Ecm for Regression and Classification Error - IT Assignment Help

Assignment Task

Task
Develop a function to obtain a complete and customised assessment of a GLM model. a GLM model. The function should therefore obtain: Exploratory analysis, using tables and graphs, of the relationship between the independent and dependent variables. independent variables and the dependent variable. Use correlation plots and graphs,Note: distinguish, according to the type of the dependent variable, the type of model, the type of dependent variable, the type of model, regression or classification (functions is.factor or is.numeric).A suitably formatted output of the coefficients estimated by the model, standard deviations, standard deviations, statistic,...
model, standard deviations, t-statistics and their p-values, both in their classical version (under model assumptions) and in their p-values. (under the model assumptions) and Bootstrap. Ditto for the goodness-of-fit measures. Consider the R2, the adjusted R2 and the ECM for regression and classification error and the pseudo R2 for classification. Results of the diagnostic tests of the model: Shapiro-Wilk, Lilliefors, Levene/Brown-Forsythe, White, Breush-Godfrey, Durbin-Watson... determining whether the residuals are normal, homoscedastic and random. Note: using plot(model) you already get the graphical tests, but you can customise the presentation using ggplot2 or any other graphical library (lattice, plotrix, plotly,...). Given the results obtained, the function will return a message advising whether or not to use the classical or Bootstrap estimators. Include an additional parameter, indicating whether or not to perform a selection of variables and, if so, a selection of variables. of variables and, if so, what type: forward, backward, stepwise. Use the BIC statistic as a criterion. ote: to obtain the Bootstrap statistics you can rely on the three packages implemented in R: rsample, bootstrap or boot, or make your own function for this purpose. For classification problems, perform the bootstrap for each class separately. Use the data frame BostonHousing from the mlbench package to test the function in the case of regression.
 

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