Dpananos

library(tidyverse)

# Genuine confounding example.  Sex confounds relationship between drug and death

set.seed(0)
n <- 1000000
is_male <- rbinom(n, 1, 0.5)
drug <- rbinom(n, 1, 0.6 + 0.3*is_male)
y <- rbinom(n, 1, 0.4 - 0.1*drug + 0.4*is_male)
d <- tibble(drug, is_male, y)

Dpananos

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#

Dpananos

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import multinomial
import cmdstanpy



def simulate(n_transitions):
    A = 1000
    B = 100

Dpananos

library(tidyverse)
library(marginaleffects)

g <- LETTERS[1:4]
N <- rpois(length(g), 100000)
theta <- c(0.1, 0.12, 0.14, 0.08)
y <- rbinom(length(g), N, theta)


d <- tibble(g, N, y)

Dpananos

library(survival)
library(tidyverse)
library(broom)
library(marginaleffects)


# Make a toy model
gr <- sample(letters[1:2], size = 10000, replace=T)
g <- as.numeric(gr=='a')
etime <- rexp(length(gr), rate = (5+1*g)/10)

Dpananos

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pymc as pm
import arviz as az


N = 50_000
treatment_conversions = np.array([541, 557, 559, 556, 530, 532, 516, 532, 528, 544, 519, 552])
control_conversions = np.array([496, 524, 486, 500, 516, 475, 507, 475, 490, 506, 512, 489])

Dpananos

import numpy as np
from statsmodels.nonparametric.smoothers_lowess import lowess

from sklearn.datasets import load_breast_cancer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, RepeatedKFold, GridSearchCV, cross_val_score
from sklearn.metrics import make_scorer, brier_score_loss
from sklearn.utils import resample

Dpananos

# LRT
data = c(27,24,3,14,9,14,4,6,7,8)
m = matrix(data, nrow = 2)

theta_null = colSums(m)/sum(m)
theta_1 = m[1,]/sum(m[1,])
theta_2 = m[2,]/sum(m[2,])

L0 = dmultinom(colSums(m), prob = theta_null, log=T)
L1 = dmultinom(m[1,], prob = theta_1, log=T) + dmultinom(m[2,], prob = theta_2, log=T)

Dpananos

library(tidyverse)

set.seed(7)
N = 1000
x = rnorm(N)
p = plogis(0.2*x - 0.8)
y = rbinom(N, 1, p)

tibble(x, y) %>%  
  mutate(z = cut_number(x, 5)) %>% 

Dpananos

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


x = np.random.normal(size = 10)
y = 2*x + 1 + np.random.normal(0, 0.3, size=x.size)

grid = np.linspace(-12, 20, 25)
b0, b1 = np.meshgrid(grid, grid)