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Showing /Users/thomasvladeck/Git/bh_pro/app/views/proposals/_cover_sheet.xlsx.axlsx where line #6 raised:

No such file or directory - /uploads/account/logo/1/me_climbing_mountains.jpg
Extracted source (around line #6):
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irb(main):012:0> account.logo.url
=> "/uploads/account/logo/1/me_climbing_mountains.jpg"

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- method: energy_cost_savings
  display: Energy Cost Savings
  tip: This is the tooltip

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    tooltips.keys.each do |key|
      define_method(key) { tooltip tooltips[key][:display], t[key][:tip] }
    end  

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mydata <- d
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
  for (i in 2:15) wss[i] <- sum(kmeans(mydata,
                                       centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
     ylab="Within groups sum of squares")

tvladeck

# NBD with t distributed beta

beta.nbd.density <- function(x, r, alpha, a, b)
{
  # params:
  #   x: count we are evaluating density at
  #   r: nbd r parameter
  #   alpha: nbd alpha parameter
  #   a: beta alpha parameter
  #   b: beta beta parameter

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beta.nbd.ll <- function(x, n_x, r, alpha, a, b)
{
  # params:
  #   x: vector of counts
  #   n_x: number of observations for each count
  #   r, alpha: nbd params
  #   a, b: beta params
  # output:
  #   the log-likelihood of the model
  probs <- sapply(x, function(y) beta.nbd.density(y, r, alpha, a, b))

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# Betas #

samples <- coda.samples(jags, c('beta_age',
                                'beta_percent_white',
                                'beta_uninsured',
                                'beta_unemployed',
                                'beta_percent_degree',
                                'beta_income',
                                'beta_health'), 10000)

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model {

  for(i in 1:n_obs){
    
    hrc_votes[i] ~ dbin(p[i], n_votes[i])
    p[i] ~ dbeta(alpha[i], beta[i])
    
    # reparameterization of the beta distribution taken from this site
    # http://bit.ly/2i880Oj
    

tvladeck

indexation <- function(A)
{
  A / ((rowSums(A)/sum(rowSums(A))) %*% 
         t(colSums(A)/sum(colSums(A))) * 
         sum(A))
}