On 2012-06-20 08:56, Petr PIKAL wrote:
Hi
Hello,
I am trying to fit a model to some "death over time" data that does not
fit the criteria for the usual LD50 type models (the counts are too
large). I am using a simple linear model in an attempt to plot a nice
line
on a scatter plot and calculate some LD values to use in designing an
experiment. Here is the basic idea of what I'm doing:
head(mort)
Time Density
0 2233333333
0 2100000000
0 1933333333
5 1900000000
5 1433333333
5 900000000
plot(Density~Time)
This plots something that looks a lot like a decay rate
mod<-lm(log(Density)~Time)
xv<-seq(0,60,0.1)
yv<-exp(predict(mod,list(time=xv)))
From help page
Usage
## S3 method for class 'lm'
predict(object, newdata, se.fit = FALSE, scale = NULL, df = Inf,
interval = c("none", "confidence", "prediction"),
level = 0.95, type = c("response", "terms"),
terms = NULL, na.action = na.pass,
pred.var = res.var/weights, weights = 1, ...)
Arguments
object Object of class inheriting from "lm"
newdata An optional data frame in which to look for variables with which
to predict. If omitted, the fitted values are used.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
yv<-exp(predict(mod, data.frame(time=xv)))
shall work.
Regards
Petr
Using list() instead of data.frame() should work, but
using 'time' instead of 'Time' should give an "object not found"
error, unless there's an object 'time' hanging around in
the workspace.
Peter Ehlers
[...]
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