IKFA: Imbrie & Kipp Factor Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/IKFA.r

Description

Functions for reconstructing (predicting) environmental values from biological assemblages using Imbrie & Kipp Factor Analysis (IKFA), as used in palaeoceanography.

Usage

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IKFA(y, x, nFact = 5, IsPoly = FALSE, IsRot = TRUE, 
      ccoef = 1:nFact, check.data=TRUE, lean=FALSE, ...)

IKFA.fit(y, x, nFact = 5, IsPoly = FALSE, IsRot = TRUE, 
      ccoef = 1:nFact, lean=FALSE)

## S3 method for class 'IKFA'
 predict(object, newdata=NULL, sse=FALSE, nboot=100,
      match.data=TRUE, verbose=TRUE, ...)

communality(object, y)

## S3 method for class 'IKFA'
crossval(object, cv.method="loo", verbose=TRUE, ngroups=10,
      nboot=100, h.cutoff=0, h.dist=NULL, ...)

## S3 method for class 'IKFA'
performance(object, ...)

## S3 method for class 'IKFA'
rand.t.test(object, n.perm=999, ...)

## S3 method for class 'IKFA'
screeplot(x, rand.test=TRUE, ...)

## S3 method for class 'IKFA'
print(x, ...)

## S3 method for class 'IKFA'
summary(object, full=FALSE, ...)

## S3 method for class 'IKFA'
plot(x, resid=FALSE, xval=FALSE, nFact=max(x$ccoef), 
      xlab="", ylab="", ylim=NULL, xlim=NULL, add.ref=TRUE,
      add.smooth=FALSE, ...)

## S3 method for class 'IKFA'
residuals(object, cv=FALSE, ...)

## S3 method for class 'IKFA'
coef(object, ...)

## S3 method for class 'IKFA'
fitted(object, ...)

Arguments

y

a data frame or matrix of biological abundance data.

x, object

a vector of environmental values to be modelled or an object of class wa.

newdata

new biological data to be predicted.

nFact

number of factor to extract.

IsRot

logical to rotate factors.

ccoef

vector of factor numbers to include in the predictions.

IsPoly

logical to include quadratic of the factors as predictors in the regression.

check.data

logical to perform simple checks on the input data.

match.data

logical indicate the function will match two species datasets by their column names. You should only set this to FALSE if you are sure the column names match exactly.

lean

logical to exclude some output from the resulting models (used when cross-validating to speed calculations).

full

logical to show head and tail of output in summaries.

resid

logical to plot residuals instead of fitted values.

xval

logical to plot cross-validation estimates.

xlab, ylab, xlim, ylim

additional graphical arguments to plot.wa.

add.ref

add 1:1 line on plot.

add.smooth

add loess smooth to plot.

cv.method

cross-validation method, either "loo", "lgo", "bootstrap" or "h-block".

verbose

logical to show feedback during cross-validation.

nboot

number of bootstrap samples.

ngroups

number of groups in leave-group-out cross-validation, or a vector contain leave-out group menbership.

h.cutoff

cutoff for h-block cross-validation. Only training samples greater than h.cutoff from each test sample will be used.

h.dist

distance matrix for use in h-block cross-validation. Usually a matrix of geographical distances between samples.

sse

logical indicating that sample specific errors should be calculated.

rand.test

logical to perform a randomisation t-test to test significance of cross validated factors.

n.perm

number of permutations for randomisation t-test.

cv

logical to indicate model or cross-validation residuals.

...

additional arguments.

Details

Function IKFA performs Imbrie and Kipp Factor Analysis, a form of Principal Components Regrssion (Imbrie & Kipp 1971).

Function predict predicts values of the environemntal variable for newdata or returns the fitted (predicted) values from the original modern dataset if newdata is NULL. Variables are matched between training and newdata by column name (if match.data is TRUE). Use compare.datasets to assess conformity of two species datasets and identify possible no-analogue samples.

IKFA has methods fitted and rediduals that return the fitted values (estimates) and residuals for the training set, performance, which returns summary performance statistics (see below), coef which returns the species coefficients, and print and summary to summarise the output. IKFA also has a plot method that produces scatter plots of predicted vs observed measurements for the training set.

Function rand.t.test performs a randomisation t-test to test the significance of the cross-validated components after van der Voet (1994).

Function screeplot displays the RMSE of prediction for the training set as a function of the number of factors and is useful for estimating the optimal number for use in prediction. By default screeplot will also carry out a randomisation t-test and add a line to scree plot indicating percentage change in RMSE with each component annotate with the p-value from the randomisation test.

Value

Function IKFA returns an object of class IKFA with the following named elements:

coefficients

species coefficients (the updated "optima").

fitted.values

fitted values for the training set.

call

original function call.

x

environmental variable used in the model.

standx, meanT sdx

additional information returned for a PLSif model.

Function crossval also returns an object of class IKFA and adds the following named elements:

predicted

predicted values of each training set sample under cross-validation.

residuals.cv

prediction residuals.

If function predict is called with newdata=NULL it returns the fitted values of the original model, otherwise it returns a list with the following named elements:

fit

predicted values for newdata.

If sample specific errors were requested the list will also include:

fit.boot

mean of the bootstrap estimates of newdata.

v1

standard error of the bootstrap estimates for each new sample.

v2

root mean squared error for the training set samples, across all bootstram samples.

SEP

standard error of prediction, calculated as the square root of v1^2 + v2^2.

Function performance returns a matrix of performance statistics for the IKFA model. See performance, for a description of the summary.

Function rand.t.test returns a matrix of performance statistics together with columns indicating the p-value and percentage change in RMSE with each higher component (see van der Veot (1994) for details).

Author(s)

Steve Juggins

References

Imbrie, J. & Kipp, N.G. (1971). A new micropaleontological method for quantitative paleoclimatology: application to a Late Pleistocene Caribbean core. In The Late Cenozoic Glacial Ages (ed K.K. Turekian), pp. 77-181. Yale University Press, New Haven.

van der Voet, H. (1994) Comparing the predictive accuracy of models uing a simple randomization test. Chemometrics and Intelligent Laboratory Systems, 25, 313-323.

See Also

WA, MAT, performance, and compare.datasets for diagnostics.

Examples

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data(IK)
spec <- IK$spec
SumSST <- IK$env$SumSST
core <- IK$core

fit <- IKFA(spec, SumSST)
fit
# cross-validate model
fit.cv <- crossval(fit, cv.method="lgo")
# How many components to use?
screeplot(fit.cv)

#predict the core
pred <- predict(fit, core, npls=2)

#plot predictions - depths are in rownames
depth <- as.numeric(rownames(core))
plot(depth, pred$fit[, 2], type="b")

# fit using only factors 1, 2, 4, & 5
# and using polynomial terms
# as Imbrie & Kipp (1971)
fit2 <- IKFA(spec, SumSST, ccoef=c(1, 2, 4, 5), IsPoly=TRUE)
fit2.cv <- crossval(fit2, cv.method="lgo")
screeplot(fit2.cv)

## Not run: 
# predictions with sample specific errors
# takes approximately 1 minute to run
pred <- predict(fit, core, sse=TRUE, nboot=1000)
pred

## End(Not run)

Example output

This is rioja 0.9-15.1

Method : Imbrie & Kipp Factor Analysis
Call   : IKFA(y = spec, x = SumSST) 

No. samples        : 61 
No. species        : 22 
Cross val.         : none 


Performance:
          RMSE      R2  Avg.Bias  Max.Bias    Skill
Fact01  2.9574  0.8225         0   10.1179  82.2514
Fact02  2.4469  0.8785         0    7.6401  87.8502
Fact03  1.7131  0.9404         0    4.5283  94.0444
Fact04  1.6994  0.9414         0    4.7206  94.1392
Fact05  1.6253  0.9464         0    4.5601  94.6391

Cross-validating:

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Cross-validating:

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Bootstrapping for SSE:

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$fit
       Fact01   Fact02   Fact03   Fact04   Fact05
0    26.17377 25.20421 25.99410 26.13490 26.69804
10   25.09155 24.09232 25.22921 25.45020 26.23556
20   26.84215 25.83762 26.45985 26.52829 26.81555
30   27.25060 26.39101 26.75764 26.74160 26.80372
40   27.06840 26.65739 26.68012 26.69782 26.93626
50   27.41843 26.87623 26.87986 26.90828 26.81295
60   27.40545 26.83915 26.91277 26.91783 26.61837
70   27.33745 26.60499 26.84098 26.83155 26.81758
80   27.33194 26.78732 26.83524 26.88406 26.77920
90   26.34908 26.30080 26.21653 26.18984 26.42555
100  27.15893 26.55885 26.68919 26.72784 26.53171
110  26.35889 25.47486 26.13716 26.08041 26.08396
120  27.08390 26.50814 26.57097 26.57363 26.28581
130  26.72332 26.25390 26.44068 26.40239 26.41039
140  27.02463 26.13311 26.61476 26.56192 26.53803
150  27.29603 26.67541 26.79420 26.78050 26.83234
160  27.09958 26.49562 26.66271 26.64107 26.75372
170  27.52321 26.65366 26.97388 26.99888 26.68452
180  26.88374 25.72244 26.53598 26.53803 26.26108
190  26.21730 25.30960 26.02204 26.13929 26.24818
200  26.55728 25.49298 26.29058 26.36278 26.47228
210  26.97663 26.03186 26.40709 26.48063 26.56242
220  26.36991 25.32111 26.13585 26.27194 26.40473
230  26.24690 25.20725 26.04836 26.18069 26.51756
240  26.00194 24.99170 25.86951 26.07703 26.17285
250  26.84451 25.81191 26.47649 26.44509 26.55745
260  26.55066 25.52414 26.28419 26.34834 26.55283
270  25.20280 24.08214 25.32924 25.46672 25.79619
280  26.45973 25.46835 26.16812 26.19986 26.31703
290  26.10925 25.00593 25.98058 26.04618 26.37786
300  26.94978 26.40884 26.58111 26.62301 26.79693
310  25.38642 25.01939 25.50527 25.51648 26.12643
320  26.68661 26.29898 26.40132 26.40425 26.72640
330  26.27284 25.94440 26.19338 26.06541 26.50952
340  27.18754 26.98244 26.77672 26.79638 26.80911
350  26.70821 25.99889 26.39082 26.44825 26.67462
360  27.30180 26.27993 26.79721 26.82240 26.64405
370  26.40551 26.01890 26.16341 26.32960 26.54581
380  26.05072 25.13324 25.88176 26.03708 26.38759
390  26.37240 25.92335 26.13120 26.20997 26.54003
400  26.42043 25.54893 26.17832 26.04335 26.47275
410  26.21215 25.25776 25.91211 26.09616 26.07068
420  26.72807 25.76251 26.30682 26.41337 26.49410
430  26.57528 25.56285 26.26248 26.36517 26.38193
440  26.13460 25.19194 25.93265 25.98060 26.35123
450  25.31568 24.38661 25.38630 25.59765 26.31514
460  25.83200 24.90281 25.76067 25.94242 26.50071
470  26.99870 26.37246 26.58904 26.61447 26.90006
480  26.83478 25.88688 26.42092 26.27235 26.48026
490  25.50233 24.62888 25.49233 25.68190 26.33555
500  25.86476 24.98946 25.74862 25.90313 26.51045
510  24.44183 23.65781 24.75296 24.92693 25.68297
520  26.55553 25.78747 26.21217 26.13490 26.38156
530  27.12141 26.93761 26.70285 26.67360 26.83006
540  27.21235 27.30647 26.77604 26.76557 26.63124
550  25.95040 26.44725 25.99310 25.80515 26.47995
560  25.80905 24.60966 25.79356 25.58104 26.16337
570  25.79344 25.01793 25.73787 25.55839 26.40845
580  26.76547 25.87410 26.42937 26.22667 26.52251
590  27.39408 26.77184 26.86015 26.94601 26.86874
600  26.26671 25.30808 26.07669 26.05410 26.53043
610  26.53827 25.50607 26.29345 26.30934 26.23679
620  25.51693 25.17398 25.59115 25.67449 26.29400
630  26.45141 25.32757 26.19930 26.10619 26.37652
640  26.09431 25.15078 25.90979 25.98406 26.47176
650  26.26684 25.34574 26.07131 26.11480 26.56666
660  26.58853 26.15195 26.29764 26.32536 26.69342
670  26.92949 25.94246 25.02847 24.82331 25.20153
680  26.46700 25.55394 25.17995 25.16976 24.96855
690  27.16540 26.29847 26.67124 26.83735 26.27961
700  26.54763 26.08583 26.31331 26.37310 26.75149
710  27.02626 26.11282 26.32692 26.44974 26.33044
720  26.59296 25.90226 26.32823 26.38677 26.07576
730  25.81301 24.87668 25.53763 25.61967 25.71619
740  25.87518 25.00099 25.76044 25.78591 26.45996
750  26.59443 25.55856 26.15966 26.12400 26.44500
760  26.07772 25.26048 25.92111 26.00814 26.55466
770  27.07338 26.11331 26.58331 26.71198 26.43254
780  25.72095 24.75433 25.62878 25.89887 25.98598
790  25.97425 24.99289 25.82928 26.03440 26.36784
800  26.31049 25.39794 26.06247 26.23585 26.57857
810  26.70623 25.72130 26.36362 26.41864 26.69934
820  25.57151 24.70065 25.46264 25.33412 26.03908
830  26.04207 26.29353 26.00934 26.02123 26.78558
840  25.18341 25.44524 25.38003 25.53102 26.41339
850  25.20352 24.48913 25.30191 25.39740 26.19718
860  27.00635 26.02771 26.55314 26.48942 26.15205
870  27.16223 26.43230 26.67814 26.74004 26.36323
880  25.91580 25.60417 25.87909 25.85742 26.38523
890  24.24023 24.61720 24.69850 24.36499 25.19128
900  25.01401 24.79497 24.89018 24.64571 25.29753
910  26.55061 26.80730 26.38507 26.23546 26.41352
920  25.32931 25.53685 25.46269 25.46015 25.74227
930  26.12719 25.49458 25.98958 26.05026 26.33928
940  26.72417 26.24455 26.40789 26.48205 26.77117
950  26.11981 25.03244 26.00586 26.07352 26.27631
960  26.84078 26.02856 26.49502 26.48090 26.76859
970  26.85241 26.34976 26.49197 26.52265 26.90281
980  26.67921 25.72444 26.36909 26.28091 26.53216
990  25.46547 24.37575 25.51791 25.48044 25.97554
1000 26.39862 25.63817 26.20525 26.14995 26.43793
1010 23.81337 22.62707 24.47845 24.32367 24.82563
1020 23.35717 24.09752 24.20139 23.93579 24.81800
1030 25.44724 24.81467 25.56292 25.24618 25.75660
1040 24.21123 23.21422 24.55945 24.26658 25.11971
1050 25.91519 25.76531 25.89266 25.82463 26.41021
1060 23.50525 23.57856 24.22337 24.14795 25.31499
1070 26.66006 26.38723 26.38114 26.11420 26.44401
1080 25.95444 25.45391 25.91193 25.90021 26.49624
1090 26.45737 25.94558 26.24486 26.14607 26.73265

$fit.boot
       Fact01   Fact02   Fact03   Fact04   Fact05
0    26.27736 25.75880 26.15214 26.29915 26.68180
10   25.23141 24.68484 25.45810 25.68417 26.22575
20   26.90846 26.42229 26.55183 26.61678 26.78754
30   27.29116 26.94187 26.78511 26.76759 26.76890
40   27.10919 26.86991 26.71592 26.74532 26.88739
50   27.43017 27.07769 26.89759 26.88760 26.77223
60   27.40652 27.10571 26.90031 26.84508 26.56189
70   27.34106 27.00480 26.84394 26.82314 26.77920
80   27.35970 26.99126 26.86866 26.86797 26.73294
90   26.38311 26.41048 26.22460 26.22645 26.37070
100  27.16195 26.79589 26.70916 26.69164 26.50188
110  26.33983 26.13988 26.11815 26.07123 26.06517
120  27.07685 26.76460 26.56071 26.50623 26.24681
130  26.74582 26.61819 26.43728 26.39903 26.36364
140  27.02547 26.75561 26.59905 26.54844 26.50020
150  27.29514 26.99770 26.79486 26.78183 26.79733
160  27.11150 26.85231 26.66886 26.65919 26.71514
170  27.51678 27.07072 26.97323 26.92950 26.64248
180  26.87762 26.46284 26.53206 26.47822 26.22068
190  26.28170 25.80852 26.13131 26.20078 26.22691
200  26.61059 26.14144 26.36949 26.41131 26.44251
210  27.03120 26.48162 26.48868 26.52755 26.54755
220  26.43609 25.88150 26.25653 26.34082 26.39724
230  26.33162 25.80126 26.18514 26.29521 26.51078
240  26.08548 25.45735 26.03321 26.15548 26.18194
250  26.85804 26.52487 26.49001 26.47259 26.54346
260  26.59807 26.15261 26.36096 26.41295 26.54136
270  25.27620 24.82564 25.46954 25.58278 25.81052
280  26.46861 26.07131 26.21501 26.23845 26.32476
290  26.16057 25.74279 26.06717 26.13786 26.36904
300  26.99129 26.67974 26.63038 26.66571 26.75410
310  25.47438 25.44090 25.58643 25.66202 26.07932
320  26.74446 26.57432 26.44754 26.47839 26.67955
330  26.30846 26.43806 26.16516 26.13405 26.44361
340  27.19640 26.99243 26.78462 26.78645 26.76140
350  26.74009 26.36483 26.45110 26.50528 26.64473
360  27.31578 26.84788 26.81862 26.79266 26.60675
370  26.47807 26.04900 26.29453 26.41162 26.52624
380  26.14782 25.61510 26.04104 26.16786 26.38037
390  26.44970 26.14892 26.23244 26.31107 26.51294
400  26.50166 26.43221 26.19563 26.15521 26.43685
410  26.29620 25.65782 26.06281 26.15268 26.06034
420  26.78844 26.23183 26.40797 26.46706 26.47598
430  26.64372 26.12231 26.36574 26.41227 26.36305
440  26.21500 25.83930 26.03709 26.10171 26.34019
450  25.45036 24.91381 25.60574 25.81753 26.32234
460  25.93379 25.39006 25.93769 26.11107 26.49963
470  27.03481 26.70829 26.63892 26.68130 26.87648
480  26.87727 26.74489 26.40820 26.33079 26.46436
490  25.62829 25.11284 25.69810 25.88867 26.35071
500  25.99466 25.50146 25.93541 26.09658 26.50963
510  24.54980 24.18628 24.94896 25.14667 25.68694
520  26.58870 26.41309 26.22976 26.20401 26.38439
530  27.18483 27.07987 26.72569 26.71224 26.77094
540  27.22356 27.15063 26.76223 26.72286 26.57236
550  25.99878 26.51090 25.94073 25.90579 26.40703
560  25.90083 25.96200 25.78765 25.71726 26.12243
570  25.89014 25.99311 25.76207 25.75424 26.38522
580  26.75825 26.75669 26.36108 26.26631 26.50254
590  27.43149 26.96415 26.92009 26.94641 26.83174
600  26.34011 26.08049 26.14411 26.18111 26.52034
610  26.58495 26.22955 26.33496 26.31532 26.20712
620  25.62944 25.46554 25.72001 25.84073 26.26329
630  26.46617 26.25480 26.19485 26.16132 26.36188
640  26.14589 25.73245 26.01379 26.11473 26.47958
650  26.32981 25.97592 26.15905 26.23333 26.55747
660  26.63402 26.39376 26.35801 26.41474 26.67468
670  26.94822 26.34625 24.96929 24.86712 25.16894
680  26.47695 25.78931 25.19020 25.13179 24.96123
690  27.17486 26.51215 26.74006 26.74794 26.24893
700  26.62715 26.36612 26.39929 26.47142 26.71504
710  27.07405 26.39823 26.41767 26.45739 26.31153
720  26.61289 26.25444 26.36262 26.33935 26.04549
730  25.88880 25.42872 25.63838 25.68171 25.69963
740  25.95270 25.66310 25.86457 25.96083 26.46345
750  26.64729 26.30277 26.20007 26.20511 26.42553
760  26.16420 25.77313 26.04617 26.16074 26.54276
770  27.11522 26.49529 26.67158 26.69445 26.42046
780  25.82286 25.11861 25.83709 25.99634 26.00509
790  26.07773 25.46200 26.01400 26.16824 26.37666
800  26.39620 25.80927 26.22128 26.35954 26.57995
810  26.76164 26.31743 26.44567 26.50289 26.68597
820  25.65064 25.62375 25.49026 25.49193 26.00758
830  26.12391 26.21181 26.07682 26.17711 26.73029
840  25.30339 25.23875 25.54384 25.74549 26.37061
850  25.33907 25.05937 25.47206 25.62479 26.18086
860  27.00829 26.70992 26.53099 26.42938 26.12856
870  27.19139 26.75075 26.71846 26.68651 26.33610
880  26.00952 25.99553 25.94449 25.98702 26.33967
890  24.34438 25.20578 24.63493 24.52644 25.11565
900  25.14063 25.48535 24.88526 24.80121 25.23993
910  26.57083 26.87787 26.31991 26.23769 26.33965
920  25.42769 25.58482 25.52803 25.54703 25.69386
930  26.21051 25.92605 26.08501 26.14479 26.30678
940  26.78657 26.45822 26.49167 26.56243 26.73715
950  26.16972 25.75943 26.08290 26.13469 26.24107
960  26.90137 26.62172 26.53980 26.55354 26.72405
970  26.91042 26.62890 26.55739 26.61496 26.86553
980  26.73813 26.55024 26.38527 26.34812 26.49374
990  25.56071 25.38391 25.59312 25.61943 25.94243
1000 26.44399 26.29784 26.22417 26.21404 26.39755
1010 23.91311 24.12383 24.49856 24.44900 24.76414
1020 23.50075 24.50840 24.18455 24.12218 24.73257
1030 25.48273 25.91653 25.46919 25.32913 25.70345
1040 24.34628 24.71070 24.57830 24.49792 25.10467
1050 25.98042 26.08394 25.92206 25.94808 26.36945
1060 23.69643 24.16085 24.34869 24.44256 25.27766
1070 26.67257 26.97366 26.28368 26.15337 26.39438
1080 26.04964 25.96982 25.98476 26.04236 26.45330
1090 26.51796 26.50607 26.26084 26.26715 26.69060

$v1.boot
        Fact01    Fact02    Fact03    Fact04    Fact05
0    0.5084664 0.7685255 0.3835502 0.3432103 0.3549043
10   0.6512529 0.9667681 0.5085261 0.4371531 0.4635051
20   0.4034041 0.6593227 0.3044495 0.2866723 0.2679141
30   0.3628928 0.5894282 0.2721065 0.2660492 0.2558569
40   0.3663826 0.3934682 0.2526331 0.2471487 0.2427169
50   0.3645347 0.3710493 0.2571531 0.2530451 0.2422081
60   0.4019394 0.4495757 0.2857077 0.2837445 0.2861211
70   0.3630924 0.4908604 0.2609283 0.2549738 0.2447169
80   0.3619576 0.3651724 0.2582826 0.2573728 0.2432293
90   0.3426487 0.3456271 0.2512849 0.2544212 0.2586417
100  0.3613400 0.3895564 0.2635770 0.2655857 0.2544108
110  0.3651211 0.7114529 0.3033627 0.2955006 0.2802072
120  0.3844357 0.4373019 0.2796330 0.2766358 0.2779934
130  0.3510917 0.4498088 0.2598795 0.2518116 0.2505053
140  0.3661598 0.6650721 0.2902007 0.2828852 0.2762794
150  0.3648016 0.4475766 0.2580831 0.2525709 0.2415221
160  0.3528402 0.4651505 0.2527023 0.2508266 0.2418386
170  0.3881641 0.5143427 0.2870292 0.2938859 0.2910096
180  0.3689932 0.7466600 0.3209676 0.3161637 0.3097435
190  0.3619690 0.5481829 0.3060031 0.2872233 0.2563797
200  0.3687172 0.6661153 0.3068986 0.2870522 0.2575483
210  0.3529340 0.5079607 0.2695006 0.2651767 0.2495216
220  0.3802079 0.6114571 0.3293806 0.3049898 0.2842093
230  0.4272175 0.6949337 0.3466131 0.3124853 0.3146228
240  0.3643474 0.5606191 0.3614831 0.3461934 0.3434361
250  0.3635751 0.7488997 0.2957666 0.2906045 0.2806560
260  0.3814172 0.6770927 0.3041768 0.2840714 0.2788342
270  0.4560034 0.8346665 0.3994315 0.3496570 0.3622127
280  0.3791115 0.6633563 0.3019484 0.2848092 0.2713321
290  0.4291810 0.8097524 0.3472479 0.3173662 0.3137500
300  0.3579153 0.4075725 0.2522294 0.2415559 0.2276705
310  0.4365776 0.6214834 0.3042319 0.3114074 0.3209494
320  0.3690268 0.4362977 0.2562501 0.2573896 0.2598599
330  0.3681959 0.6185679 0.3062821 0.3533351 0.3488571
340  0.3478773 0.3248436 0.2476521 0.2392621 0.2323926
350  0.3829726 0.5004239 0.2771639 0.2598264 0.2397869
360  0.3677591 0.5958337 0.2872536 0.2857880 0.2726075
370  0.3686246 0.4169978 0.3019948 0.2851026 0.2804608
380  0.4262050 0.6356538 0.3497569 0.3178946 0.3073520
390  0.3740126 0.4384896 0.2679397 0.2569291 0.2596227
400  0.3572100 0.9150039 0.3380617 0.3708054 0.3712043
410  0.3302828 0.4713784 0.3261310 0.3370526 0.2972053
420  0.3573380 0.5243332 0.2901306 0.2814410 0.2538288
430  0.3437725 0.5663826 0.2970001 0.2909063 0.2600693
440  0.4051157 0.7237498 0.3144205 0.2991603 0.2860337
450  0.5932443 0.8780109 0.4661105 0.4066278 0.4550420
460  0.5185901 0.7582162 0.4090292 0.3596510 0.3888543
470  0.3830430 0.4859087 0.2652551 0.2603630 0.2578584
480  0.3529781 0.8876037 0.3407361 0.3532261 0.3562835
490  0.5505683 0.8046020 0.4275629 0.3799382 0.4280223
500  0.4997882 0.7549902 0.3814897 0.3483957 0.3794819
510  0.6404011 0.9202665 0.4849813 0.4197761 0.4348153
520  0.3516579 0.6870744 0.2882567 0.3030223 0.3080666
530  0.3518049 0.3699949 0.2625380 0.2570117 0.2660448
540  0.3796596 0.4477856 0.2919999 0.2745170 0.2805751
550  0.3511162 0.4587244 0.3618875 0.4562216 0.4714381
560  0.3948793 1.3551842 0.4498409 0.5025573 0.5028436
570  0.4545590 1.1057784 0.4086828 0.5042333 0.5124915
580  0.3790722 0.9692881 0.3768590 0.4193864 0.4090007
590  0.3524561 0.3524876 0.2602642 0.2652655 0.2435034
600  0.4094004 0.8492125 0.3206347 0.3307586 0.3388976
610  0.3467314 0.6940364 0.2972495 0.2843431 0.2710185
620  0.4371744 0.5856652 0.3079954 0.2991374 0.3360713
630  0.3883410 0.9687414 0.3464009 0.3565577 0.3432071
640  0.4876803 0.7660317 0.3604752 0.3331853 0.3402771
650  0.4413106 0.7499342 0.3270116 0.3121969 0.3199197
660  0.3775174 0.4524082 0.2609818 0.2618564 0.2699472
670  0.3569613 0.7422050 0.4847178 0.4843823 0.4593675
680  0.3362841 0.4729992 0.3126185 0.2853497 0.2923319
690  0.3774172 0.4478147 0.3424662 0.4124593 0.3877849
700  0.3758109 0.4585281 0.2621733 0.2546189 0.2738281
710  0.3321586 0.3927145 0.2753224 0.2943384 0.2688614
720  0.3449300 0.4428941 0.2771808 0.2894979 0.2807673
730  0.3236185 0.5663534 0.2758133 0.2675517 0.2523013
740  0.5121834 0.8654424 0.3680272 0.3692523 0.3870709
750  0.3826654 0.7936458 0.3024025 0.3092382 0.3057158
760  0.4673315 0.7141687 0.3410916 0.3203679 0.3304659
770  0.3433815 0.4471491 0.3028451 0.3341186 0.3029419
780  0.3701406 0.5439057 0.4052463 0.3997699 0.3871323
790  0.4344933 0.6429052 0.3800558 0.3444535 0.3576772
800  0.4315471 0.6016155 0.3530221 0.3202064 0.3240680
810  0.3976122 0.6682925 0.3017106 0.2848017 0.2746882
820  0.4419353 1.0235967 0.3742003 0.4366662 0.4323833
830  0.4570859 0.5563858 0.3259635 0.3550156 0.4034146
840  0.5693484 0.7615832 0.4079103 0.3804615 0.4518517
850  0.5500861 0.8544507 0.3947035 0.3765341 0.3997368
860  0.4056663 0.7330829 0.3333796 0.3147523 0.3325344
870  0.3934608 0.4618604 0.2996496 0.3182472 0.3051406
880  0.3852986 0.5461351 0.2739791 0.2954275 0.3148103
890  0.3525478 0.7536572 0.4643342 0.5926669 0.6013268
900  0.3393849 0.7754704 0.3994878 0.4716080 0.4880425
910  0.3580814 0.4030039 0.3286776 0.3389136 0.3508662
920  0.3121458 0.3683108 0.2416184 0.2452629 0.2732189
930  0.3641963 0.5227743 0.2718273 0.2572225 0.2472794
940  0.3795063 0.4212863 0.2671519 0.2531079 0.2499726
950  0.4003458 0.7604724 0.3368521 0.3057756 0.2740713
960  0.3804141 0.6467510 0.2817599 0.2806439 0.2703537
970  0.3954364 0.4777374 0.2700272 0.2670321 0.2653000
980  0.3643952 0.8361788 0.3160620 0.3221654 0.3150761
990  0.4200626 1.0275893 0.3662888 0.3636980 0.3437631
1000 0.3717655 0.7039661 0.2930631 0.2984566 0.2881947
1010 0.4330153 1.4475072 0.4915822 0.4952685 0.4687415
1020 0.3709597 0.6184701 0.4182094 0.5446140 0.5794517
1030 0.3736415 1.1577033 0.4726913 0.5495618 0.5390304
1040 0.4152848 1.5228074 0.5288677 0.6160102 0.6078210
1050 0.3954063 0.5351932 0.2891969 0.3347012 0.3505870
1060 0.5467454 0.8939766 0.3913202 0.4920379 0.5740693
1070 0.3558354 0.7287122 0.3991263 0.4479424 0.4503866
1080 0.4217813 0.6601417 0.3001416 0.3187794 0.3398405
1090 0.4083889 0.7059293 0.3147719 0.3661637 0.3696705

$v2.boot
[1] 3.103355 2.323580 1.862341 1.871077 1.847898

$SEP.boot
       Fact01   Fact02   Fact03   Fact04   Fact05
0    3.144734 2.447378 1.901427 1.902294 1.881671
10   3.170953 2.516678 1.930522 1.921466 1.905142
20   3.129465 2.415312 1.887062 1.892910 1.867219
30   3.124501 2.397176 1.882115 1.889897 1.865527
40   3.124908 2.356659 1.879399 1.887329 1.863770
50   3.124692 2.353020 1.880011 1.888110 1.863704
60   3.129276 2.366674 1.884130 1.892469 1.869918
70   3.124524 2.374862 1.880532 1.888370 1.864032
80   3.124392 2.352100 1.880166 1.888695 1.863837
90   3.122214 2.349145 1.879218 1.888295 1.865911
100  3.124321 2.356009 1.880901 1.889832 1.865329
110  3.124760 2.430060 1.886887 1.894268 1.869022
120  3.127076 2.364373 1.883218 1.891416 1.868691
130  3.123152 2.366718 1.880386 1.887945 1.864800
140  3.124882 2.416888 1.884816 1.892341 1.868437
150  3.124723 2.366295 1.880139 1.888047 1.863615
160  3.123349 2.369681 1.879408 1.887814 1.863656
170  3.127537 2.379826 1.884330 1.894016 1.870672
180  3.125215 2.440600 1.889798 1.897601 1.873678
190  3.124393 2.387369 1.887314 1.892994 1.865599
200  3.125182 2.417175 1.887459 1.892968 1.865760
210  3.123360 2.378455 1.881740 1.889775 1.864668
220  3.126559 2.402687 1.891245 1.895771 1.869626
230  3.132623 2.425275 1.894322 1.896991 1.874491
240  3.124670 2.390255 1.897099 1.902834 1.879541
250  3.124580 2.441286 1.885681 1.893510 1.869089
260  3.126706 2.420223 1.887018 1.892518 1.868817
270  3.136679 2.468946 1.904694 1.903468 1.883063
280  3.126426 2.416416 1.886661 1.892629 1.867712
290  3.132892 2.460635 1.894438 1.897801 1.874344
300  3.123926 2.359055 1.879344 1.886605 1.861870
310  3.133913 2.405258 1.887027 1.896814 1.875563
320  3.125219 2.364187 1.879888 1.888698 1.866080
330  3.125121 2.404507 1.887359 1.904147 1.880539
340  3.122792 2.346177 1.878735 1.886313 1.862454
350  3.126896 2.376857 1.882853 1.889031 1.863391
360  3.125070 2.398759 1.884365 1.892777 1.867898
370  3.125172 2.360702 1.886668 1.892673 1.869060
380  3.132485 2.408958 1.894900 1.897890 1.873284
390  3.125812 2.364593 1.881517 1.888635 1.866047
400  3.123846 2.497250 1.892776 1.907466 1.884813
410  3.120881 2.370912 1.890682 1.901193 1.871646
420  3.123860 2.382006 1.884805 1.892125 1.865250
430  3.122338 2.391613 1.885875 1.893556 1.866109
440  3.129686 2.433688 1.888697 1.894842 1.869904
450  3.159549 2.483934 1.919785 1.914752 1.903100
460  3.146387 2.444160 1.906730 1.905329 1.888368
470  3.126905 2.373843 1.881137 1.889105 1.865802
480  3.123365 2.487341 1.893255 1.904127 1.881931
490  3.151815 2.458945 1.910792 1.909262 1.896821
500  3.143342 2.443161 1.901013 1.903236 1.886461
510  3.168742 2.499183 1.924454 1.917587 1.898366
520  3.123216 2.423035 1.884518 1.895455 1.873401
530  3.123232 2.352854 1.880756 1.888646 1.866951
540  3.126492 2.366334 1.885094 1.891108 1.869077
550  3.123155 2.368428 1.897176 1.925894 1.907087
560  3.128377 2.689898 1.915900 1.937393 1.915092
570  3.136469 2.573280 1.906656 1.937829 1.917648
580  3.126421 2.517647 1.900089 1.917502 1.892620
590  3.123306 2.350164 1.880439 1.889787 1.863873
600  3.130243 2.473901 1.889741 1.900087 1.878717
610  3.122665 2.425018 1.885914 1.892559 1.867667
620  3.133997 2.396253 1.887638 1.894838 1.878210
630  3.127558 2.517436 1.894283 1.904747 1.879500
640  3.141440 2.446596 1.896907 1.900511 1.878967
650  3.134576 2.441603 1.890834 1.896944 1.875387
660  3.126233 2.367213 1.880539 1.889311 1.867511
670  3.123817 2.439240 1.924387 1.932758 1.904139
680  3.121522 2.371235 1.888398 1.892711 1.870878
690  3.126221 2.366340 1.893568 1.915999 1.888148
700  3.126027 2.368390 1.880705 1.888322 1.868076
710  3.121080 2.356533 1.882583 1.894087 1.867355
720  3.122465 2.365413 1.882855 1.893340 1.869106
730  3.120183 2.391606 1.882655 1.890109 1.865042
740  3.145337 2.479519 1.898357 1.907164 1.888002
750  3.126859 2.455382 1.886733 1.896459 1.873016
760  3.138345 2.430856 1.893319 1.898306 1.877215
770  3.122295 2.366214 1.886804 1.900675 1.872565
780  3.125351 2.386390 1.905922 1.913307 1.888015
790  3.133624 2.410882 1.900726 1.902519 1.882196
800  3.133217 2.400201 1.895505 1.898278 1.876099
810  3.128723 2.417776 1.886623 1.892628 1.868203
820  3.134664 2.539050 1.899563 1.921355 1.897810
830  3.136836 2.389266 1.890653 1.904459 1.891420
840  3.155150 2.445206 1.906490 1.909366 1.902340
850  3.151731 2.475704 1.903709 1.908588 1.890639
860  3.129757 2.436480 1.891945 1.897366 1.877580
870  3.128198 2.369038 1.886294 1.897949 1.872922
880  3.127182 2.386899 1.882387 1.894256 1.874522
890  3.123316 2.442749 1.919354 1.962698 1.943276
900  3.121858 2.449567 1.904706 1.929597 1.911260
910  3.123945 2.358270 1.891122 1.901523 1.880913
920  3.119014 2.352590 1.877950 1.887083 1.867987
930  3.124652 2.381663 1.882075 1.888675 1.864370
940  3.126474 2.361463 1.881405 1.888119 1.864729
950  3.129072 2.444861 1.892560 1.895898 1.868112
960  3.126584 2.411910 1.883535 1.892007 1.867570
970  3.128447 2.372184 1.881816 1.890036 1.866845
980  3.124676 2.469457 1.888971 1.898610 1.874567
990  3.131655 2.540662 1.898021 1.906097 1.879601
1000 3.125544 2.427878 1.885259 1.894731 1.870236
1010 3.133419 2.737572 1.926128 1.935515 1.906422
1020 3.125448 2.404481 1.908721 1.948726 1.936619
1030 3.125767 2.596017 1.921393 1.950115 1.924911
1040 3.131018 2.778123 1.935979 1.969872 1.945295
1050 3.128444 2.384420 1.884662 1.900777 1.880861
1060 3.151150 2.489622 1.903010 1.934691 1.935015
1070 3.123689 2.435169 1.904630 1.923949 1.901993
1080 3.131886 2.415536 1.886372 1.898038 1.878888
1090 3.130111 2.428448 1.888755 1.906569 1.884512

rioja documentation built on Oct. 28, 2020, 5:07 p.m.

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