Dear Abraham,
I must admit that I don't really follow what you want to do. Disregarding
the fact that the example you provide doesn't converge to a proper solution,
the plot that you've requested will range over all values of bid at the
median home, which is 0. You may have intended home to be a categorical
variable, but you've specified it as numeric rather than a factor, and so
effect() treats it as numeric.
If you want to display the fit at all combinations of values of the two
predictors, give the first argument to effect as "bid:home", even
though this isn't a term in the model, the two terms bid and home are
marginal to it.
I hope that this helps, though I doubt it, since, as I said, I don't think
that I understand what you want to do.
John
------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/
On Sun, 8 Jul 2012 21:03:47 -0600
Abraham Mathew <abmathewks at gmail.com> wrote:> I've been looking into the effects package and it seems to be a great
tool
> for plotting the probabilities of the
> response variable by the predictors. However, I'm wonder if I can use
the
> effects package to plot the probabilities
> on the y axis and one predictor on the x axis, with the curve having the
> info for another predictor.
>
> So let's say our response variable is win, a binary variable. There are
two
> predictors, home (categorical) and
> bid (continuous). For both home and bid, I want to generate plots showing
> the predicted probabilities for all
> the "levels" of that variable.
>
> For bid, that means the probability for winning at all bid levels. For
> home, the curve for the probability for winning
> at each level.
>
> df <- data.frame(won=c(1,0,1,0,1,0,0,0,0,1),
> bid=c(150,200,135,140,130,150,200,135,140,130),
> home=c(1,0,0,0,1,1,0,0,0,1))
> df
>
> m1 = glm(won ~ bid + home, data=df,
family=binomial(link="logit"))
> summary(m1)
> eff <- effect("bid", m1, xlevels=list(bid=df$bid),
typical="median")
> print(plot(eff, rescale.axis=F))
>
> The thing I'm concerned about is the curve for home. For any logit
> equation, say with a coefficient of 2.5, that is the
> log odds change in Y regardless of the values of the other predictors. So
> I'm not sure I'm doing the write thing in that
> context.
>
> Can anyone help.
>
> Thanks
> *
> *
>
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>
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