Extract Conditional Residuals from Multivariate Linear Model Fits
cresiduals.RdResiduals from full conditionals of a Multivariate
Linear Model (mlm) object. The full conditional for each response is a
linear model with all other responses used as predictors in addition to the
regressors specified in the formula of the mlm object. This is used to
diagnose the multivariate normality assumption in plotenvelope.
Arguments
- object
a
mlmobject, typically the result of callinglmwith a matrix response.- standardize
logical defaults to
TRUE, to return studentized residuals usingrstandardso they are comparable across responses.- ...
further arguments passed to
residuals.lmorrstandard.
Details
A residuals function for mlm objects, which returns residuals from a full
conditional model, that is, a linear model of each response against all responses
as well as predictors, which can be used to diagnose the multivariate normality assumption.
These can be standardized (standardize=TRUE) to facilitate overlay plots of multiple
responses, as in plotenvelope.
References
Warton DI (2022) Eco-Stats - Data Analysis in Ecology, from t-tests to multivariate abundances. Springer, ISBN 978-3-030-88442-0
Examples
data(iris)
# fit a mlm:
iris.mlm=lm(cbind(Sepal.Length,Sepal.Width,Petal.Length,Petal.Width)~Species,data=iris)
# construct full conditional residuals:
cresiduals(iris.mlm)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 0.31367294 0.202516501 -0.301850145 -0.240802233
#> 2 0.47429637 -1.399025921 -0.182414309 0.383425946
#> 3 -0.24106926 -0.430574978 0.040805359 0.121609318
#> 4 -0.95656076 -0.526551269 0.977931661 -0.082401947
#> 5 -0.17937436 0.722221842 0.001542216 -0.444406291
#> 6 0.03684044 1.027064635 -0.035185225 0.113854365
#> 7 -1.06638351 0.302912457 0.568257718 0.231824439
#> 8 -0.12555699 0.038402502 0.247256532 -0.296982266
#> 9 -1.02163525 -1.073564006 0.928033767 0.249331951
#> 10 -0.06906266 -0.717018438 0.506389609 -0.521175728
#> 11 0.70432417 0.602598703 -0.481941948 -0.514492009
#> 12 -1.05806421 0.395862394 1.100021030 -0.558626361
#> 13 0.03856698 -1.022861007 0.285886695 -0.283412794
#> 14 -0.79359598 -0.523744394 0.301957654 -0.123918794
#> 15 2.39074532 0.983205596 -2.402668062 -0.297851087
#> 16 0.77993035 2.378404054 -1.194609506 -0.164180908
#> 17 1.14114905 0.751020347 -1.583614700 0.710204145
#> 18 0.41749985 -0.033719952 -0.533339756 0.365965061
#> 19 1.08891546 0.458914685 -0.578561536 -0.178876645
#> 20 -0.34719866 1.169090759 0.059832579 -0.222795197
#> 21 0.64680923 -0.391324618 0.081397497 -0.366435755
#> 22 -0.07882492 0.556805328 -0.242226956 0.532763437
#> 23 -0.40690250 1.013489593 -0.605385681 -0.082452599
#> 24 0.13350884 -1.053990605 0.018542848 1.434963264
#> 25 -1.88757612 0.616994229 2.254306053 -1.012183196
#> 26 0.25660863 -1.401820927 0.356892429 0.144769653
#> 27 -0.19154825 -0.363806017 0.169085164 0.769448604
#> 28 0.36994408 0.130742220 -0.151091206 -0.331372964
#> 29 0.80609842 -0.318020557 -0.605529117 -0.037514982
#> 30 -1.06283978 -0.219248419 1.196481823 -0.321242591
#> 31 -0.57013365 -0.740713087 0.894145300 -0.116686827
#> 32 1.40310765 -1.010001464 -1.156980074 1.147027061
#> 33 -0.73266508 2.638772263 0.510822238 -1.848088488
#> 34 0.50102878 2.294492202 -0.770695777 -1.068625741
#> 35 0.03518436 -0.951627009 0.273804363 0.087582529
#> 36 1.02240977 -0.931005292 -1.047316979 0.439576676
#> 37 1.90554110 -0.444227300 -1.628699251 0.133442915
#> 38 -0.61504939 1.102790030 0.469434698 -1.108612687
#> 39 -0.90738785 -0.762257934 0.607909875 0.248210421
#> 40 0.20378563 -0.104156763 0.013444430 -0.240679959
#> 41 0.36156611 0.038145328 -0.684167003 0.456755021
#> 42 0.71303380 -3.796152408 -0.368595372 2.022975618
#> 43 -1.23045090 -0.002554199 0.745811697 -0.047983180
#> 44 -0.14866091 -0.463378746 -0.227985891 1.836582177
#> 45 -1.34143584 1.223866798 1.373284194 -0.205195356
#> 46 0.24802768 -1.494071645 -0.180531025 0.936460217
#> 47 -0.72728884 1.476979480 0.680351481 -0.980021291
#> 48 -0.84426391 -0.217635235 0.660695161 -0.082138419
#> 49 0.37382592 0.744292344 -0.246893593 -0.570270905
#> 50 0.31091285 -0.409667887 -0.206831036 -0.003272779
#> 51 1.68957653 0.246746532 -0.679363521 -0.233450850
#> 52 0.35298194 0.723153609 -0.267979617 0.334498226
#> 53 1.07730540 -0.087859861 0.033747984 0.175871678
#> 54 0.01486333 -1.277495369 -0.248455591 0.673179257
#> 55 1.06426882 -0.865129455 -0.396144787 0.833865887
#> 56 -1.51087044 0.686187291 1.557510587 -0.692678269
#> 57 -0.58991947 1.153054518 0.580255443 0.446763578
#> 58 -0.53404722 0.174238166 -0.784937722 -0.618516844
#> 59 1.02240519 -0.155022005 -0.095240382 -0.476785166
#> 60 -1.25581297 0.359066972 0.117722184 0.671530577
#> 61 -0.08978165 -1.359776950 -0.524485451 -0.262794937
#> 62 -0.15036502 0.467563296 -0.391402858 0.786988763
#> 63 1.53325798 -1.675388916 -0.805518854 -0.736006984
#> 64 -0.79974572 0.395449033 1.228100512 -0.297626185
#> 65 0.46219241 0.570435769 -1.608443005 0.436784351
#> 66 1.67667024 0.081552694 -1.199771561 0.191440045
#> 67 -1.96206311 1.118912675 1.477945908 0.178343388
#> 68 -0.23741526 0.591640418 0.413217225 -1.716226501
#> 69 1.35685822 -2.774528746 -0.507540895 1.721910043
#> 70 0.08334738 -0.262174134 -0.264023989 -0.636622821
#> 71 -1.83786599 0.962988692 1.394709855 1.463152957
#> 72 1.17579011 -0.244657858 -1.302793177 0.274218337
#> 73 0.07372279 -1.508885653 1.033350712 0.727795819
#> 74 -0.84894626 0.493568978 1.625203955 -1.368949759
#> 75 1.18031586 -0.081628486 -0.783342172 -0.146666616
#> 76 1.50756664 -0.155687033 -1.031685527 0.282062506
#> 77 1.40673285 -0.919897002 -0.095304521 0.099748115
#> 78 0.51552919 -0.589188625 0.358554184 1.287247982
#> 79 -0.47746675 0.158818713 0.461729892 0.548965075
#> 80 1.25514315 -0.074702458 -1.749524151 -0.757249471
#> 81 0.19123586 -0.569569506 -0.485766942 -0.399506237
#> 82 0.36300710 -0.405304588 -0.641911527 -0.863638063
#> 83 0.52008783 -0.027666268 -0.824199807 -0.209868313
#> 84 -1.71772439 -0.419859412 2.419762790 0.583245153
#> 85 -2.62242806 1.419019355 1.959490218 0.064506930
#> 86 -1.20502772 1.820996766 0.586145012 0.427376857
#> 87 0.96078712 0.057195773 -0.270293271 0.357535896
#> 88 1.57201343 -2.136355333 -0.588794575 0.541445620
#> 89 -1.07556713 1.296600140 0.392852482 -0.452787864
#> 90 -0.31278905 -0.521110187 -0.108010667 0.376125701
#> 91 -1.67686811 0.381386630 1.736280416 -0.975366683
#> 92 -0.68898984 0.701729573 0.913641774 -0.297196839
#> 93 0.40963934 -0.334695490 -0.508764716 -0.209594801
#> 94 -0.03222012 -0.355535679 -1.090876188 -0.411674872
#> 95 -0.85522990 0.234250581 0.567293819 -0.156462455
#> 96 -1.12559242 1.462365503 0.778463848 -1.152599249
#> 97 -0.85328349 0.846491387 0.472767886 -0.394590880
#> 98 0.52123569 0.204192096 -0.313412470 -0.258944708
#> 99 0.92234629 -0.191661943 -2.581566313 0.419353141
#> 100 -0.41640415 0.397912240 0.017566294 -0.099833515
#> 101 -2.25424661 0.867643747 1.568447010 1.619394130
#> 102 -1.04844897 0.067494783 0.207494683 -0.143007886
#> 103 0.77182779 -0.561899643 -0.012243150 0.188772645
#> 104 -1.20044706 0.705365838 1.336541211 -1.499470094
#> 105 -0.83137069 -0.011808760 0.777142236 0.605625943
#> 106 0.51806305 -0.796316080 1.538184164 -0.572596556
#> 107 -2.30079610 0.668513530 0.340699854 -0.714347939
#> 108 0.19376148 -0.237768898 1.714678718 -1.987900618
#> 109 0.23466643 -1.251532201 0.897595397 -0.987400660
#> 110 -0.01166894 0.773131454 0.013575063 1.515082345
#> 111 0.55300317 0.725008281 -1.319645615 0.125839197
#> 112 0.38666929 -0.647668680 -0.428948302 -0.097758646
#> 113 0.87500786 -0.416677672 -0.849682791 0.607631007
#> 114 -0.67809909 -0.861638747 -0.319661454 0.858509859
#> 115 -0.71219374 -0.761445014 -0.912288390 2.766822883
#> 116 -0.01584990 0.299357829 -1.017180438 1.600947681
#> 117 -0.42736870 0.723753683 0.550341854 -1.381915644
#> 118 -0.66646844 1.984426717 2.036461904 -1.260233033
#> 119 0.93034168 -2.786261646 1.780196205 0.911085033
#> 120 0.29752378 -1.260306855 -0.069083416 -1.605397308
#> 121 0.54209724 -0.135129758 -0.641938840 1.290299193
#> 122 -1.22769475 0.356808241 -0.261640576 0.505755212
#> 123 0.81586426 -1.416302396 1.808551215 -0.999307925
#> 124 1.05382161 -0.557400516 -1.507438169 -0.174869139
#> 125 -0.49236091 1.000729889 0.363463489 -0.185009157
#> 126 0.19202082 0.845057354 0.987816473 -2.025383158
#> 127 0.83459110 -0.103490428 -1.589526739 -0.232166648
#> 128 -0.10475371 0.873372761 -0.832360831 -0.733036474
#> 129 -0.39013411 -0.530245163 0.332824272 0.528853749
#> 130 0.87923078 0.420105139 0.544183410 -2.659820843
#> 131 1.34934882 -1.146266442 0.387367204 -0.870902652
#> 132 0.65494937 1.962837666 0.860313912 -1.931850346
#> 133 -0.28719298 -0.768645052 0.101042833 1.136413042
#> 134 0.02476654 0.692207317 0.033388636 -2.443261221
#> 135 -1.86032534 0.841725217 2.602557556 -3.628615651
#> 136 2.43145936 -1.780791069 -1.138877601 1.479245005
#> 137 -1.40892868 1.189665443 0.294077000 1.431496706
#> 138 -0.92464944 1.246812696 0.857588193 -1.588341991
#> 139 -0.16206772 0.947945104 -0.986246691 -0.643708808
#> 140 1.31606460 -0.253524423 -1.399570167 0.667042119
#> 141 0.42640785 -0.540950046 -0.867059255 2.076104009
#> 142 2.38637729 -0.970693255 -3.034934886 2.362773567
#> 143 -1.04844897 0.067494783 0.207494683 -0.143007886
#> 144 -0.33893801 0.150908579 0.367158376 0.941244220
#> 145 -0.07740846 0.057299380 -0.575839356 2.249737400
#> 146 1.59784060 -0.978724620 -2.248738202 2.220908730
#> 147 1.21362577 -1.481214991 -1.496748247 0.588360951
#> 148 0.60367745 0.035946981 -1.069763113 0.272590818
#> 149 -1.29168602 1.423671672 -0.016470849 1.057043006
#> 150 -1.31819069 1.302294870 0.417277409 -1.142817297