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 x of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5 6 7 8 9 10 11 12
#> 1 1.645 3.295 0.918 2.282 1.791 1.213 1.044 1.311 1.598 1.564 0.694 1.844
#> 2 1.305 2.709 1.513 0.653 1.261 2.034 1.248 1.844 1.788 1.108 0.897 1.221
#> 3 1.726 1.282 1.879 0.906 2.987 1.098 1.565 1.747 1.502 1.733 2.274 2.054
#> 4 1.461 1.686 3.363 1.140 2.535 1.332 1.896 0.838 2.073 1.278 1.939 1.193
#> 5 1.117 1.928 0.861 1.183 2.333 1.509 1.327 1.048 1.397 0.921 1.801 1.953
#> 6 2.255 2.324 2.294 1.385 1.593 2.206 1.332 1.148 1.082 1.725 1.696 1.146
#> 7 1.594 1.027 1.274 2.134 1.038 2.398 1.738 1.411 1.563 2.369 1.039 2.611
#> 8 1.123 1.210 1.302 1.605 2.387 1.544 0.927 2.152 1.904 1.380 1.576 1.530
#> 9 2.003 1.315 1.766 2.128 2.201 1.836 1.556 1.499 1.012 0.865 3.636 1.067
#> 10 2.789 1.575 1.844 1.032 1.676 1.446 0.901 2.086 1.489 1.328 1.207 1.792
#> year
#> quant 13 14 15 16 17 18 19 20
#> 1 1.180 0.782 2.428 1.651 0.944 2.093 0.924 1.090
#> 2 1.900 1.765 4.463 2.029 1.086 1.651 1.886 1.649
#> 3 2.101 0.942 1.265 1.354 1.878 2.982 1.514 1.353
#> 4 1.019 1.619 2.168 1.684 1.588 1.038 1.367 1.428
#> 5 1.862 1.219 1.666 1.403 1.682 1.619 0.988 2.282
#> 6 0.960 2.076 1.551 0.966 1.664 0.821 4.544 1.285
#> 7 1.932 1.362 1.859 3.448 2.263 1.843 1.255 0.913
#> 8 1.670 3.042 0.906 1.368 2.682 2.116 1.199 2.697
#> 9 1.159 1.990 2.301 1.291 2.154 1.142 2.242 2.637
#> 10 1.390 1.807 0.892 1.796 1.456 0.825 1.176 1.464
#>
#> units: kg
var(flqp)
#> An x of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5 6 7 8
#> 1 0.7738 33.0136 0.2282 8.7541 9.3628 1.5743 0.9314 1.3296
#> 2 0.3509 3.5930 2.1718 0.1327 1.6309 4.3950 1.3963 2.1648
#> 3 4.2023 1.5636 2.1423 0.5214 4.4292 0.8097 0.8282 1.6755
#> 4 3.4829 2.8850 37.8902 0.7285 8.2979 4.5746 6.4550 0.3235
#> 5 0.6062 2.8818 0.4916 1.4400 4.6876 1.1419 0.6376 0.1906
#> 6 3.7472 3.9403 11.8870 2.6253 3.6166 5.0020 0.2721 0.6052
#> 7 0.6206 0.3943 1.6777 1.7148 1.0000 4.4651 2.2733 0.9960
#> 8 0.5369 1.1247 1.1467 1.5355 8.6006 2.5080 2.6783 8.4050
#> 9 3.3706 0.5334 4.9431 3.0002 8.1530 1.1295 1.6023 0.8877
#> 10 6.2235 1.3154 3.1922 0.6734 1.8134 1.3172 0.5215 9.4897
#> year
#> quant 9 10 11 12 13 14 15 16
#> 1 1.7362 1.3718 0.2358 4.1423 0.8477 0.3594 13.1939 2.0342
#> 2 1.0116 1.3369 0.9634 0.8483 6.7310 1.7501 13.0599 1.4912
#> 3 0.9089 5.7560 10.3468 2.7334 3.3705 0.5666 1.7752 0.7672
#> 4 4.6748 2.0813 3.8523 1.4788 0.3268 2.8231 4.3200 5.4396
#> 5 2.2163 0.9821 2.0570 4.3071 1.8138 0.2718 2.0780 1.2695
#> 6 1.3462 6.3736 1.2621 0.3095 0.5770 5.8005 1.4241 0.8087
#> 7 2.4891 6.9697 1.3699 18.7856 5.5250 1.4118 2.6565 40.4577
#> 8 3.3263 2.5325 2.0241 1.2465 1.3707 8.7698 0.2125 1.3181
#> 9 0.4262 0.3209 14.3340 1.3222 1.4824 7.9749 14.1922 0.7681
#> 10 1.5725 1.5088 0.7574 5.4289 3.1276 4.4929 0.2684 2.2944
#> year
#> quant 17 18 19 20
#> 1 0.2684 2.8645 0.0981 0.6793
#> 2 1.2403 1.2441 3.5946 0.7512
#> 3 3.6719 5.5311 0.9971 1.3137
#> 4 1.4344 0.6457 1.3351 0.6999
#> 5 3.2380 4.4319 0.3999 15.4574
#> 6 1.2130 0.2093 112.2117 1.5471
#> 7 4.6295 1.6987 1.3301 0.1888
#> 8 14.4490 1.3826 0.8438 37.1267
#> 9 7.0792 0.5956 2.7708 23.7887
#> 10 1.4330 0.2583 1.3740 1.6009
#>
#> units: kg
rnorm(200, flqp)
#> An x of class "FLQuant"
#> iters: 200
#>
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5
#> 1 1.678( 1.019) 2.797( 5.173) 0.922( 0.451) 2.752( 2.953) 1.522( 3.500)
#> 2 1.202( 0.499) 2.625( 1.805) 1.353( 1.395) 0.692( 0.392) 1.236( 1.338)
#> 3 1.253( 1.852) 1.388( 1.061) 1.816( 1.330) 0.814( 0.744) 3.142( 1.860)
#> 4 1.592( 1.823) 1.679( 1.733) 3.174( 6.112) 1.072( 0.748) 2.404( 2.587)
#> 5 1.076( 0.794) 1.976( 1.668) 0.818( 0.588) 1.067( 1.349) 2.494( 1.959)
#> 6 1.938( 1.650) 2.290( 2.097) 2.514( 3.547) 1.327( 1.439) 1.373( 1.728)
#> 7 1.596( 0.760) 1.089( 0.637) 1.310( 1.394) 2.181( 1.320) 1.045( 0.981)
#> 8 1.040( 0.769) 1.273( 0.968) 1.296( 1.023) 1.498( 1.340) 2.750( 3.190)
#> 9 2.001( 2.085) 1.329( 0.727) 1.445( 2.129) 2.215( 1.819) 2.036( 2.833)
#> 10 2.642( 2.355) 1.689( 1.227) 1.446( 1.984) 0.986( 0.781) 1.472( 1.456)
#> year
#> quant 6 7 8 9 10
#> 1 1.152( 1.120) 0.984( 0.929) 1.374( 1.149) 1.522( 1.266) 1.543( 1.190)
#> 2 1.994( 2.175) 1.260( 1.010) 1.794( 1.487) 1.834( 0.996) 1.214( 1.070)
#> 3 1.147( 0.877) 1.521( 1.013) 1.677( 1.370) 1.586( 0.977) 1.831( 2.538)
#> 4 1.251( 2.143) 1.788( 2.340) 0.890( 0.544) 2.287( 2.044) 1.246( 1.506)
#> 5 1.616( 1.045) 1.286( 0.796) 1.059( 0.446) 1.442( 1.481) 0.862( 0.837)
#> 6 2.269( 2.279) 1.327( 0.474) 1.075( 0.760) 0.931( 1.101) 1.685( 2.635)
#> 7 2.285( 2.105) 1.640( 1.636) 1.436( 0.970) 1.632( 1.629) 2.333( 2.311)
#> 8 1.774( 1.651) 1.013( 1.718) 1.771( 3.105) 1.597( 1.878) 1.426( 1.574)
#> 9 1.816( 0.995) 1.717( 1.361) 1.557( 1.020) 1.039( 0.690) 0.880( 0.548)
#> 10 1.525( 1.119) 0.956( 0.714) 2.415( 3.274) 1.355( 1.366) 1.184( 1.353)
#> year
#> quant 11 12 13 14 15
#> 1 0.639( 0.512) 1.940( 2.013) 1.046( 0.926) 0.738( 0.545) 2.290( 3.929)
#> 2 0.961( 1.040) 1.308( 1.051) 2.031( 2.859) 1.637( 1.193) 3.957( 3.334)
#> 3 1.885( 3.056) 2.161( 1.738) 2.101( 1.844) 0.907( 0.882) 1.164( 1.360)
#> 4 1.809( 1.771) 1.217( 1.239) 1.033( 0.613) 1.620( 1.682) 1.969( 2.389)
#> 5 1.745( 1.480) 1.956( 2.096) 1.922( 1.516) 1.191( 0.518) 1.816( 1.538)
#> 6 1.409( 1.254) 1.195( 0.499) 1.014( 0.867) 2.071( 2.397) 1.427( 1.332)
#> 7 1.058( 1.210) 2.826( 4.279) 1.886( 2.287) 1.458( 1.276) 2.022( 1.579)
#> 8 1.706( 1.213) 1.430( 1.029) 1.739( 0.987) 3.276( 3.193) 0.915( 0.428)
#> 9 2.888( 4.185) 0.908( 1.147) 1.028( 1.418) 2.286( 3.053) 2.676( 3.865)
#> 10 1.248( 0.859) 1.812( 2.149) 1.543( 2.026) 1.917( 1.992) 0.805( 0.526)
#> year
#> quant 16 17 18 19 20
#> 1 1.610( 1.513) 0.928( 0.486) 2.190( 1.479) 0.890( 0.358) 1.038( 0.774)
#> 2 2.033( 1.339) 0.969( 1.172) 1.472( 1.215) 2.094( 1.721) 1.650( 0.773)
#> 3 1.443( 0.927) 1.756( 1.958) 2.736( 2.236) 1.607( 1.151) 1.214( 1.024)
#> 4 1.515( 2.308) 1.521( 1.362) 0.987( 0.769) 1.344( 1.141) 1.507( 0.865)
#> 5 1.293( 1.019) 2.080( 2.104) 1.382( 2.095) 0.979( 0.644) 1.566( 4.502)
#> 6 0.854( 0.924) 1.646( 1.083) 0.866( 0.378) 5.821(10.229) 1.453( 1.226)
#> 7 3.275( 5.897) 2.474( 2.099) 2.004( 1.352) 1.283( 1.127) 0.986( 0.424)
#> 8 1.300( 1.128) 2.673( 3.564) 1.948( 1.304) 1.052( 1.027) 2.452( 5.420)
#> 9 1.208( 0.848) 2.437( 2.605) 1.172( 0.706) 2.561( 1.818) 2.397( 5.306)
#> 10 1.754( 1.676) 1.526( 0.980) 0.790( 0.482) 1.202( 1.196) 1.456( 1.110)
#>
#> units: kg