pima: Pima Indian diabetes dataset

In danno11/SMVCIR: Sliced Mean Variance Covariance Inverse Regression

Description

Pima Indian diabetes dataset

Usage

data(pima)

Format

An object of class data.frame with 768 rows and 9 columns.

Source

UCI Machine Learning Repository

Class Variable: "diabetes" 0 = no diabetes, 1 = diabetes

Attributes used:

1. Number of times pregnant

2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test

3. Diastolic blood pressure (mm Hg)

4. Triceps skin fold thickness (mm)

5. 2-Hour serum insulin (mu U/ml)

6. Body mass index (weight in kg/(height in m)^2)

7. Diabetes pedigree function

8. Age (years)

References

UCI Machine Learning Repository

Examples

data(pima)
library(caret)
train<-createDataPartition(pima$diabetes, p = .8, list = F)###Create a training set using 80% of dataset
pim.smv<-smvcir("diabetes", data = pima[train,], test = T) ###Build smvcir model on training set
preds<-predict(pim.smv, newdata = pima[-train,], type = "class")
table(preds, pima$diabetes[-train])  ###Check accuracy


###Get Coordinates
pred_coords<-predict(pim.smv, newdata = pima, coordinates_only = TRUE)
pred_coords$diabetes<-pima$diabetes

library(e1071)
svm_mod<-svm(diabetes~., data = pred_coords[train,], kernel = "radial")  ###Build an SVM model and check accuracy
svmp<-predict(svm_mod, newdata = pred_coords[-train,])
confusionMatrix(svmp, pred_coords$diabetes[-train], positive = "1")

danno11/SMVCIR documentation built on May 14, 2019, 6:06 p.m.