Intro

Question 1

Load libraries into R

Answer 1

library(Caret)

Question 2

Edit data in R

Answer 2

fix()

Question 3

View names in data frame

Answer 3

names()

Question 4

Load Variable Names Into Environment So Don’t have to type the name of columns

Answer 4

attach(DataFrame)

Question 5

basic linear model function R

Answer 5

lm.(y~x, data = DataFrame)

Question 6

display statistics on model

Answer 6

first send model output to variable lm.output = lm(y~x, data=Dataframe)
Then, summary(lm.output)

Question 7

Show information after fitting a model

Answer 7

names(model.output)

summary(model.output)

Question 8

Show model coefficients and confidence intervals

Answer 8

coef(model.output) #shows the coeff

confint(model.output) #shows the 95% conf interval for the coefficients

Question 9

Use model to predict new values

Answer 9

Predict()

Predict(model.output, dataframeofx’s, interval=”confidence)

Question 10

Prediction vs. Confidence Interval

Answer 10

When predicting a new data point, want prediction interval. Confidence Interval is about the where the average of future values lie.
To get PI, Predict(model.output, dataframeofx’s, interval = “prediction”)

Question 11

Scatter Plot with Regresion Line

Answer 11

Plot(x, y)
abline(model.output, lwd=3, col= “red”) #adds line to scatterplot

lwd is for width

Question 12

See diagnostic plots of linear regression

Answer 12

plot(model.output) #Automatically does it, b/c model output contains it, wow!
if there are 4 graphs, first create 4 tiles, so first do:
par(mfrow=c(2,2))

Question 13

how does predict() work

Answer 13

predict(model.output) will return a vector of predicted Y values
predict(model.output,

Question 14

inspect functions

Answer 14

type function name predict

if there is call to method, use methods(methodname)

Question 15

Get max of vector

Answer 15

which.max(vector), returns index of max position

Question 16

Shorthand formula for regression in R

Answer 16

formula = lm(Yvariable ~ ., data = DATAFRAME)

instead of writing x1 + x2 + etc you can just put a dot.

Question 17

Function to use when there is Colinearity

Answer 17

Need to see the Variance Inflation Factor VIF, part of car package.
library(car)
vif(lm.fit) #use on model output
remember VIF > 5 indicates colinearity

Question 18

How to see a correlation matrix

Answer 18

cor() all columns must be numeric, if a column isn’t numeric use matrix notation such as cor(data.frame[,-9])

Question 19

Logistic Regression R

Answer 19

logreg = glm(Direction ~Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume, data = SMarket, family = binomial) key is family = binomial, using the glm() genaralized linear model function

Question 20

When doing logistic regression on a factor variable with two levels, how do we handle the dummy coding?

Answer 20

we dont have to do anything, the glm function will dummy code it for you automatically! However, you can retireve what the dummy coding values are by using contrasts()
attach(Smarket)
contrasts(Direction)

Question 21

How to use logistic regression output to predict values in your dataset

Answer 21

predict(logreg, type = ‘response’)

the type = ‘response’ tells R to output probabilities instead of other variables.

Question 22

How to convert Logistic regression probabilities into actual predictions.

Answer 22

1. Create a vector corresponding to “zero” probability.
   logreg. predict = rep(‘Down’, 1250)
2. Rename vector based on probability
   logreg. predict[logreg.probs > .5] = ‘Up’

Question 23

How to create a confusion matrix

Answer 23

use the table function
table(VectorOfPredictions, Vector of True Values)
table(logreg.predict, Direction)

The two vectors have to have the same values like “Up/Down”, so make sure they are converted to same values.

Question 24

sub select training data in time series

Answer 24

train = (Year<2005)  #Stores binary vector in trainingset
Smarket.2005 = Smarket[!train, ] #Data before 2005
Direction.2005 = Direction[!train]

Question 25

How to train a linear regression model on a subset of the data

Answer 25

Use the subset argument to glm

logreg. train = glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume, data=Smarket, family=binomial, subset=train)

Then predict using the predict function

Question 26

making prediction on test set, logistic regression

Answer 26

predict(glm.fit, Smarket.2005, type=’response’)

Question 27

LDA with R

Answer 27

library(MASS) #library for LDA
lda.fit = lda(Direction~Lag1+Lag2, data=Smarket, subset=train)
lda.pred = predict(lda.fit, Smarket.2005)
names(lda.pred)
lda.class = lda.pred$class #uses 50% probability
lda.probs = lda.pred$posterior #these are the probabilities

Question 28

How to center and scale variables for distance based learning methods KNN, K Means, etc

Answer 28

scale()

gives each variable a mean of zero and stdev of 1

Question 29

create vector

Answer 29

a = c('Hamel', 'Bob', 'Tom')  OR
a = 1:10   OR
a = c(1:10)

Question 30

Create a matrix

Answer 30

matrix(nrow, ncol)

matrix(1:6, 2, 3)

Question 31

see attributes of an object

Answer 31

attributes()

Question 32

combine two vectors of equal length into data frame

Answer 32

rbind(x,y) OR
cbind(x,y)
* trick question, doesn’t have to be equal length the shorter vector if not equal will just be duplicated to accommodate the bigger vector

Question 33

Factor Vector

Answer 33

x ))
The order of the levels can be set using the levels argument to factor(). This can
be important in linear modelling because the rst level is used as the baseline level.

Question 34

find missing values

Answer 34

is.na()

returns boolean vector

Question 35

differences b/w mattrices and data frames in R

Answer 35

in matrices, entire matrix has to be same class whereas data frame can store dierent classes of objects in each column

Question 36

convert data frame to matrix

Answer 36

data.matrix()

Question 37

count # of columns and rows

Answer 37

nrow(DataFrame)

ncol(DataFrame)

Question 38

how to change names of columns in R

Answer 38

names(x) )

Question 39

find the class or data type of each column

Answer 39

sapply(dataframe, class)

Question 40

connections to files in R

Answer 40

1. file - python like interface to file
2. gzfile opens to connection to gzipped file
3. url
   see ?file

Question 41

Readlines from a file

Answer 41

con <- readLines(con, 10)

Question 42

readlines from webpage

Answer 42

con <- readLines(con)

see ?file

Question 43

Find number of missing values in column

Answer 43

sum(is.na(Dataframe$column))

Question 44

what is model.matrix()

Answer 44

#Create Model Matrix, which is normally used behind the scense
#by regression models to predict things, just converts your dataset
# to a model-freiendly format - adds a column of 1's for intercept and 
#dummy codes all variables

Question 45

What do you do when you perform 10 k-fold cross validation and you are left with 10 different error terms for a particular tuning parameter

Answer 45

you take the mean of the statistic in question for all 10 folds. You average the 10 folds together, because you can’t use any one fold by itself.

Question 46

What happens after you find the best model using k-fold cross validation or LOOCV?

Answer 46

Once you find the best model, you should fit the model over the ENTIRE dataset as a last step and use that model. You only used CV so that you could find the optimal model that generalizes, but you can go ahead and fit the model over the entire dataset when you are done.

Question 47

what package has lasso and ridge regression

Answer 47

library(glmnet)