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# mrecos / Put_a_prior_on_it-Blog_post

Code, markdown, and data for blog post on fitting beta distribution to song lyrics data

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# Puttin'a Prior on It: Stan Beta Estmation of Song Lyrics

MDH 10/6/2016

## Introduction

### Estimating a Beta Distribution with Stan HMC

#### Repository for data, analysis (markdown), and R code

This is a repo to hold the data and code for my blog post based on Julia Silge's Singing the Bayesian Beginner Blues. The post by Silge is a really fun and interesting analysis of the rate at which song lyrics from Billboard Hot-100 songs (1958 to present) mention U.S. States by name. In her second post on the subject, Silge used a beta distribution to model this rate. After reading that post, I was inspired to learn more about her method and to follow up on this model with my interests in Bayesian modeling with Stan, a probabilistic programming language. I hoped to repeat Silge's findings while learning how to code her model in Stan. This was also a fun opportunity to work on more `dplyr` data munging techniques. The result was a good learning experience and perhaps a few additional insights into the distribution of state name mentions.

If you are interested in this, please see my blog post and Julia's posts. Note: the code and analytical process here is based on Silge's workflow, but alterations and additions were made to focus on different aspects. Any errors, sloppiness, or misunderstandings are my own. Also, I am sure there are other ways to model these data and while I'd be very happy to hear about them, this post is intended to explore this particular method. Please contact me if you see errors and just to say hi. [@md_harris](https://twitter.com/Md_Harris)

### Differences from Silge's Analysis

1. Is zero inflated to include states not mentioned in lyrics
2. Incorporates mentions of cities with >= 100k population aggregated to their state and compares to analysis without city counts
3. Utilizes Hamiltonian Monte Carlo via `rstan` to estimate parameters, propagated uncertainty, and predict new values.

### Why Go Bayes?

Aside from being an interesting data set due to cultural relevance, it is also a really great example of for why one may want to use a Bayesian approach. As described in Silge's post, the simple calculation of a mention rate is unsatisfying because it does not consider the magnitude of each states population. We would prefer a method that incorporates this information and can make a new estimate based on the number of mentions and population each state that therefore regularizes the estimates based on all of the information available. This include prior information and empirical data.

In my humble opinion this is a really key point; the reason that Silge utilized the beta distribution to model the data (as opposed to simply describe) is because she wanted to show the uncertainty for each estimate based on a states mentions and population. The problem this solves is a state that has a very high rate, but few mentions because it has a low population. This is in opposition to a state that is mentioned many times, but has a lower rate because of a large population. This is the classic "batting average" from illuminated by David Robinson here.

What the Bayesian approach does is incorporate prior knowledge (e.g. priors) and the uncertainty that comes with balancing values based on only a few data points against values based on many data points; which are likely more reliable. By this, the expectation for a states rate of mention is regularized or drawn towards a central global expectation. States with fewer data points are regularized more than more stable states with lots of data. Finally, the Credible Interval captures the uncertainty that propagates through this system. Regularization and uncertainty estimation are key reasons why someone may want to use Bayesian methods.

Let's get started!

#### Packages

```library("ggplot2")
library("grid")
library("dplyr")
library("tools")
library("tidyverse")
library("acs")
library("reshape2")
library("tidytext")
library("scales")
library("ggplot2")
library("ggrepel")
library("broom")
library("fitdistrplus")
library("rstan")
library("knitr")
library("rmarkdown")```

## Get Data

1. Use `acs` package to get state population data from 2014

1. requires an API from the Census
2. Grab song lyrics from Kaylin Walker's repo

3. Load `./data/cities_over_100k_pop.csv`, names of cities with >= 100k population

1. UNCOMMENT that line `download.file(lyrics_url, save_lyrics_loc)` to run
2. you may have to adjust the path
4. Load state name abbreviation for simplifying the plots later

```#api.key.install("YOUR KEY HERE!")
## population data
stategeo <- geo.make(state = "*")
popfetch <- acs.fetch(geography = stategeo,
endyear = 2014,
span = 5,
table.number = "B01003",
col.names = "pretty")
## song lyrics
lyrics_url <- "https://raw.githubusercontent.com/walkerkq/musiclyrics/master/billboard_lyrics_1964-2015.csv"
save_lyrics_loc <- "~/Documents/R_Local/Put_a_prior_on_it-Blog_post/data/billboard_lyrics_1964-2015.csv"
``````Parsed with column specification:
cols(
Rank = col_integer(),
Song = col_character(),
Artist = col_character(),
Year = col_integer(),
Lyrics = col_character(),
Source = col_integer()
)
``````
```cities_dat_loc <- "~/Documents/R_Local/Put_a_prior_on_it-Blog_post/data/cities_over_100k_pop.csv"
state_abbrev_loc <- "~/Documents/R_Local/Put_a_prior_on_it-Blog_post/data/state_abbrev.csv"
``````Parsed with column specification:
cols(
name = col_character(),
abbrev = col_character()
)
``````

## Join, mutate, and munge the data

### Prepare data for extracting lyrics

The `tidytext` package uses `unnest_tokens` to do the heavy lifting here. Thanks David Robinson and Julia Silge for this package!

```# extract desired info from acs data
pop_df <- tbl_df(melt(estimate(popfetch))) %>%
mutate(name = as.character(Var1),
state_name = tolower(Var1),
pop2014 = value) %>%
dplyr::select(name, state_name, pop2014) %>%
filter(state_name != "puerto rico") %>%
left_join(state_abbrev)```
``````Joining, by = "name"
``````
```# clean in city names
mutate(city = gsub("\x96", "-", city),
city_name = tolower(city),
state_name = tolower(state))```
``````Parsed with column specification:
cols(
rank = col_integer(),
city = col_character(),
state = col_character(),
pop_2014 = col_integer()
)
``````
```# extract and tidy lyrics from songs data
tidy_lyrics <- bind_rows(song_lyrics %>%
unnest_tokens(lyric, Lyrics),
song_lyrics %>%
unnest_tokens(lyric, Lyrics,
token = "ngrams", n = 2))```

### Join lyrics to geography

Here the lyrics are joined to the state names to find the songs that contain those names. An `inner_join` will return only the songs and states that match. Here I use a `right_join` to retain all 50 state names even if they are not mentioned by any lyrics. The `zeros` data set is then filtered for a data set that only has matching songs. Also, I use `distinct(Song, Artist, lyric, ...)` to aggregate the data set to only a single row for each song even if it mentions a song many times. This choice if distinct criteria also allows for songs that mentioned many different states to remain, but collapses an edge case where a song was on the charts in different years.

```# join and retain songs whether or not they are in the lyrics
tidy_lyrics_state_zeros <- right_join(tidy_lyrics, pop_df,
by = c("lyric" = "state_name")) %>%
distinct(Song, Artist, lyric, .keep_all = TRUE) %>%
mutate(cnt = ifelse(is.na(Source), 0, 1)) %>%
filter(lyric != "district of columbia") %>%
dplyr::rename(state_name = lyric)

tidy_lyrics_state <- filter(tidy_lyrics_state_zeros, cnt > 0)

## count the states up
zero_rate <- 0.0000001 # beta hates zeros

# group, summarise, and calculate rate per 100k population
state_counts_zeros <- tidy_lyrics_state_zeros %>%
group_by(state_name) %>%
dplyr::summarise(n = sum(cnt))  %>% # sum(cnt)
left_join(pop_df, by = c("state_name" = "state_name")) %>%
mutate(rate = (n / (pop2014 / 100000)) + zero_rate) %>%
arrange(desc(n))

# create another data set with
state_counts <- filter(state_counts_zeros, rate > zero_rate)
print(dplyr::select(state_counts, state_name, n ,pop2014, rate))```
``````# A tibble: 33 × 4
state_name     n  pop2014       rate
<chr> <dbl>    <dbl>      <dbl>
1     new york    61 19594330 0.31131465
2   california    34 38066920 0.08931649
3      georgia    22  9907756 0.22204836
4    tennessee    14  6451365 0.21700844
5        texas    13 26092033 0.04982374
6      alabama    12  4817678 0.24908275
7  mississippi    10  2984345 0.33508200
8     kentucky     7  4383272 0.15969815
9       hawaii     6  1392704 0.43081670
10    illinois     6 12868747 0.04662469
# ... with 23 more rows
``````

#### Do the same as above, but for cities

Note the use of `inner_join` here because I am not interested in cities that are not mentioned.

```### Cities
## join cities together - inner_join b/c I don't care about cities with zero mentions (right_join otherwise)
tidy_lyrics_city <- inner_join(tidy_lyrics, cities,
by = c("lyric" = "city_name")) %>%
distinct(Song, Artist, lyric, .keep_all = TRUE) %>%
filter(!city %in% c("Surprise", "Hollywood",
"District of Columbia", "Jackson",
"Independence")) %>%
mutate(cnt = ifelse(is.na(Source), 0, 1)) %>%
dplyr::rename(city_name = lyric)

# count cities mentions. No need for a rate; not of use now
city_counts <- tidy_lyrics_city %>%
group_by(city_name) %>%
dplyr::summarise(n = sum(cnt)) %>%
arrange(desc(n))
print(city_counts)```
``````# A tibble: 84 × 2
city_name     n
<chr> <dbl>
1     new york    61
2        miami    20
3  new orleans    19
4      chicago    17
5      atlanta    14
6      memphis    14
7      houston    11
8       orange     9
9      detroit     7
10      dallas     6
# ... with 74 more rows
``````
```# count of states that host the cities mentioned
city_state_counts <- tidy_lyrics_city %>%
group_by(state_name) %>%
dplyr::summarise(n = sum(cnt)) %>%
arrange(desc(n))
print(city_state_counts)```
``````# A tibble: 33 × 2
state_name     n
<chr> <dbl>
1    new york    66
2  california    38
3       texas    33
4   tennessee    22
5     florida    21
6   louisiana    21
7     georgia    18
8    illinois    17
9    michigan     9
# ... with 23 more rows
``````

#### Join city counts to state counts and compute new rate

```state_city_counts_zeros <- left_join(state_counts_zeros,
city_state_counts,
by = "state_name") %>%
dplyr::rename(n_state = n.x, n_city = n.y) %>%
mutate(n_city = ifelse(is.na(n_city), 0, n_city),
n_city_state = n_state + n_city,
city_state_rate = (n_city_state / (pop2014 / 100000)) + zero_rate)

# same as above, but no states with zero mentioned by city or state
state_city_counts <- filter(state_city_counts_zeros, n_city_state > zero_rate)```

## Fun facts...

### Some interesting things about these data

#### 1. States mentioned by their cities, but not the state itself

```# Boston = most mentioned city without its state
all_the_cities <- filter(state_city_counts, !state_name %in% state_counts\$state_name) %>%
dplyr::select(name) %>%
mutate_if(is.factor, as.character) %>%
left_join(tidy_lyrics_city, by = c("name" = "state")) %>%
dplyr::select(name, Song, Artist, city)

kable(all_the_cities)```
name Song Artist city
Massachusetts dirty water the standells Boston
Massachusetts please come to boston dave loggins Boston
Massachusetts hey nineteen steely dan Boston
Massachusetts the heart of rock roll huey lewis and the news Boston
Massachusetts dazzey duks duice Boston
Missouri georgy girl the seekers Springfield
Missouri living in america james brown Kansas City
New Mexico bring em out ti Albuquerque
North Carolina hush hush sweet charlotte patti page Charlotte
North Carolina georgy girl the seekers Charlotte
North Carolina raise the roof luke featuring no good but so good Fayetteville
North Carolina wagon wheel darius rucker Raleigh
Rhode Island meant to live switchfoot Providence
South Carolina the beat goes on sonny cher Charleston
South Carolina no no song ringo starr Columbia
South Carolina forgot about dre dr dre featuring eminem Charleston

#### 2. What song mentions the most unique state names?

```n_states_mentioned <- tidy_lyrics_state %>%
group_by(Artist, Song) %>%
dplyr::summarise(n = n()) %>%
arrange(desc(n)) %>%
ungroup() %>%
top_n(5)```
``````Selecting by n
``````
`kable(n_states_mentioned)`
Artist Song n
red hot chili peppers dani california 7
duice dazzey duks 5
jason aldean fly over states 4
nelly country grammar hot shit 4
arlo guthrie city of new orleans 3
crazy elephant gimme gimme good lovin 3
ludacris featuring bobby valentino pimpin all over the world 3
```# Top song is...
filter(tidy_lyrics_state, Song == as.character(n_states_mentioned[1,1])) %>%
dplyr::select(Song, Artist, Year, state_name)```
``````# A tibble: 0 × 4
# ... with 4 variables: Song <chr>, Artist <chr>, Year <int>,
#   state_name <chr>
``````

#### 3. What song mentions a single state the most number of times?

```most_repeated_in_song <- right_join(tidy_lyrics, pop_df,
by = c("lyric" = "state_name")) %>%
group_by(Song, Artist, lyric) %>%
dplyr::summarise(n = n()) %>%
arrange(desc(n)) %>%
ungroup() %>%
filter(row_number() <= 10)

kable(most_repeated_in_song)```
Song Artist lyric n
empire state of mind jayz and alicia keys new york 34
new york groove ace frehley new york 20
arthurs theme best that you can do christopher cross new york 16
arizona mark lindsay arizona 13
all summer long kid rock alabama 12
mississippi girl faith hill mississippi 10
california girls david lee roth california 9
california girls the beach boys california 9
midnight train to georgia gladys knight the pips georgia 9
california dreamin the mamas the papas california 8

## Parameter estimation

### Estimate the parameters of the beta distribution in 3 ways:

1. Maximum-likelihood Estimation (MLE) with `fitdistr` package
2. Limited memory BFGS with the `rstan` package
3. Hamiltonian Monte Carlo (HMC) with `Stan` and `rstan` package

#### MLE with `fitdistr`

Here I use `fitdist` to compare the parameter estimates and Log likelihood of three different distributions, beta, exponential, and log-normal. This method is very fast, but not as accurate as the full Bayesian estimate. Here the beta has the best fit. You could explore different distributions that make sense for the data, but since Silge used beta, I am also.

```## beta boot for comparison
beta_fit <- fitdist(state_counts_zeros\$rate,"beta") # best logLik
summary(beta_fit)```
``````Fitting of the distribution ' beta ' by maximum likelihood
Parameters :
estimate Std. Error
shape1 0.1617485 0.02453031
shape2 2.2298802 0.71328680
Loglikelihood:  212.6346   AIC:  -421.2692   BIC:  -417.4452
Correlation matrix:
shape1  shape2
shape1 1.00000 0.38538
shape2 0.38538 1.00000
``````
```exp_fit <- fitdist(state_counts_zeros\$rate,"exp")
summary(exp_fit)```
``````Fitting of the distribution ' exp ' by maximum likelihood
Parameters :
estimate Std. Error
rate  12.9374   1.829625
Loglikelihood:  78.00613   AIC:  -154.0123   BIC:  -152.1002
``````
```lnorm_fit <- fitdist(state_counts_zeros\$rate,"lnorm")
summary(lnorm_fit)```
``````Fitting of the distribution ' lnorm ' by maximum likelihood
Parameters :
estimate Std. Error
meanlog -7.170250  0.9150467
sdlog    6.470357  0.6470357
Loglikelihood:  194.204   AIC:  -384.408   BIC:  -380.584
Correlation matrix:
meanlog         sdlog
meanlog  1.000000e+00 -4.206895e-09
sdlog   -4.206895e-09  1.000000e+00
``````

#### Optimizing in `rstan`

This is the first introduction to a Stan model contained in the character string `opt_chr1`. Typically the Stan model is saved in a separate file and called, but I kept it all in-house for this analysis.

```opt_chr1 <- "
data {
int<lower=0> N;
real x[N];
}
parameters {
real<lower = 0> alpha0;
real<lower = 0> beta0;
}
model {
alpha0 ~ normal(0, 1);
beta0 ~ normal(0, 10);
//target += beta_lpdf(x | alpha0, beta0); // same as below
x ~ beta(alpha0, beta0);
}
"
# initialize parameter values (based on knowledge or fitdist results)
init_list <- list(alpha0 = 0.1, beta0 = 1)
# compile model (~ 10 to 15 seconds)
opt_mod1 <- stan_model(model_code = opt_chr1)
# optimize data given model
opt1 <- optimizing(object = opt_mod1, as_vector = FALSE,
data = list(x = state_counts_zeros\$rate,
N = length(state_counts_zeros\$rate)),
hessian = TRUE,
draws = 2500)```

#### Parameters estimates and log likelihood

```# view results
opt1\$par ```
``````\$alpha0
 0.1614814

\$beta0
 2.217158
``````
`opt1\$value #compare to LogLikelihood of summary(beta_fit)`
`````` 212.5968
``````

plot distribution of parameters to see dispersal and correlation

```ggplot(data.frame(opt1\$theta_tilde), aes(x = alpha0, y = beta0)) +
geom_point(color = "skyblue3", alpha = 0.35) +
geom_density2d(aes(colour =..level..)) +
scale_x_continuous(breaks = seq(0,0.3,0.025)) +
scale_y_continuous(breaks = seq(0,7,0.5)) +
theme_bw() +
labs(x = "alpha0",
y = "beta0",
title = "Distribution of Alpha and Beta Shape Parameters",
subtitle = "2500 samples from MLE optimized beta model posterior") +
theme(
panel.border = element_rect(colour = "gray90"),
axis.text.x = element_text(size = 8, family = "Trebuchet MS"),
axis.text.y = element_text(size = 8, family = "Trebuchet MS"),
axis.title = element_text(size = 10, family = "Trebuchet MS"),
plot.caption = element_text(size = 7, hjust=0, margin=margin(t=5),
family = "Trebuchet MS"),
plot.title=element_text(family="TrebuchetMS-Bold"),
legend.position = "none",
panel.grid.minor = element_blank()
)``` ## Hamiltonian Monte Carlo (HMC)

#### Estimation and prediction

As the third method of estimation, we use a full Stan model and include a `generated quantities` block to make predictions of new rates for each state. In this block, new parameters for each state are drawn and calculated into expected mention rates based on the observed number of mentions and population. Estimating this within the model allows for the full integration over uncertainty. Another approach is to take the `alpha` and `beta` parameters distributions, sample those outside the model and calculate the state estimates.

```model_string1_pred <- "
data {
int<lower=1> N;
vector[N] x;
int<lower=1> M;
vector[M] new_success;
vector[M] new_attempts;
}
parameters {
real<lower=0> alpha0;
real<lower=0> beta0;
}
model {
alpha0 ~ normal(0, 1);
beta0 ~ normal(0, 10);
x ~ beta(alpha0, beta0);
} generated quantities {
vector[M] x_tilde;
for (n in 1:M)
x_tilde[n] = beta_rng((new_success[n] + alpha0),
(new_attempts[n] - new_success[n]  + beta0));
}
"```

### Run the Stan model

```new_success = state_counts_zeros\$n
new_attempts = (state_counts_zeros\$pop2014)/100000
model_dat1_pred <- list(x = state_counts_zeros\$rate,
N = length(state_counts_zeros\$rate),
new_success = new_success,
new_attempts = new_attempts,
M = length(new_success))
fit1_pred <- stan(model_code = model_string1_pred,
data = model_dat1_pred,
iter = 10000, chains = 4,  warmup=2500)```

#### Extract results

Show the 95% Credible Interval for `alpha`, `beta`, each state, and the log posterior `lp__`

```fit1_pred_summary <- data.frame(summary(fit1_pred)[["summary"]]) %>%
rownames_to_column() %>%
mutate(Parameter = c("alpha0", "beta0",
as.character(state_counts_zeros\$name), "lp__")) %>%
dplyr::select(Parameter, mean, sd, X2.5., X97.5., n_eff, Rhat) %>%
dplyr::rename(Mean = mean,
SD = sd,
`2.5%` = X2.5.,
`97.5%` = X97.5.)
kable(fit1_pred_summary, digits = 3)```
Parameter Mean SD 2.5% 97.5% n_eff Rhat
alpha0 0.167 0.024 0.124 0.217 21096.54 1
beta0 2.490 0.718 1.303 4.110 22051.02 1
New York 0.308 0.033 0.245 0.373 29857.22 1
California 0.089 0.014 0.063 0.119 29855.18 1
Georgia 0.218 0.040 0.144 0.302 29343.73 1
Tennessee 0.211 0.049 0.123 0.315 29874.16 1
Texas 0.050 0.013 0.027 0.079 30000.00 1
Alabama 0.240 0.059 0.134 0.365 30000.00 1
Mississippi 0.313 0.080 0.167 0.478 28211.54 1
Kentucky 0.154 0.052 0.066 0.270 29731.69 1
Hawaii 0.372 0.117 0.162 0.610 29625.26 1
Illinois 0.047 0.018 0.018 0.089 30000.00 1
Indiana 0.091 0.035 0.035 0.169 28416.92 1
Virginia 0.073 0.028 0.028 0.137 30000.00 1
Louisiana 0.106 0.044 0.037 0.207 30000.00 1
Arizona 0.061 0.029 0.018 0.127 29321.09 1
Florida 0.021 0.010 0.006 0.045 28980.35 1
Ohio 0.035 0.017 0.010 0.075 29935.66 1
Oklahoma 0.102 0.047 0.031 0.210 29909.97 1
Colorado 0.058 0.031 0.013 0.134 29557.50 1
Maine 0.199 0.097 0.050 0.418 30000.00 1
Nebraska 0.150 0.075 0.035 0.322 29598.46 1
Washington 0.044 0.024 0.010 0.104 30000.00 1
Kansas 0.068 0.044 0.010 0.176 29175.99 1
Montana 0.172 0.103 0.026 0.415 29853.08 1
Nevada 0.072 0.046 0.010 0.184 29922.60 1
New Jersey 0.024 0.016 0.003 0.063 30000.00 1
Pennsylvania 0.017 0.011 0.002 0.044 30000.00 1
West Virginia 0.103 0.064 0.015 0.256 30000.00 1
Arkansas 0.036 0.033 0.001 0.124 28833.05 1
Idaho 0.063 0.055 0.003 0.206 29375.92 1
Maryland 0.019 0.017 0.001 0.063 28763.70 1
Michigan 0.012 0.011 0.000 0.040 30000.00 1
Minnesota 0.021 0.019 0.001 0.071 29458.81 1
North Dakota 0.121 0.101 0.006 0.379 29224.08 1
Alaska 0.017 0.039 0.000 0.135 30000.00 1
Connecticut 0.004 0.011 0.000 0.035 29449.01 1
Delaware 0.014 0.033 0.000 0.113 28917.46 1
Iowa 0.005 0.012 0.000 0.041 29729.80 1
Massachusetts 0.002 0.006 0.000 0.020 29606.32 1
Missouri 0.003 0.007 0.000 0.022 29339.44 1
New Hampshire 0.010 0.024 0.000 0.082 30000.00 1
New Mexico 0.007 0.017 0.000 0.056 29956.60 1
North Carolina 0.002 0.004 0.000 0.014 30000.00 1
Oregon 0.004 0.010 0.000 0.032 30000.00 1
Rhode Island 0.012 0.030 0.000 0.101 27519.67 1
South Carolina 0.003 0.008 0.000 0.027 29300.92 1
South Dakota 0.015 0.035 0.000 0.120 30000.00 1
Utah 0.005 0.013 0.000 0.044 30000.00 1
Vermont 0.019 0.043 0.000 0.149 29881.66 1
Wisconsin 0.003 0.007 0.000 0.022 29677.02 1
Wyoming 0.020 0.046 0.000 0.160 28480.49 1
lp__ 210.722 0.954 208.133 211.627 14563.81 1

#### Realtionship of Bayesian to observed estimate

```state_estimates <- rstan::extract(fit1_pred, pars = "x_tilde") %>%
data.frame() %>%
rename_(.dots=setNames(names(.),state_counts_zeros\$state_name)) %>%
gather() %>%
dplyr::rename(state_name = key) %>%
group_by(state_name) %>%
dplyr::summarise(q025 = quantile(value, probs = 0.025),
q5 = quantile(value, probs = 0.5),
q975 = quantile(value, probs = 0.975),
mean = mean(value)) %>%
left_join(.,state_counts_zeros)```

The plot design here is based entirely on Silge's visualization

```### could melt and add q025,q5,q975 by color/shape
### could also predict across range of rates and show areas

ggplot(state_estimates, aes(rate, mean)) +
geom_abline(intercept = 0, slope = 1, color = "gray70", linetype = 2) +
geom_point(size = 4, aes(color = n)) +
geom_text_repel(aes(label = abbrev), stat = "identity",
max.iter = 5000) +
scale_color_gradient(low = "midnightblue", high = "pink",
name="Number\nof songs") +
labs(title = "States in Song Lyrics with Empirical Bayes",
subtitle = "States like Montana and Hawaii (high rates, few mentions) are shifted the most",
x = "Measured rate of mentions per 100k population",
y = "Mean predicted rate per 100k population",
caption = "plot design by @juliasilge") +
theme_minimal(base_family = "Trebuchet MS") +
theme(plot.title=element_text(family="Trebuchet MS"))``` #### Bayesian vs. Observed estimate and 95% CI by state

The plot design here is based entirely on Silge's visualization

```state_estimates %>%
arrange(desc(mean)) %>%
mutate(state_name = factor(name, levels = rev(unique(name)))) %>%
dplyr::select(state_name, 'Measured rate' = rate,
'Bayesian estimate' = mean, q025, q975) %>%
gather(type, rate, `Measured rate`, `Bayesian estimate`) %>%
ggplot(aes(rate, state_name, color = type)) +
geom_errorbarh(aes(xmin = q025, xmax = q975), color = "gray50") +
geom_point(size = 3) +
xlim(0, NA) +
labs(x = "Rate of mentions per 100k population",
y = NULL, title = "Measured Rates, Bayesian Estimates (HMC), and 95% Credible Intervals",
subtitle = "Mention rate for states sorted by descending posterior mean",
caption = "plot design by @juliasilge") +
theme_minimal(base_family = "Trebuchet MS") +
theme(plot.title=element_text(family="Trebuchet MS", face = "bold")) +
theme(legend.title=element_blank())``` ### Below is a repeate of the analysis above, but using that dataset that includes the additional counts of cities mentions: `state_city_counts_zeros`

```new_success_SC = state_city_counts_zeros\$n_city_state
new_attempts_SC = (state_city_counts_zeros\$pop2014)/100000
model_SC_pred <- list(x = state_city_counts_zeros\$city_state_rate,
N = length(state_city_counts_zeros\$city_state_rate),
new_success = new_success_SC,
new_attempts = new_attempts_SC,
M = length(new_success_SC))```

#### Fit same Stan model as above, but new data.

```fit_SC_pred <- stan(model_code = model_string1_pred,
data = model_SC_pred,
iter = 10000, chains = 4,  warmup=2500)```

#### Summarise Stan fit

```fit_SC_pred_summary <- data.frame(summary(fit_SC_pred)[["summary"]]) %>%
rownames_to_column() %>%
mutate(Parameter = c("alpha0", "beta0",
as.character(state_counts_zeros\$name), "lp__")) %>%
dplyr::select(Parameter, mean, sd, X2.5., X97.5., n_eff, Rhat) %>%
dplyr::rename(Mean = mean,
SD = sd,
`2.5%` = X2.5.,
`97.5%` = X97.5.)
kable(fit_SC_pred_summary,  digits = 3)```
Parameter Mean SD 2.5% 97.5% n_eff Rhat
alpha0 0.225 0.033 0.165 0.296 17279.23 1
beta0 1.646 0.406 0.951 2.549 16973.93 1
New York 0.643 0.034 0.575 0.708 29848.10 1
California 0.189 0.020 0.151 0.230 30000.00 1
Georgia 0.398 0.049 0.305 0.496 30000.00 1
Tennessee 0.545 0.061 0.425 0.661 30000.00 1
Texas 0.176 0.023 0.133 0.224 30000.00 1
Alabama 0.344 0.067 0.220 0.479 29206.28 1
Mississippi 0.322 0.081 0.175 0.491 30000.00 1
Kentucky 0.223 0.061 0.116 0.355 29811.61 1
Hawaii 0.394 0.120 0.176 0.638 29736.12 1
Illinois 0.178 0.033 0.117 0.247 30000.00 1
Indiana 0.107 0.038 0.045 0.192 30000.00 1
Virginia 0.098 0.033 0.045 0.171 30000.00 1
Louisiana 0.547 0.072 0.407 0.685 30000.00 1
Arizona 0.122 0.039 0.056 0.209 29910.51 1
Florida 0.129 0.024 0.086 0.181 29215.51 1
Ohio 0.053 0.021 0.020 0.100 30000.00 1
Oklahoma 0.206 0.063 0.098 0.342 30000.00 1
Colorado 0.153 0.049 0.071 0.259 30000.00 1
Maine 0.214 0.102 0.054 0.448 30000.00 1
Nebraska 0.452 0.108 0.246 0.666 29657.57 1
Washington 0.074 0.031 0.026 0.145 29910.37 1
Kansas 0.073 0.046 0.011 0.187 30000.00 1
Montana 0.186 0.109 0.030 0.445 29356.45 1
Nevada 0.245 0.078 0.111 0.411 29610.70 1
New Jersey 0.036 0.019 0.008 0.082 28206.21 1
Pennsylvania 0.063 0.021 0.028 0.112 29625.22 1
West Virginia 0.109 0.068 0.016 0.272 30000.00 1
Arkansas 0.135 0.060 0.040 0.272 29560.75 1
Idaho 0.124 0.075 0.019 0.305 29396.58 1
Maryland 0.070 0.033 0.020 0.146 30000.00 1
Michigan 0.102 0.030 0.051 0.167 29490.48 1
Minnesota 0.040 0.026 0.006 0.104 30000.00 1
North Dakota 0.137 0.109 0.007 0.412 29612.46 1
Alaska 0.025 0.049 0.000 0.174 28267.34 1
Connecticut 0.006 0.012 0.000 0.041 29777.99 1
Delaware 0.020 0.041 0.000 0.147 30000.00 1
Iowa 0.007 0.014 0.000 0.050 30000.00 1
Massachusetts 0.076 0.032 0.026 0.148 29567.38 1
Missouri 0.068 0.032 0.020 0.142 29661.64 1
New Hampshire 0.015 0.030 0.000 0.106 30000.00 1
New Mexico 0.055 0.047 0.003 0.177 29817.16 1
North Carolina 0.043 0.020 0.012 0.090 30000.00 1
Oregon 0.006 0.012 0.000 0.040 29704.75 1
Rhode Island 0.099 0.082 0.005 0.306 29375.50 1
South Carolina 0.066 0.035 0.015 0.149 27237.50 1
South Dakota 0.022 0.044 0.000 0.155 30000.00 1
Utah 0.008 0.015 0.000 0.054 30000.00 1
Vermont 0.027 0.054 0.000 0.191 29276.90 1
Wisconsin 0.004 0.008 0.000 0.028 30000.00 1
Wyoming 0.030 0.059 0.000 0.211 29559.97 1
lp__ 122.002 0.964 119.375 122.919 14550.35 1

#### Prepare fit estimates for plotting

```state_city_estimates <- rstan::extract(fit_SC_pred, pars = "x_tilde") %>%
data.frame() %>%
rename_(.dots=setNames(names(.),state_city_counts_zeros\$state_name)) %>%
gather() %>%
dplyr::rename(state_name = key) %>%
group_by(state_name) %>%
dplyr::summarise(q025 = quantile(value, probs = 0.025),
q5 = quantile(value, probs = 0.5),
q975 = quantile(value, probs = 0.975),
mean = mean(value)) %>%
left_join(.,state_city_counts_zeros)```
``````Joining, by = "state_name"
``````

#### Realtionship of Bayesian to observed state + city estimate

The plot design here is based entirely on Silge's visualization

```### could melt and add q025,q5,q975 by color/shape
### could also predict across range of rates and show areas
ggplot(state_city_estimates, aes(city_state_rate, mean)) +
geom_abline(intercept = 0, slope = 1, color = "gray70", linetype = 2) +
geom_point(size = 4, aes(color = n_city_state)) +
geom_text_repel(aes(label = abbrev), stat = "identity",
max.iter = 5000) +
scale_color_gradient(low = "midnightblue", high = "pink",
name="Number\nof songs") +
labs(title = "States & Cities in Song Lyrics Modeled with Bayes (HMC)",
subtitle = "States like Nebraska and Hawaii (high rates, few mentions) are shifted the most",
x = "Measured rate of mentions per 100k population",
y = "Mean predicted rate per 100k population",
caption = "plot design by @juliasilge") +
theme_minimal(base_family = "Trebuchet MS") +
theme(plot.title=element_text(family="Trebuchet MS"))``` #### Bayesian vs. Observed state + city estimate and 95% CI by state

The plot design here is based entirely on Silge's visualization

```### range estiamtes plot
state_city_estimates %>%
arrange(desc(mean)) %>%
mutate(state_name = factor(name, levels = rev(unique(name)))) %>%
dplyr::select(state_name, 'Measured rate' = city_state_rate,
'Bayesian estimate' = mean, q025, q975) %>%
gather(type, city_state_rate, `Measured rate`, `Bayesian estimate`) %>%
ggplot(aes(city_state_rate, state_name, color = type)) +
geom_errorbarh(aes(xmin = q025, xmax = q975), color = "gray50") +
geom_point(size = 3) +
xlim(0, NA) +
labs(x = "Rate of mentions per 100k population",
y = NULL, title = "Measured Rates, Bayesian Estimates (HMC), and 95% Credible Intervals",
subtitle = "Mention rate for states & cities sorted by descending posterior mean",
caption = "plot design by @juliasilge") +
theme_minimal(base_family = "Trebuchet MS") +
theme(plot.title=element_text(family="Trebuchet MS", face = "bold")) +
theme(legend.title=element_blank())``` ## Model Comparison

There are lots of ways to compare which model is "better", but I am not going to go crazy with it. Log posteriors from the Stan models can be compared, but I do not believe they can be used for inference directly from the form they are reported from the fit. We could also do things like hold-out samples, cross-validations, Leave-One-Out CV, or use information criteria such as WAIC. See Gelman and go down the rabbit hole from there.

Here I take a simple approach and calculate a few metrics based a few things:

• Loss Metrics
• Mean Absolute Error (MAE)
• Root Mean Square Error (RMSE)
• Credible Interval (CI)
• mean width of 95% CI
• Predicted Mentions
• RMSE of expected mentions vs observed mentions
• MAE of the same

These result show that the state counts only model may have the slightest advantage over the states + cities counts, but it really is very small. For me, the choice of model would simply be which fits my purpose better; both are pretty darn good at estimating the number of mentions. the MAE of expected mentions per state is less than 0.2 of a mention; rounded down to no real error at all in that category. The beta distribution accurately described these data

```city_state_error <- state_city_estimates %>%
mutate(rate_error = mean - city_state_rate,
pred_mentions = round(mean * (pop2014/100000),1),
mention_error = n_city_state - pred_mentions,
CI_width = q975 - q025) %>%
dplyr::summarise(RMSE_rate = sqrt(mean(rate_error^2)),
MAE_rate = mean(abs(rate_error)),
mean_CI = mean(CI_width),
RMSE_mentions = sqrt(mean(mention_error^2)),
MAE_mentions = mean(abs(mention_error))) %>%
as.numeric()

state_error <- state_estimates %>%
mutate(rate_error = mean - rate,
pred_mentions = round(mean * (pop2014/100000),1),
mention_error = n - pred_mentions,
CI_width = q975 - q025) %>%
dplyr::summarise(RMSE_rate = sqrt(mean(rate_error^2)),
MAE_rate = mean(abs(rate_error)),
median_CI = median(CI_width),
RMSE_mentions = sqrt(mean(mention_error^2)),
MAE_mentions = mean(abs(mention_error))) %>%
as.numeric()

#print
model_rmse <- data.frame(model = c("States Only", "City and States"),
RMSE_rate =  c(state_error, city_state_error),
MAE_rate =  c(state_error, city_state_error),
Median_CI =  c(state_error, city_state_error),
RMSE_mentions =  c(state_error, city_state_error),
MAE_mentions =  c(state_error, city_state_error))
kable(model_rmse, digits = 3)```
model RMSE_rate MAE_rate Median_CI RMSE_mentions MAE_mentions
States Only 0.013 0.007 0.111 0.246 0.174
City and States 0.013 0.008 0.182 0.416 0.236

## Create R scipt

From this markdown, I use the `purl()` function in the `rmarkdown` package to extract the code. This is run just after the `*.Rmd` file is created. Otherwise, it trys to read an already open file and fails to `knit()`

```rmd_loc <- "/Users/mattharris/Documents/R_Local/Put_a_prior_on_it-Blog_post/"

### Environment

`sessionInfo()`
``````R version 3.3.1 (2016-06-21)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)

locale:
 en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 tools     grid      stats     graphics  grDevices utils     datasets
 methods   base

other attached packages:
 rmarkdown_1.0      knitr_1.14         rstan_2.11.1
 MASS_7.3-45        broom_0.4.1        ggrepel_0.6.2
 scales_0.4.0.9003  tidytext_0.1.1     reshape2_1.4.1
 acs_2.0            XML_3.98-1.4       plyr_1.8.4
 tidyr_0.6.0        tibble_1.2         tidyverse_1.0.0
 dplyr_0.5.0        ggplot2_2.1.0.9001

loaded via a namespace (and not attached):
 Rcpp_0.12.7       highr_0.6         formatR_1.4
 bitops_1.0-6      tokenizers_0.1.4  digest_0.6.10
 evaluate_0.9      gtable_0.2.0      nlme_3.1-128
 lattice_0.20-33   psych_1.6.9       Matrix_1.2-6
 DBI_0.5           parallel_3.3.1    yaml_2.1.13
 gridExtra_2.2.1   janeaustenr_0.1.2 stats4_3.3.1
 inline_0.3.14     R6_2.1.3          foreign_0.8-66
 magrittr_1.5      codetools_0.2-14  splines_3.3.1
 SnowballC_0.5.1   htmltools_0.3.5   mnormt_1.5-4
 assertthat_0.1    colorspace_1.2-6  labeling_0.3
 stringi_1.1.1     RCurl_1.95-4.8    lazyeval_0.2.0
 munsell_0.4.3
``````

Code, markdown, and data for blog post on fitting beta distribution to song lyrics data

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