Even when I try to predict y values from x, let's say I want to predict y at
x=0. Looking at the graph from the provided syntax, I would expect y to be
about 0.85. Is this right:

predict(mylogit,newdata=as.data.frame(0),type="response")

# I get:

Warning message:
'newdata' had 1 rows but variable(s) found have 34 rows

# Why do I need 34 rows? So I try:

v=vector()
v[1:34]=0
predict(mylogit,newdata=as.data.frame(v),type="response")

# And I get a matrix of 34 values that appear to fit a logistic function,
instead of 0.85..




On Wed, Jan 26, 2011 at 6:59 PM, David Winsemius <dwinsemius@comcast.net>wrote:

>
> 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))
>>
>>        [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> David Winsemius, MD
> West Hartford, CT
>
>

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