I am trying to fit a nonlinear model using nlm().
My application of nlm() is a bit complicated.
Here is the story behind the model being fit:
The observer is trying to detect a signal corrupted by noise.
On each trial, the observer gets stim=signal+rnorm().
In the simulation below I have 500
I have the following problem. I have measured a dose response curve
(binary response, continuous dose) on a grid of x,y positions. I would
like to produce a grey-level plot that shows the LD50 at each (x,y)
position.
I am thinking that I have to do something like
fit-glm(resp ~ x*y + dose, family
I have used lme() on data from a between-within subjects experiment. The correct
ANOVA table is known because this is a textbook example (Experimental Design by
Roger Kirk Chapter 12: Split-Plot Factorial Design). The lme() F-values differ
from
the known results. Please help me understand why.
Your lme statement is OK. To get the usual split-plot anova, your aov
statement should be
fit2 - aov(y ~ a*b*c + Error(s), data = d)
No, this gives wrong F-values. By wrong I mean it does not agree with the
published table.
Table 12.10-2, page 559:
Number of obs =
Thanks very much Peter!
Your lme statement is OK. To get the usual split-plot anova, your aov
statement should be
fit2 - aov(y ~ a*b*c + Error(s), data = d)
No, this gives wrong F-values. By wrong I mean it does not agree with the
published table.
Well, it's the model that is
Well nobody answered :-(
Nobody on R-help is doing anovas I guess -- I don't blame them! (It's just for
aggies.)
In the absence of any response and for no good reason I am doing:
fitn1 - aov(amplitude ~ stereo*site*stimulus + Error(subject), stereon1) This
is
Bill Venables's way.
And when the
I have 2 questions on ANOVA with 1 between subjects factor and 2 within factors.
1. I am confused on how to do the analysis with aov because I have seen two
examples
on the web with different solutions.
a) Jon Baron (http://www.psych.upenn.edu/~baron/rpsych/rpsych.html) does
6.8.5 Example 5:
