
TkPredict(fit.glm1)

(you may need to install the TeachingDemos package first if you don't

You will now see a plot that shows the predicted probability compared
to one of the predictor variables, there are controls that you can
then change which variable is shown on the x axis and what the value
of the other variables are.  Play with the controls to see the effects
of the different variables.  You can now do the same thing with other
logistic regression models.  This also works to show nonlinear
(polynomial, spline, etc.) fits of the variables and interactions.
There is a button that you can click that will show the command to
create the same plot in regular R graphics, and you can then use that
command (and change add=TRUE to overlay multiple ones) to create a
static plot showing the relationship.

On Fri, Jul 6, 2012 at 2:30 PM, Abraham Mathew <abmathewks at gmail.com> wrote:
> Ok, so let's say I have a logit equation outlined as Y= 2.5 + 3X1 + 2.3X2 +
> 4X3 + 3.6X4 + 2.2X5
>
> So a one unit increase in X2 is associated with a 2.3 increase in Y,
> regardless of what the other
> predictor values are. So I guess instead of trying to plot of curve with
> all the predictors accounted
> for, I should plot each curve by itself.
>
> I'm still not sure how to do that with so many predictors.
>
> Any help would be appreciated.
>
>
>
>
> On Thu, Jul 5, 2012 at 4:23 PM, Bert Gunter <gunter.berton at gene.com> wrote:
>
>> You have an about 11-D response surface, not a curve!
>>
>> -- Bert
>>
>> On Thu, Jul 5, 2012 at 2:39 PM, Abraham Mathew <abmathewks at gmail.com>wrote:
>>
>>> I have a logit model with about 10 predictors and I am trying to plot the
>>> probability curve for the model.
>>>
>>> Y=1 = 1 / 1+e^-z  where  z=B0 + B1X1 + ... + BnXi
>>>
>>> If the model had only one predictor, I know to do something like below.
>>>
>>> mod1 = glm(factor(won) ~ as.numeric(bid), data=mydat,
>>>
>>> all.x <- expand.grid(won=unique(won), bid=unique(bid))
>>> y.hat.new <- predict(mod1, newdata=all.x, type="response")
>>>
>>> plot(bid<-000:250,predict(mod1,newdata=data.frame(bid<-c(000:250)),type="response"),
>>> lwd=5, col="blue", type="l")
>>>
>>>
>>> I'm not sure how to proceed when I have 10 or so predictors in the logit
>>> model. Do I simply expand the
>>> expand.grid() function to include all the variables?
>>>
>>> So my question is how do I form a plot of a logit probability curve when I
>>> have 10 predictors?
>>>
>>> would be nice to do this in ggplot2.
>>>
>>> Thanks!
>>>
>>>
>>> --
>>> *Abraham Mathew
>>> Statistical Analyst
>>> www.amathew.com
>>> 720-648-0108
>>> @abmathewks*
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>>
>>
>> --
>>
>> Bert Gunter
>> Genentech Nonclinical Biostatistics
>>
>> Internal Contact Info:
>> Phone: 467-7374
>> Website:
>>
>>
>>
>>
>
>
> --
> *Abraham Mathew
> Statistical Analyst
> www.amathew.com
> 720-648-0108
> @abmathewks*
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help