alexgleith

adrianolszewski

Despite the widespread and nonsensical claim, that "logistic regression is not a regression", it constitutes one of the key regression and hypothesis testing tools used in the experimental research (like clinical trials).

Let me show you how the logistic regression (with a few extensions) can be used to test hypotheses about fractions (%) of successes, repacling the classic "test for proportions". Namely, it can replicate the results of:

1. the Wald's (normal approximation) z test for 2 proportions with non-pooled standard errors (common in clinical trials) via LS-means on the prediction scale or AME (average marginal effect)
2. the Rao's score (normal appr.) z test for 2 proportions with pooled standard errors (just what the prop.test() does in R)
3. the z test for multiple (2+) proportions
4. ANOVA-like (joint) test for multiple caterogical predictors (n-way ANOVA). Also (n-way) ANCOVA if you employ numerical covariates.
5. [the **Cochran-Mantel-Haenszel

MartGro

snakers4

# https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
#Import libraries:
import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import cross_validation, metrics   #Additional scklearn functions
from sklearn.grid_search import GridSearchCV   #Perforing grid search

import matplotlib.pylab as plt

grofit

public class Account
{
	Guid Id {get;set;}
	string Name {get;set;}
}

dbetebenner

require(splines)
require(quantreg)

### Create some data

x <- rnorm(100, mean=50, sd=10)
noise <- rnorm(100, sd=5)
y <- x + noise