HP.pred: Prediction of Heligman-Pollard model.
In strandCet: Estimation of Biological Parameters from Stranded Cetaceans

Description Usage Arguments Details Value References See Also Examples

Predicts Heligman-Pollard model from Heligman-Pollard's parameters.

1	HP.pred(life, HPout, M = "med", age = seq(0, 29, 0.1), rm = 0)

`life`	A life table created with life.tab or a dataframe with a vector of ages in the first column.
`HPout`	A model object created with HP.mod with the Heligman-Pollard estimated params.
`M`	Defines the statistic to predict. Median by default (med), Low CI (low) or High CI (high)
`age`	A vector containing the ages at which each age interval begins. See Details
`rm`	The number of age classes that want to be removed from optimization.

Mx is returned only if number of ages required for prediction is equal to the number of ages in the life table.

A dataframe with seven columns:

`age`	Age at the beginning of the interval.
`Mx`	Number of observed deaths at age x.
`qx.tot`	Total probability of death between ages x and x + n.
`qx.nat`	Natural probability of death between ages x and x + n.
`qx.young`	Young probability of death between ages x and x + n.
`qx.risk`	Probability of death due to an externl risk between ages x and x + n.
`qx.adult`	Adult or senescent probability of death between ages x and x + n.

Heligman, L. and Pollard, J.H. (1980). The Age Pattern of Mortality. Journal of the Institute of Actuaries 107:49<e2><80><93>80.

Sharrow, D.J., Clark, S.J., Collinson, M.A., Kahn, K. and Tollman, S.M. (2013). The Age Pattern of Increases in Mortality Affected by HIV: Bayesian Fit of the Heligman-Pollard Model to Data from the Agincourt HDSS Field Site in Rural Northeast South Africa. Demogr. Res. 29, 1039<e2><80><93>1096.

HP.mod

lifeN <- life.tab(cetaceans)

modSi <- Si.mod(data = cetaceans, rm = 2,
                par = c(0.3159462,  0.1860541, -1.2802880,  1.1733226,  0.0170314))

dataSi <- Si.pred(data = cetaceans, Sout = modSi, rm = 2)

priors <- data.frame(priors.lo = c(0,0.5,0,0,0,0,6,0,1),
                     priors.hi = c(0.1,1,1,0.15,0.15,50,10,0.01,1.5))

q0 <- HP.priors(pri.lo = priors$priors.lo,
                pri.hi = priors$priors.hi,
                theta.dim = 9)

modHP <- HP.mod(prior = q0, lifeTab = lifeN, rm = 2, K = 10, d = 10, B = 10, CI = 90)

HP.pred(life = lifeN, HPout = modHP, age = seq(0,29,1), rm = 2)