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 prosopis dataset set that we will work throughout all the examples.
The prosopis dataset contains information from an experiment containing information from germination experiment with Prosopis juliflor under different osmotic potentials and temperatures evaluated during 10 days.
library(GerminaR)
dim(prosopis)## [1] 80 15str(prosopis)## 'data.frame': 80 obs. of 15 variables:
## $ rep : int 1 2 3 4 1 2 3 4 1 2 ...
## $ nacl : num 0 0 0 0 0 0 0 0 0.5 0.5 ...
## $ temp : int 25 25 25 25 30 30 30 30 25 25 ...
## $ seeds: int 50 50 50 50 50 50 50 50 50 50 ...
## $ D0 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ D1 : int 39 40 34 43 48 47 50 49 10 18 ...
## $ D2 : int 8 9 16 7 2 3 0 1 37 30 ...
## $ D3 : int 3 1 0 0 0 0 0 0 1 1 ...
## $ D4 : int 0 0 0 0 0 0 0 0 2 1 ...
## $ D5 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ D6 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ D7 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ D8 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ D9 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ D10 : int 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 15
## .. ..$ rep : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ nacl : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ temp : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ seeds: list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D0 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D1 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D2 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D3 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D4 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D5 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D6 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D7 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D8 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D9 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ D10 : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"GerminaQuantger_summaryger_GRSger_GRPger_ASGger_MGTger_MGRger_GSPger_SYNger_UNCger_SDGger_CVGger_VGTger_intimefplotThe functionGerminaQuant() 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
GerminaQuant()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 <- prosopis
smr <- ger_summary(SeedN = "seeds", evalName = "D", data = dt)## Warning: package 'bindrcpp' was built under R version 3.3.3knitr::kable(head(smr, 10),align = "c")| rep | nacl | temp | seeds | GRS | GRP | MGT | MGR | GSP | UNC | SYN | VGT | SDG | CVG |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.0 | 25 | 50 | 50 | 100 | 1.28 | 0.7812500 | 78.12500 | 0.9461447 | 0.6302041 | 0.3281633 | 0.5728554 | 44.75433 |
| 2 | 0.0 | 25 | 50 | 50 | 100 | 1.22 | 0.8196721 | 81.96721 | 0.8157272 | 0.6661224 | 0.2159184 | 0.4646702 | 38.08772 |
| 3 | 0.0 | 25 | 50 | 50 | 100 | 1.32 | 0.7575758 | 75.75758 | 0.9043815 | 0.5559184 | 0.2220408 | 0.4712121 | 35.69788 |
| 4 | 0.0 | 25 | 50 | 50 | 100 | 1.14 | 0.8771930 | 87.71930 | 0.5842388 | 0.7542857 | 0.1228571 | 0.3505098 | 30.74648 |
| 1 | 0.0 | 30 | 50 | 50 | 100 | 1.04 | 0.9615385 | 96.15385 | 0.2422922 | 0.9216327 | 0.0391837 | 0.1979487 | 19.03353 |
| 2 | 0.0 | 30 | 50 | 50 | 100 | 1.06 | 0.9433962 | 94.33962 | 0.3274449 | 0.8848980 | 0.0575510 | 0.2398979 | 22.63188 |
| 3 | 0.0 | 30 | 50 | 50 | 100 | 1.00 | 1.0000000 | 100.00000 | 0.0000000 | 1.0000000 | 0.0000000 | 0.0000000 | 0.00000 |
| 4 | 0.0 | 30 | 50 | 50 | 100 | 1.02 | 0.9803922 | 98.03922 | 0.1414405 | 0.9600000 | 0.0200000 | 0.1414214 | 13.86484 |
| 1 | 0.5 | 25 | 50 | 50 | 100 | 1.90 | 0.5263158 | 52.63158 | 1.0844751 | 0.5812245 | 0.3775510 | 0.6144518 | 32.33957 |
| 2 | 0.5 | 25 | 50 | 50 | 100 | 1.70 | 0.5882353 | 58.82353 | 1.1985488 | 0.4800000 | 0.3775510 | 0.6144518 | 36.14422 |
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 <- prosopis
grs <- ger_GRS(evalName = "D", data = dt)
grs## [1] 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 48 48 47 49 49 50 48
## [24] 50 47 48 48 49 50 50 50 47 47 47 46 49 46 48 47 47 50 50 50 50 50 50
## [47] 50 50 50 48 50 48 48 49 48 47 49 48 46 49 50 48 49 50 50 48 50 49 6
## [70] 6 4 5 10 10 9 11 0 0 0 0ger_GRP() calculates the germination percentage related at total seed sown for experimental unit.
dt <- prosopis
grp <- ger_GRP(SeedN = "seeds",evalName = "D", data = dt)
grp## [1] 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 96
## [18] 96 94 98 98 100 96 100 94 96 96 98 100 100 100 94 94 94
## [35] 92 98 92 96 94 94 100 100 100 100 100 100 100 100 100 96 100
## [52] 96 96 98 96 94 98 96 92 98 100 96 98 100 100 96 100 98
## [69] 12 12 8 10 20 20 18 22 0 0 0 0ger_ASG() calculates the arc sin of germination percentage for normalization.
dt <- prosopis
gas <- ger_ASG(SeedN = "seeds", evalName = "D", data = dt)
gas## [1] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963
## [8] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963
## [15] 1.5707963 1.5707963 1.3694384 1.3694384 1.3233293 1.4288993 1.4288993
## [22] 1.5707963 1.3694384 1.5707963 1.3233293 1.3694384 1.3694384 1.4288993
## [29] 1.5707963 1.5707963 1.5707963 1.3233293 1.3233293 1.3233293 1.2840398
## [36] 1.4288993 1.2840398 1.3694384 1.3233293 1.3233293 1.5707963 1.5707963
## [43] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963
## [50] 1.3694384 1.5707963 1.3694384 1.3694384 1.4288993 1.3694384 1.3233293
## [57] 1.4288993 1.3694384 1.2840398 1.4288993 1.5707963 1.3694384 1.4288993
## [64] 1.5707963 1.5707963 1.3694384 1.5707963 1.4288993 0.3537416 0.3537416
## [71] 0.2867566 0.3217506 0.4636476 0.4636476 0.4381490 0.4882053 0.0000000
## [78] 0.0000000 0.0000000 0.0000000ger_MGT() estimates the mean germination time according at the time lapse of the evaluations
dt <- prosopis
mgt <- ger_MGT(evalName = "D", data = dt)
mgt## [1] 1.280000 1.220000 1.320000 1.140000 1.040000 1.060000 1.000000
## [8] 1.020000 1.900000 1.700000 1.880000 1.840000 1.100000 1.160000
## [15] 1.080000 1.060000 2.666667 2.708333 2.531915 2.897959 1.959184
## [22] 1.940000 1.833333 1.860000 5.382979 5.458333 5.479167 5.448980
## [29] 3.160000 2.960000 2.900000 2.957447 6.680851 6.063830 6.695652
## [36] 6.653061 4.326087 4.333333 4.446809 4.446809 1.040000 1.000000
## [43] 1.020000 1.000000 1.060000 1.040000 1.020000 1.020000 1.120000
## [50] 1.062500 1.060000 1.062500 2.333333 2.346939 2.375000 2.255319
## [57] 1.653061 1.729167 2.173913 1.714286 2.940000 2.520833 2.714286
## [64] 2.740000 3.380000 3.354167 3.360000 3.387755 3.333333 3.166667
## [71] 3.250000 3.400000 6.800000 7.400000 6.555556 7.181818 NaN
## [78] NaN NaN NaNger_MGR() estimates the mean of germination rate
dt <- prosopis
mgr <- ger_MGR(evalName = "D", data = dt)
mgr## [1] 0.7812500 0.8196721 0.7575758 0.8771930 0.9615385 0.9433962 1.0000000
## [8] 0.9803922 0.5263158 0.5882353 0.5319149 0.5434783 0.9090909 0.8620690
## [15] 0.9259259 0.9433962 0.3750000 0.3692308 0.3949580 0.3450704 0.5104167
## [22] 0.5154639 0.5454545 0.5376344 0.1857708 0.1832061 0.1825095 0.1835206
## [29] 0.3164557 0.3378378 0.3448276 0.3381295 0.1496815 0.1649123 0.1493506
## [36] 0.1503067 0.2311558 0.2307692 0.2248804 0.2248804 0.9615385 1.0000000
## [43] 0.9803922 1.0000000 0.9433962 0.9615385 0.9803922 0.9803922 0.8928571
## [50] 0.9411765 0.9433962 0.9411765 0.4285714 0.4260870 0.4210526 0.4433962
## [57] 0.6049383 0.5783133 0.4600000 0.5833333 0.3401361 0.3966942 0.3684211
## [64] 0.3649635 0.2958580 0.2981366 0.2976190 0.2951807 0.3000000 0.3157895
## [71] 0.3076923 0.2941176 0.1470588 0.1351351 0.1525424 0.1392405 NaN
## [78] NaN NaN NaNger_GSP() performs the calculation of germination speed according at the time lapse of the evaluations.
dt <- prosopis
gsp <- ger_GSP(evalName = "D", data = dt)
gsp## [1] 78.12500 81.96721 75.75758 87.71930 96.15385 94.33962 100.00000
## [8] 98.03922 52.63158 58.82353 53.19149 54.34783 90.90909 86.20690
## [15] 92.59259 94.33962 37.50000 36.92308 39.49580 34.50704 51.04167
## [22] 51.54639 54.54545 53.76344 18.57708 18.32061 18.25095 18.35206
## [29] 31.64557 33.78378 34.48276 33.81295 14.96815 16.49123 14.93506
## [36] 15.03067 23.11558 23.07692 22.48804 22.48804 96.15385 100.00000
## [43] 98.03922 100.00000 94.33962 96.15385 98.03922 98.03922 89.28571
## [50] 94.11765 94.33962 94.11765 42.85714 42.60870 42.10526 44.33962
## [57] 60.49383 57.83133 46.00000 58.33333 34.01361 39.66942 36.84211
## [64] 36.49635 29.58580 29.81366 29.76190 29.51807 30.00000 31.57895
## [71] 30.76923 29.41176 14.70588 13.51351 15.25424 13.92405 NaN
## [78] NaN NaN NaNger_SYN() calculates germination synchronization of the germination process.
dt <- prosopis
syn <- ger_SYN(evalName = "D", data = dt)
syn## [1] 0.63020408 0.66612245 0.55591837 0.75428571 0.92163265 0.88489796
## [7] 1.00000000 0.96000000 0.58122449 0.48000000 0.78448980 0.72571429
## [13] 0.81632653 0.72571429 0.84979592 0.88489796 0.38652482 0.40514184
## [19] 0.39222942 0.36224490 0.64795918 0.74612245 0.71631206 0.75428571
## [25] 0.30712303 0.29521277 0.34485816 0.35374150 0.48081633 0.59918367
## [31] 0.50775510 0.57909343 0.39037928 0.26734505 0.29855072 0.40816327
## [37] 0.44057971 0.39982270 0.36077706 0.28029602 0.92163265 1.00000000
## [43] 0.96000000 1.00000000 0.88489796 0.92163265 0.96000000 0.96000000
## [49] 0.88489796 0.88031915 0.92081633 0.88031915 0.50620567 0.42517007
## [55] 0.45744681 0.36725254 0.46768707 0.37322695 0.35458937 0.53826531
## [61] 0.47183673 0.36258865 0.30527211 0.59102041 0.32897959 0.40780142
## [67] 0.37387755 0.36309524 0.26666667 0.26666667 0.16666667 0.30000000
## [73] 0.08888889 0.31111111 0.36111111 0.16363636 NaN NaN
## [79] NaN NaNger_UNC() measures the germination uncertainty into the germination process.
dt <- prosopis
unc <- ger_UNC(evalName = "D", data = dt)
unc## [1] 0.9461447 0.8157272 0.9043815 0.5842388 0.2422922 0.3274449 0.0000000
## [8] 0.1414405 1.0844751 1.1985488 0.5293609 0.6343096 0.4689956 0.6343096
## [15] 0.4021792 0.3274449 1.4171327 1.4081359 1.5822405 1.4950825 0.9281698
## [22] 0.7050757 0.6500224 0.5842388 1.6951591 1.8213883 1.6026878 1.5043742
## [29] 1.5468954 1.0302088 1.2633065 1.0697797 1.6179042 1.9947498 1.9243519
## [36] 1.5071571 1.2646502 1.3775500 1.6034362 1.9685404 0.2422922 0.0000000
## [43] 0.1414405 0.0000000 0.3274449 0.2422922 0.1414405 0.1414405 0.3274449
## [50] 0.3372901 0.2822922 0.3372901 1.1228074 1.3718323 1.2987949 1.4883676
## [57] 1.1796780 1.4531143 1.5098718 1.0214779 1.4015264 1.6336284 1.8160410
## [64] 1.1510457 1.7518407 1.5487081 1.5566689 1.6343886 1.4591479 1.4591479
## [71] 1.5000000 1.3709506 2.6464393 1.4854753 1.3516441 2.1626441 0.0000000
## [78] 0.0000000 0.0000000 0.0000000ger_SDG() estimates the standard deviation of the mean germination time.
dt <- prosopis
sdg <- ger_SDG(evalName = "D", data = dt)
sdg## [1] 0.5728554 0.4646702 0.4712121 0.3505098 0.1979487 0.2398979 0.0000000
## [8] 0.1414214 0.6144518 0.6144518 0.3282607 0.3703280 0.3030458 0.3703280
## [15] 0.2740475 0.2398979 0.6944563 0.8741764 0.9290262 0.7142857 0.4545686
## [22] 0.3730733 0.3766218 0.3505098 1.3113748 1.4433757 1.4438363 1.4869043
## [29] 0.8417668 0.4932193 0.6144518 0.5089395 0.7831481 1.0300750 0.9630868
## [36] 0.9906021 0.5983068 0.6631111 0.7462518 1.0174245 0.1979487 0.0000000
## [43] 0.1414214 0.0000000 0.2398979 0.1979487 0.1414214 0.1414214 0.4797959
## [50] 0.2446230 0.3136357 0.2446230 0.9301872 0.8551564 0.8660254 0.8461700
## [57] 0.5609152 0.7067933 0.8769733 0.5000000 0.6824326 0.7986580 0.9128709
## [64] 0.6942916 0.8545198 0.7576443 0.7216761 0.8615956 0.8164966 0.9831921
## [71] 0.9574271 0.8944272 2.2010099 1.8378732 1.9436506 1.6624188 0.0000000
## [78] 0.0000000 0.0000000 0.0000000ger_CVG() Coefficient of Variance of the Mean Germination Time
dt <- prosopis
cvg <- ger_CVG(evalName = "D", data = dt)
cvg## [1] 44.75433 38.08772 35.69788 30.74648 19.03353 22.63188 0.00000
## [8] 13.86484 32.33957 36.14422 17.46068 20.12652 27.54961 31.92483
## [15] 25.37477 22.63188 26.04211 32.27728 36.69263 24.64789 23.20194
## [22] 19.23058 20.54301 18.84461 24.36151 26.44352 26.35138 27.28776
## [29] 26.63819 16.66282 21.18799 17.20875 11.72228 16.98720 14.38376
## [36] 14.88942 13.83021 15.30256 16.78174 22.87988 19.03353 0.00000
## [43] 13.86484 0.00000 22.63188 19.03353 13.86484 13.86484 42.83892
## [50] 23.02334 29.58827 23.02334 39.86517 36.43710 36.46423 37.51886
## [57] 33.93191 40.87479 40.34077 29.16667 23.21199 31.68230 33.63209
## [64] 25.33911 25.28165 22.58815 21.47845 25.43264 24.49490 31.04817
## [71] 29.45930 26.30668 32.36779 24.83612 29.64891 23.14760 NaN
## [78] NaN NaN NaNger_VGT() compute the variance of the mean during germination time.
dt <- prosopis
vgt <- ger_VGT(evalName = "D", data = dt)
vgt## [1] 0.32816327 0.21591837 0.22204082 0.12285714 0.03918367 0.05755102
## [7] 0.00000000 0.02000000 0.37755102 0.37755102 0.10775510 0.13714286
## [13] 0.09183673 0.13714286 0.07510204 0.05755102 0.48226950 0.76418440
## [19] 0.86308973 0.51020408 0.20663265 0.13918367 0.14184397 0.12285714
## [25] 1.71970398 2.08333333 2.08466312 2.21088435 0.70857143 0.24326531
## [31] 0.37755102 0.25901943 0.61332100 1.06105458 0.92753623 0.98129252
## [37] 0.35797101 0.43971631 0.55689177 1.03515264 0.03918367 0.00000000
## [43] 0.02000000 0.00000000 0.05755102 0.03918367 0.02000000 0.02000000
## [49] 0.23020408 0.05984043 0.09836735 0.05984043 0.86524823 0.73129252
## [55] 0.75000000 0.71600370 0.31462585 0.49955674 0.76908213 0.25000000
## [61] 0.46571429 0.63785461 0.83333333 0.48204082 0.73020408 0.57402482
## [67] 0.52081633 0.74234694 0.66666667 0.96666667 0.91666667 0.80000000
## [73] 4.84444444 3.37777778 3.77777778 2.76363636 0.00000000 0.00000000
## [79] 0.00000000 0.00000000ger_intime() Allow to calculate the accumulative germination
dt <- prosopis
grt <- ger_intime(Factor = "nacl", SeedN = "seeds", evalName = "D", method = "percentage", data = dt)
head(grt, 10)## nacl evaluation mean r std ste
## 1 0 0 0.0 16 0.000000 0.0000000
## 2 0 1 92.5 16 9.451631 2.3629078
## 3 0 10 100.0 16 0.000000 0.0000000
## 4 0 2 99.5 16 1.549193 0.3872983
## 5 0 3 100.0 16 0.000000 0.0000000
## 6 0 4 100.0 16 0.000000 0.0000000
## 7 0 5 100.0 16 0.000000 0.0000000
## 8 0 6 100.0 16 0.000000 0.0000000
## 9 0 7 100.0 16 0.000000 0.0000000
## 10 0 8 100.0 16 0.000000 0.0000000fplot() is generic plot function optimized for publication graphs
dt <- prosopis
grt <- ger_intime(Factor = "nacl", SeedN = "seeds", evalName = "D", method = "percentage", data = dt)
fplot(data = grt, type = "line",
x = "evaluation",
y = "mean",
z = "nacl",
ylab = "Germination ('%')",
xlab = "days", lgl = "NaCl (mM)",
lgd = "top", color = F)