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CV Lab

Dr. D’Agostino McGowan

1 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

RStudio Pro

rstudio.hpc.ar53.wfu.edu:8787

pw: R2D2Star!

2 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Practice getting your lab into RStudio Pro

3 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Now get your lab into RStudio Cloud

4 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Practice pushing and pulling in GitHub

  • So far we have just been pushing to GitHub, but a nice feature is that you can then pull your results at any time.
  • Make a change to your Lab file in RStudio Pro and push this to GitHub (check the GitHub repo to make sure your changes showed up)
  • Check RStudio cloud - did those changes show up?
5 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Practice pushing and pulling in GitHub

  • So far we have just been pushing to GitHub, but a nice feature is that you can then pull your results at any time.
  • Make a change to your Lab file in RStudio Pro and push this to GitHub (check the GitHub repo to make sure your changes showed up)
  • Check RStudio cloud - did those changes show up?
  • Click the pull (down) arrow in RStudio cloud. Do you see the changes now?
5 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Some R functions

sample(10)
##  [1]  3  1  8  5  7  2  4  9  6 10
sample(10)
##  [1]  8  3  9  1  7 10  4  6  2  5
6 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Some R functions

set.seed(1)
sample(10)
##  [1]  3  4  5  7  2  8  9  6 10  1
set.seed(1)
sample(10)
##  [1]  3  4  5  7  2  8  9  6 10  1
7 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Use the set.seed() function before performing cross validation so you get consistent (reproducible) answers!

8 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Polynomial regression in R

\(y = \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3 + \epsilon\)

lm(y ~ x + I(x^2) + I(x^3), data = data)
## 
## Call:
## lm(formula = y ~ x + I(x^2) + I(x^3), data = data)
## 
## Coefficients:
## (Intercept)            x       I(x^2)       I(x^3)  
##     0.10276     -0.06669     -0.13772      0.02546
9 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Polynomial regression in R

\(y = \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3 + \epsilon\)

lm(y ~ x + I(x^2) + I(x^3), data = data)
## 
## Call:
## lm(formula = y ~ x + I(x^2) + I(x^3), data = data)
## 
## Coefficients:
## (Intercept)            x       I(x^2)       I(x^3)  
##     0.10276     -0.06669     -0.13772      0.02546
lm(y ~ poly(x, 3), data = data)
## 
## Call:
## lm(formula = y ~ poly(x, 3), data = data)
## 
## Coefficients:
## (Intercept)  poly(x, 3)1  poly(x, 3)2  poly(x, 3)3  
##   -0.007264    -0.205147    -1.465710     0.382521
9 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

Polynomial regression in R

\(y = \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3 + \epsilon\)

model1 <- lm(y ~ x + I(x^2) + I(x^3), data = data)
model2 <- lm(y ~ poly(x, 3), data = data)
all.equal(predict(model1), predict(model2))
## [1] TRUE
10 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

When doing polynomial regression, you can use the poly() function in R

11 / 11
Dr. Lucy D'Agostino McGowan adapted from slides by Hastie & Tibshirani

RStudio Pro

rstudio.hpc.ar53.wfu.edu:8787

pw: R2D2Star!

2 / 11
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