Actually,
I think it worked with chemCal, it was the drawing that was a bit
rough. I sort it with:
```
library(MASS)
lod <- dose.p(model,p=.95)
plot(positivity ~ concentration, data = df, log = "x",
xlab=expression(bold(paste("Concentration (c/", mu, "L)"))),
ylab=expression(bold("Proportion of
Hello,
I also tried with
```
library(MASS)
> dose.p(model,p=.95)
Dose SE
p = 0.95: 1.70912 96.26511
```
which is closer to the expected 1.72 but with a very large error (I
expected 1.10-2.34). Is this regression correct?
On Sat, Oct 2, 2021 at 10:14 AM Luigi Marongiu wrote:
I tried with:
```
library(chemCal)
inverse.predict(model, 0.95)
> inverse.predict(model, 0.95)
$Prediction
[1] 0.4565871
$`Standard Error`
[1] 5.525725e-10
$Confidence
[1] 1.758532e-09
$`Confidence Limits`
[1] 0.4565871 0.4565871
```
but the value 0.457 does not sound good, it should be about
Hello,
I have set a glm model using probit. I would like to use it to predict
X given Y. I have followed this example:
```
f2<-data.frame(age=c(10,20,30),weight=c(100,200,300))
f3<-data.frame(age=c(15,25))
f4<-data.frame(age=18)
mod<-lm(weight~age,data=f2)
> predict(mod,f3)
1
150
> predict(mod,f4)
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