R/FourPHFfit.bulk.R
FourPHFfit.bulk.RdFit four-parameter hill function from a data frame of germination counts recorded at specific time intervals for multiple samples in batch.
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
)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).
The name of the column in data with the total
number of seeds tested.
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).
The time intervals.
logical. If TRUE, germ.counts is considered as
partial and if FALSE, it is considered as cumulative. Default is
TRUE.
Force the intercept of the y axis through 0.
Fix a as the actual maximum germination percentage at the end of the experiment.
The time up to which AUC is to be computed.
Germination percentage value(s) for which the corresponding time is
to be computed as a numeric vector. Default is c(10, 60).
The minimum germination percentage value for computing
uniformity. Default is 10. Seed Details.
The maximum germination percentage value for computing
uniformity. Default is 90. Seed Details.
The number of tries to be attempted to fit the curve. Default is 3.
A data frame with the original data along with the various parameters of the respective fitted four-parameter hill function.
El-Kassaby YA, Moss I, Kolotelo D, Stoehr M (2008). “Seed germination: Mathematical representation and parameters extraction.” Forest Science, 54(2), 220--227.
This function is a wrapper around the
FourPHFfit function for fitting
four-parameter hill curve.
# \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
#> 1 1 0 0 0 50 80 9.88193689239939
#> 2 0 1 0 0 51 82.3529411764706 9.22766646359687
#> 3 1 1 0 0 48 93.75 7.79305096503615
#> 4 1 1 0 0 51 90.1960784313725 8.92565503394839
#> 5 0 1 1 0 50 96 9.4191816695981
#> 6 1 1 0 0 49 93.8775510204082 9.45014899129514
#> 7 1 0 0 0 48 87.5 10.1724586100529
#> 8 0 1 0 0 47 85.1063829787234 8.94069602989349
#> 9 1 1 0 0 52 86.5384615384615 8.6173913532163
#> 10 0 1 0 0 50 90 9.60884373087692
#> 11 0 1 1 0 51 94.1176470588235 9.40021183872586
#> 12 1 1 0 0 51 86.2745098039216 9.16252658054406
#> 13 0 1 1 0 49 95.9183673469388 8.99520959277319
#> 14 1 1 0 0 48 91.6666666666667 10.3918447990981
#> 15 1 0 0 0 48 87.5 9.13674439831543
#> c y0 lag Dlag50 t50.total txp.total_10
#> 1 6.03495355423622 0 0 6.03495355423622 6.35512149738159 4.95626430996844
#> 2 6.17519294875847 0 0 6.17519294875847 6.47349043979169 4.98323617961634
#> 3 6.13811027323845 0 0 6.13811027323845 6.24419102980473 4.67302155333174
#> 4 6.12517308176588 0 0 6.12517308176588 6.27679437746254 4.85087548237175
#> 5 6.04964210720327 0 0 6.04964210720327 6.10343321091848 4.81412549010201
#> 6 6.09741485213496 0 0 6.09741485213496 6.18227860747235 4.86863251431733
#> 7 6.02985089631599 0 0 6.02985089631599 6.20281219696422 4.93042184740182
#> 8 6.18977354961439 0 0 6.18977354961439 6.43951015764455 4.94005695310539
#> 9 6.12512151399929 0 0 6.12512151399929 6.35217197764166 4.83665841861718
#> 10 6.10950363575767 0 0 6.10950363575767 6.25304320794668 4.92062915221628
#> 11 6.01875974061195 0 0 6.01875974061195 6.09943499335382 4.79862683383817
#> 12 6.1084516820797 0 0 6.1084516820797 6.32618435705024 4.89359557090626
#> 13 6.14901168803061 0 0 6.14901168803061 6.20750091288263 4.84130798267247
#> 14 6.01591019543247 0 0 6.01591019543247 6.12238872898609 4.91514013767951
#> 15 6.12157936163499 0 0 6.12157936163499 6.31739163301497 4.89250226946576
#> txp.total_60 t50.Germinated txp.Germinated_10 txp.Germinated_60
#> 1 6.74459834631771 6.03495355423622 4.8318073794034 6.28772357367188
#> 2 6.87260337968692 6.17519294875847 4.86675518549505 6.45258151256586
#> 3 6.60843809251509 6.13811027323845 4.63006208020014 6.46592435698387
#> 4 6.61496814302537 6.12517308176588 4.78859693817119 6.40983765941072
#> 5 6.38678874941426 6.04964210720327 4.79094574322756 6.31574586639992
#> 6 6.47759860932629 6.09741485213496 4.83247140619904 6.36472210249767
#> 7 6.51049505523 6.02985089631599 4.85847638047658 6.2750496018235
#> 8 6.82329908278267 6.18977354961439 4.84110536088622 6.47694540370958
#> 9 6.73327569782723 6.12512151399929 4.74657350251934 6.42020821882777
#> 10 6.5665061956458 6.10950363575767 4.86068135560336 6.37282341572477
#> 11 6.3912906236839 6.01875974061195 4.76424552194859 6.2840509537431
#> 12 6.68452626570581 6.1084516820797 4.80601279742022 6.38483647023757
#> 13 6.50995387022312 6.1490116880306 4.81639290881553 6.43252427366119
#> 14 6.39749097966013 6.01591019543247 4.86939775646343 6.2552761045868
#> 15 6.66724718740801 6.12157936163499 4.81308335438754 6.39935718177504
#> Uniformity_90 Uniformity_10 Uniformity TMGR
#> 1 7.53768963494679 4.8318073794034 2.70588225554338 5.91219440465559
#> 2 7.83540706301571 4.86675518549505 2.96865187752066 6.03128155417137
#> 3 8.13734180531911 4.63006208020014 3.50727972511896 5.93817948829363
#> 4 7.83480960415051 4.78859693817119 3.04621266597932 5.97268622562109
#> 5 7.63902819750811 4.79094574322756 2.84808245428055 5.91428884333636
#> 6 7.69346877693759 4.83247140619904 2.86099737073854 5.96187868562603
#> 7 7.48364280989593 4.85847638047658 2.62516642941935 5.91405695229978
#> 8 7.91416293168472 4.84110536088622 3.07305757079851 6.03619216805867
#> 9 7.90404141879274 4.74657350251934 3.1574679162734 5.9616310497804
#> 10 7.67917745365204 4.86068135560336 2.81849609804867 5.97811533002658
#> 11 7.60361082322955 4.76424552194859 2.83936530128096 5.88355748786772
#> 12 7.76385405638773 4.80601279742022 2.95784125896751 5.9640804983933
#> 13 7.85034474042432 4.81639290881553 3.03395183160878 5.99827012388754
#> 14 7.43237198716613 4.86939775646343 2.5629742307027 5.90518049089766
#> 15 7.78580612916975 4.81308335438754 2.97272277478221 5.97608676470078
#> AUC MGT Skewness msg
#> 1 1108.97550938753 6.6322519662712 1.09897315806444 #1. success
#> 2 1128.55880088539 6.78440735640875 1.09865512101493 #1. success
#> 3 1283.69307344325 6.7727423279724 1.10339209080373 #1. success
#> 4 1239.88674124826 6.73966592721389 1.10032252758331 #1. success
#> 5 1328.32820017628 6.65498075748102 1.10006189449736 #1. success
#> 6 1294.46271443954 6.70247312592894 1.0992319349211 #1. success
#> 7 1213.90764565674 6.62241708548249 1.09827211308468 #1. success
#> 8 1164.34586106316 6.80400021213917 1.09923249333783 #1. success
#> 9 1188.79304149759 6.7452410863068 1.10124200326315 #1. success
#> 10 1240.22733172773 6.71189998816383 1.09859988442931 #1. success
#> 11 1305.20007906005 6.62424817630914 1.10060020033889 #1. success
#> 12 1188.0211599463 6.71863893649018 1.09989229450739 #1. success
#> 13 1316.40687295539 6.76227360647556 1.09973341238539 #1. success
#> 14 1273.38526597424 6.60496678828807 1.0979164538232 #1. success
#> 15 1203.66421628837 6.73226579042194 1.09975961965212 #1. success
#> Fit_sigma Fit_isConv Fit_finTol Fit_logLik
#> 1 1.61522002910957 TRUE 2.8528290840768e-12 -25.498681342686
#> 2 1.11537185901124 TRUE 5.11413134063332e-12 -20.3147146781893
#> 3 2.43270386985341 TRUE 8.43982661535847e-11 -31.2321314996742
#> 4 2.39658164351394 TRUE 3.38218342221808e-12 -31.0226924019787
#> 5 2.39966172990826 TRUE 6.74447164783487e-11 -31.0406736477542
#> 6 3.03496223650969 TRUE 3.95630195271224e-11 -34.328870450832
#> 7 1.66301938705135 TRUE 3.90798504668055e-12 -25.9069727183683
#> 8 1.12070433595621 TRUE 4.32720526077901e-12 -20.3814877326307
#> 9 2.42996010854989 TRUE 1.77209358298569e-11 -31.2163324798379
#> 10 1.68665620116432 TRUE 8.19966317067156e-12 -26.1045565628368
#> 11 2.62811272107047 TRUE 1.32729383039987e-11 -32.3138085946749
#> 12 2.87814601795845 TRUE 3.51434437106946e-11 -33.5861335093548
#> 13 2.60458797517776 TRUE 1.00897068477934e-11 -32.1879276469568
#> 14 2.76475621724483 TRUE 9.80548975348938e-13 -33.023419198233
#> 15 1.95400807212262 TRUE 8.73967564984923e-13 -28.1644422917083
#> Fit_AIC Fit_BIC Fit_deviance Fit_df.residual Fit_nobs
#> 1 56.9973626853719 58.9145346742177 31.3072289092405 12 14
#> 2 46.6294293563787 48.5466013452244 14.9286526064904 12 14
#> 3 68.4642629993484 70.3814349881942 71.0165774207973 12 14
#> 4 68.0453848039574 69.9625567928032 68.923242888336 12 14
#> 5 68.0813472955084 69.9985192843541 69.1005170158358 12 14
#> 6 74.6577409016639 76.5749128905097 110.531949324479 12 14
#> 7 57.8139454367367 59.7311174255824 33.1876017805038 12 14
#> 8 46.7629754652615 48.6801474541073 15.0717385035725 12 14
#> 9 68.4326649596759 70.3498369485217 70.8564735497253 12 14
#> 10 58.2091131256735 60.1262851145193 34.1377096911126 12 14
#> 11 70.6276171893498 72.5447891781956 82.8837176958294 12 14
#> 12 73.1722670187096 75.0894390075554 99.4046940082808 12 14
#> 13 70.3758552939136 72.2930272827594 81.4065422452872 12 14
#> 14 72.0468383964661 73.9640103853119 91.7265232895271 12 14
#> 15 62.3288845834165 64.2460565722623 45.8177705510444 12 14
# }