Difference in Differences Regressions - Error Analysis and Graphs for data from Snow 1855
Introduction
This notebook discusses the count regressions (Poisson and Negative Binomial) for 1849 versus 1854 data that are run in the notebook Snow1855_DiDRegression1.Rmd.
The variation across sub-districts and within sub-districts across time (1849 versus 1854) are too large to be accounted for simply by random variation in counts with fixed mortality rates, as assumed for Poisson regression. We seem to be pushed towards the conclusion that the rates themselves are random, which would be consistent with Negative Binomial regression.
This notebook calculates and graphs the actual versus predicted rates under three assumptions:
- Poisson regression with rates the same across sub-districts
- Poisson regression but allowing rates to differ across sub-districts (fixed effects)
- Negative Binomial regression so that underlying Poisson rates are themsleves Gamma-distributed across sub-districts and time
For a brief introduction to Snow’s work, see:
- Snow’s original 1855 monograph (it is masterful): Snow, John. 1855. On the Mode of Communication of Cholera. 2nd ed. London: John Churchill. http://archive.org/details/b28985266.
- The best popular exposition I have found: Johnson, Steven. 2007. The Ghost Map: The Story of London’s Most Terrifying Epidemic–and How It Changed Science, Cities, and the Modern World. Reprint edition. New York: Riverhead Books.
- Another good popular version: Hempel, Sandra. 2007. The Strange Case of the Broad Street Pump: John Snow and the Mystery of Cholera. First edition. Berkeley: University of California Press.
- Tufte’s classic discussion of Snow’s mapping (a topic I don’t cover here): Tufte, Edward R. 1997. Visual Explanations: Images and Quantities, Evidence and Narrative. 1st edition. Graphics Press.
- Biography: Vinten-Johansen, Peter, Howard Brody, Nigel Paneth, Stephen Rachman, and Michael Russell Rip. 2003. Cholera, Chloroform and the Science of Medicine: A Life of John Snow. Oxford; New York: Oxford University Press. Linked on-line resources https://johnsnow.matrix.msu.edu/snowworks.php
This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code. The results are also saved in a self-contained html document with the suffix .nb.html. If you want pure r code (for example to run outside RStudio) you can easily extract code with the command knit(‘notebook.Rmd’,tangle=TRUE) which will save a file ‘notebook.R’ under your working directory.
Try executing the chunk below by clicking the Run button within the chunk or by placing your cursor inside it and pressing Cmd+Shift+Enter.
# Copyright (c) 2019, Thomas Coleman
#
# ------- Licensed under BSD 2-Clause "Simplified" License -------
#
# Results and discussion in "Causality in the Time of Cholera: John Snow as a Prototype
# for Causal Inference (Working Paper)" available at SSRN: https://papers.ssrn.com/abstract=3262234
rm(list=ls()) # starts a fresh workspace
#
library(knitr)
options(scipen=5)
# The following libraries are used for the Negative Binomial regression and the robust standard error analysis
#install.packages("sandwich")
#install.packages("lmtest")
library("MASS")
library("sandwich")
library("lmtest")
# Read in the data
tablevii <- read.csv(file="Snow1855_TableVII.csv", header=TRUE, sep=",", skip=5,comment.char="#")
tableviii <- read.csv(file="Snow1855_TableVIII.csv", header=TRUE, sep=",", skip=5,comment.char="#")
tableix <- read.csv(file="Snow1855_TableIX.csv", header=TRUE, sep=",", skip=5,comment.char="#")
tablexii <- read.csv(file="Snow1855_TableXII.csv", header=TRUE, sep=",", skip=5,comment.char="#")
“Worker” Functions for Plotting
The main object of this notebook is to plot mortality rates for sub-districts, say comparing 1849 versus 1854, with (approximate) error bars overlaid. The graphs are basically all the same with just different input data. So I have put together some simple functions that produce a standard format graph. These are in the file “SnowPlotFns.r” and then ’source’d into this notebook.
preperrdata Before graphing, however, we need to prepare the data
- Takes in fittedmodel - a regression that has been already run. From this it extracts the necessary parameters. Also single, a string which for “single” says that there is a single treatment effect.
- Calculates the 1849 and 1854 predicted counts and rates
- Calculates approximate 95% error bars around the predicted rates, based on whether the fitted model is Poisson or Negative Binomial
- Produces an adjusted 1854 predicted rate, adjusting for the 1854 time effect and treatment effect, so that it is comparable to the 1849 predicted rate (for purposes of plotting with error bars)
This function changes global data (the x1849 & x1854 dataframes) using the “<<-” instead of “<-” assignment. This is poor programming style but I could not find another easy way of doing what I wanted.
plot2_worker Plots actual vs predicted, with error bars around the predicted
plot2_worker Is a cover function which unpacks the actual versus predicted mean from the appropriate dataframe
plot3 Plots actual 1849, 1854 (adjusted for time & treatment effects), predicted, with error bars
plotcomp Plots actual 1849 versus 1854, with error bars around actual 1849
ploterrbars is NOT a function you should use - I use it to print out .pdf versions of the graphs I want to use
source('SnowPlotFns.r')
Create Regression Data
Now we create the we need for running count regressions, from Snow’s Tables XII and VIII. This is the same as in the notebook *Snow1855_DiDRegression1.Rmd.
x1 <- subset(tableviii,supplier == "SouthwarkVauxhall" | supplier == "SouthwarkVauxhall_Lambeth")
x1849 <- x1[c("subDistrict","pop1851","supplier","lambethdegree")]
x1 <- subset(tablexii,supplier == "SouthwarkVauxhall" | supplier == "SouthwarkVauxhall_Lambeth")
x1849$deaths <- x1$deaths1849
x1849$rate <- 10000 * x1$deaths1849 / x1849$pop1851
x1849$seq <- c(seq(1,length(x1849$deaths)))
#x1849$dum1854 <- 0
xyear <- factor(c(rep(1849,28),rep(1854,28)))
x1849$year <- xyear[1:28]
x1854 <- x1849
#x1849$lambethdegree <- "dirty"
x1854$deaths <- x1$deaths1854
x1854$rate <- 10000 * x1$deaths1854 / x1849$pop1851
x1854$seq <- c(seq(1,length(x1849$deaths)))
#x1854$dum1854 <- 1
x1854$year <- xyear[29:56]
regdata <- rbind(x1849,x1854)
regdata
1849 vs 1854 DiD, Poisson Regression
Now we are ready for running regressions and plotting. First, basic Poisson regression with single treatment effect:
\(ln(Rate) = ln(Count) - ln(Population) = \mu + \delta 54*I(54) + \gamma*I(joint) + \beta*I(54)*I(joint) + \epsilon\)
- an overall constant (\(\mu\))
- a difference for 1854 (\(\delta54\))
- a difference for joint “next 16” region (\(\gamma\))
- an interaction for 1854 and joint (\(\beta\))
# Poisson with single "Lambeth effect" and same rate for all sub-districts (no sub-district fixed effects)
pois1single <- glm(deaths ~ supplier * year
+ offset(log(pop1851)), family=poisson, data=regdata)
summary(pois1single)
Call:
glm(formula = deaths ~ supplier * year + offset(log(pop1851)),
family = poisson, data = regdata)
Deviance Residuals:
Min 1Q Median 3Q Max
-12.1542 -3.0953 0.0306 2.7398 10.2518
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.30610 0.02103 -204.755 < 2e-16 ***
supplierSouthwarkVauxhall_Lambeth -0.03593 0.02643 -1.359 0.17400
year1854 0.08354 0.02914 2.867 0.00414 **
supplierSouthwarkVauxhall_Lambeth:year1854 -0.51088 0.03870 -13.201 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 2032.6 on 55 degrees of freedom
Residual deviance: 1541.6 on 52 degrees of freedom
AIC: 1937.4
Number of Fisher Scoring iterations: 4
This regression calculates the same parameter as the simple table (-0.5109, from notebooks Snow1855_DiDRegression1.Rmd or Snow1855_SimpleDID_QRCT.Rmd). The Poisson regression says the z value is -13.2 but this is in fact a huge over-estimate. Graphing the actual versus fitted mortality rates by sub-district help to show why.
xfamily <- preperrdata(pois1single,"single") # this function modifies global data
plot3(x1849,x1854,"SouthwarkVauxhall",paste("First-12 Southwark-only ",xfamily," 1849vs1854 "))
![](data:image/png;base64,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)
plot3(x1849,x1854,"SouthwarkVauxhall_Lambeth",paste("Next-16 Jointly-Supplied ",xfamily," 1849vs1854 "))
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)
The empty circles are the fitted with error bars; red circles are 1849; blue diamonds are 1854 (adjusted for year and treatment effects to be comparable with 1849). The first graph shows the “first-12” Southwark-onl sub-districs and the second the “next-12” jointly-supplied sub-districts. (The sequence numbers match the sequence or IDs in Table XII or Table VIII.) The error bars are estimated 95% limits assuming that the counts are Poisson-distributed (which is for all practical purposes the same as assuming counts are Binomial, generated from a Bernoulli process). Note that smaller sub-districts, such as Putney (ID 10, population 5,280) have wider error bars.
The problem is obvious from these graphs: the observed rates are almost all outside the error bars. There is simply too much variation, both across sub-districts and within sub-districts, to be consistent with a Poisson process with all sub-districts having the same mortality.
The regression statistic we need to use is the “Residual Deviance” which essentially measures the sum-of-squared differences between actual and predicted - larger when the data fit less well. This will be approximately chi-squared distributed, with 52 degrees of freedom in this case. The value is 1541.6 which is very large - the 5% right-tail quantile for a chi-squared with 52-degrees of freedom is 69.8 - a value larger than this will only be observed with 5% probability. A value of 1541.6 is far out in the right tail, with miniscule probability of being observed. In sum, it would be exceedinlgy unlikely to observe a Residual Deviance of 1541.6 if the data were Poisson-distributed - we can reject the hypothesis that the observed counts are generated by a Poisson process.
We have to abandon the assumption that rates are Poisson with a constant rate for all sub-districts.
Our regerssion equation is
\(ln(Rate) = ln(Count) - ln(Population) = \mu + \delta 54*I(54) + \gamma*I(joint) + \beta*I(54)*I(joint) + \epsilon\)
One direction we can go is to allow the mean rate \(\mu\) to vary by sub-district: sub-district fixed effects. The other is to generalize the error process and allow \(\epsilon\) to be other than Poisson-distributed: say Negative Binomial
1849 vs 1854 DiD, Poisson Regression with Fixed effects
So our first generalization is sub-district fixed effects. Our data has a “subDistric” factor that we can use in our regression:
# Poisson with single "Lambeth effect" and different rates by sub-district (fixed effects)
pois2single <- glm(deaths ~ subDistrict + supplier * year
+ offset(log(pop1851)), family=poisson, data=regdata)
summary(pois2single)
Call:
glm(formula = deaths ~ subDistrict + supplier * year + offset(log(pop1851)),
family = poisson, data = regdata)
Deviance Residuals:
Min 1Q Median 3Q Max
-6.8307 -2.1772 -0.0007 2.1558 7.2691
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.358711 0.061453 -70.928 < 2e-16 ***
subDistrictBorough Road 0.551368 0.074805 7.371 1.70e-13 ***
subDistrictBrixton -0.873070 0.107840 -8.096 5.68e-16 ***
subDistrictCamberwell 0.002546 0.075176 0.034 0.972980
subDistrictChristchurch, Southwark 0.085657 0.081164 1.055 0.291260
subDistrictClapham -0.210516 0.086382 -2.437 0.014808 *
subDistrictKennington (1st) -0.045641 0.076931 -0.593 0.552997
subDistrictKennington (2nd) -0.300608 0.085249 -3.526 0.000422 ***
subDistrictKent Road 0.140521 0.078391 1.793 0.073042 .
subDistrictLambeth Church (1st) -0.388067 0.087552 -4.432 9.32e-06 ***
subDistrictLambeth Church (2nd) 0.263607 0.072349 3.644 0.000269 ***
subDistrictLeather Market 0.125367 0.075538 1.660 0.096981 .
subDistrictLondon Road -0.074462 0.082065 -0.907 0.364220
subDistrictPeckham -0.668876 0.085472 -7.826 5.05e-15 ***
subDistrictPutney -2.115547 0.249739 -8.471 < 2e-16 ***
subDistrictRotherhithe 0.287736 0.071578 4.020 5.82e-05 ***
subDistrictSt. George, Camberwell -0.084183 0.084406 -0.997 0.318588
subDistrictSt. James, Bermondsey 0.191154 0.071991 2.655 0.007925 **
subDistrictSt. John, Horsleydown 0.114013 0.080544 1.416 0.156908
subDistrictSt. Mary Magdalen 0.301424 0.074392 4.052 5.08e-05 ***
subDistrictSt. Mary, Newington -0.233003 0.090182 -2.584 0.009775 **
subDistrictSt. Olave, Southwark 0.395903 0.081797 4.840 1.30e-06 ***
subDistrictSt. Peter, Walworth 0.278504 0.071250 3.909 9.28e-05 ***
subDistrictSt. Saviour, Southwark 0.217198 0.071240 3.049 0.002297 **
subDistrictTrinity, Newington 0.177119 0.075970 2.331 0.019731 *
subDistrictWandsworth -0.497886 0.099782 -4.990 6.05e-07 ***
subDistrictWaterloo Road (1st) -0.171047 0.088666 -1.929 0.053716 .
subDistrictWaterloo Road (2nd) -0.074593 0.081580 -0.914 0.360532
supplierSouthwarkVauxhall_Lambeth NA NA NA NA
year1854 0.083541 0.029140 2.867 0.004145 **
supplierSouthwarkVauxhall_Lambeth:year1854 -0.510882 0.038702 -13.201 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 2032.61 on 55 degrees of freedom
Residual deviance: 456.78 on 26 degrees of freedom
AIC: 904.6
Number of Fisher Scoring iterations: 4
We can plot this with our plot3 function to examine the actual versus predicted.
xfamily <- preperrdata(pois2single,"single") # this function modifies global data
plot3(x1849,x1854,"SouthwarkVauxhall",paste("First-12 Southwark-only ",xfamily," 1849vs1854 "))
![](data:image/png;base64,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)
plot3(x1849,x1854,"SouthwarkVauxhall_Lambeth",paste("Next-16 Jointly-Supplied ",xfamily," 1849vs1854 "))
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)
Now the individual sub-districts all have different rates. But still the data do not fit well. There are too many of the red circles or blue diamonds outside the error bars - there should only be 5% or roughly 3 out of 56. And the residual deviance, 456.8 is still too large compared with the chi-squared (with 26 df) of 38.9
1849 vs 1854 DiD, Negative Binomial Regression
Instead of allowing each sub-district to have its own, fixed, rate, we are going to take another direction. For our regression equation:
\(ln(Rate) = ln(Count) - ln(Population) = \mu + \delta 54*I(54) + \gamma*I(joint) + \beta*I(54)*I(joint) + \epsilon\)
we will allow the error \(\epsilon\) to have a more general distribution, Negative Binomial in this case. A Negative Binomial distribution is actually a mixture of Poisson distributions but with the underling Poisson rate itself now random, chosen from a Gamma distribution. (See my working paper at https://papers.ssrn.com/abstract=3262234 and references there for more details.)
# Negative Binomial with single "Lambeth effect"
nb1single <- glm.nb(deaths ~ supplier * year
+ offset(log(pop1851)), data=regdata)
summary(nb1single)
Call:
glm.nb(formula = deaths ~ supplier * year + offset(log(pop1851)),
data = regdata, init.theta = 4.956204025, link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.2718 -0.5282 0.0652 0.4958 1.8326
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.33237 0.13169 -32.898 <2e-16 ***
supplierSouthwarkVauxhall_Lambeth -0.03182 0.17386 -0.183 0.8548
year1854 0.05734 0.18616 0.308 0.7581
supplierSouthwarkVauxhall_Lambeth:year1854 -0.50027 0.24612 -2.033 0.0421 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Negative Binomial(4.9562) family taken to be 1)
Null deviance: 71.825 on 55 degrees of freedom
Residual deviance: 59.764 on 52 degrees of freedom
AIC: 657.19
Number of Fisher Scoring iterations: 1
Theta: 4.956
Std. Err.: 0.973
2 x log-likelihood: -647.185
One thing we want to immediately note is the Residual Deviance is only 59.8 which is well below the 5% right-tail quantile of 69.8, meaning that such a value would not be unusual for Negative Binomial counts. (In fact the right-tail probability is 0.214.)
xfamily <- preperrdata(nb1single,"single") # this function modifies global data
plot3(x1849,x1854,"SouthwarkVauxhall",paste("First-12 Southwark-only ",xfamily," 1849vs1854 "))
![](data:image/png;base64,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)
plot3(x1849,x1854,"SouthwarkVauxhall_Lambeth",paste("Next-16 Jointly-Supplied ",xfamily," 1849vs1854 "))
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)
The graphs show exactly what is hapenning: the error bars are wide and capture all except perhaps 3 of our observed values - close to what we would expect for 95% confidence bands.
---
title: "John Snow Project - DiD Error Analysis"
author: "[Thomas Coleman](http://www.hilerun.org/econ)"
output: html_notebook
---
# Difference in Differences Regressions - Error Analysis and Graphs for data from Snow 1855

#### See "Causality in the Time of Cholera" working paper at https://papers.ssrn.com/abstract=3262234 and my [John Snow project website](http://www.hilerun.org/econ/papers/snow)

#### This notebook is licensed under the [BSD 2-Clause License](https://opensource.org/licenses/BSD-2-Clause)

### Introduction

This notebook discusses the count regressions (Poisson and Negative Binomial) for 1849 versus 1854 data that are run in the notebook *Snow1855_DiDRegression1.Rmd*. 

The variation across sub-districts and within sub-districts across time (1849 versus 1854) are too large to be accounted for simply by random variation in counts with *fixed* mortality rates, as assumed for Poisson regression. We seem to be pushed towards the conclusion that the rates themselves are random, which would be consistent with Negative Binomial regression. 

This notebook calculates and graphs the actual versus predicted rates under three assumptions:

* Poisson regression with rates the same across sub-districts
* Poisson regression but allowing rates to differ across sub-districts (fixed effects)
* Negative Binomial regression so that underlying Poisson rates are themsleves Gamma-distributed across sub-districts and time


For a brief introduction to Snow's work, see:

+ **Snow's original 1855 monograph** (it is masterful): Snow, John. 1855. *On the Mode of Communication of Cholera*. 2nd ed. London: John Churchill. http://archive.org/details/b28985266.
+ **The best popular exposition I have found**: Johnson, Steven. 2007. *The Ghost Map: The Story of London’s Most Terrifying Epidemic--and How It Changed Science, Cities, and the Modern World*. Reprint edition. New York: Riverhead Books.
+ **Another good popular version**: Hempel, Sandra. 2007. *The Strange Case of the Broad Street Pump: John Snow and the Mystery of Cholera*. First edition. Berkeley: University of California Press.
+ **Tufte's classic discussion of Snow's mapping** (a topic I don't cover here): Tufte, Edward R. 1997. *Visual Explanations: Images and Quantities, Evidence and Narrative*. 1st edition. Graphics Press.
+ **Biography**: Vinten-Johansen, Peter, Howard Brody, Nigel Paneth, Stephen Rachman, and Michael Russell Rip. 2003. *Cholera, Chloroform and the Science of Medicine: A Life of John Snow*. Oxford; New York: Oxford University Press. Linked on-line resources https://johnsnow.matrix.msu.edu/snowworks.php




This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. The results are also saved in a self-contained html document with the suffix *.nb.html*. If you want pure r code (for example to run outside RStudio) you can easily extract code with the command *knit('notebook.Rmd',tangle=TRUE)* which will save a file 'notebook.R' under your working directory.

Try executing the chunk below by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

``````{r message=FALSE, results='hide'}
# Copyright (c) 2019, Thomas Coleman
#
#  -------  Licensed under BSD 2-Clause "Simplified" License  -------
#
# Results and discussion in "Causality in the Time of Cholera: John Snow as a Prototype 
# for Causal Inference (Working Paper)" available at SSRN: https://papers.ssrn.com/abstract=3262234

rm(list=ls())    # starts a fresh workspace
#
library(knitr)
options(scipen=5)
# The following libraries are used for the Negative Binomial regression and the robust standard error analysis
#install.packages("sandwich")
#install.packages("lmtest")
library("MASS")
library("sandwich") 
library("lmtest") 

# Read in the data
tablevii <- read.csv(file="Snow1855_TableVII.csv", header=TRUE, sep=",", skip=5,comment.char="#")
tableviii <- read.csv(file="Snow1855_TableVIII.csv", header=TRUE, sep=",", skip=5,comment.char="#")
tableix <- read.csv(file="Snow1855_TableIX.csv", header=TRUE, sep=",", skip=5,comment.char="#")
tablexii <- read.csv(file="Snow1855_TableXII.csv", header=TRUE, sep=",", skip=5,comment.char="#")

```

###"Worker" Functions for Plotting

The main object of this notebook is to plot mortality rates for sub-districts, say comparing 1849 versus 1854, with (approximate) error bars overlaid. The graphs are basically all the same with just different input data. So I have put together some simple functions that produce a standard format graph. These are in the file "SnowPlotFns.r" and then 'source'd into this notebook. 

**preperrdata** Before graphing, however, we need to prepare the data

* Takes in *fittedmodel* - a regression that has been already run. From this it extracts the necessary parameters. Also *single*, a string which for "single" says that there is a single treatment effect. 
* Calculates the 1849 and 1854 predicted counts and rates
* Calculates *approximate* 95% error bars around the predicted rates, based on whether the fitted model is Poisson or Negative Binomial
* Produces an adjusted 1854 predicted rate, adjusting for the 1854 time effect and treatment effect, so that it is comparable to the 1849 predicted rate (for purposes of plotting with error bars)

This function changes global data (the x1849 & x1854 dataframes) using the "<<-" instead of "<-" assignment. This is poor programming style but I could not find another easy way of doing what I wanted. 

**plot2_worker** Plots actual vs predicted, with error bars around the predicted

* Actually does the plotting, given all the data as input arguments (sequence no. for sub-districts; the actual mean or rate; predicted; the 2.5% and 97.5% points; title)
* btw, the hack for plotting error bars is from https://stackoverflow.com/questions/13032777/scatter-plot-with-error-bars

**plot2_worker** Is a cover function which unpacks the actual versus predicted mean from the appropriate dataframe

**plot3** Plots actual 1849, 1854 (adjusted for time & treatment effects), predicted, with error bars

**plotcomp** Plots actual 1849 versus 1854, with error bars around actual 1849

**ploterrbars** is NOT a function you should use - I use it to print out .pdf versions of the graphs I want to use


```{r}
source('SnowPlotFns.r') 


```


###Create Regression Data

Now we create the we need for running count regressions, from Snow's Tables XII and VIII. This is the same as in the notebook *Snow1855_DiDRegression1.Rmd.


```{r}
x1 <- subset(tableviii,supplier == "SouthwarkVauxhall" | supplier == "SouthwarkVauxhall_Lambeth")
x1849 <- x1[c("subDistrict","pop1851","supplier","lambethdegree")]
x1 <- subset(tablexii,supplier == "SouthwarkVauxhall" | supplier == "SouthwarkVauxhall_Lambeth")
x1849$deaths <- x1$deaths1849
x1849$rate <- 10000 * x1$deaths1849 / x1849$pop1851
x1849$seq <- c(seq(1,length(x1849$deaths)))
#x1849$dum1854 <- 0
xyear <- factor(c(rep(1849,28),rep(1854,28)))
x1849$year <- xyear[1:28]
x1854 <- x1849
#x1849$lambethdegree <- "dirty"
x1854$deaths <- x1$deaths1854
x1854$rate <- 10000 * x1$deaths1854 / x1849$pop1851
x1854$seq <- c(seq(1,length(x1849$deaths)))
#x1854$dum1854 <- 1
x1854$year <- xyear[29:56]

regdata <- rbind(x1849,x1854)
regdata
```

###1849 vs 1854 DiD, Poisson Regression

Now we are ready for running regressions and plotting. First, basic Poisson regression with single treatment effect:

$ln(Rate) = ln(Count) - ln(Population) = \mu + \delta 54*I(54) + \gamma*I(joint) + \beta*I(54)*I(joint) + \epsilon$

* an overall constant ($\mu$)
* a difference for 1854 ($\delta54$) 
* a difference for joint "next 16" region ($\gamma$)
* an interaction for 1854 and joint ($\beta$)


```{r}
# Poisson with single "Lambeth effect" and same rate for all sub-districts (no sub-district fixed effects)
pois1single <- glm(deaths ~ supplier * year 
	+ offset(log(pop1851)), family=poisson, data=regdata) 
summary(pois1single)

```

This regression calculates the same parameter as the simple table (`r round(pois1single$coefficients[4],4)`, from notebooks *Snow1855_DiDRegression1.Rmd* or *Snow1855_SimpleDID_QRCT.Rmd*). The Poisson regression says the *z value* is `r round(summary(pois1single)$coefficients[4,3],2)` but this is in fact a huge over-estimate. Graphing the actual versus fitted mortality rates by sub-district help to show why.

```{r}
xfamily <- preperrdata(pois1single,"single")  # this function modifies global data

plot3(x1849,x1854,"SouthwarkVauxhall",paste("First-12 Southwark-only ",xfamily," 1849vs1854 "))
plot3(x1849,x1854,"SouthwarkVauxhall_Lambeth",paste("Next-16 Jointly-Supplied ",xfamily," 1849vs1854 "))
```

The empty circles are the fitted with error bars; red circles are 1849; blue diamonds are 1854 (adjusted for year and treatment effects to be comparable with 1849). The first graph shows the "first-12" Southwark-onl sub-districs and the second the "next-12" jointly-supplied sub-districts. (The sequence numbers match the sequence or IDs in Table XII or Table VIII.) The error bars are estimated 95% limits assuming that the counts are Poisson-distributed (which is for all practical purposes the same as assuming counts are Binomial, generated from a Bernoulli process). Note that smaller sub-districts, such as Putney (ID 10, population 5,280) have wider error bars. 

The problem is obvious from these graphs: the observed rates are almost all outside the error bars. There is simply too much variation, both across sub-districts and within sub-districts, to be consistent with a Poisson process with all sub-districts having the same mortality. 

The regression statistic we need to use is the "Residual Deviance" which essentially measures the sum-of-squared differences between actual and predicted - larger when the data fit less well. This will be approximately chi-squared distributed, with 52 degrees of freedom in this case. The value is `r round(pois1single$deviance,1)` which is very large - the 5% right-tail quantile for a chi-squared with 52-degrees of freedom is `r round(qchisq(.95,52),1)` - a value larger than this will only be observed with 5% probability. A value of `r round(pois1single$deviance,1)` is far out in the right tail, with miniscule probability of being observed. In sum, it would be exceedinlgy unlikely to observe a Residual Deviance of `r round(pois1single$deviance,1)` if the data were Poisson-distributed - we can reject the hypothesis that the observed counts are generated by a Poisson process. 

We have to abandon the assumption that rates are Poisson with a constant rate for all sub-districts. 

Our regerssion equation is

$ln(Rate) = ln(Count) - ln(Population) = \mu + \delta 54*I(54) + \gamma*I(joint) + \beta*I(54)*I(joint) + \epsilon$

One direction we can go is to allow the mean rate $\mu$ to vary by sub-district: sub-district fixed effects. The other is to generalize the error process and allow $\epsilon$ to be other than Poisson-distributed: say Negative Binomial


###1849 vs 1854 DiD, Poisson Regression with Fixed effects

So our first generalization is sub-district fixed effects. Our data has a "subDistric" factor that we can use in our regression:

```{r}
# Poisson with single "Lambeth effect" and different rates by sub-district (fixed effects)
pois2single <- glm(deaths ~ subDistrict + supplier * year 
	+ offset(log(pop1851)), family=poisson, data=regdata) 
summary(pois2single)
```

We can plot this with our *plot3* function to examine the actual versus predicted.

```{r}
xfamily <- preperrdata(pois2single,"single")  # this function modifies global data

plot3(x1849,x1854,"SouthwarkVauxhall",paste("First-12 Southwark-only ",xfamily," 1849vs1854 "))
plot3(x1849,x1854,"SouthwarkVauxhall_Lambeth",paste("Next-16 Jointly-Supplied ",xfamily," 1849vs1854 "))
```

Now the individual sub-districts all have different rates. But still the data do not fit well. There are too many of the red circles or blue diamonds outside the error bars - there should only be 5% or roughly 3 out of 56. And the residual deviance, `r round(pois2single$deviance,1)` is still too large compared with the chi-squared (with 26 df) of `r round(qchisq(.95,26),1)` 

###1849 vs 1854 DiD, Negative Binomial Regression

Instead of allowing each sub-district to have its own, fixed, rate, we are going to take another direction. For our regression equation:

$ln(Rate) = ln(Count) - ln(Population) = \mu + \delta 54*I(54) + \gamma*I(joint) + \beta*I(54)*I(joint) + \epsilon$

we will allow the error $\epsilon$ to have a more general distribution, Negative Binomial in this case. A Negative Binomial distribution is actually a mixture of Poisson distributions but with the underling Poisson rate itself now random, chosen from a Gamma distribution. (See my working paper at https://papers.ssrn.com/abstract=3262234 and references there for more details.)

```{r}
# Negative Binomial with single "Lambeth effect" 
nb1single <- glm.nb(deaths ~ supplier * year 
	+ offset(log(pop1851)), data=regdata) 
summary(nb1single)
```

One thing we want to immediately note is the Residual Deviance is only `r round(nb1single$deviance,1)` which is well below the 5% right-tail quantile of `r round(qchisq(.95,52),1)`, meaning that such a value would not be unusual for Negative Binomial counts. (In fact the right-tail probability is  `r round(1-pchisq(nb1single$deviance,52),3)`.)

```{r}
xfamily <- preperrdata(nb1single,"single")  # this function modifies global data

plot3(x1849,x1854,"SouthwarkVauxhall",paste("First-12 Southwark-only ",xfamily," 1849vs1854 "))
plot3(x1849,x1854,"SouthwarkVauxhall_Lambeth",paste("Next-16 Jointly-Supplied ",xfamily," 1849vs1854 "))
```

The graphs show exactly what is hapenning: the error bars are wide and capture all except perhaps 3 of our observed values - close to what we would expect for 95% confidence bands. 


