larsoner

"""Hotelling T2 example.

Following the notation in
https://en.wikipedia.org/wiki/Hotelling%27s_T-squared_distribution#Two-sample_statistic
"""

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

agramfort

JoshCheek

Why I chose Fish over Bash for students

I'm currently the lead instructor at Code Platoon and an instructor/developer at the Turing School of Software and Design.

I've been advocating the Fish shell and when the choice is up to me, I choose that for my students. Enough people ask about the decision, particularly in relation to the preinstalled Bash shell, that I figured it's worth laying out my reasoning.

TL;DR

tmalsburg

Predict vs simulate in lme4

For this investigation we are going to use the sleepdata data set from the lme4 package. Here is the head of the data frame:

lmullen

SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md))
HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md))

all : $(SLIDES) $(HANDOUTS)

%.md.slides.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -o $@

%.md.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@ 

casallas

lmm

Assumptions

Code based on: http://pages.stat.wisc.edu/~fyang/stat572/disc9.R

Homoscedasticity

# checking that e_i all have the same variance:
gsame_var <- function(flmer){

even4void

library(lme4)
data(sleepstudy)

## Fit individual regression lines for each subject
dfrm <- coef(lmList(Reaction ~ Days | Subject, sleepstudy))

## Estimate parameters of a random intercept and random intercept and slope model
m1 <- lmer(Reaction ~ Days + (1 | Subject), data=sleepstudy)
m2 <- lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy)

mastbaum

'''Example of a custom ReST directive in Python docutils'''
import docutils.core
from docutils.nodes import TextElement, Inline
from docutils.parsers.rst import Directive, directives
from docutils.writers.html4css1 import Writer, HTMLTranslator

class foo(Inline, TextElement):
    '''This node class is a no-op -- just a fun way to define some parameters.
    There are lots of base classes to choose from in `docutils.nodes`.

avram

#!/usr/bin/python
# -*- coding: utf8 -*-

# We'll use the pyzotero project for server access
# See packages.python.org/Pyzotero/
from pyzotero import zotero

import sys

# Python's built-in CSV support is pretty nice