This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(fitdistrplus) | |
dplyr::select -> select | |
.Machine$double.eps -> eps | |
1975 -> N # number of responses | |
tibble(lower = c(0, 0.25, 0.5, 0.75), # lower bins | |
upper = c(0.25 + eps, 0.5 + eps, 0.75 + eps, 1), # upper bins | |
pct = c(0.32, 0.51, 0.15, 1 - sum(0.32, 0.51, 0.15)), # response shares | |
n = floor(pct * N)) %>% # implied responses + eps |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(rvest) | |
library(gamlss) | |
library(brms) | |
library(tidybayes) | |
select <- dplyr::select | |
#################################################################################### | |
# Model the market capitalizations of members of the S&P 500. | |
#################################################################################### |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(rvest) | |
library(gamlss) | |
library(brms) | |
library(tidybayes) | |
select <- dplyr::select | |
#################################################################################### | |
# Model the market capitalizations of members of the S&P 500. | |
#################################################################################### |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(quantmod) | |
library(gamlss) | |
select <- dplyr::select | |
posix <- function(x) { as.POSIXct(x, origin = "1970-01-01") } | |
## "Fat tails" | |
## Here we compare the residuals from the normal and t distributional models. | |
## Notice standardized error is worse in the normal model. This happens | |
## because returns are leptokurtic (large surprises should be expected, there's risk in stock returns), |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(ggthemes) | |
expand.grid( | |
risk = seq(0.1/5e3, 1/5e3, 1e-05), # average daily risk e.g. - 1,000 infected per day in Alabama / 5,000,000 AL population | |
units_of_exposure = seq_len(31) # days of exposure (up to 31 days) | |
) %>% as_tibble() %>% | |
mutate(total_risk = map2_dbl(risk, units_of_exposure, ~ 1 - (1 - .x)^(.y)), | |
total_odds = 1/total_risk, | |
risk_threshold = case_when(total_odds <= 5e2 ~ "Worse than 1 in 500", | |
total_odds <= 1e3 ~ "Worse than 1 in 1k chance", |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(brms) | |
library(tidybayes) | |
1e7 -> N # obs | |
1 -> J # groups of members | |
10 -> K # members | |
0.5 -> base_p # base rate, this is logistic regression | |
# sample member coefficients |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import sklearn | |
from sklearn import naive_bayes | |
import pandas as pd | |
import numpy as np | |
d = pd.read_csv("data.csv") | |
y = d.iloc[:, 1] | |
X = d.iloc[:,list(range(2, d.shape[1]))] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(brms) | |
library(tidybayes) | |
3e4 -> N | |
40 -> K | |
rnorm(K) -> group_coefs | |
tibble(K = factor(rep(paste0("group_", seq_len(K)), length.out = N))) %>% | |
mutate(coef = rep(group_coefs, N/40)) %>% |