hw05
114035129_Mochammad Raffy Anugerah
2026-03-27
library(tidymodels)## ── Attaching packages ────────────────────────────────────── tidymodels 1.4.1 ──## ✔ broom 1.0.12 ✔ recipes 1.3.1
## ✔ dials 1.4.2 ✔ rsample 1.3.2
## ✔ dplyr 1.2.0 ✔ tailor 0.1.0
## ✔ ggplot2 4.0.2 ✔ tidyr 1.3.1
## ✔ infer 1.1.0 ✔ tune 2.0.1
## ✔ modeldata 1.5.1 ✔ workflows 1.3.0
## ✔ parsnip 1.4.1 ✔ workflowsets 1.1.1
## ✔ purrr 1.1.0 ✔ yardstick 1.3.2## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ purrr::discard() masks scales::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ recipes::step() masks stats::step()library(ISLR)
library(corrr)
library(ggplot2)
data(Smarket)glimpse(Smarket)## Rows: 1,250
## Columns: 9
## $ Year <dbl> 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, …
## $ Lag1 <dbl> 0.381, 0.959, 1.032, -0.623, 0.614, 0.213, 1.392, -0.403, 0.…
## $ Lag2 <dbl> -0.192, 0.381, 0.959, 1.032, -0.623, 0.614, 0.213, 1.392, -0…
## $ Lag3 <dbl> -2.624, -0.192, 0.381, 0.959, 1.032, -0.623, 0.614, 0.213, 1…
## $ Lag4 <dbl> -1.055, -2.624, -0.192, 0.381, 0.959, 1.032, -0.623, 0.614, …
## $ Lag5 <dbl> 5.010, -1.055, -2.624, -0.192, 0.381, 0.959, 1.032, -0.623, …
## $ Volume <dbl> 1.1913, 1.2965, 1.4112, 1.2760, 1.2057, 1.3491, 1.4450, 1.40…
## $ Today <dbl> 0.959, 1.032, -0.623, 0.614, 0.213, 1.392, -0.403, 0.027, 1.…
## $ Direction <fct> Up, Up, Down, Up, Up, Up, Down, Up, Up, Up, Down, Down, Up, …summary(Smarket)## Year Lag1 Lag2 Lag3
## Min. :2001 Min. :-4.922000 Min. :-4.922000 Min. :-4.922000
## 1st Qu.:2002 1st Qu.:-0.639500 1st Qu.:-0.639500 1st Qu.:-0.640000
## Median :2003 Median : 0.039000 Median : 0.039000 Median : 0.038500
## Mean :2003 Mean : 0.003834 Mean : 0.003919 Mean : 0.001716
## 3rd Qu.:2004 3rd Qu.: 0.596750 3rd Qu.: 0.596750 3rd Qu.: 0.596750
## Max. :2005 Max. : 5.733000 Max. : 5.733000 Max. : 5.733000
## Lag4 Lag5 Volume Today
## Min. :-4.922000 Min. :-4.92200 Min. :0.3561 Min. :-4.922000
## 1st Qu.:-0.640000 1st Qu.:-0.64000 1st Qu.:1.2574 1st Qu.:-0.639500
## Median : 0.038500 Median : 0.03850 Median :1.4229 Median : 0.038500
## Mean : 0.001636 Mean : 0.00561 Mean :1.4783 Mean : 0.003138
## 3rd Qu.: 0.596750 3rd Qu.: 0.59700 3rd Qu.:1.6417 3rd Qu.: 0.596750
## Max. : 5.733000 Max. : 5.73300 Max. :3.1525 Max. : 5.733000
## Direction
## Down:602
## Up :648
##
##
##
## cor_Smarket <- Smarket %>%
select(-Direction) %>%
correlate()## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'cor_Smarket## # A tibble: 8 × 9
## term Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume Today
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Year NA 0.0297 0.0306 0.0332 0.0357 0.0298 0.539 0.0301
## 2 Lag1 0.0297 NA -0.0263 -0.0108 -0.00299 -0.00567 0.0409 -0.0262
## 3 Lag2 0.0306 -0.0263 NA -0.0259 -0.0109 -0.00356 -0.0434 -0.0103
## 4 Lag3 0.0332 -0.0108 -0.0259 NA -0.0241 -0.0188 -0.0418 -0.00245
## 5 Lag4 0.0357 -0.00299 -0.0109 -0.0241 NA -0.0271 -0.0484 -0.00690
## 6 Lag5 0.0298 -0.00567 -0.00356 -0.0188 -0.0271 NA -0.0220 -0.0349
## 7 Volume 0.539 0.0409 -0.0434 -0.0418 -0.0484 -0.0220 NA 0.0146
## 8 Today 0.0301 -0.0262 -0.0103 -0.00245 -0.00690 -0.0349 0.0146 NArplot(cor_Smarket)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the corrr package.
## Please report the issue at <https://github.com/tidymodels/corrr/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
ggplot(Smarket, aes(x = Year, y = Volume)) +
geom_point()
lr_spec <- logistic_reg() %>%
set_engine("glm") %>%
set_mode("classification")lr_fit <- lr_spec %>%
fit(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume,
data = Smarket)
lr_fit## parsnip model object
##
##
## Call: stats::glm(formula = Direction ~ Lag1 + Lag2 + Lag3 + Lag4 +
## Lag5 + Volume, family = stats::binomial, data = data)
##
## Coefficients:
## (Intercept) Lag1 Lag2 Lag3 Lag4 Lag5
## -0.126000 -0.073074 -0.042301 0.011085 0.009359 0.010313
## Volume
## 0.135441
##
## Degrees of Freedom: 1249 Total (i.e. Null); 1243 Residual
## Null Deviance: 1731
## Residual Deviance: 1728 AIC: 1742summary(lr_fit$fit)##
## Call:
## stats::glm(formula = Direction ~ Lag1 + Lag2 + Lag3 + Lag4 +
## Lag5 + Volume, family = stats::binomial, data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.126000 0.240736 -0.523 0.601
## Lag1 -0.073074 0.050167 -1.457 0.145
## Lag2 -0.042301 0.050086 -0.845 0.398
## Lag3 0.011085 0.049939 0.222 0.824
## Lag4 0.009359 0.049974 0.187 0.851
## Lag5 0.010313 0.049511 0.208 0.835
## Volume 0.135441 0.158360 0.855 0.392
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1731.2 on 1249 degrees of freedom
## Residual deviance: 1727.6 on 1243 degrees of freedom
## AIC: 1741.6
##
## Number of Fisher Scoring iterations: 3library(broom)
tidy(lr_fit)## # A tibble: 7 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.126 0.241 -0.523 0.601
## 2 Lag1 -0.0731 0.0502 -1.46 0.145
## 3 Lag2 -0.0423 0.0501 -0.845 0.398
## 4 Lag3 0.0111 0.0499 0.222 0.824
## 5 Lag4 0.00936 0.0500 0.187 0.851
## 6 Lag5 0.0103 0.0495 0.208 0.835
## 7 Volume 0.135 0.158 0.855 0.392pred_class <- predict(lr_fit, new_data = Smarket)
head(pred_class)## # A tibble: 6 × 1
## .pred_class
## <fct>
## 1 Up
## 2 Down
## 3 Down
## 4 Up
## 5 Up
## 6 Uppred_prob <- predict(lr_fit, new_data = Smarket, type = "prob")
head(pred_prob)## # A tibble: 6 × 2
## .pred_Down .pred_Up
## <dbl> <dbl>
## 1 0.493 0.507
## 2 0.519 0.481
## 3 0.519 0.481
## 4 0.485 0.515
## 5 0.489 0.511
## 6 0.493 0.507Section 4.1 The Smarket dataset contains stock market data with variables such as lagged returns and trading volume. Correlation analysis shows that most variables are weakly correlated, except for Year and Volume, which show a slight positive relationship. This indicates that predictors may not strongly explain the market direction.
Section 4.2 A logistic regression model was used to predict the market direction (Up/Down) based on lag variables and volume. The results show that most predictors are not statistically significant, meaning they do not strongly influence the prediction. The model can generate both class predictions and probabilities for each observation.