GerminaR 1.0

Flavio Lozano-Isla, Omar Benites, Marcelo Francisco Pompelli

2016-12-23

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.

Data: GerminaR

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 ...

List of the principal functions

Interactive shiny application

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()

Summary of Germination Variables

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.

Germinated Seed Number

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

Germination Seed Percentage

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

ArcSino of the germination

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

Mean Germination Time

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

Mean Germination Rate

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

Germination Speed

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

Germination Synchronization Index

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

Germination Uncertainty

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

Standard deviation of the Mean Germination Time

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

Coefficient of Variance of the Mean Germination Time

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

Variance of the Mean Germination Time

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

Accumulative germination

ger_intime() Allow to calculate the accumulative germination

dt <- GerminaR
grt <- ger_intime(Factor = "Salt", SeedN = "NSeeds", evalName = "Ev", method = "percentage", data = GerminaR)