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I frequently want to test for differences between animal size frequency distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov two sample test (provided in R as the function ks.test in package ctest). The KS test is for continuous variables and this obviously includes length, weight etc. However, limitations in measuring (e.g length to the nearest cm/mm, weight to the nearest

...stion and it's only relation to R is that I probably need R after I understand what to do. Both models are delta y_t = Beta + epslion and suppose I have a null hypothesis and alternative hypothesis H_0 : delta y_t = zero + epsilon epsilon is normal ( 0, sigmazero^2 ) H_1 delta y_t = beta + epsilon epsilon is normal ( sigmabeta^2 ) ------------------------------------------------------------------------ ------------------------------------------------------------------------ ------ so, i calculate the MLE's under the null and the alterna...

Dear All I am trying to replicate a numerical application (not computed on R) from an article. Using, ks.test() I computed the exact D value shown in the article but the p-values I obtain are quite different from the one shown in the article. The tests are performed on a sample of 37 values (please see "[0] DATA" below) for truncated Exponential, Pareto and truncated LogNormal

Hi all, this is OT, but maybe someone can give me a clue. I've got data from eye tracker experiments (750 data points). These figures show how the data lock like |** * ***** |*** ****** | ** **** | * * | | * * | | * |-------------- |--------------- Y axes display velocity, x axes display time, * are the data points.

Hi everyone, I have devise 1.5.4 working with rails 3.0.20 and ruby 1.8.7 . I have extended the destroy action from the RegistrationController, to soft delete users instead of really deleting them from the database. def destroy # raise resouce.inspect # this is just to see if the test hits the action resource.soft_delete set_flash_message :notice, :destroyed sign_out resource

Buenos dias para todos, Estoy trabajando en una aplicación que involucra análisis de clusters. Básicamente el objetivo es determinar a qué cluster pertenece cada observación de una matriz de datos "mydata" y luego generar muestras aleatorias de los mismos datos para determinar la proporción de veces que cada observación es clasificada en el cluster k. Este tipo de análisis es muy común