Statistical Analysis Commands in R

12 Questions

What is the purpose of the 'chisq.test(a$target,a$sex)' function in the provided code snippet?

To test the relationship between the target variable and gender.

What does the 'anov()' function with 'target~(resting.bp.s)' as an argument do?

Performs analysis of variance on the target variable and resting blood pressure data.

In the given code, what is the purpose of 'ifelse(testset$predicted_probability>0.50,1,0)'?

Classifying test data based on predicted probabilities.

What information does the 'table(testset$target,testset$binary)' provide?

It displays a contingency table of observed and predicted target values.

Why is 'sample.split(a$target,SplitRatio = 0.80)' used in the code?

To split the dataset into training and testing sets for model evaluation.

What does 'model=randomForest(target~.,data=trainingset)' achieve in the code snippet?

Fits a random forest model predicting the target variable using all other variables.

What does 'sum(is.na(a))' in the code snippet do?

Calculates the sum of logical NA values in the dataframe a

What is the purpose of 'chisq.test(a$target,a$chest.pain.type)' in the code snippet?

Conducts a chi-squared test between target and chest pain type

What does 'testset$predicted_probability>0.50' determine in the code snippet?

Predicted probability greater than 0.50 in testset

What information does 'table(a$resting.ecg)' provide in the code snippet?

Creates a contingency table for resting ECG values

What is the purpose of 'anova=aov(target~(age),data=a)' in the code snippet?

Conducts an analysis of variance (ANOVA) for target with respect to age

What does 'model=randomForest(target~,.,data=trainingset)' achieve in the code snippet?

Fits a random forest model with all predictors to predict the target variable

Study Notes

R Script Steps

Set working directory to C:/Users/nishitha1/Desktop/231BCADA23
Read CSV file newppt.csv into a with na.strings set to empty string
Calculate sum of NA values in a using sum(is.na(a))
Create frequency table for a$resting.ecg using table(a$resting.ecg)

Handling Missing Values

Replace NA values in a$resting.ecg with 0

ANOVA Models

Create ANOVA model for target vs age using aov function
Summarize ANOVA model using summary function
Create ANOVA models for target vs resting.bp.s, cholesterol, max.heart.rate, and oldpeak

Chi-Square Tests

Perform chi-square tests for target vs sex, chest.pain.type, fasting.blood.sugar, resting.ecg, exercise.angina, and ST.slope

Random Forest Model

Split data into training set (80%) and test set (20%) using sample.split function
Create random forest model using randomForest function with target as response variable
Summarize random forest model using summary function
Predict probabilities for test set using predict function
Create binary predictions using ifelse function

Confusion Matrix

Create confusion matrix for test set using table function