The FLQuantPoint class summarizes the contents of an FLQuant
object with multiple iterations along its sixth dimension using a number of
descriptive statistics.
Details
An object of this class has a set structure along its sixth dimension (iter), which will always be of length 5, and with dimnames mean, median, var, uppq and lowq. They refer, respectively, to the sample mean, sample median, variance, and lower (0.25) and upper (0.75) quantiles.
Objects of this class wil be typically created from an FLQuant. The
various statistics are calculated along the iter dimension of the
original FLQuant using apply.
Slots
- .Data
The main array holding the computed statistics.
array.- units
Units of measurement.
character.
Accesors
- mean,mean<-:
'mean' element on 6th dimension, arithmetic mean.
- median,median<-:
'median' element on 6th dimension, median.
- var,var<-:
'var' element on 6th dimension, variance.
- lowq,lowq<-:
'lowq' element on 6th dimension, lower quantile (0.25 by default).
- uppq,uppq<-:
'uppq' element on 6th dimension, upper quantile (0.75 by default).
- quantile:
returns the 'lowq' or 'uppq' iter, depending on the value of 'probs' (0.25 or 0.75).
Validity
- iter:
iter dimension is of length 5.
- Dimnames:
iter dimnames are 'mean', 'median', 'var', 'uppq' and'lowq'
Examples
flq <- FLQuant(rlnorm(2000), dim=c(10,20,1,1,1,200), units="kg")
flqp <- FLQuantPoint(flq)
flqp <- FLQuantPoint(flq, probs=c(0.05, 0.95))
summary(flqp)
#> An object of class "FLQuantPoint" with:
#> dim : 10 20 1 1 1 5
#> quant: quant
#> units: kg
#>
#> 1st Qu.: 0.2798872
#> Mean : 1.652017
#> Median : 1.072711
#> Var : 4.229131
#> 3rd Qu.: 5.681206
mean(flqp)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5 6 7 8 9
#> 1 1.64518 3.29503 0.91802 2.28167 1.79148 1.21329 1.04418 1.31054 1.59807
#> 2 1.30500 2.70891 1.51304 0.65337 1.26083 2.03442 1.24816 1.84421 1.78849
#> 3 1.72608 1.28226 1.87914 0.90563 2.98732 1.09821 1.56460 1.74708 1.50160
#> 4 1.46081 1.68573 3.36316 1.14047 2.53486 1.33153 1.89569 0.83789 2.07323
#> 5 1.11749 1.92763 0.86117 1.18337 2.33317 1.50922 1.32713 1.04796 1.39750
#> 6 2.25480 2.32396 2.29371 1.38517 1.59304 2.20574 1.33174 1.14754 1.08192
#> 7 1.59368 1.02706 1.27412 2.13379 1.03769 2.39800 1.73798 1.41104 1.56261
#> 8 1.12317 1.20965 1.30224 1.60504 2.38692 1.54433 0.92678 2.15185 1.90446
#> 9 2.00305 1.31471 1.76567 2.12799 2.20142 1.83648 1.55606 1.49897 1.01159
#> 10 2.78900 1.57454 1.84430 1.03200 1.67608 1.44640 0.90141 2.08572 1.48879
#> year
#> quant 10 11 12 13 14 15 16 17 18
#> 1 1.56385 0.69410 1.84355 1.17989 0.78158 2.42777 1.65084 0.94388 2.09289
#> 2 1.10761 0.89736 1.22099 1.89994 1.76549 4.46254 2.02941 1.08578 1.65101
#> 3 1.73257 2.27441 2.05426 2.10051 0.94218 1.26533 1.35407 1.87850 2.98190
#> 4 1.27797 1.93892 1.19289 1.01922 1.61901 2.16790 1.68390 1.58832 1.03811
#> 5 0.92071 1.80061 1.95344 1.86233 1.21863 1.66589 1.40279 1.68220 1.61940
#> 6 1.72523 1.69635 1.14565 0.96003 2.07596 1.55076 0.96603 1.66420 0.82132
#> 7 2.36870 1.03941 2.61099 1.93239 1.36250 1.85875 3.44759 2.26322 1.84290
#> 8 1.37974 1.57554 1.53029 1.66965 3.04222 0.90560 1.36770 2.68153 2.11631
#> 9 0.86522 3.63644 1.06735 1.15863 1.99009 2.30144 1.29083 2.15369 1.14241
#> 10 1.32750 1.20672 1.79150 1.39005 1.80739 0.89218 1.79581 1.45571 0.82504
#> year
#> quant 19 20
#> 1 0.92426 1.09014
#> 2 1.88627 1.64884
#> 3 1.51402 1.35284
#> 4 1.36715 1.42840
#> 5 0.98822 2.28207
#> 6 4.54409 1.28512
#> 7 1.25515 0.91270
#> 8 1.19856 2.69694
#> 9 2.24212 2.63695
#> 10 1.17621 1.46418
#>
#> units: kg
var(flqp)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5 6
#> 1 0.773828 33.013582 0.228222 8.754118 9.362816 1.574253
#> 2 0.350902 3.593010 2.171765 0.132745 1.630882 4.395036
#> 3 4.202308 1.563639 2.142253 0.521406 4.429209 0.809702
#> 4 3.482882 2.885003 37.890217 0.728480 8.297854 4.574640
#> 5 0.606156 2.881837 0.491559 1.439986 4.687646 1.141950
#> 6 3.747210 3.940274 11.886978 2.625321 3.616634 5.002037
#> 7 0.620601 0.394341 1.677674 1.714849 1.000042 4.465067
#> 8 0.536856 1.124677 1.146664 1.535522 8.600610 2.507955
#> 9 3.370610 0.533425 4.943101 3.000160 8.153028 1.129520
#> 10 6.223457 1.315425 3.192170 0.673410 1.813376 1.317185
#> year
#> quant 7 8 9 10 11 12
#> 1 0.931391 1.329629 1.736199 1.371791 0.235844 4.142285
#> 2 1.396252 2.164757 1.011559 1.336895 0.963449 0.848331
#> 3 0.828194 1.675462 0.908875 5.756017 10.346841 2.733438
#> 4 6.455043 0.323461 4.674825 2.081296 3.852284 1.478778
#> 5 0.637611 0.190585 2.216282 0.982077 2.057045 4.307113
#> 6 0.272101 0.605174 1.346165 6.373578 1.262074 0.309458
#> 7 2.273265 0.995984 2.489106 6.969697 1.369903 18.785591
#> 8 2.678302 8.404959 3.326250 2.532460 2.024053 1.246508
#> 9 1.602273 0.887670 0.426192 0.320899 14.333967 1.322233
#> 10 0.521493 9.489667 1.572506 1.508843 0.757442 5.428854
#> year
#> quant 13 14 15 16 17 18
#> 1 0.847675 0.359355 13.193909 2.034201 0.268396 2.864472
#> 2 6.730991 1.750119 13.059929 1.491208 1.240315 1.244067
#> 3 3.370519 0.566554 1.775179 0.767225 3.671908 5.531114
#> 4 0.326756 2.823079 4.319991 5.439551 1.434438 0.645693
#> 5 1.813838 0.271813 2.077980 1.269502 3.237958 4.431929
#> 6 0.576954 5.800504 1.424055 0.808698 1.213016 0.209303
#> 7 5.524967 1.411770 2.656463 40.457672 4.629545 1.698692
#> 8 1.370653 8.769808 0.212540 1.318109 14.448975 1.382577
#> 9 1.482416 7.974917 14.192154 0.768127 7.079203 0.595614
#> 10 3.127598 4.492923 0.268376 2.294440 1.433046 0.258266
#> year
#> quant 19 20
#> 1 0.098087 0.679265
#> 2 3.594618 0.751188
#> 3 0.997108 1.313655
#> 4 1.335136 0.699928
#> 5 0.399927 15.457441
#> 6 112.211678 1.547067
#> 7 1.330136 0.188752
#> 8 0.843808 37.126654
#> 9 2.770756 23.788691
#> 10 1.374000 1.600941
#>
#> units: kg
rnorm(200, flqp)
#> An object of class "FLQuant"
#> iters: 200
#>
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4
#> 1 1.67773( 1.019) 2.79742( 5.173) 0.92174( 0.451) 2.75226( 2.953)
#> 2 1.20156( 0.499) 2.62503( 1.805) 1.35318( 1.395) 0.69246( 0.392)
#> 3 1.25338( 1.852) 1.38833( 1.061) 1.81636( 1.330) 0.81357( 0.744)
#> 4 1.59239( 1.823) 1.67853( 1.733) 3.17443( 6.112) 1.07165( 0.748)
#> 5 1.07626( 0.794) 1.97588( 1.668) 0.81847( 0.588) 1.06652( 1.349)
#> 6 1.93827( 1.650) 2.29044( 2.097) 2.51356( 3.547) 1.32694( 1.439)
#> 7 1.59633( 0.760) 1.08860( 0.637) 1.30988( 1.394) 2.18072( 1.320)
#> 8 1.03983( 0.769) 1.27310( 0.968) 1.29644( 1.023) 1.49768( 1.340)
#> 9 2.00135( 2.085) 1.32907( 0.727) 1.44506( 2.129) 2.21526( 1.819)
#> 10 2.64249( 2.355) 1.68938( 1.227) 1.44610( 1.984) 0.98585( 0.781)
#> year
#> quant 5 6 7 8
#> 1 1.52225( 3.500) 1.15206( 1.120) 0.98372( 0.929) 1.37355( 1.149)
#> 2 1.23594( 1.338) 1.99427( 2.175) 1.25985( 1.010) 1.79425( 1.487)
#> 3 3.14167( 1.860) 1.14661( 0.877) 1.52139( 1.013) 1.67668( 1.370)
#> 4 2.40385( 2.587) 1.25102( 2.143) 1.78835( 2.340) 0.89011( 0.544)
#> 5 2.49375( 1.959) 1.61615( 1.045) 1.28612( 0.796) 1.05873( 0.446)
#> 6 1.37344( 1.728) 2.26892( 2.279) 1.32682( 0.474) 1.07545( 0.760)
#> 7 1.04509( 0.981) 2.28524( 2.105) 1.64015( 1.636) 1.43620( 0.970)
#> 8 2.75003( 3.190) 1.77417( 1.651) 1.01307( 1.718) 1.77110( 3.105)
#> 9 2.03563( 2.833) 1.81603( 0.995) 1.71698( 1.361) 1.55714( 1.020)
#> 10 1.47178( 1.456) 1.52543( 1.119) 0.95600( 0.714) 2.41489( 3.274)
#> year
#> quant 9 10 11 12
#> 1 1.52213( 1.266) 1.54261( 1.190) 0.63911( 0.512) 1.94004( 2.013)
#> 2 1.83411( 0.996) 1.21350( 1.070) 0.96055( 1.040) 1.30801( 1.051)
#> 3 1.58561( 0.977) 1.83113( 2.538) 1.88451( 3.056) 2.16063( 1.738)
#> 4 2.28693( 2.044) 1.24561( 1.506) 1.80938( 1.771) 1.21654( 1.239)
#> 5 1.44150( 1.481) 0.86161( 0.837) 1.74544( 1.480) 1.95573( 2.096)
#> 6 0.93106( 1.101) 1.68451( 2.635) 1.40890( 1.254) 1.19512( 0.499)
#> 7 1.63187( 1.629) 2.33292( 2.311) 1.05828( 1.210) 2.82641( 4.279)
#> 8 1.59665( 1.878) 1.42625( 1.574) 1.70571( 1.213) 1.42958( 1.029)
#> 9 1.03911( 0.690) 0.88049( 0.548) 2.88800( 4.185) 0.90842( 1.147)
#> 10 1.35472( 1.366) 1.18378( 1.353) 1.24765( 0.859) 1.81168( 2.149)
#> year
#> quant 13 14 15 16
#> 1 1.04557( 0.926) 0.73765( 0.545) 2.29027( 3.929) 1.61029( 1.513)
#> 2 2.03138( 2.859) 1.63707( 1.193) 3.95733( 3.334) 2.03255( 1.339)
#> 3 2.10061( 1.844) 0.90685( 0.882) 1.16351( 1.360) 1.44278( 0.927)
#> 4 1.03296( 0.613) 1.61995( 1.682) 1.96936( 2.389) 1.51504( 2.308)
#> 5 1.92218( 1.516) 1.19131( 0.518) 1.81635( 1.538) 1.29339( 1.019)
#> 6 1.01386( 0.867) 2.07077( 2.397) 1.42662( 1.332) 0.85362( 0.924)
#> 7 1.88584( 2.287) 1.45788( 1.276) 2.02199( 1.579) 3.27482( 5.897)
#> 8 1.73950( 0.987) 3.27551( 3.193) 0.91520( 0.428) 1.29996( 1.128)
#> 9 1.02784( 1.418) 2.28569( 3.053) 2.67612( 3.865) 1.20793( 0.848)
#> 10 1.54345( 2.026) 1.91687( 1.992) 0.80519( 0.526) 1.75438( 1.676)
#> year
#> quant 17 18 19 20
#> 1 0.92830( 0.486) 2.19043( 1.479) 0.88994( 0.358) 1.03843( 0.774)
#> 2 0.96903( 1.172) 1.47229( 1.215) 2.09426( 1.721) 1.65030( 0.773)
#> 3 1.75553( 1.958) 2.73600( 2.236) 1.60659( 1.151) 1.21382( 1.024)
#> 4 1.52115( 1.362) 0.98726( 0.769) 1.34391( 1.141) 1.50673( 0.865)
#> 5 2.07968( 2.104) 1.38206( 2.095) 0.97864( 0.644) 1.56586( 4.502)
#> 6 1.64590( 1.083) 0.86576( 0.378) 5.82077(10.229) 1.45342( 1.226)
#> 7 2.47404( 2.099) 2.00410( 1.352) 1.28344( 1.127) 0.98570( 0.424)
#> 8 2.67289( 3.564) 1.94825( 1.304) 1.05250( 1.027) 2.45178( 5.420)
#> 9 2.43746( 2.605) 1.17187( 0.706) 2.56057( 1.818) 2.39662( 5.306)
#> 10 1.52610( 0.980) 0.78980( 0.482) 1.20188( 1.196) 1.45607( 1.110)
#>
#> units: kg