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 ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
