- Fri 20 September 2013
- notebook
- Jason K. Moore
- #notebook, #google calendar, #rss, #sample size, #plone

- Test out being perturbed on the treadmill while walking
- Go over Tarun's final PRs and decide what's in and out for the GSoC deadline
- Meet with Tarun
- Run the JS tests in pydy-viz
- Contact Mike about senior design projects
- Help Sandy with visualizing the inertial compensation results
- Tidy up PyDy
- Embed the google calendar on the website
- Do FERPA course, due Sept 20
- Took photos of high school kids that visited the lab

## Google Calendar RSS Feeds

I use this feed for the rss portlet in plone:

The parameters show the latest upcoming events, but Plone rss feed portlet displays the date of create of the event instead of the start time of the event, which I need to figure out. Might need to be a custom portlet that copies the RSS portlet, but changes the displayed date.

## Sample Size Determination

Obinna has a model that computes joint angles given accelerometer data. Ton has a model that computes joint angles given marker data. We believe the marker data model is pretty accurate, but the joint angles from accelerometers is a new idea. Obinna would like to know how many samples (subjects, speeds, cadences, of walking) are needed to ensure that his model is equivalent to Ton's model.

Obinna picks some statistics like peak joint angles in a gait cycle and computes them with both methods. If he plots the peak joint angle computed from each trial with joint angle on both the x an y axes and where the x axes is the joint angle computed from Ton's method and the y axes computed from Obinna's model then you'd expect to see a line form with slope = 1 and y-intercept = 0.

y = m x + b

y = obinna's output x = ton's output

hat{m} and hat{b} are the estiamted slope and intercept given the actual data.

Thus we'd like to make some hypotheses.

H_0 (null hypothesis): m_o = m_t and b_o = b_t H_1 (alternative hypothesis): m_o neq m_t and b_o neq b_t

If null hyptothesis is rejected when it is acutally true:

alpha = P(type I error) = P(reject H_o|H_0 is true)

and if the null hypothesis is not rejected when it is false then

beta = P(type II error) = P(fail to reject H_0|H_0 is false)

power is often talked about instead of beta:

Power = 1 - beta = P(reject H_0|H_0 is false)

If we had only one variable in the hypotheses we could compute the power for a t-test and try to estimate sample size there.

For example the R pwr package gives:

> pwr.t.test(d=0.8, sig.level=.05, power=.95) Two-sample t test power calculation n = 41.59414 d = 0.8 sig.level = 0.05 power = 0.95 alternative = two.sided NOTE: n is number in *each* group

where d is set for large effects (Cohen recommends 0.8).

On page 275 of Discovering Statistics using R, Field 2012 the authors show a plot of sample size as a function of predictors in the regression equation for different effect sizes. For 2 predictors and large effect, it recommends at least 40 samples. This graph comes form Miles and Shelvin 2001. But in the preceding text he gives two rules of thumb from Green 1991:

50 + 8k if you only want to test only the overall fit of your regression

and

104 + k if you want to test the individual predictors

For 2 predictors we need then either 66 or 106 samples.

https://stat.ethz.ch/pipermail/r-help/2006-November/117902.html

Applying Regression and Correlation: A Guide for Students and Researchers Miles and Shelvin 2001

Green 1991 How many subjects does it take to a regression analysis?

R pwr package examples:

http://www.statmethods.net/stats/power.html

Related to what we are after, i.e. testing hypotheses of the relationship in the two models with simple linear model:

http://www.weibull.com/DOEWeb/hypothesis_tests_in_simple_linear_regression.htm