I have reviewed several previous answers here, but I do not understand how to calculate the misclassification rate for each model. The notes I received for this topic said that the ClassProp function calculates accuracy and NOT the misclassification rate $($ClassProp$($id$.$true, id$.$kmeans$)).$ So then how is the misclassification rate produced? And how to solve the below please:

In the package "MASS", there is a dataset called "iris".

The goal of this assignment is to perform a model$-$based cluster analysis based on the $4$ numeric variables $($first four columns$)$: Sepal.Length, Sepal.Width, Petal.Length, and Petal.Width.

$1.$ Create a true.id variable based on Species variable from the data.

$2.$ Form a data set based on the first $4$ columns, i$.$e$.,$ without the Species variable.

$3.$ Apply the following clustering algorithms:

a$.$ Hierarchical Clustering with Single Linkage

b$.$ Hierarchical Clustering with Complete Linkage

c$.$ K$-$means

d$.$ Model$-$based clustering from package $$mclust$$

$4.$ Create a plot using function clusplot$()$ from package $$cluster$.$

HINT: clusplot$($x$,$ y$,$ lines $=0,$ color $=$ TRUE, plotchar $=$ FALSE, main $=)$

where x is the object with interval variables and y represents the factor variable.

$5.$ Summarize the misclassification rates of each model in a table. Report your best model based on the results.