1. R Data Manipulation Bootstrapping
Lab 3
R Data Manipulation
Bootstrapping

2. Today is the last day for basic R language.
Working directory
Combining datasets
Creating and deleting variables
Bootstrapping
How to make a function in R!

3. Get/Set Working Directory
Set working directory on hard drive to be the location of a library and reference output files
Can also use top menu
Session -> set working directory
Useful to have working directory set before analyses
Won’t have to specify path for writing output

4. Manipulating Datasets
Load up SchoolA, SchoolB, and SchoolC for following examples
SchoolA our original dataset
SchoolB has two additional variables
CGPA
Credit
SchoolC has additional rows of data
We will merge these datasets together

5. Rows might or might not have matches in each data set.
Combining R Data Sets
You will look at combining data sets in two ways, concatenation and merging.
Stacking observations from multiple data sets
(concatenation)
Joining multiple data sets side by side (merging)
Data Set 1
Data Set 2
Data Set 3
Data Set 1
Data Set 2
Data Set 3
Data
Set 3
Data Set 3
Rows might or might not have matches in each data set.

6. Merging R Datasets
A merge combines two or more existing data sets by joining observations side-by-side.
Datasets are linked together by some key variable
Merge function

7. Merging R Datasets Use the merge function
General form of a merge procedure
New object is defined
Use merge function
Specify datasets to merge
Specify name of key variable

8. Concatenating R Data Sets
Use the rbind function (binds rows)
General form of a rbind procedure
New object is defined
Use rbind function
Specify datasets
Need to make sure datasets have same variables

9. Create Or Delete Variables
As an example, suppose the data set SchoolAB contains variables HSGPA and CGPA.
A new variable avgGPA can be added to the data set as follows:
Variables can be deleted just as easily

10. Eliminating variables from an existing data set
Using subset:
Specify dataset
Use select option to define which variables to keep in new dataset.

11. Bootstrapping!

12. Bootstrapping?
Bootstrapping is a nonparametric technique free from any distributional assumption of data such as normality.
We do not rely on any seriously restrictive assumptions concerning the shape of the sampled populations.
This technique is useful when we are uncomfortable with:
Small sample size.
Non-normal distribution of the sample.
Statistic has complicated or no confidence interval calculation

13. General Bootstrap Procedure
Sample with replacement from original data where each resampled N equals original N
Calculate statistic of interest with resampled data
Repeat step 1-2 with many replications (~10,000)
Sort the empirical values in ascending order, find the values corresponding to the .025 and .975 percentile ranks, and use them as lower and upper critical values for two-tailed null hypothesis significance testing.
If the interval does not contain zero, then the effect is statistically significant (p<.05).

14. Bootstrap Procedure In R
Install boot package to use boot function
Boot function requires three things
Dataset for resampling
A function that specifies the statistic returned
Number of bootstrap replications

15. How to create a function
Specify object name for function
Specify arguments or parameters fed into function
Write code that uses arguments provided
Return some object
Can be of any type (single value, list, dataset…)
myfunction <- function(arg1, arg2, ... ){
code
return(object) }
myfunction(arg1,arg2)

16. Example Bootstrap Function
Requires two specific parameters
Data
Indices
Within function, need to define d, the resampled datasets
Write code to obtain some statistic from d
Return object that serves as output

17. Running Bootstrap
Let’s get bootstrapped mean for CGPA in our final school dataset
Use boot function
Specify dataset or variable, function you created, and number of replications
Print results to see all resampled results
Plot results to get histogram of results

18. Getting 95% Confidence Interval
Boot.ci typically requires three parameters
Output from boot function (results)
Confidence level
Type of bootstrap
Recommend bias-corrected and accelerated bootstrap (BCA)

19. Other Notes on Bootstrap
Can return more than one bootstrapped statistic
Just return a vector instead of one value in boot function
Lab code includes example of bootstrapped regression
Produces bootstrapped beta weights
Requires additional formula parameter in boot function
For your own personal interest/use

20. Homework3
Due next lab (Sept. 21st)