The package GerminaR
has been developed to calculate different germination indices and graphical functions to analyze punctual and accumulative germination. For calculating the indices is necessary acumulative germination data. For more details, you can read the description of each index, the seed germination dataset and analysis in the germinar’s book. (GerminaQuant)
First we load the GerminaR 1.0
package. It provides the GerminaR
dataset set that we will work throughout all the examples.
The GerminaR
dataset contains information from an experiment with 6 genotypes Jatropha curcas under 3 levels of salinity stress evaluated during 25 days.
library(GerminaR)
dim(GerminaR)
## [1] 66 30
str(GerminaR)
## 'data.frame': 66 obs. of 30 variables:
## $ Repetition: chr "R1" "R1" "R1" "R1" ...
## $ Genotype : chr "G5" "G1" "G6" "G3" ...
## $ Salt : num 0 0 0 0 0 0 100 100 100 100 ...
## $ NSeeds : num 25 25 25 25 25 25 25 25 25 25 ...
## $ Ev00 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev01 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev02 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev03 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev04 : num 0 0 6 0 0 0 0 0 0 0 ...
## $ Ev05 : num 2 1 5 4 5 4 0 0 0 0 ...
## $ Ev06 : num 6 5 7 4 5 3 1 0 0 0 ...
## $ Ev07 : num 10 2 3 3 2 10 0 1 1 0 ...
## $ Ev08 : num 1 3 0 0 2 2 0 0 0 0 ...
## $ Ev09 : num 1 7 0 1 1 1 0 0 0 0 ...
## $ Ev10 : num 2 2 2 2 1 2 2 0 1 1 ...
## $ Ev11 : num 0 2 0 3 1 1 0 0 0 0 ...
## $ Ev12 : num 0 0 0 2 0 0 0 2 0 1 ...
## $ Ev13 : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Ev14 : num 1 0 0 1 0 1 1 1 0 0 ...
## $ Ev15 : num 0 0 0 0 0 0 1 2 2 0 ...
## $ Ev16 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev17 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev18 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev19 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev20 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev21 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev22 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev23 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev24 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Ev25 : num 0 0 0 0 0 0 0 0 0 0 ...
runGerminaQuant
ger_summary
ger_GRS
ger_GRP
ger_ASG
ger_MGT
ger_MGR
ger_GSP
ger_SYN
ger_UNC
ger_SDG
ger_CVG
ger_VGT
ger_intime
fplot
The functionrunGerminaQuant()
activates an interactive application with friendly interface for performing the different germination, statistical and graphic analysis. For activation of some function could be necessary internet connection
runGerminaQuant()
The function ger_summary()
, according to the accumulative germination data, calculates eleven germination indices maintaining the values of each experimental unit and experiments factor for statistical analysis
dt <- GerminaR
smr <- ger_summary(SeedN = "NSeeds", evalName = "Ev", data = dt)
knitr::kable(head(smr, 10),align = "c")
Repetition | Genotype | Salt | NSeeds | GRS | GRP | ASG | MGT | MGR | GSP | UNC | SYN | VGT | SDG | CVG |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R1 | G5 | 0 | 25 | 23 | 92 | 1.2840398 | 7.260870 | 0.1377246 | 13.772455 | 2.230994 | 0.2450593 | 3.837945 | 1.959067 | 26.98117 |
R1 | G1 | 0 | 25 | 22 | 88 | 1.2170547 | 8.090909 | 0.1235955 | 12.359551 | 2.549613 | 0.1601732 | 3.038961 | 1.743262 | 21.54593 |
R1 | G6 | 0 | 25 | 23 | 92 | 1.2840398 | 5.739130 | 0.1742424 | 17.424242 | 2.196354 | 0.1976285 | 2.837945 | 1.684620 | 29.35323 |
R1 | G3 | 0 | 25 | 20 | 80 | 1.1071487 | 8.250000 | 0.1212121 | 12.121212 | 2.846439 | 0.1052632 | 8.302632 | 2.881429 | 34.92641 |
R1 | G4 | 0 | 25 | 17 | 68 | 0.9695321 | 6.764706 | 0.1478261 | 14.782609 | 2.486329 | 0.1617647 | 3.441177 | 1.855041 | 27.42234 |
R1 | G2 | 0 | 25 | 24 | 96 | 1.3694384 | 7.416667 | 0.1348315 | 13.483146 | 2.502705 | 0.2028986 | 4.514493 | 2.124734 | 28.64809 |
R1 | G5 | 100 | 25 | 5 | 20 | 0.4636476 | 11.000000 | 0.0909091 | 9.090909 | 1.921928 | 0.1000000 | 13.000000 | 3.605551 | 32.77774 |
R1 | G1 | 100 | 25 | 7 | 28 | 0.5575988 | 12.571429 | 0.0795455 | 7.954546 | 2.235926 | 0.0952381 | 7.619048 | 2.760262 | 21.95663 |
R1 | G6 | 100 | 25 | 4 | 16 | 0.4115168 | 11.750000 | 0.0851064 | 8.510638 | 1.500000 | 0.1666667 | 15.583333 | 3.947573 | 33.59637 |
R1 | G3 | 100 | 25 | 2 | 8 | 0.2867566 | 11.000000 | 0.0909091 | 9.090909 | 1.000000 | 0.0000000 | 2.000000 | 1.414214 | 12.85649 |
On the other hand, you can analyze each variable independently using the following germination indexes.
ger_GRS()
allows you to calculate the number of seed germinated.
dt <- GerminaR
grs <- ger_GRS(evalName = "Ev", data = dt)
grs
## [1] 23 22 23 20 17 24 5 7 4 2 2 19 20 23 18 15 22 23 23 23 18 21 24
## [24] 5 15 1 1 21 15 19 4 10 21 24 23 23 20 19 20 4 2 2 3 23 19 19
## [47] 14 8 24 20 19 20 19 17 15 6 2 1 1 3 21 18 22 4 13 21
ger_GRP()
calculates the germination percentage related at total seed sown for experimental unit.
dt <- GerminaR
grp <- ger_GRP(SeedN = "NSeeds",evalName = "Ev", data = dt)
grp
## [1] 92 88 92 80 68 96 20 28 16 8 8 76 80 92 72 60 88 92 92 92 72 84 96
## [24] 20 60 4 4 84 60 76 16 40 84 96 92 92 80 76 80 16 8 8 12 92 76 76
## [47] 56 32 96 80 76 80 76 68 60 24 8 4 4 12 84 72 88 16 52 84
ger_ASG()
calculates the arc sin of germination percentage for normalization.
dt <- GerminaR
gas <- ger_ASG(SeedN = "NSeeds", evalName = "Ev", data = dt)
gas
## [1] 1.2840398 1.2170547 1.2840398 1.1071487 0.9695321 1.3694384 0.4636476
## [8] 0.5575988 0.4115168 0.2867566 0.2867566 1.0588236 1.1071487 1.2840398
## [15] 1.0131975 0.8860771 1.2170547 1.2840398 1.2840398 1.2840398 1.0131975
## [22] 1.1592795 1.3694384 0.4636476 0.8860771 0.2013579 0.2013579 1.1592795
## [29] 0.8860771 1.0588236 0.4115168 0.6847192 1.1592795 1.3694384 1.2840398
## [36] 1.2840398 1.1071487 1.0588236 1.1071487 0.4115168 0.2867566 0.2867566
## [43] 0.3537416 1.2840398 1.0588236 1.0588236 0.8455431 0.6012642 1.3694384
## [50] 1.1071487 1.0588236 1.1071487 1.0588236 0.9695321 0.8860771 0.5119727
## [57] 0.2867566 0.2013579 0.2013579 0.3537416 1.1592795 1.0131975 1.2170547
## [64] 0.4115168 0.8054035 1.1592795
ger_MGT()
estimates the mean germination time according at the time lapse of the evaluations
dt <- GerminaR
mgt <- ger_MGT(evalName = "Ev", data = dt)
mgt
## [1] 7.260870 8.090909 5.739130 8.250000 6.764706 7.416667 11.000000
## [8] 12.571429 11.750000 11.000000 9.000000 9.421053 10.450000 8.217391
## [15] 10.833333 11.400000 8.590909 7.217391 6.000000 7.043478 6.333333
## [22] 8.190476 6.291667 10.400000 10.466667 7.000000 13.000000 9.238095
## [29] 10.800000 9.263158 8.750000 10.400000 7.523810 6.333333 6.652174
## [36] 7.478261 7.950000 7.263158 6.350000 11.250000 9.000000 13.000000
## [43] 8.333333 8.391304 10.736842 8.736842 9.071429 11.500000 8.125000
## [50] 6.200000 8.894737 7.050000 7.894737 8.294118 6.733333 12.000000
## [57] 13.000000 12.000000 11.000000 12.666667 10.142857 10.611111 9.545455
## [64] 8.750000 9.307692 7.857143
ger_MGR()
estimates the mean of germination rate
dt <- GerminaR
mgr <- ger_MGR(evalName = "Ev", data = dt)
mgr
## [1] 0.13772455 0.12359551 0.17424242 0.12121212 0.14782609 0.13483146
## [7] 0.09090909 0.07954545 0.08510638 0.09090909 0.11111111 0.10614525
## [13] 0.09569378 0.12169312 0.09230769 0.08771930 0.11640212 0.13855422
## [19] 0.16666667 0.14197531 0.15789474 0.12209302 0.15894040 0.09615385
## [25] 0.09554140 0.14285714 0.07692308 0.10824742 0.09259259 0.10795455
## [31] 0.11428571 0.09615385 0.13291139 0.15789474 0.15032680 0.13372093
## [37] 0.12578616 0.13768116 0.15748031 0.08888889 0.11111111 0.07692308
## [43] 0.12000000 0.11917098 0.09313725 0.11445783 0.11023622 0.08695652
## [49] 0.12307692 0.16129032 0.11242604 0.14184397 0.12666667 0.12056738
## [55] 0.14851485 0.08333333 0.07692308 0.08333333 0.09090909 0.07894737
## [61] 0.09859155 0.09424084 0.10476190 0.11428571 0.10743802 0.12727273
ger_GSP()
performs the calculation of germination speed according at the time lapse of the evaluations.
dt <- GerminaR
gsp <- ger_GSP(evalName = "Ev", data = dt)
gsp
## [1] 13.772455 12.359551 17.424242 12.121212 14.782609 13.483146 9.090909
## [8] 7.954545 8.510638 9.090909 11.111111 10.614525 9.569378 12.169312
## [15] 9.230769 8.771930 11.640212 13.855422 16.666667 14.197531 15.789474
## [22] 12.209302 15.894040 9.615385 9.554140 14.285714 7.692308 10.824742
## [29] 9.259259 10.795455 11.428571 9.615385 13.291139 15.789474 15.032680
## [36] 13.372093 12.578616 13.768116 15.748031 8.888889 11.111111 7.692308
## [43] 12.000000 11.917098 9.313725 11.445783 11.023622 8.695652 12.307692
## [50] 16.129032 11.242604 14.184397 12.666667 12.056738 14.851485 8.333333
## [57] 7.692308 8.333333 9.090909 7.894737 9.859155 9.424084 10.476190
## [64] 11.428571 10.743802 12.727273
ger_SYN()
calculates germination synchronization of the germination process.
dt <- GerminaR
syn <- ger_SYN(evalName = "Ev", data = dt)
syn
## [1] 0.24505929 0.16017316 0.19762846 0.10526316 0.16176471 0.20289855
## [7] 0.10000000 0.09523810 0.16666667 0.00000000 0.00000000 0.12280702
## [13] 0.13157895 0.10276680 0.09803922 0.14285714 0.13419913 0.16996047
## [19] 0.31620553 0.19367589 0.19607843 0.18095238 0.17753623 0.10000000
## [25] 0.15238095 NaN NaN 0.21428571 0.08571429 0.07602339
## [31] 0.00000000 0.11111111 0.16190476 0.27173913 0.28063241 0.13043478
## [37] 0.12631579 0.33918129 0.23684211 0.16666667 0.00000000 1.00000000
## [43] 0.33333333 0.14229249 0.09356725 0.12280702 0.15384615 0.25000000
## [49] 0.13405797 0.14736842 0.18713450 0.21578947 0.18128655 0.21323529
## [55] 0.20000000 0.20000000 0.00000000 NaN NaN 0.33333333
## [61] 0.10476190 0.07843137 0.10389610 0.16666667 0.08974359 0.40952381
ger_UNC()
measures the germination uncertainty into the germination process.
dt <- GerminaR
unc <- ger_UNC(evalName = "Ev", data = dt)
unc
## [1] 2.2309943 2.5496129 2.1963538 2.8464393 2.4863287 2.5027055 1.9219281
## [8] 2.2359264 1.5000000 1.0000000 1.0000000 2.6550505 2.7464393 2.9638097
## [15] 2.9477028 2.4225798 2.7201288 2.4943865 1.7209481 2.3143122 2.3695710
## [22] 2.4275019 2.4380547 1.9219281 2.4989296 0.0000000 0.0000000 2.3204234
## [29] 2.9735573 3.1416354 2.0000000 2.4464393 2.5993896 1.8291723 1.9508157
## [36] 2.6657769 2.8232197 1.9293092 2.0567796 1.5000000 1.0000000 0.0000000
## [43] 0.9182958 2.5246851 2.9847696 2.6755958 2.2709424 1.7500000 2.7254129
## [50] 2.5414461 2.2942741 2.2219281 2.4370658 2.0948568 2.3395723 1.7924813
## [57] 1.0000000 0.0000000 0.0000000 0.9182958 2.9463875 3.1279868 2.8853775
## [64] 1.5000000 2.7192945 1.5301405
ger_SDG()
estimates the standard deviation of the mean germination time.
dt <- GerminaR
sdg <- ger_SDG(evalName = "Ev", data = dt)
sdg
## [1] 1.9590673 1.7432616 1.6846200 2.8814287 1.8550408 2.1247336 3.6055513
## [8] 2.7602622 3.9475731 1.4142136 2.8284271 2.0633250 2.6051568 2.6104079
## [15] 3.0726497 2.9228166 2.1527190 1.7309094 0.9045340 1.3306957 1.7149859
## [22] 2.4620936 2.2356629 2.6076810 3.3565856 NaN NaN 2.2562084
## [29] 3.5294678 2.5131234 3.5000000 3.8355066 2.6385422 0.9168313 1.6406400
## [36] 1.9275412 2.7999060 1.1470787 1.5652476 1.8929694 1.4142136 0.0000000
## [43] 0.5773503 1.7251912 2.9970746 2.0505312 1.5915298 1.9272482 2.5928413
## [50] 2.5256578 1.5949482 1.8488973 2.0247013 2.3120528 2.7894359 3.3466401
## [57] 2.8284271 NaN NaN 0.5773503 3.6371103 3.7438403 2.6498387
## [64] 1.8929694 2.4625399 1.5259657
ger_CVG()
Coefficient of Variance of the Mean Germination Time
dt <- GerminaR
cvg <- ger_CVG(evalName = "Ev", data = dt)
cvg
## [1] 26.981166 21.545930 29.353228 34.926409 27.422343 28.648093 32.777739
## [8] 21.956631 33.596367 12.856487 31.426968 21.901215 24.929730 31.766868
## [15] 28.362921 25.638743 25.058105 23.982480 15.075567 18.892593 27.078724
## [22] 30.060445 35.533714 25.073855 32.069289 NaN NaN 24.422874
## [29] 32.680257 27.130310 40.000000 36.879871 35.069231 14.476284 24.663216
## [36] 25.775260 35.218944 15.793112 24.649568 16.826395 15.713484 0.000000
## [43] 6.928203 20.559273 27.913930 23.469936 17.544423 16.758680 31.911893
## [50] 40.736416 17.931370 26.225493 25.646216 27.875814 41.427265 27.888668
## [57] 21.757132 NaN NaN 4.558028 35.858834 35.282264 27.760215
## [64] 21.633937 26.457040 19.421382
ger_VGT()
compute the variance of the mean during germination time.
dt <- GerminaR
vgt <- ger_VGT(evalName = "Ev", data = dt)
vgt
## [1] 3.8379447 3.0389610 2.8379447 8.3026316 3.4411765 4.5144928
## [7] 13.0000000 7.6190476 15.5833333 2.0000000 8.0000000 4.2573099
## [13] 6.7868421 6.8142292 9.4411765 8.5428571 4.6341991 2.9960474
## [19] 0.8181818 1.7707510 2.9411765 6.0619048 4.9981884 6.8000000
## [25] 11.2666667 NaN NaN 5.0904762 12.4571429 6.3157895
## [31] 12.2500000 14.7111111 6.9619048 0.8405797 2.6916996 3.7154150
## [37] 7.8394737 1.3157895 2.4500000 3.5833333 2.0000000 0.0000000
## [43] 0.3333333 2.9762846 8.9824561 4.2046784 2.5329670 3.7142857
## [49] 6.7228261 6.3789474 2.5438596 3.4184211 4.0994152 5.3455882
## [55] 7.7809524 11.2000000 8.0000000 NaN NaN 0.3333333
## [61] 13.2285714 14.0163399 7.0216450 3.5833333 6.0641026 2.3285714
ger_intime()
Allow to calculate the accumulative germination
dt <- GerminaR
grt <- ger_intime(Factor = "Salt", SeedN = "NSeeds", evalName = "Ev", method = "percentage", data = GerminaR)