how to predict X given Y using logit regresion in R?
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 <marongiu.luigi at gmail.com> wrote:
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)
1 180 ``` I have set the following: ``` df <- data.frame(concentration = c(1, 10, 100, 1000, 10000), positivity = c(0.86, 1, 1, 1, 1)) model <- glm(positivity~concentration,family = binomial(link = "logit"), data=df)
e3<-data.frame(concentration=c(11, 101), positivity=c(1, 1)) predict(model, e3)
1 2 5.645045 46.727573 ``` but: ```
e4<-data.frame(positivity=0.95) e4
positivity 1 0.95
predict(model, e4)
Error in eval(predvars, data, env) : object 'concentration' not found ``` Why did the thing worked for f4 but not e4? How do I get X given Y? Do I need to find the inverse function of logit (which one?) and apply this to the regression or is there a simpler method? Also, is it possible to plot the model to get a smooter line than `plot(positivity ~ concentration, data = df, log = "x", type="o")`? Thanks -- Best regards, Luigi
Best regards, Luigi