# ConfirmConvergence: Check R hat criterion In BayesianFROC: FROC Analysis by Bayesian Approaches

## Description

Calculates the maximum and the minimal values of R hat over all parameters. In addition, it returns a loginal R object whether R hat is good (`TRUE`) or bad (`FALSE`).

## Usage

 `1` ```ConfirmConvergence(StanS4class, summary = TRUE, digits = 2) ```

## Arguments

 `StanS4class` An S4 object of the class `stanfit`. No need that it is the S4 class ` stanfitExtended`. `summary` Logical: `TRUE` of `FALSE`. Whether to print the verbose summary. If `TRUE` then verbose summary is printed in the R console. If `FALSE`, the output is minimal. I regret, this variable name should be verbose. `digits` A positive integer, indicating digits for R hat statistics.

## Details

Evaluates convergence criterion based on only the R hat statistics for a fitted model object. Revised Nov 23.

## Value

Logical: `TRUE` of `FALSE`. If model converges ( all R hat are closed to 1) then it is `TRUE`, and if not( some R hat is far from 1), then `FALSE`.

## References

Gelman A. & Rubin, D.B. (1992). Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, Volume 7, Number 4, 457-472.

`check_rhat()`, which is made by Betanalpha.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76``` ```## Not run: #================The first example====================================== #((Primitive way)). #1) Build the data for a singler reader and a single modality case. dat <- list(c=c(3,2,1), #Confidence level h=c(97,32,31), #Number of hits for each confidence level f=c(1,14,74), #Number of false alarms for each confidence level NL=259, #Number of lesions NI=57, #Number of images C=3) #Number of confidence level # where, c denotes Confidence level, # h denotes number of Hits for each confidence level, # f denotes number of False alarms for each confidence level, # NL denotes Number of Lesions, # NI denotes Number of Images, #2) Fit the FROC model. #Since the above dataset "dat" are single reader and single modality, #the following function fit the non hierarchical model. fit <- BayesianFROC::fit_Bayesian_FROC(dat,ite=1111) # Where, the variable "ite" specifies the iteration of MCMC samplings. # Larger iteration is better. #3.1) Confirm whether our estimates converge. ConfirmConvergence(fit) # By the above R script, # the diagnosis of convergence will be printed in the R (R-studio) console. # The diagnosis is based on only the R hat statistic. # It also return the logical vector indicating whether or not the MCMC converge, # if MCMC converges, then the return value is TRUE and if not, then FALSE. # This logical return value is used in this package development # and the user should not be interested. # The following was useful for programming. #3.2) The return value is TRUE or FALSE. x <- ConfirmConvergence(fit) #3.3) If you do not want to print the results in the R (Studio) console, then x <- ConfirmConvergence(fit,summary=FALSE) # 2019.05.21 Revised. # 2019.12.02 Revised. ## End(Not run)# dontrun ```