# Short Introduction to BAMP

#### Volker Schmid

#### 2022-05-04

## Data example

BAMP includes a data example.

    data(apc)
    plot(cases[,1],type="l",ylim=range(cases), ylab="cases", xlab="year", main="cases per age group")
    for (i in 2:8)lines(cases[,i], col=i)

For simulating APC data, see vignette(“simulation”, package=“bamp”).

## APC model with random walk first order prior

    model1 <- bamp(cases, population, age="rw1", period="rw1", cohort="rw1",
                  periods_per_agegroup = 5)

bamp() automatically performs a check for MCMC convergence using Gelman and Rubin’s convergence diagnostic. We can manually check the convergence again:

    checkConvergence(model1)

    ## [1] TRUE

Now we have a look at the model results. This includes estimates of smoothing parameters and deviance and DIC:

    print(model1)

    ##
    ##  Model:
    ## age (rw1)  - period (rw1)  - cohort (rw1) model
    ## Deviance:     231.17
    ## pD:            36.75
    ## DIC:          267.91
    ##
    ##
    ##  Hyper parameters:                 5%           50%          95%
    ## age                              0.346        0.911        1.921
    ## period                          66.679      204.020      637.133
    ## cohort                          34.832       59.757       96.270
    ##
    ##
    ## Markov Chains convergence checked succesfully using Gelman's R (potential scale reduction factor).

We can plot the main APC effects using point-wise quantiles:

    plot(model1)

More quantiles are possible:

    plot(model1, quantiles = c(0.025,0.1,0.5,0.9,0.975))

For other models see vignette(“modeling”,package=“bamp”).

## Prediction

Using the prior assumption of a random walk for the period and cohort effect, one can predict cases for upcoming years.

    pred <- predict_apc(object=model1, periods=3)

    m<-max(pred$pr[2,,])
    plot(pred$pr[2,,8],type="l", ylab="probability", xlab="year", ylim=c(0,m))
    for (i in 7:1)
      lines(pred$pr[2,,i],col=8-i)
    legend(1,m,col=8:1,legend=paste("Age group",1:8),lwd=2,cex=0.6)
    lines(c(10.5,10.5),c(0,1),lty=2)

More details see vignette(“prediction”,package=“bamp”).