tfr.dl.coverage {bayesTFR} | R Documentation |
The function computes coverage, i.e. the ratio of observed data fitted within the given probability intervals of the predictive posterior distribution of the double logistic function, as well as the root mean square error and mean absolute error of the simulation.
tfr.dl.coverage(sim.dir, pi = c(80, 90, 95), burnin = 2000, verbose = TRUE)
sim.dir |
Directory with the MCMC simulation results. If a prediction and its corresponding thinned MCMCs are available in the simulation directory, those are taken for assessing the goodness of fit. |
pi |
Probability interval. It can be a single number or an array. |
burnin |
Burnin. Only relevant if |
verbose |
Logical switching log messages on and off. |
List with the following components:
total.coverage |
Vector of the coverage, one element per probability interval. For each |
time.coverage |
Matrix corresponding to the coverage computed per time period. (Rows correspond to probability intervals, columns correspond to time.) It is derived like |
country.coverage |
Matrix corresponding to the coverage computed per country. (Rows correspond to probability intervals, columns correspond to countries.) It is derived like |
total.rmse |
Root mean square error as √{(1/n∑(x-m)^2)} where x are observed data points, m is the mean of the posterior distribution and n is the number of data points. Here the sum is taken over all countries and historical time periods. |
time.rmse |
Like |
country.rmse |
Like |
total.mae |
Mean absolute error as 1/n∑|x-m| where x are observed data points, m is the median of the posterior distribution and n is the number of data points. Here the sum is taken over all countries and historical time periods. |
time.mae |
Like |
country.mae |
Like |
pred.cdf |
TxC matrix (with T being the number of time periods and C being the number of countries), containing the predictive CDF of the observation, i.e. the quantile of each data point within the predictive posterior distribution. |
n |
0-1 TxC matrix indicating if the corresponding data point was included in the goodness of fit computation. Zeros indicate missing historical values. |
To see the fit visually per country, use DLcurve.plot(..., predictive.distr=TRUE,...)
.
Hana Sevcikova
## Not run: sim.dir <- file.path(find.package("bayesTFR"), "ex-data", "bayesTFR.output") tfr <- get.tfr.mcmc(sim.dir) # Note that this simulation is a toy example and thus has not converged. gof <- tfr.dl.coverage(sim.dir) gof$time.coverage DLcurve.plot(tfr, country=608, predictive.distr=TRUE, pi=c(80, 90, 95)) ## End(Not run)