The leukemia gene expression dataset available in the url below consists of 72 sub- jects/patients and 3571 genes. http://www.ams.sunysb.edu/~pfkuan/Teaching/AMS597/Data/leukemiaDataSet.txt

Each patient is of either type aLL (Acute lymphocytic leukemia) or type aML (Acute myelogenous leukemia). Using the genes as covariates, we will construct a model that can predict the two types of leukemia as follows:

1. (a) First split the data randomly into two subsets containing 50 (trainData) and 22 (testData) subjects, respectively as follows:

 dat 'http://www.ams.sunysb.edu /~pfkuan/Teaching/AMS597/Data/leukemiaDataSet.txt' ,header=T,sep='\t') ### please read this as a single line in R, I break this into ### 3 lines to avoid overflowing outside paper margin

1. str(dat)

 set.seed(123) trainID 
(b) Build your best model on trainData using the Group variable as response and the genes as predictors/covariates.
(c) Evaluate your model from (2) on the testData by computing the percentage of AML correctly predicted, the percentage of ALL correctly predicted and the overall percentage of AML and ALL correctly predicted.
 1. The leukemia gene expression dataset available in the url below consists of 72 sub- jects/patients and 3571 genes. http://www. ams . sunysb . edu/~pfkuan/Teaching/AMS597/Data/leukemiaDataSet. txt Each patient is of either type ALL (Acute lymphocytec leukemia) or type AML (Acute myelogenous leukemia). Using the genes as covariates, we will construct a model that can predict the two types of leukemia as follows: (a) First split the data randomly into two subsets containing 50 (trainData) and 22 (testData) subjects, respectively as follows: dat