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
already have it installed)
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@> 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@> 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@>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,
family=binomial(link="logit"))
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*
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