R commands y2

Question 1

What is the command to read data in from file x.txt and call it y?

Answer 1

y <- read.table(“x.txt”, h=T)

Question 2

How do you specify that a dataset has a header?

Answer 2

h=T or header=TRUE

Question 3

What is the command of printing dataset x?

Answer 3

print(x)

Question 4

How do you plot every possible pair of variables in a dataset together?

Answer 4

plot(datasetname)

Question 5

How do you see the correlation coefficient of each pair of variables in a dataset?

Answer 5

cor(datasetname)

Question 6

How do you get the ANOVA for a model?

Answer 6

anova(modelname)

Question 7

How do you know which variable to remove from a model after an looking at the significance of every variable?

Answer 7

Look at the values in the Pr(>|t|) column and remove the independent variable with the highest

Question 8

When do you stop removing variables from a model when looking at the variance caused by specific variables?

Answer 8

When the Pr(>|t|) value of every variable remaining is significant (<0.05)

Question 9

How do you make the model that you are using for looking at the variance provided by each variable?

Answer 9

m1=lm(DV~ IV1 + IV2 + IV3, data = datasetname)

Question 10

How do you create the second model for a variance analysis after removing the first IV?

Answer 10

m1=lm(DV~ IV1 + IV2, data = datasetname)

Question 11

How do you get the formula for the model after you have removed the final variable?

Answer 11

print(modelname)

Question 12

What is a way to see if the model is significant without looking at the Pr(>|t|)?

Answer 12

Do an ANOVA between two models (e.g. ANOVA(m7,m8) and if the Pr(>F) value is significant then the penultimate model is significant

Question 13

What does AIC stand for?

Answer 13

Akaike’s Information Criterion

Question 14

What is the goal of running an AIC?

Answer 14

To minimise the AIC value

Question 15

What are the benefits of an AIC?

Answer 15

The program runs to completion, so it is quicker than by hand

Question 16

How do you run an AIC on a model?

Answer 16

backward=step(modelname,direction=”backward”)

Question 17

How do you see the formula from the model after doing an AIC?

Answer 17

summary(modelname)

Question 18

What is PCA used for?

Answer 18

To explore the structure of multivariate data, it is a data reduction technique

Question 19

Limitations of PCA?

Answer 19

no null hypothesis

Question 20

Assumptions of PCA?

Answer 20

variables are continuous or on an interval scale

Question 21

How to remove a column from a dataset?

Answer 21

newdatasetname=olddatasetname[,-1]
where the column you are removing is the first column

Question 22

What are the two types of PCA?

Answer 22

covariance or correlation matrix

Question 23

What is a covariance PCA?

Answer 23

It gives some more weight to some variables than others depending on their variance

Question 24

What is a correlation matrix PCA?

Answer 24

it expresses every variable in standard deviation units so each one is weighted equally

Question 25

How to do a correlation matrix?

Answer 25

yourname=princomp(dataset,cor=TRUE)

Question 26

How to do a covariance matrix?

Answer 26

yourname=princomp(dataset)