Hi:
Are you trying to find something analogous to the median dose? If so, look
at function dose.p() in the MASS package and see if it does what you're
looking for. It can take a vector of quantiles as input.
HTH,
Dennis
On Wed, Jan 26, 2011 at 7:52 PM, Ahnate Lim ahn...@gmail.com wrote:
Dear
On Wed, 2011-01-26 at 19:25 -1000, Ahnate Lim wrote:
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)
On Thu, 2011-01-27 at 00:10 -1000, Ahnate Lim wrote:
Thank you for the clarification, this makes sense now! dose.p from
MASS also does the job perfectly, in returning x values for specified
proportions. I'm curious, if I use the other parameter in predict.glm
type=link (instead of
Thank you for the clarification, this makes sense now! dose.p from MASS also
does the job perfectly, in returning x values for specified proportions. I'm
curious, if I use the other parameter in predict.glm type=link (instead of
type=response), in the case of a logistic binomial, what does this
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
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
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
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