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Fit four-parameter hill function from a data frame of germination counts recorded at specific time intervals for multiple samples in batch.

Usage

FourPHFfit.bulk(
  data,
  total.seeds.col,
  counts.intervals.cols,
  intervals,
  partial = TRUE,
  fix.y0 = TRUE,
  fix.a = TRUE,
  tmax,
  xp = c(10, 60),
  umin = 10,
  umax = 90,
  tries = 3
)

Arguments

data

A data frame with the germination count data. It should possess columns with

  • Partial or cumulative germination counts per time interval (to be indicated by the argument counts.intervals.cols and

  • Total number of seeds tested (to be indicated by the argument total.seeds.col).

total.seeds.col

The name of the column in data with the total number of seeds tested.

counts.intervals.cols

The names of columns in data with the germination counts (partial or cumulative, as indicated by the argument partial) per time interval (indicated by the argument intervals).

intervals

The time intervals.

partial

logical. If TRUE, germ.counts is considered as partial and if FALSE, it is considered as cumulative. Default is TRUE.

fix.y0

Force the intercept of the y axis through 0.

fix.a

Fix a as the actual maximum germination percentage at the end of the experiment.

tmax

The time up to which AUC is to be computed.

xp

Germination percentage value(s) for which the corresponding time is to be computed as a numeric vector. Default is c(10, 60).

umin

The minimum germination percentage value for computing uniformity. Default is 10. Seed Details.

umax

The maximum germination percentage value for computing uniformity. Default is 90. Seed Details.

tries

The number of tries to be attempted to fit the curve. Default is 3.

Value

A data frame with the original data along with the various parameters of the respective fitted four-parameter hill function.

References

El-Kassaby YA, Moss I, Kolotelo D, Stoehr M (2008). “Seed germination: Mathematical representation and parameters extraction.” Forest Science, 54(2), 220–227.

See also

This function is a wrapper around the FourPHFfit function for fitting four-parameter hill curve.

Examples


# \donttest{
data(gcdata)

counts.per.intervals <- c("Day01", "Day02", "Day03", "Day04", "Day05",
                          "Day06", "Day07", "Day08", "Day09", "Day10",
                          "Day11", "Day12", "Day13", "Day14")

FourPHFfit.bulk(gcdata, total.seeds.col = "Total Seeds",
                    counts.intervals.cols = counts.per.intervals,
                    intervals = 1:14, partial = TRUE,
                    fix.y0 = TRUE, fix.a = TRUE, xp = c(10, 60),
                    tmax = 20, tries = 3, umax = 90, umin = 10)
#>    Genotype Rep Day01 Day02 Day03 Day04 Day05 Day06 Day07 Day08 Day09 Day10
#> 1        G1   1     0     0     0     0     4    17    10     7     1     0
#> 2        G2   1     0     0     0     1     3    15    13     6     2     1
#> 3        G3   1     0     0     0     2     3    18     9     8     2     1
#> 4        G4   1     0     0     0     0     4    19    12     6     2     1
#> 5        G5   1     0     0     0     0     5    20    12     8     1     0
#> 6        G1   2     0     0     0     0     3    21    11     7     1     1
#> 7        G2   2     0     0     0     0     4    18    11     7     1     0
#> 8        G3   2     0     0     0     1     3    14    12     6     2     1
#> 9        G4   2     0     0     0     1     3    19    10     8     1     1
#> 10       G5   2     0     0     0     0     4    18    13     6     2     1
#> 11       G1   3     0     0     0     0     5    21    11     8     1     0
#> 12       G2   3     0     0     0     0     3    20    10     7     1     1
#> 13       G3   3     0     0     0     0     4    19    12     8     1     1
#> 14       G4   3     0     0     0     0     3    21    11     6     1     0
#> 15       G5   3     0     0     0     0     4    17    10     8     1     1
#>    Day11 Day12 Day13 Day14 Total Seeds        a         b        c y0 lag
#> 1      1     0     0     0          50 80.00000  9.881937 6.034954  0   0
#> 2      0     1     0     0          51 82.35294  9.227666 6.175193  0   0
#> 3      1     1     0     0          48 93.75000  7.793051 6.138110  0   0
#> 4      1     1     0     0          51 90.19608  8.925655 6.125173  0   0
#> 5      0     1     1     0          50 96.00000  9.419181 6.049642  0   0
#> 6      1     1     0     0          49 93.87755  9.450149 6.097415  0   0
#> 7      1     0     0     0          48 87.50000 10.172459 6.029851  0   0
#> 8      0     1     0     0          47 85.10638  8.940696 6.189774  0   0
#> 9      1     1     0     0          52 86.53846  8.617391 6.125122  0   0
#> 10     0     1     0     0          50 90.00000  9.608844 6.109504  0   0
#> 11     0     1     1     0          51 94.11765  9.400212 6.018760  0   0
#> 12     1     1     0     0          51 86.27451  9.162526 6.108452  0   0
#> 13     0     1     1     0          49 95.91837  8.995210 6.149012  0   0
#> 14     1     1     0     0          48 91.66667 10.391845 6.015910  0   0
#> 15     1     0     0     0          48 87.50000  9.136744 6.121579  0   0
#>      Dlag50 t50.total t10.total t60.total t50.Germinated t10.Germinated
#> 1  6.034954  6.355121  4.956264  6.744598       6.034954       4.831807
#> 2  6.175193  6.473490  4.983236  6.872603       6.175193       4.866755
#> 3  6.138110  6.244191  4.673022  6.608438       6.138110       4.630062
#> 4  6.125173  6.276794  4.850875  6.614968       6.125173       4.788597
#> 5  6.049642  6.103433  4.814125  6.386789       6.049642       4.790946
#> 6  6.097415  6.182279  4.868632  6.477599       6.097415       4.832471
#> 7  6.029851  6.202812  4.930422  6.510495       6.029851       4.858476
#> 8  6.189774  6.439510  4.940057  6.823299       6.189774       4.841105
#> 9  6.125122  6.352172  4.836658  6.733276       6.125122       4.746573
#> 10 6.109504  6.253043  4.920629  6.566506       6.109504       4.860681
#> 11 6.018760  6.099435  4.798627  6.391291       6.018760       4.764246
#> 12 6.108452  6.326184  4.893596  6.684526       6.108452       4.806013
#> 13 6.149012  6.207501  4.841308  6.509954       6.149012       4.816393
#> 14 6.015910  6.122389  4.915140  6.397491       6.015910       4.869398
#> 15 6.121579  6.317392  4.892502  6.667247       6.121579       4.813083
#>    t60.Germinated Uniformity_90 Uniformity_10 Uniformity     TMGR      AUC
#> 1        6.287724      7.537690      4.831807   2.705882 5.912194 1108.976
#> 2        6.452582      7.835407      4.866755   2.968652 6.031282 1128.559
#> 3        6.465924      8.137342      4.630062   3.507280 5.938180 1283.693
#> 4        6.409838      7.834810      4.788597   3.046213 5.972686 1239.887
#> 5        6.315746      7.639028      4.790946   2.848083 5.914289 1328.328
#> 6        6.364722      7.693469      4.832471   2.860997 5.961879 1294.463
#> 7        6.275050      7.483643      4.858476   2.625166 5.914057 1213.908
#> 8        6.476945      7.914163      4.841105   3.073058 6.036192 1164.346
#> 9        6.420208      7.904041      4.746573   3.157468 5.961631 1188.793
#> 10       6.372823      7.679177      4.860681   2.818496 5.978115 1240.227
#> 11       6.284051      7.603611      4.764246   2.839365 5.883557 1305.200
#> 12       6.384836      7.763854      4.806013   2.957841 5.964080 1188.021
#> 13       6.432524      7.850345      4.816393   3.033952 5.998270 1316.407
#> 14       6.255276      7.432372      4.869398   2.562974 5.905180 1273.385
#> 15       6.399357      7.785806      4.813083   2.972723 5.976087 1203.664
#>         MGT Skewness          msg    sigma       finTol    logLik      AIC
#> 1  6.632252 1.098973 #1. success  1.615220 6.039613e-14 -25.49868 56.99736
#> 2  6.784407 1.098655 #1. success  1.115372 6.217249e-14 -20.31471 46.62943
#> 3  6.772742 1.103392 #1. success  2.432704 1.790568e-12 -31.23213 68.46426
#> 4  6.739666 1.100323 #1. success  2.396582 8.526513e-14 -31.02269 68.04538
#> 5  6.654981 1.100062 #1. success  2.399662 1.094236e-12 -31.04067 68.08135
#> 6  6.702473 1.099232 #1. success  3.034962 1.392664e-12 -34.32887 74.65774
#> 7  6.622417 1.098272 #1. success  1.663019 7.105427e-14 -25.90697 57.81395
#> 8  6.804000 1.099232 #1. success  1.120704 4.302336e-12 -20.38149 46.76298
#> 9  6.745241 1.101242 #1. success  2.429960 8.810730e-13 -31.21633 68.43266
#> 10 6.711900 1.098600 #1. success  1.686656 8.199663e-12 -26.10456 58.20911
#> 11 6.624248 1.100600 #1. success  2.628113 2.984279e-13 -32.31381 70.62762
#> 12 6.718639 1.099892 #1. success  2.878146 1.008971e-12 -33.58613 73.17227
#> 13 6.762274 1.099733 #1. success  2.604588 3.126388e-13 -32.18793 70.37586
#> 14 6.604967 1.097916 #1. success  2.764756 9.379164e-13 -33.02342 72.04684
#> 15 6.732266 1.099760 #1. success  1.954008 7.602807e-13 -28.16444 62.32888
#>         BIC  deviance df.residual nobs
#> 1  58.91453  31.30723          12   14
#> 2  48.54660  14.92865          12   14
#> 3  70.38143  71.01658          12   14
#> 4  69.96256  68.92324          12   14
#> 5  69.99852  69.10052          12   14
#> 6  76.57491 110.53195          12   14
#> 7  59.73112  33.18760          12   14
#> 8  48.68015  15.07174          12   14
#> 9  70.34984  70.85647          12   14
#> 10 60.12629  34.13771          12   14
#> 11 72.54479  82.88372          12   14
#> 12 75.08944  99.40469          12   14
#> 13 72.29303  81.40654          12   14
#> 14 73.96401  91.72652          12   14
#> 15 64.24606  45.81777          12   14
# }