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Computes the limits of the felling date range

Source: R/sw_interval.R
sw_interval.Rd

This function computes the probability density function (PDF) and the highest posterior density interval (hdi) of the felling date range based on the observed number of sapwood rings, their chronological dating, and the selected sapwood data and model.

Usage

sw_interval(
  n_sapwood = NA,
  last = 1,
  hdi = FALSE,
  cred_mass = 0.954,
  sw_data = "Hollstein_1980",
  densfun = "lognormal",
  plot = FALSE
)

Arguments

n_sapwood

A numeric. The number of observed sapwood rings.

last

A numeric. The calendar year assigned to the outermost sapwood ring (optional, default = 0).

hdi

A logical. If TRUE, the lower and upper limit of the highest density interval (credible interval) are returned. When FALSE, a matrix is returned with scaled p-values for each number of observed sapwood rings.

cred_mass

A scalar in the range of [0, 1] specifying the mass within the credible interval (default = .954)

sw_data

The name of the sapwood data set to use for modelling. It should be one of the data sets listed in sw_data_overview(), or the name of a data.frame with sapwood data in columns n_sapwood and count.

densfun

Name of the density function fitted to the sapwood data set. Should be one of:

  • lognormal (the default value),

  • normal,

  • weibull,

  • gammma.

plot

A logical.

  • If TRUE, a ggplot-style graph is returned of the individual sapwood model and estimate of the felling date range.

  • If FALSE, a list with the numeric output of the modelling process is returned.

Value

Depends on the value of hdi.

  • If hdi = TRUE, a numeric vector reporting the upper and lower limit of the hdi (attributes provide more detail on cred_mass and the applied sapwood model (sw_data)).

  • If hdi = FALSE, a data.frame with scaled p values for each number of observed sapwood rings.

Examples

# 10 sapwood rings observed and the Wazny 1990 sapwood model:
sw_interval(
  n_sapwood = 10,
  last = 1234,
  hdi = TRUE,
  cred_mass = .95,
  sw_data = "Wazny_1990",
  densfun = "lognormal",
  plot = FALSE
)
#>   lower upper         p
#> 1  1234  1250 0.9611793
# same example as above, but with numerical output (hdi = FALSE):
sw_interval(
  n_sapwood = 10,
  last = 1234,
  hdi = FALSE,
  cred_mass = .95,
  sw_data = "Wazny_1990",
  densfun = "lognormal",
  plot = FALSE
)
#>     year n_sapwood            p
#> 1   1234        10 4.387611e-02
#> 2   1235        11 6.219097e-02
#> 3   1236        12 7.801314e-02
#> 4   1237        13 8.894482e-02
#> 5   1238        14 9.400306e-02
#> 6   1239        15 9.347466e-02
#> 7   1240        16 8.845988e-02
#> 8   1241        17 8.038628e-02
#> 9   1242        18 7.064400e-02
#> 10  1243        19 6.037958e-02
#> 11  1244        20 5.042220e-02
#> 12  1245        21 4.129538e-02
#> 13  1246        22 3.327159e-02
#> 14  1247        23 2.643959e-02
#> 15  1248        24 2.076722e-02
#> 16  1249        25 1.615214e-02
#> 17  1250        26 1.245872e-02
#> 18  1251        27 9.542758e-03
#> 19  1252        28 7.266294e-03
#> 20  1253        29 5.505594e-03
#> 21  1254        30 4.154352e-03
#> 22  1255        31 3.124045e-03
#> 23  1256        32 2.342677e-03
#> 24  1257        33 1.752749e-03
#> 25  1258        34 1.309002e-03
#> 26  1259        35 9.762245e-04
#> 27  1260        36 7.272799e-04
#> 28  1261        37 5.414149e-04
#> 29  1262        38 4.028596e-04
#> 30  1263        39 2.996922e-04
#> 31  1264        40 2.229393e-04
#> 32  1265        41 1.658698e-04
#> 33  1266        42 1.234492e-04
#> 34  1267        43 9.192023e-05
#> 35  1268        44 6.848429e-05
#> 36  1269        45 5.105942e-05
#> 37  1270        46 3.809853e-05
#> 38  1271        47 2.845283e-05
#> 39  1272        48 2.126964e-05
#> 40  1273        49 1.591626e-05
#> 41  1274        50 1.192322e-05
#> 42  1275        51 8.942096e-06
#> 43  1276        52 6.714252e-06
#> 44  1277        53 5.047602e-06
#> 45  1278        54 3.799408e-06
#> 46  1279        55 2.863535e-06
#> 47  1280        56 2.161002e-06
#> 48  1281        57 1.632988e-06
#> 49  1282        58 1.235643e-06
#> 50  1283        59 9.362498e-07
#> 51  1284        60 7.103687e-07
#> 52  1285        61 5.397257e-07
#> 53  1286        62 4.106413e-07
#> 54  1287        63 3.128631e-07
#> 55  1288        64 2.386985e-07
#> 56  1289        65 1.823682e-07
#> 57  1290        66 1.395252e-07
#> 58  1291        67 1.068956e-07
#> 59  1292        68 8.201055e-08
#> 60  1293        69 6.300581e-08
#> 61  1294        70 4.847197e-08
#> 62  1295        71 3.734200e-08
#> 63  1296        72 2.880707e-08
#> 64  1297        73 2.225318e-08
#> 65  1298        74 1.721367e-08
#> 66  1299        75 1.333338e-08
#> 67  1300        76 1.034162e-08
#> 68  1301        77 8.031835e-09
#> 69  1302        78 6.246191e-09
#> 70  1303        79 4.863914e-09
#> 71  1304        80 3.792475e-09
#> 72  1305        81 2.960884e-09
#> 73  1306        82 2.314610e-09
#> 74  1307        83 1.811705e-09
#> 75  1308        84 1.419861e-09
#> 76  1309        85 1.114164e-09
#> 77  1310        86 8.753705e-10
#> 78  1311        87 6.886052e-10
#> 79  1312        88 5.423498e-10
#> 80  1313        89 4.276761e-10
#> 81  1314        90 3.376542e-10
#> 82  1315        91 2.668986e-10
#> 83  1316        92 2.112190e-10
#> 84  1317        93 1.673507e-10
#> 85  1318        94 1.327473e-10
#> 86  1319        95 1.054198e-10
#> 87  1320        96 8.381333e-11
#> 88  1321        97 6.671042e-11
#> 89  1322        98 5.315687e-11
#> 90  1323        99 4.240389e-11
#> 91  1324       100 3.386323e-11
#> 92  1325       101 2.707215e-11
#> 93  1326       102 2.166629e-11
#> 94  1327       103 1.735839e-11
#> 95  1328       104 1.392172e-11
#> 96  1329       105 1.117714e-11
#> 97  1330       106 8.982951e-12
#> 98  1331       107 7.226918e-12
#> 99  1332       108 5.820081e-12
#> 100 1333       109 4.691834e-12
#> 101 1334       110 3.786080e-12