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n = 1000; | |
coins = rep(0, n) | |
for (i in 2:n) { | |
whatToFlip = seq(from = i, to = n, by = i) | |
coins[whatToFlip] = coins[whatToFlip] + 1 | |
} | |
sqrt(which(!as.logical(coins%%2))) | |
# [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 |
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require(lavaan) | |
bifactor <- " | |
general.factor =~ Easy_Reservation + Preferred_Seats + Flight_Options + Ticket_Prices | |
+ Seat_Comfort + Seat_Roominess + Overhead_Storage | |
+ Clean_Aircraft + Courtesy + Friendliness + Helpfulness + Service | |
ticketing =~ Easy_Reservation + Preferred_Seats + Flight_Options + Ticket_Prices | |
aircraft =~ Seat_Comfort + Seat_Roominess + Overhead_Storage + Clean_Aircraft | |
service =~ Courtesy + Friendliness + Helpfulness + Service" | |
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library(MASS) | |
library(ggplot2) | |
# move n around to alter sample size | |
# move r around to alter effect size | |
n = 1000; r = .5 | |
desiredCovMatrix = matrix(c(1,r,r, 1) ,nrow=2, ncol=2); | |
count = 1000 # number of replications | |
out = rep(NA,count) # array to store the results | |
for (i in 1:count) { |
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#!/usr/bin/ruby | |
VERSION = 1.3 | |
require 'net/https' | |
require 'rubygems' | |
require 'json' | |
require 'cgi' | |
# set to true to force inline links | |
inline = false |
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umxSaturated <- function(m1, evaluate = T) { | |
# Use case | |
# m1_sat = umxSaturated(m1) | |
# summary(m1, SaturatedLikelihood=m1_sat$SaturatedLikelihood, IndependenceLikelihood=m1_sat$IndependenceLikelihood) | |
manifests = m1@manifestVars | |
nVar = length(manifests) | |
theData = m1@data@observed | |
dataMeans = colMeans(theData) | |
meansLabels = paste("mean", 1:nVar, sep="") | |
loadingsLabels = paste("F", 1:nVar, "loading", sep="") |
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setMethod("imxVerifyModel", "MxRAMModel", | |
function(model) { | |
if ((length(model$A) == 0) || | |
(length(model$S) == 0) || | |
(length(model$F) == 0)) { | |
msg <- paste("The RAM model", omxQuotes(model@name), | |
"does not contain any paths.", | |
" Are you just starting out? you need to add paths like", |
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if (identical(options[["Standard Errors"]], "Yes") && | |
identical(options[["Calculate Hessian"]], "No")) { | |
msg <- paste('The "Standard Errors" option is enabled and', | |
'the "Calculate Hessian" option is disabled. Generating', | |
'standard errors requires the Hessian calculation. Please', | |
'disable standard errors or enable the Hessian calculation.\n', | |
'You can do this with\n', | |
'model <- mxOption(model, "Standard Errors", "No")\n', | |
'or\n', | |
'model <- mxOption(model, "Calculate Hessian", "Yes")' |
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persons = rnorm(100, 20,10) | |
nYearsToRun = 500 | |
births = deaths = pop = rep(NA,nYearsToRun) | |
for (y in 1:nYearsToRun) { | |
populationSize = length(persons) | |
persons = persons+1 # age everybody | |
# death | |
persons <- persons[persons < rnorm(populationSize, 77, 15)] |
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# Above are some results from the a matrix of a | |
# trivariate cholesky with the above diag cells filled in | |
A = matrix(nrow = 3, byrow = T, c(.71, -.28, .15, -.28, .61, -.38, .15, -.38, .000001)) | |
[,1] [,2] [,3] | |
[1,] 0.71 -0.28 1.5e-01 | |
[2,] -0.28 0.61 -3.8e-01 | |
[3,] 0.15 -0.38 1.0e-06 | |
# Yeah... so that's wrong. that's the lower a matrix. |
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x <- data.frame(matrix(ncol = 2, byrow = T, c( | |
-0.7, 0.2, | |
2.1, 2.7, | |
1.7, 2.3, | |
1.4, 0.8, | |
1.9, 2.0, | |
1.8, 1.0, | |
0.4, -0.4, | |
1.1, 0.3, | |
0.9, 0.4, |
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