Blog Archives

Optimizing a multi-modal function with a two step anneal method.

February 24, 2013
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Optimizing a multi-modal function with a two step anneal method.

I have been working on a reliable optimization method for this crazy function. f.egg<-function(x,y){ (2+cos(x)+cos(y))/(100+x^2+y^2) }I noticed that if I had a large variance in the random normal generator, the optimizer would jump all over t...

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Optimizing a multi-modal function with a two step anneal method.

February 24, 2013
By
Optimizing a multi-modal function with a two step anneal method.

I have been working on a reliable optimization method for this crazy function. f.egg<-function(x,y){ (2+cos(x)+cos(y))/(100+x^2+y^2) }I noticed that if I had a large variance in the random normal generator, the optimizer would jump all over t...

Read more »

Revisiting Cronbach 1951 via Simulation with Shiny

January 9, 2013
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Revisiting Cronbach 1951 via Simulation with Shiny

At the time of the creation of this blog, Cronbach’s 1951 piece on coefficient alpha has 18,132 citations according to google scholar.  The main use of coefficient alpha is to assess internal consistency reliability of a test or survey.   Although it may have been forgotten, the proof Cronbach demonstrated established that coefficient alpha is the mean of all split...

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Revisiting Cronbach 1951 via Simulation with Shiny

January 9, 2013
By
Revisiting Cronbach 1951 via Simulation with Shiny

At the time of the creation of this blog, Cronbach’s 1951 piece on coefficient alpha has 18,132 citations according to google scholar.  The main use of coefficient alpha is to assess internal consistency reliability of a test or survey.   Although it may have been forgotten, the proof Cronbach demonstrated established that coefficient alpha is the mean of all split...

Read more »

Creating a Covariance Matrix from Scratch

January 7, 2013
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I have been conducting several simulations that use a covariance matrix.  I needed to expand the code that I found in the psych package to have more than 2 latent variables (the code probably allows it but I didn’t figure it out).  I ran across Joreskog’s 1971 paper and realized that I could use the confirmatory factor analysis...

Read more »

Creating a Covariance Matrix from Scratch

January 7, 2013
By

I have been conducting several simulations that use a covariance matrix.  I needed to expand the code that I found in the psych package to have more than 2 latent variables (the code probably allows it but I didn’t figure it out).  I ran across Joreskog’s 1971 paper and realized that I could use the confirmatory factor analysis...

Read more »

Internal Consistency Reliability in R with Lambda4

January 6, 2013
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For the last year I have been developing a package “Lambda4” to improve internal consistency reliability estimation.  In the package’s conception my primary concern centered on H.G. Osburn’s maximized lambda4 estimator.  Despite a very thorough search I could not find a stats package that could utilized Osburn’s method.  I wanted to learn R and so I jumped in and...

Read more »

Internal Consistency Reliability in R with Lambda4

January 6, 2013
By

For the last year I have been developing a package “Lambda4” to improve internal consistency reliability estimation.  In the package’s conception my primary concern centered on H.G. Osburn’s maximized lambda4 estimator.  Despite a very thorough search I could not find a stats package that could utilized Osburn’s method.  I wanted to learn R and so I jumped in and...

Read more »

Demonstrating Confidence Intervals with Shiny

January 6, 2013
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Demonstrating Confidence Intervals with Shiny

For the introductory statistic student confidence intervals can seem a daunting concept to grasp.  Quite simply put it is an interval that we have a certain measure of confidence that the population parameter falls into.  The 95% confidence is the most common value chosen in my academic circle.  Nevertheless, many others may be viable as well as long as...

Read more »

Demonstrating Confidence Intervals with Shiny

January 6, 2013
By
Demonstrating Confidence Intervals with Shiny

For the introductory statistic student confidence intervals can seem a daunting concept to grasp.  Quite simply put it is an interval that we have a certain measure of confidence that the population parameter falls into.  The 95% confidence is the most common value chosen in my academic circle.  Nevertheless, many others may be viable as well as long as...

Read more »