This is a motion chart I created using the life and life domain satisfaction data from the 2003 longitudinal study of Australian young people. The satisfaction measures cover the ages 16 to 22. [cf]Satisfaction Chart[/cf]
This is a motion chart I created using the life and life domain satisfaction data from the 2003 longitudinal study of Australian young people. The satisfaction measures cover the ages 16 to 22. [cf]Satisfaction Chart[/cf]
#For use when comparing the fit of nested models with complex data, # (e.g. TYPE = COMPLEX is mplus) # The Scaled Difference Chi-square Test Statistic can be found at #http://preprints.stat.ucla.edu/260/chisquare.pdf # This function provides scaled differences tests based on … Continue reading →
library (MASS) covar<-mvrnorm(250, c(0, 0), matrix(c(1, 0.00, 0.00, 1), 2, 2)) mydata<-data.frame(covar) names(mydata)<-c("sat", "mot") mydata$admin<- mydata$sat + mydata$mot mydata$admin2<- ifelse (mydata$admin >=quantile(mydata$admin, .85), "pass", "fail") library(ggplot2) qplot(sat,mot, data = mydata, color = admin2) Created by Pretty R at inside-R.org
I am sure it is out there but for my record keeping purposes and for anyone interested here is a quick function to find Stirling’s approximation of all possible permutations Stirling<- function (n)((2*pi*n)^.5)*(n/exp(1))^n Created by Pretty R at inside-R.org
R does not give a Pseudo R square value and neither does Mplus. As such, here is a real quick R function to calculate McFadden’s and Adjusted McFadden’s Pseudo R square value: #Pesudo R square #x = full model … Continue reading →
I have reworked the graphing and summarizing functions I wrote for the matchit package to be cleaner and to provide more information. Currently I have functions for a sorted by size summary table of the post matching mean differences: meandifftable<- … Continue reading →
Have been attending a seminar on propensity score matching this week, when I was asked if I could write a function to reduce the complexity of the matchit package output. I produced two functions. First I wrote a function to … Continue reading →
I am a fan of K-means approaches to clustering data particularly when you have a theoretical reason to expect a certain number of clusters and you have a large data set. However, I think ploting the cluster means can be … Continue reading →
There maybe times when you need scores corrected for measurement error and latent factor loading. In these instances it can be beneficial to use composite scores derived from one factor congeneric models. While easy they are time consuming to calculate by hand … Continue reading →
Calculating Cohen’s D is a pain particularly when you have data which is not independent (e.g. is longitudinal). So I have written a function which take away the hassle. It is in early form but produces a normal Cohen’s D and … Continue reading →
