Learning objectives

Datacamp

• Explore on your own
• No specific required class this week

Assignment 2

• Looking at Singaporean retail firms
  • Mostly focused on time and cyclicality
  • Some visualization
  • A little of what we cover today
• Optional:
  • You can work in pairs on this assignment
    • If you choose to do this, please only make 1 submission and include both your names on the submission

What are binary outcomes?

• Thus far we have talked about events with continuous outcomes
  • Revenue: Some positive number
  • Earnings: Some number
  • ROA: Some percentage
• Binary outcomes only have two possible outcomes
  • Did something happen, yes or no?
  • Is a statement true or false?

Accounting examples of binary outcomes

• Financial accounting:
  • Will the company’s earnings meet analysts’ expectations?
  • Will the company have positive earnings?
• Managerial accounting:
  • Will we have ___ problem with our supply chain?
  • Will our customer go bankrupt?
• Audit:
  • Is the company committing fraud?
• Taxation:
  • Is the company too aggressive in their tax positions?

We can assign a probability to any of these

Regression approach: Logistic regression

• When modeling a binary outcome, we use logistic regression
  • A.k.a. logit model
• The logit function is \(logit(x) = \text{log}\left(\frac{x}{1-x}\right)\)
  • Also called log odds

\[ \text{log}\left(\frac{\text{Prob}(y=1|X)}{1-\text{Prob}(y=1|X)}\right)=\alpha + \beta_1 x_1 + \beta_2 x_2 + \ldots + \varepsilon \]

There are other ways to model this though, such as probit

Implementation: Logistic regression

• The logistic model is related to our previous linear models as such:

Both linear and logit models are under the class of General Linear Models (GLMs)

• To regress a GLM, we use the glm() command.
  • In fact, the lm() command we have been using is actually glm() when you specify the option family=gaussian
• To run a logit regression:

mod <- glm(y ~ x1 + x2 + x3 + ..., data=df, family=binomial)

summary(mod)

family=binomial is what sets the model to be a logit

Interpreting logit values

• The sign of the coefficients means the same as before
  • +: increases the likelihood of \(y\) occurring
  • -: decreases the likelihood of \(y\) occurring
• The level of a coefficient is different
  • The relationship isn’t linear between \(x_i\) and \(y\) now
  • Instead, coefficients are in log odds
    • Thus, \(e^{\beta_i}\) gives you the odds, \(o\)
• You can interpret the odds for a coefficient
  • Increased by \([o-1]\%\)
• You need to sum all relevant log odds before converting to a probability!

Odds vs probability

Example logit regression

Do holidays increase the likelihood that a department more than doubles its store’s average weekly sales across departments?

# Create the binary variable from Walmart sales data
df$double <- ifelse(df$Weekly_Sales > df$store_avg*2,1,0)
fit <- glm(double ~ IsHoliday, data=df, family=binomial)
tidy(fit)

## # A tibble: 2 x 5
##   term          estimate std.error statistic  p.value
##   <chr>            <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)     -3.45    0.00924    -373.  0.      
## 2 IsHolidayTRUE    0.539   0.0278       19.4 1.09e-83

Holidays increase the odds… but by how much?

A simple interpretation

• The model we just saw the following model:

\[ logodds(Double~sales) = -3.44 + 0.54 IsHoliday \]

• There are two ways to interpret this:
  1. Coefficient by coefficient
  2. In total

Interpretting specific coefficients

\[ logodds(Double~sales) = -3.44 + 0.54 IsHoliday \]

• Interpreting specific coefficients is easiest done manually
• Odds for the \(IsHoliday\) coefficient are exp(0.54) = 1.72
  • This means that having a holiday modifies the baseline (i.e., non-Holiday) odds by 1.72 to 1
    • Where 1 to 1 is considered no change
  • Baseline is 0.032 to 1

# Automating the above:
exp(coef(fit))

##   (Intercept) IsHolidayTRUE 
##    0.03184725    1.71367497

Interpretting in total

• It is important to note that log odds are additive
  • So, calculate a new log odd by plugging in values for variables and adding it all up
    • Holiday: \(-3.44 + 0.54 * 1 = -2.9\)
    • No holiday: \(-3.44 + 0.54 * 0 = -3.44\)
• Then calculate odds and log odds like before
  • With holiday: exp(-2.9) = 0.055
  • Without holiday: exp(-3.44) = 0.032
  • Ratio of holiday to without: 1.72!
    • This is the individual log odds for holiday

We need to specify values to calculate log odds in total

Converting to probabilities

• We can calculate a probability at any given point using the log odds

\[ Probability = \frac{log\ odds}{log\ odds + 1} \]

• Probability of double sales…
  • With a holiday: 0.055 / (0.55 + 1) = 0.052
  • Without a holiday: 0.032 / (0.032 + 1) = 0.031

These are easier to interpret, but require specifying values

Using predict() to simplify it

• predict() can calculate log odds and probabilities for us with minimal effort
  • Specify type="response" to get probabilities

test_data <- as.data.frame(IsHoliday = c(0,1))
predict(model, test_data)  # log odds

## [1] -3.44 -2.90

predict(model, test_data, type="response")  #probabilities

## [1] 0.03106848 0.05215356

• Here, we see the baseline probability is 3.1%
• The probability of doubling sales on a holiday is higher, at 5.2%

R practice: Logit

• A continuation of last week’s practices answering:
  • Is Walmart more likely to see a year over year decrease in quarterly revenue during a recession?
• Practice using mutate() and glm()
• Do exercises 1 and 2 in today’s practice file
  • R Practice
  • Shortlink: rmc.link/420r4

What about more complex models?

• Continuous inputs in the model
  • What values do we pick to determine probabilities?
• Multiple inputs?
  • We can scale up what we did, but things get messy
    • Mathematically, the inputs get interacted within the inner workings of logit…
      • So the impact of each input depends on the values of the others!

Consider this model

model2 <- glm(double ~ IsHoliday + Temperature + Fuel_Price, data=df, family=binomial)
summary(model2)

## 
## Call:
## glm(formula = double ~ IsHoliday + Temperature + Fuel_Price, 
##     family = binomial, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4113  -0.2738  -0.2464  -0.2213   2.8562  
## 
## Coefficients:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.7764917  0.0673246  -26.39   <2e-16 ***
## IsHolidayTRUE  0.3704298  0.0284395   13.03   <2e-16 ***
## Temperature   -0.0108268  0.0004698  -23.04   <2e-16 ***
## Fuel_Price    -0.3091950  0.0196234  -15.76   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 120370  on 421569  degrees of freedom
## Residual deviance: 119146  on 421566  degrees of freedom
## AIC: 119154
## 
## Number of Fisher Scoring iterations: 6

Odds and probabilities

# Odds
exp(coef(model2))

##   (Intercept) IsHolidayTRUE   Temperature    Fuel_Price 
##     0.1692308     1.4483570     0.9892316     0.7340376

# Typical September days
hday_sep    <- mean(predict(model2, filter(df, IsHoliday, month==9), type="response"))
no_hday_sep <-  mean(predict(model2, filter(df, !IsHoliday, month==9), type="response"))
# Typical December days
hday_dec    <- mean(predict(model2, filter(df, IsHoliday, month==12), type="response"))
no_hday_dec <-  mean(predict(model2, filter(df, !IsHoliday, month==12), type="response"))

html_df(data.frame(Month=c(9,9,12,12),
                   IsHoliday=c(FALSE,TRUE,FALSE,TRUE),
                   Probability=c(no_hday_sep, hday_sep, no_hday_dec, hday_dec)))

A bit easier: Marginal effects

Marginal effects tell us the average change in our output for a change of 1 to an input

• The above definition is very similar to how we interpret linear regression coefficients
  • The only difference is the word average – the effect changes a bit depending on the input data
• Using margins, we can calculate marginal effects
• There are a few types that we could calculate:
  • An Average Marginal Effect tells us what the average effect of an input is across all values in our data
    • This is the default method in the package
  • We can also specify a specific value to calculate marginal effects at (like with our probabilities last slides)

Marginal effects in action

# Calculate AME marginal effects
library(margins)
m <- margins(model2)
m

##  Temperature Fuel_Price IsHoliday
##   -0.0003377  -0.009644   0.01334

• A holiday increase the probability of doubling by a flat 1.33%
  • Not too bad when you consider that the probability of doubling is 3.23%
• If the temperature goes up by 1°F (0.55°C), the probability of doubling changes by -0.03%
• If the fuel price increases by 1 USD for 1 gallon of gas, the probability of doubling changes by -0.96%

margins niceties

• We can get some extra information about our marginal effects through summary():

summary(m) %>%
  html_df()

• Those p-values work just like with our linear models
• We also get a confidence interval
  • Which we can plot!

Plotting marginal effects

plot(m, which=summary(m)$factor)

Note: The which... part is absolutely necessary at the moment due to a bug in the package

Marginal effects at a specified value

margins(model2, at = list(IsHoliday = c(TRUE, FALSE)),
        variables = c("Temperature", "Fuel_Price")) %>%
  summary() %>%
  html_df()

margins(model2, at = list(Temperature = c(0, 20, 40, 60, 80, 100)),
        variables = c("IsHoliday")) %>%
  summary() %>%
  html_df()

The question

Can we leverage global weather data to predict shipping delays?

A bit about shipping data

• WRDS doesn’t have shipping data
• There are, however, vendors for shipping data, such as:

• They pretty much have any data you could need:
  • Over 650,000 ships tracked using ground and satellite based AIS
    • AIS: Automatic Identification System
  • Live mapping
  • Weather data
  • Fleet tracking
  • Port congestion
  • Inmarsat support for ship operators

What can we see from naval data?

What can we see from naval data?

What can we see from naval data?

Examining Singaporean owned ships

Code for last slide’s map

library(plotly)  # for plotting
library(RColorBrewer)  # for colors
# plot with boats, ports, and typhoons
# Note: geo is defined in the appendix -- it controls layout
palette = brewer.pal(8, "Dark2")[c(1,8,3,2)]
p <- plot_geo(colors=palette) %>%
  add_markers(data=df_ports, x = ~port_lon, y = ~port_lat, color = "Port") %>%
  add_markers(data=df_Aug31, x = ~lon, y = ~lat, color = ~ship_type,
              text=~paste('Ship name',shipname)) %>%
  add_markers(data=typhoon_Aug31, x = ~lon, y = ~lat, color="TYPHOON",
              text=~paste("Name", typhoon_name)) %>%
   layout(showlegend = TRUE, geo = geo,
    title = 'Singaporean owned container and tanker ships, August 31, 2018')
p

• plot_geo() is from plotly
• add_markers() adds points to the map
• layout() adjusts the layout
• Within geo, a list, the following makes the map a globe
  • projection=list(type="orthographic")

Singaporean ship movement

Link to ship movement animation

Code for last slide’s map

library(sf)  # Note: very difficult to install except on Windows
library(maps)
# Requires separately installing "maptools" and "rgeos" as well
# This graph requires ~7GB of RAM to render
world1 <- sf::st_as_sf(map('world', plot = FALSE, fill = TRUE))

df_all <- df_all %>% arrange(run, imo)

p <- ggplot(data = world1) + 
  geom_sf() + 
  geom_point(data = df_all, aes(x = lon, y = lat, frame=frame,
                                text=paste("name:",shipname)))
ggplotly(p) %>%
   animation_opts(
    1000, easing = "linear", redraw = FALSE)

• world1 contains the map data
• geom_sf() plots map data passed to ggplot()
• geom_point() plots ship locations as longitude and latitude
• ggplotly() converts the graph to html and animates it
  • Animation follows the frame aesthetic

What might matter for shipping?

What observable events or data might provide insight as to whether a naval shipment will be delayed or not?

Typhoon Jebi

• link

Typhoons in the data

Code for last slide’s map

# plot with boats and typhoons
palette = brewer.pal(8, "Dark2")[c(1,3,2)]
p <- plot_geo(colors=palette) %>%
  add_markers(data=df_all[df_all$frame == 14,], x = ~lon, y = ~lat,
              color = ~ship_type, text=~paste('Ship name',shipname)) %>%
  add_markers(data=typhoon_Jebi, x = ~lon,
              y = ~lat, color="Typhoon Jebi",
              text=~paste("Name", typhoon_name, "</br>Time: ", date)) %>%
   layout(showlegend = TRUE, geo = geo,
    title = 'Singaporean container/tanker ships, September 4, 2018, evening')
p

• This map is made the same way as the first map

Typhoons in the data using leaflet

Code for last slide’s map

library(leaflet)
library(leaflet.extras)

# typhoon icons
icons <- pulseIcons(color='red',
  heartbeat = ifelse(typhoon_Jebi$intensity_vmax > 150/1.852, 0.8,
    ifelse(typhoon$intensity_vmax < 118/1.852, 1.6, 1.2)),
  iconSize=ifelse(typhoon_Jebi$intensity_vmax > 150/1.852, 5,
    ifelse(typhoon_Jebi$intensity_vmax < 118/1.852, 2, 3)))

# ship icons
shipicons <- iconList(
  ship = makeIcon("../Figures/ship.png", NULL, 18, 18)
)
leaflet() %>%
  addTiles() %>% 
  setView(lng = 136, lat = 34, zoom=4) %>%
  addPulseMarkers(data=typhoon_Jebi[seq(1,nrow(typhoon_Jebi),5),], lng=~lon,
                  lat=~lat, label=~date, icon=icons) %>%
  addCircleMarkers(data=typhoon_Jebi[typhoon_Jebi$intensity_vmax > 150/1.852,],
    lng=~lon, lat=~lat,stroke = TRUE, radius=3, color="red", label=~date) %>%
  addCircleMarkers(data=typhoon_Jebi[typhoon_Jebi$intensity_vmax <= 150/1.852 &
    typhoon_Jebi$intensity_vmax > 118/1.852,], lng=~lon, lat=~lat,
    stroke = TRUE, radius=2, color="red", label=~date) %>%
  addCircleMarkers(data=typhoon_Jebi[typhoon_Jebi$intensity_vmax <=118/1.852,],
    lng=~lon, lat=~lat, stroke = TRUE, radius=1, color="red", label=~date) %>%
  addMarkers(data=df_all[df_all$frame == 14,], lng=~lon, lat=~lat,
             label=~shipname, icon=shipicons)

• pulseIcons(): pulsing icons from leaflet.extras
• iconList(): pulls icons stored on your computer
• leaflet(): start the map; addTiles() pulls from OpenStreetMap
• setView(): sets the frame for the map
• addPulseMarkers(): adds pulsing markers
• addCircleMarkers(): adds circular markers

R Practice on mapping

• Practice mapping typhoon data
  • 1 map using plotly
  • 1 map using leaflet
• Practice using plotly and leaflet
  • No practice using ggplot2 as sf is missing on DataCamp light
    • And sf can be tough to install for anyone on a Mac
• Do exercises 3 and 4 in today’s practice file
  • R Practice
  • Shortlink: rmc.link/420r4

Data

• If the ship will report a delay of at least 3 hours some time in the next 12-24 hours
• What we have:
  • Ship location
  • Typhoon location
  • Typhoon wind speed

We need to calculate distance between ships and typhoons

Distance for geo

• There are a number of formulas for this
  • Haversine for a simple calculation
  • Vincenty’s formulae for a complex, incredibly accurate calculation
    • Accurate within 0.5mm
• Use distVincentyEllipsoid() from geosphere to get a reasonably quick and accurate calculation
  • Calculates distance between two sets of points, x and y, structured as matrices
  • Matrices must have longitude in the first column and latitude in the second column
  • Provides distance in meters by default

library(geosphere)
x <- as.matrix(df3[,c("lon","lat")])  # ship location
y <- as.matrix(df3[,c("ty_lon","ty_lat")]) # typhoon location

df3$dist_typhoon <- distVincentyEllipsoid(x, y) / 1000

Clean up

• Some indicators to cleanly capture how far away the typhoon is

df3$typhoon_500 = ifelse(df3$dist_typhoon < 500 & 
                         df3$dist_typhoon >= 0, 1, 0)
df3$typhoon_1000 = ifelse(df3$dist_typhoon < 1000 &
                          df3$dist_typhoon >= 500, 1, 0)
df3$typhoon_2000 = ifelse(df3$dist_typhoon < 2000 &
                          df3$dist_typhoon >= 1000, 1, 0)

Do typhoons delay shipments?

fit1 <- glm(delayed ~ typhoon_500 + typhoon_1000 + typhoon_2000, data=df3,
            family=binomial)
summary(fit1)

## 
## Call:
## glm(formula = delayed ~ typhoon_500 + typhoon_1000 + typhoon_2000, 
##     family = binomial, data = df3)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.2502  -0.2261  -0.2261  -0.2261   2.7127  
## 
## Coefficients:
##              Estimate Std. Error  z value Pr(>|z|)    
## (Intercept)  -3.65377    0.02934 -124.547   <2e-16 ***
## typhoon_500   0.14073    0.16311    0.863   0.3883    
## typhoon_1000  0.20539    0.12575    1.633   0.1024    
## typhoon_2000  0.16059    0.07106    2.260   0.0238 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 14329  on 59184  degrees of freedom
## Residual deviance: 14322  on 59181  degrees of freedom
##   (3866 observations deleted due to missingness)
## AIC: 14330
## 
## Number of Fisher Scoring iterations: 6

It appears so!

Interpretation of coefficients

odds1 <- exp(coef(fit1))
odds1

##  (Intercept)  typhoon_500 typhoon_1000 typhoon_2000 
##   0.02589334   1.15111673   1.22800815   1.17420736

• Ships 1,000 to 2,000 km from a typhoon have a 17% increased odds of having a delay

m1 <- margins(fit1)
summary(m1)

##        factor    AME     SE      z      p   lower  upper
##  typhoon_1000 0.0052 0.0032 1.6322 0.1026 -0.0010 0.0115
##  typhoon_2000 0.0041 0.0018 2.2570 0.0240  0.0005 0.0076
##   typhoon_500 0.0036 0.0042 0.8626 0.3883 -0.0046 0.0117

• Ships 1,000 to 2,000 km from a typhoon have an extra 0.41% chance of having a delay (baseline of 2.61%)

What about typhoon intensity?

• Hong Kong’s typhoon classification: Official source
  1. 41-62 km/h: Tropical depression
  2. 63-87 km/h: Tropical storm
  3. 88-117 km/h: Severe tropical storm
  4. 118-149 km/h: Typhoon
  5. 150-184 km/h: Severe typhoon
  6. 185+km/h: Super typhoon

# Cut makes a categorical variable out of a numerical variable using specified bins
df3$Super <- ifelse(df3$intensity_vmax * 1.852 > 185, 1, 0)
df3$Moderate <- ifelse(df3$intensity_vmax * 1.852 >= 88 &
                         df3$intensity_vmax * 1.852 < 185, 1, 0)
df3$Weak <- ifelse(df3$intensity_vmax * 1.852 >= 41 & 
                   df3$intensity_vmax * 1.852 < 88, 1, 0)
df3$HK_intensity <- cut(df3$intensity_vmax * 1.852 ,c(-1,41, 62, 87, 117, 149, 999))
table(df3$HK_intensity)

## 
##   (-1,41]   (41,62]   (62,87]  (87,117] (117,149] (149,999] 
##      3398     12039     12615     11527      2255     21141

Typhoon intensity and delays

fit2 <- glm(delayed ~ (typhoon_500 + typhoon_1000 + typhoon_2000) : 
              (Weak + Moderate + Super), data=df3,
            family=binomial)
tidy(fit2)

## # A tibble: 10 x 5
##    term                  estimate std.error statistic p.value
##    <chr>                    <dbl>     <dbl>     <dbl>   <dbl>
##  1 (Intercept)           -3.65       0.0290 -126.     0      
##  2 typhoon_500:Weak      -0.00879    0.213    -0.0413 0.967  
##  3 typhoon_500:Moderate   0.715      0.251     2.86   0.00430
##  4 typhoon_500:Super     -8.91     123.       -0.0726 0.942  
##  5 typhoon_1000:Weak      0.250      0.161     1.55   0.121  
##  6 typhoon_1000:Moderate  0.123      0.273     0.451  0.652  
##  7 typhoon_1000:Super    -0.0269     0.414    -0.0648 0.948  
##  8 typhoon_2000:Weak      0.182      0.101     1.80   0.0723 
##  9 typhoon_2000:Moderate  0.0253     0.134     0.189  0.850  
## 10 typhoon_2000:Super     0.311      0.136     2.29   0.0217

Moderate storms predict delays when within 500km

Super typhoons predict delays when 1,000 to 2,000km away

Interpretation of coefficients

odds2 <- exp(coef(fit2))
odds2[c(1, 3, 8, 10)]

##          (Intercept) typhoon_500:Moderate    typhoon_2000:Weak 
##           0.02589637           2.04505487           1.19923751 
##   typhoon_2000:Super 
##           1.36507575

• Ships within 500km of a moderately strong storm have 105% higher odds of a delay
• Ships 1,000 to 2,000km from a weak typhoon have 20% higher odds of a delay
• Ships 1,000 to 2,000km from a super typhoon have 37% higher odds of a delay

Marginal effects

m2 <- margins(fit2)
summary(m2) %>%
  html_df()

• Delays appear to be driven mostly by 2 factors:
  1. A typhoon 1,000 to 2,000 km away from the ship
  2. Weak typhoons

What might matter for shipping?

What other observable events or data might provide insight as to whether a naval shipment will be delayed or not?

• What is the reason that this event or data would be useful in predicting delays?
  • I.e., how does it fit into your mental model?

For next week

• For next week:
  • Second individual assignment
    • Finish by the end of next Thursday
    • Submit on eLearn
  • Think about who you want to work with for the project

Packages used for these slides

Custom code

# styling for plotly maps
geo <- list(
  showland = TRUE,
  showlakes = TRUE,
  showcountries = TRUE,
  showocean = TRUE,
  countrywidth = 0.5,
  landcolor = toRGB("grey90"),
  lakecolor = toRGB("aliceblue"),
  oceancolor = toRGB("aliceblue"),
  projection = list(
    type = 'orthographic',  # detailed at https://plot.ly/r/reference/#layout-geo-projection
    rotation = list(
      lon = 100,
      lat = 1,
      roll = 0
    )
  ),
  lonaxis = list(
    showgrid = TRUE,
    gridcolor = toRGB("gray40"),
    gridwidth = 0.5
  ),
  lataxis = list(
    showgrid = TRUE,
    gridcolor = toRGB("gray40"),
    gridwidth = 0.5
  )
)