Description Usage Arguments Value Examples
For now this is just a wrapper that calls the DICE plotFitCDCPercentILI.ggplot2
function
Plot the results of an a coupled or uncoupled DICE run. For each of the fit regions we plot the % ILI of the region along with
our predictions for it based on randomly selected results from the history of the MCMC chain of each region. Using the predictions
for the fit regions we then show the results for the model region as a weighted sum of the fit regions. The last panel
shows our prediction for the model region using a direct fit to the model data. The function also writes a binary RData file with
all the profile predictions for the model and fit regions. Note that in the case of a coupled run the fit regions are never individually
optimized. It is their weighted sum that is optimized, with the weights given by the relative population of each fit region.
1 2 3 |
rtn |
A 1D numeric array with the best in-direct prediction to the model region |
profile |
A 3D numeric array holding random predictions for each of the fit regions based on the history of their MCMC chains. |
model_rtn |
A 1D numeric array with the best direct prediction to the model region |
model_profile |
A 2D numeric array with randomly chosen predicted profiles obtained by fitting the model region directly. |
mydata |
A dataframe with all the data available for this |
ireal |
Integer - the MCMC chain number |
run.list |
A list with various run parameters |
idevice |
Integer - the index of the device in the device array. Default is 1 - make only one format of plot results |
Returns err = 0 if successful
1 2 | plotFitCDCPercentILI{ rtn = rtn, profile = profile, model_rtn = model_rtn, model_profile = model_profile,
mydata = mydata, ireal = ireal, device = device, idevice = 1}
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.