make.leslie.matrix: Make Leslie Matrix

Description Usage Arguments Details Value Vignettes Author(s) References See Also Examples

View source: R/pop_reconstruction_functions.R

Description

Constructs the Leslie Matrix needed for cohort component projection.

Usage

1
make.leslie.matrix(pop, surv, fert, srb = 1.05, age.int = 5, label.dims = FALSE)

Arguments

pop

Population count at baseline.

surv

Survivorship probabilities: the probability of reaching the age at the start of the interval. The first row should be nL0/(n*l0). The last row is survival for age.int years in the open interval.

fert

Matrix of age specific fertility rates NOT yet mulitplied by age.int.

srb

Sex ratio at birth (matrix or scalar).

age.int

Width of the age intervals; needed for correct interpretation of survival probabilities and fertility rates.

label.dims

Should row and column names be set? Aesthetic.

Details

This function is used in the calculation of the average annual net number of migrants. See the vignette burkina-faso-females for an example of its use.

Value

A Leslie matrix as a matrix object.

Vignettes

burkina-faso-females

Author(s)

Mark C. Wheldon

References

Preston, S. H., Heuveline, P. and Guillot, M. (2001) Demography, chapter 6. Malden, MA: Blackwell.

See Also

popRecon.ccmp.female, net.number.migrants

Examples

1
2
3
4
5
6
7
example(popRecon.ccmp.female)

(Lk <- make.leslie.matrix(pop = pop.input.mat[,1]
                       ,surv = burkina.faso.females$survival.proportions[,1]
                       ,fert = burkina.faso.females$fertility.rates[,1]
                       ,srb = 1.05
                       ,age.int = 5))

Example output

Loading required package: coda

ppRc..> data(burkina_faso_females)

ppRc..> (pop.input.mat <-
ppRc..+     popRecon.ccmp.female(pop=burkina.faso.females$baseline.pop.counts
ppRc..+                       ,surv=burkina.faso.females$survival.proportions
ppRc..+                       ,fert=burkina.faso.females$fertility.rates
ppRc..+                       ,mig=burkina.faso.females$migration.proportions
ppRc..+                       ))
        [,1]       [,2]       [,3]       [,4]       [,5]       [,6]       [,7]
 [1,] 386000 496963.688 553279.776 605122.263 687150.308 795208.536 914376.040
 [2,] 292000 338995.727 444396.528 500213.483 552487.573 632644.932 735566.439
 [3,] 260000 283642.516 330447.405 434412.243 490189.352 542556.373 622294.440
 [4,] 244000 246278.270 270115.663 316003.052 417493.093 470378.768 521239.005
 [5,] 207000 221576.949 224450.169 247736.574 291322.622 387113.613 435658.724
 [6,] 175000 186062.343 200439.586 203773.553 226440.844 267704.942 357791.216
 [7,] 153000 156791.159 167916.942 181898.908 185506.394 207461.542 246427.789
 [8,] 135000 136059.686 140456.826 151475.723 164992.858 168764.539 189907.129
 [9,] 117000 118338.831 120232.254 125079.907 135905.215 148891.568 152765.468
[10,]  98000 100304.139 102441.076 105028.794 110220.189 120751.178 133131.487
[11,]  78000  81282.772  84201.624  86990.038  90142.111  95552.187 105667.976
[12,]  60000  63137.000  66810.877  70264.782  73614.763  77258.457  82846.087
[13,]  43000  46416.989  49774.054  53708.538  57529.826  61306.324  65380.083
[14,]  29000  29954.307  33064.417  36336.814  40137.388  43957.286  47879.074
[15,]  17000  17193.530  18217.493  20722.535  23444.345  26639.236  30047.166
[16,]   8000   7730.870   8046.969   8842.486  10420.995  12213.805  14430.576
[17,]   2000   2965.314   3186.973   3494.269   4008.592   4906.748   6111.374
             [,8]        [,9]      [,10]
 [1,] 1033996.879 1158134.105 1369041.17
 [2,]  848330.217  964879.639 1088715.23
 [3,]  724519.032  836697.861  952860.73
 [4,]  599148.299  698673.579  846073.20
 [5,]  483615.125  557494.775  719894.38
 [6,]  402180.255  447437.234  572001.86
 [7,]  331145.222  371710.173  458905.67
 [8,]  226610.907  306195.173  379925.83
 [9,]  173019.647  207467.392  309642.33
[10,]  137082.748  156368.113  208006.24
[11,]  117356.990  121365.361  154298.67
[12,]   92546.531  103651.454  114066.08
[13,]   71116.143   80367.432   90879.78
[14,]   52091.893   57560.954   65876.04
[15,]   33648.268   37389.225   41985.79
[16,]   16905.919   19459.431   22044.43
[17,]    7694.051    9487.132   11332.85
           [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]
 [1,] 0.0000000 0.0000000 0.2090608 0.5400452 0.6110685 0.5131988 0.3952854
 [2,] 0.8782273 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
 [3,] 0.0000000 0.9713785 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
 [4,] 0.0000000 0.0000000 0.9730318 0.0000000 0.0000000 0.0000000 0.0000000
 [5,] 0.0000000 0.0000000 0.0000000 0.9577709 0.0000000 0.0000000 0.0000000
 [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.9481755 0.0000000 0.0000000
 [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.9460075 0.0000000
 [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.9393766
 [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[14,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
           [,8]      [,9]      [,10]    [,11]     [,12]     [,13]     [,14]
 [1,] 0.2440665 0.1012326 0.01816255 0.000000 0.0000000 0.0000000 0.0000000
 [2,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
 [3,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
 [4,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
 [5,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
 [6,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
 [7,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
 [8,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
 [9,] 0.9258789 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
[10,] 0.0000000 0.9052283 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
[11,] 0.0000000 0.0000000 0.87537666 0.000000 0.0000000 0.0000000 0.0000000
[12,] 0.0000000 0.0000000 0.00000000 0.832338 0.0000000 0.0000000 0.0000000
[13,] 0.0000000 0.0000000 0.00000000 0.000000 0.7736165 0.0000000 0.0000000
[14,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.6966118 0.0000000
[15,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.5928803
[16,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
[17,] 0.0000000 0.0000000 0.00000000 0.000000 0.0000000 0.0000000 0.0000000
          [,15]     [,16]     [,17]
 [1,] 0.0000000 0.0000000 0.0000000
 [2,] 0.0000000 0.0000000 0.0000000
 [3,] 0.0000000 0.0000000 0.0000000
 [4,] 0.0000000 0.0000000 0.0000000
 [5,] 0.0000000 0.0000000 0.0000000
 [6,] 0.0000000 0.0000000 0.0000000
 [7,] 0.0000000 0.0000000 0.0000000
 [8,] 0.0000000 0.0000000 0.0000000
 [9,] 0.0000000 0.0000000 0.0000000
[10,] 0.0000000 0.0000000 0.0000000
[11,] 0.0000000 0.0000000 0.0000000
[12,] 0.0000000 0.0000000 0.0000000
[13,] 0.0000000 0.0000000 0.0000000
[14,] 0.0000000 0.0000000 0.0000000
[15,] 0.0000000 0.0000000 0.0000000
[16,] 0.4547571 0.0000000 0.0000000
[17,] 0.0000000 0.3181678 0.2099861

popReconstruct documentation built on Dec. 1, 2019, 1:27 a.m.