| cdffeedback | R Documentation | 
Report the median and 100(1-alpha)% credible interval for point on the population CDF
cdffeedback(
  medianfit,
  precisionfit,
  quantiles = c(0.05, 0.95),
  vals = NA,
  alpha = 0.05,
  median.dist = "best",
  precision.dist = "gamma",
  n.rep = 10000
)
| medianfit | The output of a fitdist command following elicitation of the expert's beliefs about the population median. | 
| precisionfit | The output of a fitprecision command following elicitation of the expert's beliefs about the population precision. | 
| quantiles | A vector of quantiles  | 
| vals | A vector of population values  | 
| alpha | The size of the 100(1-alpha)% credible interval | 
| median.dist | The fitted distribution for the population median. Can be one of  | 
| precision.dist | The fitted distribution for the population precision. Can either be  | 
| n.rep | The number of randomly sampled CDFs used to estimated the median and credible interval. | 
Denote the uncertain population CDF by
P(X \le x | \mu, \sigma^2),
where \mu
is the uncertain population median and \sigma^(-2) is the uncertain population precision.
Feedback can be reported in the form of the median and 100(1-alpha)% credible interval for
(a) an uncertain probability P(X \le x | \mu, \sigma^2), where x is a specified 
population value and (b) an uncertain quantile x_q defined by P(X \le x_q | \mu, \sigma^2) = q, where q is a specified 
population probability.
Fitted median and 100(1-alpha)% credible interval for population quantiles and probabilities.
| $quantiles | Each row gives the fitted median 
and 100(1-alpha)% credible interval for each uncertain population quantile 
specified in  | 
| $probs | Each row gives the fitted median 
and 100(1-alpha)% credible interval for each uncertain population probability 
specified in  | 
## Not run: 
prfit <- fitprecision(interval = c(60, 70), propvals = c(0.2, 0.4), trans = "log")
medianfit <- fitdist(vals = c(50, 60, 70), probs = c(0.05, 0.5,  0.95), lower = 0)
cdffeedback(medianfit, prfit, quantiles = c(0.01, 0.99),
            vals = c(65, 75), alpha = 0.05, n.rep = 10000)
 
## End(Not run)  
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