On Jan 26, 2011, at 10:52 PM, Ahnate Lim wrote:

Dear R-help,

I have fitted a glm logistic function to dichotomous forced choices
responses varying according to time interval between two stimulus. x values
are time separation in miliseconds, and the y values are proportion
responses for one of the stimulus. Now I am trying to extrapolate x values
for the y value (proportion) at .25, .5, and .75. I have tried several
predict parameters, and they don't appear to be working. Is this correct use and understanding of the predict function? It would be nice to know the parameters for the glm best fit, but all I really need are the extrapolated x values for those proportions. Thank you for your help. Here is the code:

x <-
c(-283.9, -267.2, -250.5, -233.8, -217.1, -200.4, -183.7, -167,
-150.3, -133.6, -116.9, -100.2, -83.5, -66.8, -50.1, -33.4, -16.7,
16.7, 33.4, 50.1, 66.8, 83.5, 100.2, 116.9, 133.6, 150.3, 167,
183.7, 200.4, 217.1, 233.8, 250.5, 267.2, 283.9)

y <-
c(0, 0.333333333333333, 0, 0, 0, 0, 0, 0, 0, 0.333333333333333,
0, 0.133333333333333, 0.238095238095238, 0.527777777777778,
0.566666666666667,
0.845238095238095, 0.55, 1, 0.888888888888889, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5)

weight <-
c(1, 3, 2, 5, 4, 4, 3, 5, 5, 4, 5, 11, 22, 11, 15, 16, 11, 7,
14, 10, 16, 19, 11, 5, 4, 5, 6, 9, 4, 2, 5, 5, 2, 2)

mylogit <- glm(y~x,weights=weight, family = binomial)

# now I try plotting the predicted value, and it looks like a good fit,
hopefully I can access what the glm is doing

ypred <- predict(mylogit,newdata=as.data.frame(x),type="response")
plot(x, ypred,type="l")
points(x,y)

# so I try to predict the x value when y (proportion) is at .5, but
something is wrong..

Right. Predict goes in the other direction ... x predicts y.

Perhaps if you created a function of y w.r.t. x and then inverted it.

?approxfun # and see the posting earlier this week "Inverse Prediction with splines" where it was demonstrated how to reverse the roles of x and y.

predict(mylogit,newdata=as.data.frame(0.5))

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