Fit four-parameter hill function for multiple samples
Source:R/FourPHFfit.bulk.R
FourPHFfit.bulk.Rd
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
andTotal 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 argumentpartial
) per time interval (indicated by the argumentintervals
).- intervals
The time intervals.
- partial
logical. If
TRUE
,germ.counts
is considered as partial and ifFALSE
, it is considered as cumulative. Default isTRUE
.- 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
. SeedDetails
.- umax
The maximum germination percentage value for computing uniformity. Default is
90
. SeedDetails
.- 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
# }