Lab 2

If you collaborated with anyone, you must include “Collaborated with: FIRSTNAME LASTNAME” at the top of your lab!

Part 1. More on Functions (8 points)

1a. (2 points) The hard-threshold function is defined as \[f_\lambda(x) = \begin{cases} x & |x| \geq \lambda\\ 0 & |x| < \lambda \end{cases}\] Write an R function that takes two parameters, numeric input x and a threshold lambda. Your function should return the value of $f_\lambda(x)$ and work for vector input x of any length.

1b. (1 point) Set $\lambda = 4$, demonstrate your function on the vector c(-5, -3, 0, 3, 5).

(Hint: the output should be the vector -5, 0, 0, 0, 5)

1c. (1 point) Set $\lambda = 2$, demonstrate your function on the vector c(-7, -5, -3, 0, 3, 5, 7).

2a. (2 points) The soft-threshold function is defined as \[g_\lambda(x) = \begin{cases} sign(x)(|x| - \lambda) & |x| \geq \lambda\\ 0 & |x| < \lambda \end{cases}\] Write an R function that takes two parameters, numeric input x and a threshold lambda. Your function should return the value of $g_\lambda(x)$ and work for vector input x of any length.

2b. (1 point) Set $\lambda = 4$, demonstrate your function on the vector c(-5, -3, 0, 3, 5).

(Hint: the output should be the vector -1, 0, 0, 0, 1)

2c. (1 point) Set $\lambda = 2$, demonstrate your function on the vector c(-7, -5, -3, 0, 3, 5, 7).

Part 2. Lists (4 points)

Many popular functions in R output lists in order to return multiple objects of different types and lengths. Here we will look at the function lm, which performs linear regression. Don’t worry if you aren’t familiar with linear regression, we will just be analyzing the output of the function.

First, run the following code to create an object of class lm.

linearMod <- lm(dist ~ speed, data = cars)

3a. (2 points) What are the names of the items in the list linearMod?

3b. (2 points) Use two different methods to store the coefficients within linearMod as a variable. One method should use the dollar sign $, the other should use the word coefficients. Print out your variables.

Part 3. Data (8 points)

4. (2 points) Create a data frame that includes at least 3 observations and variables of at least 3 different types. Print out your data frame.

For problem 5, we will use an adapted version of the weather data from Tidyverse.

library(kableExtra)
weather <- data.frame("station" = rep(c("A", "B", "C"), each = 4),
                      "element" = rep(c("temp_min", "temp_max"), 2),
                      "month1"  = c(11.4, 25.6, NA, NA, 17.7, 28.0,
                                    NA, NA, 20.0, 24.9, NA, NA),
                      "month2"  = c(NA, NA, 16.8, 28.7, NA, NA,
                                    11.1, 26.8, NA, NA, 14.7, 33.4))
kable_styling(kable(weather))

station	element	month1	month2
A	temp_min	11.4	NA
A	temp_max	25.6	NA
A	temp_min	NA	16.8
A	temp_max	NA	28.7
B	temp_min	17.7	NA
B	temp_max	28.0	NA
B	temp_min	NA	11.1
B	temp_max	NA	26.8
C	temp_min	20.0	NA
C	temp_max	24.9	NA
C	temp_min	NA	14.7
C	temp_max	NA	33.4

5a. (2 points) Identify and list the observations and the variables in this data set.

(Hint: the variables are not station, element, month1, and month2. These are the column names in the untidy data, and need to be fixed!)

5b. (4 points) Use kable() to present a tidied up version of this data. (Hint: My table has 4 variables, 6 observations, and no NA values. I only kept one of the variables unchanged.)

Lab 2

STAT 302

Due Date Here

Part 1. More on Functions (8 points)

Part 2. Lists (4 points)

Part 3. Data (8 points)