Description Usage Arguments Examples
This function merges the results of multiple experts' distributions using a numeric linear pool approach. It samples from the distributions of all experts individually many times (e.g. 100,000), then calculates the overall quantiles and medians from the samples. The function returns a matrix representing the lower, median and upper limits of the pooled distribution. This can then be fed into fitModCM() to generate a modified Connor-Mosimann distribution representing the overall spread of the experts' beliefs.
1 | mergeMultiplemCM(NrSamples, RawData)
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NrSamples |
Vector of length 1. Sets the number of samples to draw from each expert's mCM distribution |
RawData |
Data frame of mCM parameters. Must have six columns: expert, outcome, a, b, L, U of modified Connor-Mosimann distribution. Note last row of parameters will always be zero. Columns 1:6 of the output from function fitMultipleCM() are in the correct format for this. |
1 2 3 4 5 6 7 8 9 | NrSamples <- 100000
RawData <- data.frame(expert = as.character(c(1,1,1,2,2,2)),
Outcome = as.factor(c("Remission","Progression","Dead",
"Remission","Progression","Dead")),
a = as.numeric(c(6.0786, 0.2245, 0, 6.9214, 4.5259, 0)),
b = as.numeric(c(7.5900, 0.5866, 0, 1.7187, 3.1892, 0)),
L = as.numeric(c(0.3400, 0.4839, 0, 0.0152, 0.2390, 0)),
U = as.numeric(c(0.7917, 0.9213, 0, 0.7106, 0.9970, 0)))
mergeMultiplemCM(NrSamples,RawData)
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