ETC5521: Exploratory Data Analysis

.info-box.w-50.bg-white[
These slides are viewed best by Chrome or Firefox and occasionally need to be refreshed if elements did not load properly. See <a href=lecture-08B.pdf>here for the PDF </a>. 
]

---

# .monash-blue[ETC5521: Exploratory Data Analysis]

<h2 style="font-weight:900!important;">Sculpting data using models, checking assumptions, co-dependency and performing diagnostics</h2>

.bottom_abs.width100[

Lecturer: *Emi Tanaka*

ETC5521.Clayton-x@monash.edu

Week 8 - Session 2

]

---

# Revisiting outliers

.w-50[
* We defined outliers in week 4 as "observations that are significantly different from the majority" when studying univariate variables.
* There is actually no hard and fast definition.

.info-box[
We can also define an outlier as a data point that emanates from a different model than do the rest of the data.
]

]

* Notice that this makes this definition *dependent on the model* in question.

---

# Pop Quiz

Would you consider the yellow points below as outliers?

---

# Outlying values

.grid[
.item.border-right[
* As with simple linear regression the fitted model should not be used to predict `$Y$` values for `$\boldsymbol{x}$` combinations that are well away from the set of observed `$\boldsymbol{x}_i$` values. 
* This is not always easy to detect!

* Here, a point labelled P has `$x_1$` and `$x_2$` coordinates well within their respective ranges but P is not close to the observed sample values in 2-dimensional space.

* In higher dimensions this type of behaviour is even harder to detect but we need to be on guard against extrapolating to extreme values.

]
.item[

]
]

---

# Leverage

.w-70[
* The matrix `$\mathbf{H} = \mathbf{X}(\mathbf{X}^\top\mathbf{X})^{-1}\mathbf{X}^\top$` is referred to as the .monash-blue[**hat matrix**].
* The `$i$`-th diagonal element of `$\mathbf{H}$`, `$h_{ii}$`, is called the .monash-blue[**leverage**] of the `$i$`-th observation.
* Leverages are always between zero and one,
`$$0 \leq h_{ii} \leq 1.$$`
* Notice that leverages are not dependent on the response!
* Points with high leverage can exert a lot of influence on the parameter estimates

]

---

# Studentized residuals

.w-70[
* In order to obtain residuals with equal variance, many texts recommend using the .monash-blue[**studentised residuals**]
`$$R_i^* = \dfrac{R_i} {\hat{\sigma} \sqrt{1 - h_{ii}}}$$` for diagnostic checks.

]

---

# Cook's distance

.w-70[
* .brand-blue[Cook's distance], `$D$`, is another measure of influence: 
`\begin{eqnarray*}
D_i &=& \dfrac{(\hat{\boldsymbol{\beta}}- \hat{\boldsymbol{\beta}}_{[-i]})^\top Var(\hat{\boldsymbol{\beta}})^{-1}(\hat{\boldsymbol{\beta}}- \hat{\boldsymbol{\beta}}_{[-i]})}{p}\\
&=&\frac{R_i^2 h_{ii}}{(1-h_{ii})^2p\hat\sigma^2},
\end{eqnarray*}`
where `$p$` is the number of elements in `$\boldsymbol{\beta}$`, `$\hat{\boldsymbol{\beta}}_{[-i]}$` and `$\hat Y_{j[-i]}$` are least squares estimates and the fitted value obtained by fitting the model ignoring the `$i$`-th data point `$(\boldsymbol{x}_i,Y_i)$`, respectively.

]

---

# .orange[Case study] .circle.bg-orange.white[2] Social media marketing

Data collected from advertising experiment to study the impact of three advertising medias (youtube, facebook and newspaper) on sales.

]
.panel[.panel-name[data]
.h200.scroll-sign[

```r
data(marketing, package="datarium")
skimr::skim(marketing)
```

```
## ── Data Summary ────────────────────────
##                            Values   
## Name                       marketing
## Number of rows             200      
## Number of columns          4        
## _______________________             
## Column type frequency:              
##   numeric                  4        
## ________________________            
## Group variables            None     
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate  mean     sd    p0   p25   p50   p75  p100 hist 
## 1 youtube               0             1 176.  103.    0.84  89.2 180.  263.  356.  ▇▆▆▇▆
## 2 facebook              0             1  27.9  17.8   0     12.0  27.5  43.8  59.5 ▇▆▆▆▆
## 3 newspaper             0             1  36.7  26.1   0.36  15.3  30.9  54.1 137.  ▇▆▃▁▁
## 4 sales                 0             1  16.8   6.26  1.92  12.4  15.5  20.9  32.4 ▁▇▇▅▂
```

]]
.panel[.panel-name[R]

```r
GGally::ggpairs(marketing, progress=F)
```

]

---

# Extracting values from models in R

* The leverage value, studentised residual and Cook's distance can be easily extracted from a model object using `broom::augment`.
  * `.hat` is the leverage value
  * `.std.resid` is the studentised residual
  * `.cooksd` is the Cook's distance

```r
fit <- lm(sales ~ youtube * facebook, data = marketing)
(out <- broom::augment(fit))
```

```
## # A tibble: 200 × 9
## sales youtube facebook .fitted .resid .hat .sigma .cooksd .std.resid
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 26.5 276. 45.4 26.0 0.496 0.0174 1.13 0.000864 0.442 
## 2 12.5 53.4 47.2 12.8 -0.281 0.0264 1.13 0.000431 -0.252 
## 3 11.2 20.6 55.1 11.1 0.0465 0.0543 1.14 0.0000256 0.0423
## 4 22.2 182. 49.6 21.2 1.04 0.0124 1.13 0.00268 0.923 
## 5 15.5 217. 13.0 15.2 0.316 0.0104 1.13 0.000207 0.280 
## 6 8.64 10.4 58.7 10.5 -1.91 0.0709 1.13 0.0583 -1.75 
## 7 14.2 69 39.4 13.0 1.15 0.0149 1.13 0.00395 1.02 
## 8 15.8 144. 23.5 14.6 1.23 0.00577 1.13 0.00173 1.09 
## 9 5.76 10.3 2.52 8.39 -2.63 0.0553 1.12 0.0838 -2.39 
## 10 12.7 240. 3.12 13.4 -0.727 0.0219 1.13 0.00236 -0.649 
## # … with 190 more rows
```
]

---

# Examining the leverage values

---

# Examining the Cook's distance

---

# Non-parametric regression

---

# LOESS

.grid[
.item.border-right[
* LOESS (LOcal regrESSion) and LOWESS (LOcally WEighted
Scatterplot Smoothing) are .monash-blue[**non-parametric regression**] methods (LOESS is a generalisation of LOWESS)
* **LOESS fits a low order polynomial to a subset of neighbouring data** and can be fitted using `loess` function in `R`
* a user specified "bandwidth" or "smoothing parameter" `$\color{blue}{\alpha}$` determines how much of the data is used to fit each local polynomial.

]
.item[

* `$\alpha \in \left(\frac{\lambda + 1}{n}, 1\right)$` (default `span=0.75`) where `$\lambda$` is the degree of the local polynomial (default `degree=2`) and `$n$` is the number of observations.
* Large `$\alpha$` produce a smoother fit.
* Small `$\alpha$` overfits the data with the fitted regression capturing the random error in the data.

]

---

# How `span` changes the loess fit

.footnote.f4[
Code inspired by http://varianceexplained.org/files/loess.html
]

---

# How `loess` works

.footnote.f4[
Code inspired by http://varianceexplained.org/files/loess.html
]
---

# .orange[Case study] .circle.bg-orange.white[3] US economic time series

This dataset was produced from US economic time series data available from http://research.stlouisfed.org/fred2.

]
.panel[.panel-name[data]
.h200.scroll-sign.f4[

```r
data(economics, package = "ggplot2")
skimr::skim(economics)
```

```
## ── Data Summary ────────────────────────
##                            Values   
## Name                       economics
## Number of rows             574      
## Number of columns          6        
## _______________________             
## Column type frequency:              
##   Date                     1        
##   numeric                  5        
## ________________________            
## Group variables            None     
## 
## ── Variable type: Date ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate min        max        median     n_unique
## 1 date                  0             1 1967-07-01 2015-04-01 1991-05-16      574
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate      mean       sd       p0      p25      p50      p75     p100 hist 
## 1 pce                   0             1   4820.    3557.      507.    1578.    3937.    7626.   12194.  ▇▅▃▂▃
## 2 pop                   0             1 257160.   36682.   198712   224896   253060   290291.  320402.  ▇▇▆▆▇
## 3 psavert               0             1      8.57     2.96      2.2      6.4      8.4     11.1     17.3 ▃▇▆▅▁
## 4 uempmed               0             1      8.61     4.11      4        6        7.5      9.1     25.2 ▇▃▁▁▁
## 5 unemploy              0             1   7771.    2642.     2685     6284     7494     8686.   15352   ▃▇▆▂▁
```

]]
.panel[.panel-name.f4[R]

```r
ggplot(economics, aes(date, uempmed)) + 
  geom_point() +
  geom_smooth(method = loess, se = FALSE,
              method.args = list(span = 0.1)) +
  labs(x = "Date", y = "Median unemployment duration")
```

]

---

# How to fit LOESS curves in R?

## Model fitting

The model can be fitted using the `loess` function where

* the default  span is 0.75 and 
* the default local polynomial degree is 2.

```r
fit <- economics %>% 
 mutate(index = 1:n()) %>% 
* loess(uempmed ~ index,
* data = .,
* span = 0.75,
* degree = 2)
```

]
.w-50.pl3.f4[

]

## Showing it on the plot

In `ggplot`, you can add the loess using `geom_smooth` with `method = loess` and method arguments passed as list:

```r
ggplot(economics, aes(date, uempmed)) +
  geom_point() + 
* geom_smooth(method = loess,
*             method.args = list(span = 0.75,
*                                degree = 2))
```

---

# Why non-parametric regression?

.w-70[
* Fitting a line to a scatter plot where noisy data values, sparse data points or weak inter-relationships interfere with your ability to see a line of best fit.
{{content}}
]
--

* Linear regression where least squares fitting doesn't create a line of good fit or is too labour intensive to use.
{{content}}
--

* Data exploration and analysis.
{{content}}
--

* Recall: In a parametric regression, some type of distribution is assumed in advance; therefore fitted model can lead to fitting a smooth curve that
misrepresents the data.
{{content}}
--

* In those cases, non-parametric regression may be a better choice. 
{{content}}
--

* *Can you think of where it might be useful?*

---

# .orange[Case study] .circle.bg-orange.white[4] Bluegills .font_small[Part 1/3]

Data were collected on length (in mm) and the age (in years) of 78 bluegills captured from Lake Mary, Minnesota in 1981.

Which fit looks better?

]
.item[
<img src="images/week8B/bluegills-plot2-1.png" width="432" style="display: block; margin: auto;" />
]
]
]
.panel[.panel-name[data]
.h200.scroll-sign.f4[

```r
bg_df <- read.table(here::here("data/bluegills.txt"),
 header = TRUE)
skimr::skim(bg_df)
```

```
## ── Data Summary ────────────────────────
##                            Values
## Name                       bg_df 
## Number of rows             78    
## Number of columns          2     
## _______________________          
## Column type frequency:           
##   numeric                  2     
## ________________________         
## Group variables            None  
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate   mean     sd    p0   p25   p50   p75  p100 hist 
## 1 age                   0             1   3.63  0.927     1    3      4     4     6 ▂▃▇▂▁
## 2 length                0             1 144.   24.1      62  137.   150   160   188 ▁▁▂▇▂
```

]]
.panel[.panel-name[R]
.f4[

```r
ggplot(bg_df, aes(age, length)) + 
  geom_point() +
  geom_smooth(method = lm, se = FALSE) +
  labs(tag = "(A)", title = "Linear regression", x = "Age (in years)", y = "Length (in mm)")
ggplot(bg_df, aes(age, length)) + 
  geom_point() +
  geom_smooth(method = lm, se = FALSE,
              formula = y ~ poly(x, 2)) +
  labs(tag = "(B)", title = "Quadratic regression",
       x = "Age (in years)", y = "Length (in mm)")
```

]]

]

.footnote[
Weisberg (1986) A linear model approach to backcalculation of fish length, *Journal of the American Statistical Association* **81** (196) 922-929
]

---

# .orange[Case study] .circle.bg-orange.white[4] Bluegills .font_small[Part 2/3]

* Let's have a look at the residual plots.
* Do you see any patterns on either residual plot?

]
.item[
<img src="images/week8B/bluegills-resplot2-1.png" width="432" style="display: block; margin: auto;" />
]
]
]
.panel[.panel-name[data]
.h200.scroll-sign.f4[

```r
fit1 <- lm(length ~ age, data = bg_df)
fit2 <- lm(length ~ poly(age, 2), data = bg_df)
df1 <- augment(fit1)
df2 <- mutate(augment(fit2), age = bg_df$age)
summary(fit1)
```

```
## 
## Call:
## lm(formula = length ~ age, data = bg_df)
## 
## Residuals:
## Min 1Q Median 3Q Max 
## -26.523 -7.586 0.258 10.102 20.414 
## 
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) 
## (Intercept) 62.649 5.755 10.89 <2e-16 ***
## age 22.312 1.537 14.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.51 on 76 degrees of freedom
## Multiple R-squared: 0.7349,	Adjusted R-squared: 0.7314 
## F-statistic: 210.7 on 1 and 76 DF, p-value: < 2.2e-16
```

```r
summary(fit2)
```

```
## 
## Call:
## lm(formula = length ~ poly(age, 2), data = bg_df)
## 
## Residuals:
## Min 1Q Median 3Q Max 
## -19.846 -8.321 -1.137 6.698 22.098 
## 
## Coefficients:
## Estimate Std. Error t value Pr(>|t|) 
## (Intercept) 143.603 1.235 116.290 < 2e-16 ***
## poly(age, 2)1 181.565 10.906 16.648 < 2e-16 ***
## poly(age, 2)2 -54.517 10.906 -4.999 3.67e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.91 on 75 degrees of freedom
## Multiple R-squared: 0.8011,	Adjusted R-squared: 0.7958 
## F-statistic: 151.1 on 2 and 75 DF, p-value: < 2.2e-16
```

]]
.panel[.panel-name[R]
.f4[

```r
ggplot(df1, aes(age, .std.resid)) + 
  geom_point() +
  geom_hline(yintercept = 0) +
  labs(x = "Age", y = "Residual",
       tag = "(A)", title = "Linear regression")
ggplot(df2, aes(age, .std.resid)) + 
  geom_point() +
  geom_hline(yintercept = 0) +
  labs(x = "Age", y = "Residual",
       tag = "(B)", title = "Quadratic regression")
```

]]

]

.footnote[
Weisberg (1986) A linear model approach to backcalculation of fish length, *Journal of the American Statistical Association* **81** (196) 922-929
]

---

# .orange[Case study] .circle.bg-orange.white[4] Bluegills .font_small[Part 3/3]

The structure is easily visible with the LOESS curve:

.grid[.item[
<img src="images/week8B/bluegills-lresplot1-1.png" width="432" style="display: block; margin: auto;" />

]
.item[
<img src="images/week8B/bluegills-lresplot2-1.png" width="432" style="display: block; margin: auto;" />
]
]
]
.panel[.panel-name[data]
.h200.scroll-sign.f4[

```r
fit1 <- lm(length ~ age, data = bg_df)
fit2 <- lm(length ~ poly(age, 2), data = bg_df)
df1 <- augment(fit1)
df2 <- mutate(augment(fit2), age = bg_df$age)
summary(fit1)
```

```r
summary(fit2)
```

]]
.panel[.panel-name[R]
.f4[

```r
ggplot(df1, aes(age, .std.resid)) + 
  geom_point() +
  geom_hline(yintercept = 0) +
  labs(x = "Age", y = "Residual",
       tag = "(A)", title = "Linear regression") +
  geom_smooth(method = loess, color = "red",
              se = FALSE)
ggplot(df2, aes(age, .std.resid)) + 
  geom_point() +
  geom_hline(yintercept = 0) +
  labs(x = "Age", y = "Residual",
       tag = "(B)", title = "Quadratic regression") +
  geom_smooth(method = loess, color = "red",
              se = FALSE)
```

]]

]

.footnote[
Weisberg (1986) A linear model approach to backcalculation of fish length, *Journal of the American Statistical Association* **81** (196) 922-929
]

---

# .orange[Case study] .circle.bg-orange.white[5] Soil resistivity in a field

This data contains measurement of soil resistivity of an agricultural field.

.grid[.item[
<img src="images/week8B/cleveland-plot1-1.png" width="288" style="display: block; margin: auto;" />
]
.item[
<img src="images/week8B/cleveland-plot2-1.png" width="504" style="display: block; margin: auto;" />

]
]

]
.panel[.panel-name[data]
.h200.scroll-sign.f4[

```r
data(cleveland.soil, package = "agridat")
skimr::skim(cleveland.soil)
```

```
## ── Data Summary ────────────────────────
##                            Values        
## Name                       cleveland.soil
## Number of rows             8641          
## Number of columns          5             
## _______________________                  
## Column type frequency:                   
##   logical                  1             
##   numeric                  4             
## ________________________                 
## Group variables            None          
## 
## ── Variable type: logical ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate  mean count               
## 1 is.ns                 0             1 0.242 FAL: 6553, TRU: 2088
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate   mean     sd     p0    p25    p50   p75   p100 hist 
## 1 northing              0             1  1.90   1.11  -0.01   0.978  1.81   2.91   3.81 ▆▇▅▇▆
## 2 easting               0             1  0.739  0.429 -0.004  0.362  0.729  1.10   1.56 ▆▇▆▆▅
## 3 resistivity           0             1 50.9   28.8    0.89  29.6   47.8   71.0  166.   ▇▇▅▁▁
## 4 track                 0             1 16.9   12.4    1      5     14     29     40    ▇▃▂▃▃
```

]]
.panel[.panel-name[R]
.f4[

```r
ggplot(cleveland.soil, aes(easting, northing)) +
  geom_point()
library(lattice)
cloud(resistivity ~ easting * northing, pch = ".", data = cleveland.soil)
```
]
]

]

---

# Conditioning plots (Coplots)

```r
library(lattice)
xyplot(resistivity ~ northing | equal.count(easting, 12),
       data = cleveland.soil, cex = 0.2,  
       type = c("p", "smooth"), col.line = "red", 
       col = "gray", lwd = 2)
```

<img src="images/week8B/coplots-1.png" width="720" style="display: block; margin: auto;" />
]

.footnote.f4[
See also: https://homepage.divms.uiowa.edu/~luke/classes/STAT4580/threenum.html
]

---

# Coplots via `ggplot2`

* Coplots with `ggplot2` where the panels have overlapping observations is tricky.
* Below creates a plot for non-overlapping intervals of `easting`:

```r
ggplot(cleveland.soil, aes(northing, resistivity)) +
  geom_point(color = "gray") + 
  geom_smooth(method = "loess", color = "red", se = FALSE) +
  facet_wrap(~ cut_number(easting, 12))
```

---

# Take away messages

<ul class="fa-ul">
{{content}}
</ul>

]
]

<li> You can use leverage values and Cook's distance to query possible unusal values in the data
</li>
{{content}}
--

<li> Non-parametric regression, such as LOESS, can be useful in data exploration and analysis although parameters must be carefully chosen not to overfit the data
</li>
{{content}}
--

<li> Conditioning plots are useful in understanding the relationship between pairs of variables given at particular intervals of other variables
</li>

---

background-size: cover
class: title-slide
background-image: url("images/bg-01.png")

<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

.bottom_abs.width100[

Lecturer: *Emi Tanaka*

ETC5521.Clayton-x@monash.edu

Week 8 - Session 2

]