Hello,
I am in the process of making a predictive nonlinear model for my data with one dependent variable and 3 independent variables. I am currently using SPSS and R but the best model i have at the moment is not descriptive enough. My R2 is about 0.8 and my goal is above 0.9. I know getting a model means a lot of trials which i have attempted but i havent gotten better fit. is there a script or code that i can use to make a more effective model?
Have a look at caret package by Max Kuhn in R, which as a great resource with lots of functions and also a great help pages https://topepo.github.io/caret/ .
For example here you have a minimal working example for easy logistic regression with repeated cross validation using ROC AUC as performance metric.
library(caret)
library(mlbench)
data(Sonar)
model
Thank you @Michael Heming, i will go through carefully and keep you posted.
Hello, one would have liked to know the types of your dependent variables. However, since you have access SPSS softwares package, you really can use Discriminant Analysis to obtain your desired model. It theoretically requires one categorical dependent variable and three interval data independent variables.
Regression analysis is ruled out because it requires interval data dependent and independent variables. If your dependent variable is interval type and the independent variables are categorical type, you are now thinking about Analysis of Variance which deals with classification and not modelling.
Indeed, Discriminant Analysis does both classification and modelling. It is really a powerful but uncommon technique.
I hope my input is helpful ?
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