Also: require(rms); ?plot.Predict
Frank
Greg Snow wrote
>
> 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 instal
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
On Jul 6, 2012, at 4:30 PM, Abraham Mathew 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,
Assuming, that is, you also understand what Y is. From you comments so
fa
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
fo
Look at the Predict.Plot and TkPredict functions in the TeachingDemos
package. These will not plot all 11 dimensions at once, but will plot
2 of the dimensions conditioned on the others. You can then change
the conditioning to see relationships.
These use base rather than ggplot graphics.
On Th
You have an about 11-D response surface, not a curve!
-- Bert
On Thu, Jul 5, 2012 at 2:39 PM, Abraham Mathew 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 mod
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=binomi
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