Dear knowledgeable people,
I am running rlq analyses for two different datasets. Even though these
datasets are quite similar, for one of them I receive the error: "Error in
randtest.rlq(xtest, modeltype = 2, nrepet = nrepet, ...) :
Not yet available" when applying randtest(pres.rlq)
My first dataset, for which the test works, looks like this:
> str(birdsX)
'data.frame': 116 obs. of 51 variables:
$ Acrocephalus_palustris : int 1 0 0 0 0 0 0 0 0 0 ...
$ Aegithalos_caudatus : int 0 0 0 0 0 0 0 0 1 1 ...
$ Alauda_arvensis : int 0 1 0 1 1 1 0 0 0 0 ...
$ Anthus_campestris : int 0 0 0 0 0 0 0 0 0 0 ...
$ Anthus_trivialis : int 0 1 0 0 0 1 1 0 0 0 ...
$ Carduelis_cannabina : int 0 0 0 0 0 0 0 0 0 0 ...
$ Carduelis_carduelis : int 0 0 0 0 0 0 0 0 0 0 ...
$ Carduelis_chloris : int 0 0 0 0 0 0 0 0 0 0 ...
$ Coccothraustes_coccothraustes: int 0 1 0 0 0 0 1 0 0 1 ...
$ Columba_palumbus : int 0 0 0 0 0 0 0 0 0 0 ...
$ Crex_crex : int 0 0 0 0 0 0 0 0 0 0 ...
$ Emberiza_calandra : int 0 1 0 0 0 0 0 0 0 0 ...
$ Emberiza_citrinella : int 0 1 1 0 1 1 1 0 1 0 ...
$ Erithacus_rubecula : int 0 0 1 0 0 0 0 0 0 0 ...
$ Fringilla_coelebs : int 0 0 0 0 0 0 0 0 1 0 ...
$ Garrulus_glandarius : int 0 0 0 0 0 0 0 0 1 0 ...
$ Hippolais_pallida : int 0 0 0 0 0 0 0 0 0 0 ...
$ Lanius_collurio : int 0 0 1 0 0 1 0 0 0 0 ...
$ Lanius_excubitor : int 0 0 0 0 0 0 0 0 0 0 ...
$ Lanius_minor : int 0 0 0 0 0 0 0 0 0 0 ...
$ Locustella_fluviatilis : int 0 0 0 0 0 0 0 0 1 0 ...
$ Lullula_arborea : int 0 0 0 0 0 0 0 0 0 0 ...
$ Luscinia_luscinia : int 0 0 0 0 0 0 0 0 1 1 ...
$ Motacilla_alba : int 1 0 0 0 0 0 0 0 0 0 ...
$ Motacilla_flava : int 0 0 0 0 0 0 0 0 0 0 ...
$ Oriolus_oriolus : int 1 0 0 0 0 0 0 0 0 0 ...
$ Parus_caeruleus : int 0 0 0 0 0 0 0 0 1 0 ...
$ Parus_major : int 1 1 1 0 0 0 1 0 1 0 ...
$ Parus_palustris : int 0 0 1 0 0 0 0 0 1 0 ...
$ Passer_domesticus : int 0 0 0 0 0 0 0 0 0 0 ...
$ Passer_montanus : int 0 0 1 0 0 0 0 0 0 0 ...
$ Phylloscopus_collybita : int 0 1 0 0 0 0 1 0 1 0 ...
$ Phylloscopus_sibilatrix : int 0 0 0 0 0 0 0 0 0 0 ...
$ Phylloscopus_trochilus : int 0 0 0 0 0 0 0 0 0 0 ...
$ Pica_pica : int 0 0 0 0 0 0 0 0 0 0 ...
$ Picus_canus : int 0 0 0 0 0 0 0 0 0 0 ...
$ Picus_viridis : int 0 0 0 0 0 0 0 0 0 0 ...
$ Saxicola_rubetra : int 0 0 0 0 1 0 0 1 0 0 ...
$ Saxicola_torquata : int 0 0 0 0 0 0 0 0 0 0 ...
$ Sitta_europea : int 0 0 0 0 0 0 0 0 0 0 ...
$ Sturnus_vulgaris : int 0 0 0 0 0 0 0 0 1 1 ...
$ Sylvia_atricapilla : int 1 0 1 0 0 0 0 0 1 0 ...
$ Sylvia_borin : int 1 0 0 0 0 0 1 0 0 1 ...
$ Sylvia_communis : int 1 1 0 0 0 0 0 0 0 0 ...
$ Sylvia_curruca : int 0 0 0 0 0 0 0 0 0 1 ...
$ Sylvia_nisoria : int 0 0 0 0 0 0 0 0 0 0 ...
$ Troglodytes_troglodytes : int 0 0 0 0 0 0 0 0 0 0 ...
$ Turdus_merula : int 0 0 0 0 0 0 1 0 0 0 ...
$ Turdus_philomelos : int 0 0 0 0 0 0 0 0 0 0 ...
$ Turdus_viscivorus : int 0 0 0 0 0 0 0 0 0 0 ...
$ Upupa_epops : int 0 0 0 0 0 0 0 0 0 0 ...
> str(traitX)
'data.frame': 51 obs. of 15 variables:
$ family : Factor w/ 25 levels "Buntings","Chats",..: 23 20 7
13 13 4 4 4 21 4 ...
$ habitat : Factor w/ 5 levels "aquatic","forest",..: 3 3 4 4
3 4 3 3 2 3 ...
$ nest_location.cosmin : Factor w/ 4 levels "ground","herbaceous",..: 2 4 1
1 1 3 4 3 4 4 ...
$ type_of_nest_._cosmin: Factor w/ 4 levels
"build_nest","escavate_hollow",..: 4 1 4 4 4 4 4 4 3 4 ...
$ diet : Factor w/ 8 levels "Aerial_insect",..: 8 2 6 4 2 7
7 6 2 7 ...
$ diet.cosmin : Factor w/ 4 levels "carnivorous",..: 2 3 3 2 2 4 4
3 2 4 ...
$ migratory_Cosmin : Factor w/ 3 levels "long","resident",..: 1 2 1 1 1
2 2 2 2 2 ...
$ foraging_technique. : Factor w/ 12 levels "bark_forager",..: 12 12 6 6 6
11 11 10 1 11 ...
$ homerange : Factor w/ 3 levels "1-4ha","less1ha",..: 2 2 2 2 2
2 2 2 2 2 ...
$ bodysize : Factor w/ 6 levels "100-500g","15-24g",..: 5 5 3 2
2 2 2 3 5 4 ...
$ clutchsize : Factor w/ 3 levels "3-6eggs","less3eggs",..: 1 3 1
1 1 1 1 1 1 1 ...
$ laying_date : Factor w/ 7 levels "Early_April",..: 6 1 1 6 2 3 3
3 6 1 ...
$ rarity : Factor w/ 3 levels "75-95%","less75%",..: 2 1 1 1
2 3 3 3 2 1 ...
$ national_trend : Factor w/ 5 levels "increasing","Increasing",..: 4
4 3 4 4 1 4 3 4 4 ...
$ Ecological_type : Factor w/ 4 levels
"Farmland_whit_bushes_and_trees",..: 3 2 3 3 1 1 1 1 2 2 ...
> str(envir)
'data.frame': 116 obs. of 13 variables:
$ woody_1ha : num 0.349 0.247 0.439 -1.24 -1.24 ...
$ spot_1ha : num -0.154 -0.308 0.91 -0.308 1.263 ...
$ TWI : num 1.773 -0.641 -0.297 0.459 1.21 ...
$ heatload : num 0.788 -0.986 -1.24 0.366 0.704 ...
$ sidi_50ha : num -1.846 0.622 -1.115 -1.267 0.346 ...
$ woody_50ha : num -0.381 1.443 0.476 -1.857 -1.286 ...
$ rugg_50ha : num -1.2455 0.6073 0.197 -0.6771 -0.0291 ...
$ ed_50ha : num -1.122 0.715 -0.407 -0.509 0.409 ...
$ woodypercatch: num 0.586 0.586 0.586 -1.063 -1.063 ...
$ catch_rugged : num 0.227 0.227 0.227 2.048 2.048 ...
$ Edfine : num 0.902 0.902 0.902 0.434 0.434 ...
$ SIDIfine : num 0.739 0.739 0.739 -0.407 -0.407 ...
$ pastpercatch : num -1.141 -1.141 -1.141 0.679 0.679 ...
> summary(pres.rlq)
Eigenvalues decomposition:
eig covar sdR sdQ corr
1 1.9050088 1.3802205 1.730708 2.194411 0.3634181
2 0.3471101 0.5891605 1.156380 1.889246 0.2696775
Inertia & coinertia R:
inertia max ratio
1 2.995351 3.379328 0.8863749
12 4.332566 5.234932 0.8276260
Inertia & coinertia Q:
inertia max ratio
1 4.815442 6.36944 0.7560228
12 8.384691 12.42140 0.6750197
Correlation L:
corr max ratio
1 0.3634181 0.7633403 0.4760892
2 0.2696775 0.6986529 0.3859963
The second one, for which it doesn´t work, looks like this:
str(buttX)
'data.frame': 120 obs. of 88 variables:
$ Aglais_urticae : num 0 0 0 0 0 0 0 0 0 0 ...
$ Antocharis_cardamines: num 0 0 0 0 0 0 0 0 0 0 ...
$ Apatura_ilia : num 0 0 0 0 0 0 0 0 0 0 ...
$ Apatura_iris : num 0 0 0 0 0 0 0 0 0 0 ...
$ Aphantopus_hyperantus: num 0 1 1 1 0 1 1 0 1 1 ...
$ Aporia_crataegi : num 0 0 1 0 0 0 0 0 0 0 ...
$ Araschnia_levana : num 0 0 0 0 0 0 0 0 0 0 ...
$ Argynnis_adippe : num 0 0 0 0 0 0 0 0 0 0 ...
$ Argynnis_aglaja : num 0 0 0 1 0 1 1 0 0 0 ...
$ Argynnis_niobe : num 0 0 0 0 0 1 0 0 0 0 ...
$ Argynnis_paphia : num 1 1 0 0 0 0 1 0 1 0 ...
$ Aricia_agestis : num 0 0 0 0 0 0 0 0 1 0 ...
$ Aricia_artaxerxes : num 0 0 0 0 0 0 0 0 0 0 ...
$ Boloria_dia : num 0 0 0 0 1 1 1 0 0 0 ...
$ Boloria_euphrosyne : num 0 0 0 0 0 0 0 0 0 0 ...
$ Boloria_selene : num 0 0 1 0 0 1 0 0 0 0 ...
$ Brenthis_daphne : num 0 0 0 0 0 0 0 0 1 0 ...
$ Brenthis_ino : num 0 0 0 0 0 0 0 0 0 0 ...
$ Aulocera_circe : num 0 0 0 0 0 1 0 0 0 0 ...
$ Callophrys_rubi : num 0 0 0 0 1 0 0 0 0 0 ...
$ Celastrina_argiolus : num 0 0 0 0 0 0 0 0 1 0 ...
$ Coenonympha_arcania : num 0 0 0 0 0 0 0 0 0 0 ...
$ Coenonympha_glycerion: num 0 1 1 1 1 1 0 0 1 1 ...
$ Coenonympha_pamphilus: num 1 1 1 1 1 1 1 0 1 1 ...
$ Colias_alfacariensis : num 0 0 0 0 0 0 0 0 0 0 ...
$ Colias_croceus : num 1 0 0 0 1 0 0 0 0 0 ...
$ Colias_hyale : num 0 0 0 0 0 0 0 0 0 0 ...
$ Cupido_minimus : num 0 0 0 0 0 0 0 0 0 0 ...
$ Cyaniris_semiargus : num 0 0 0 0 0 0 0 0 0 0 ...
$ Erebia_medusa : num 0 0 0 0 0 0 0 0 0 0 ...
$ Erynnis_tages : num 0 0 0 0 1 0 1 1 1 0 ...
$ Aricia_eumedon : num 0 0 0 0 0 0 0 0 0 0 ...
$ Euphydryas_aurinia : num 0 0 0 0 0 1 0 0 0 0 ...
$ Cupido_argiades : num 0 1 1 1 1 0 0 0 1 0 ...
$ Glaucopsyche_alexis : num 0 0 0 0 0 0 0 0 0 0 ...
$ Gonepteryx_rhamni : num 0 0 0 0 0 0 0 0 0 0 ...
$ Hamearis_lucina : num 0 0 0 0 0 0 0 0 0 0 ...
$ Hesperia_comma : num 0 1 1 1 1 1 0 0 0 0 ...
$ Heteropterus_morpheus: num 0 0 0 0 0 0 0 0 1 0 ...
$ Inachis_io : num 0 0 0 0 0 0 0 0 0 0 ...
$ Iphiclides_podalirius: num 0 0 0 1 1 0 1 0 0 0 ...
$ Issoria_lathonia : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lasiommata_megera : num 0 0 0 0 0 0 1 0 0 0 ...
$ Leptidea_sinapis : num 1 1 1 1 1 1 1 0 0 1 ...
$ Limenitis_camilla : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lopinga_achine : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lycaena_alciphron : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lycaena_dispar : num 0 0 0 0 0 0 0 0 0 1 ...
$ Lycaena_phlaeas : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lycaena_tityrus : num 0 1 0 0 0 0 0 0 0 0 ...
$ Lycaena_virgaureae : num 0 1 0 0 0 0 0 0 0 0 ...
$ Polyommatus_bellargus: num 0 0 0 0 0 0 0 0 0 0 ...
$ Maculinea_arion : num 0 0 0 0 0 0 0 0 0 0 ...
$ Maniola_jurtina : num 1 1 1 1 1 1 1 1 1 1 ...
$ Melanargia_galathea : num 0 1 1 1 1 1 1 0 1 1 ...
$ Polyommatus_daphnis : num 0 0 0 0 0 0 0 0 0 0 ...
$ Melitaea_athalia : num 0 0 1 0 0 1 0 0 1 1 ...
$ Melitaea_aurelia : num 0 0 0 0 0 0 1 0 1 1 ...
$ Melitaea_britomartis : num 0 0 1 0 0 0 0 0 0 0 ...
$ Melitaea_cinxia : num 0 0 0 0 0 0 0 0 0 0 ...
$ Melitaea_diamina : num 0 0 0 0 0 0 0 0 0 0 ...
$ Melitaea_didyma : num 0 0 0 0 0 0 1 0 0 0 ...
$ Melitaea_phoebe : num 0 0 0 0 0 1 0 0 0 0 ...
$ Minois_dryas : num 1 0 1 0 0 1 1 0 0 0 ...
$ Nymphalis_antiopa : num 0 0 0 0 0 0 0 0 0 0 ...
$ Ochlodes_sylvanus : num 0 0 0 0 0 0 0 0 0 0 ...
$ Papilio_machaon : num 0 0 0 1 0 0 0 0 0 0 ...
$ Pieris_brassicae : num 1 0 0 0 0 0 0 1 0 0 ...
$ Pieris_napi : num 0 0 1 0 0 0 0 0 0 0 ...
$ Pieris_rapae : num 1 1 0 1 0 0 0 0 1 0 ...
$ Plebejus_argus : num 1 1 1 1 1 1 1 1 1 1 ...
$ Plebejus_argyrognomon: num 0 1 0 0 0 0 0 0 0 0 ...
$ Plebejus_idas : num 0 1 0 0 0 0 1 0 0 0 ...
$ Polygonia_calbum : num 1 0 0 0 0 0 0 0 1 0 ...
$ Polyommatus_amandus : num 0 0 0 0 0 0 0 0 0 0 ...
$ Polyommatus_coridon : num 0 0 0 0 0 1 0 0 0 0 ...
$ Polyommatus_dorylas : num 0 0 0 0 0 0 0 0 0 0 ...
$ Polyommatus_icarus : num 1 1 1 1 1 1 1 0 1 1 ...
$ Polyommatus_thersites: num 0 0 0 0 0 1 0 0 0 0 ...
$ Pyrgus_armoricanus : num 0 0 0 0 0 0 0 0 0 0 ...
$ Pyrgus_alveus : num 0 0 0 0 0 0 0 0 0 0 ...
$ Pyrgus_malvae : num 0 0 1 0 0 0 1 0 0 1 ...
$ Satyrium_acaciae : num 0 0 0 0 0 0 0 0 0 0 ...
$ Satyrium_ilicis : num 0 0 0 0 0 0 0 0 0 0 ...
$ Thymelicus_lineola : num 0 1 1 1 1 0 1 1 1 0 ...
$ Thymelicus_sylvestris: num 0 1 1 0 0 0 1 0 1 1 ...
$ Vanessa_atalanta : num 0 0 0 0 0 0 0 0 0 0 ...
$ Vanessa_cardui : num 0 0 0 0 0 0 0 0 0 0 ...
> str(envir)
'data.frame': 120 obs. of 13 variables:
$ woody_1ha : num 0.36 0.257 0.451 -1.245 -1.245 ...
$ spot_1ha : num -0.159 -0.313 0.909 -0.313 1.265 ...
$ TWI : num 1.635 -0.636 -0.312 0.398 1.105 ...
$ heatload : num 0.789 -1.001 -1.257 0.363 0.704 ...
$ sidi_50ha : num -1.879 0.621 -1.139 -1.292 0.341 ...
$ woody_50ha : num -0.374 1.437 0.477 -1.838 -1.272 ...
$ rugg_50ha : num -1.271 0.609 0.192 -0.695 -0.037 ...
$ ed_50ha : num -1.125 0.704 -0.414 -0.516 0.399 ...
$ woodypercatch: num 0.566 0.566 0.566 -1.076 -1.076 ...
$ catch_rugged : num 0.236 0.236 0.236 2.084 2.084 ...
$ Edfine : num 0.926 0.926 0.926 0.453 0.453 ...
$ SIDIfine : num 0.719 0.719 0.719 -0.401 -0.401 ...
$ pastpercatch : num -1.111 -1.111 -1.111 0.703 0.703 ...
> str(butttraitX)
'data.frame': 88 obs. of 11 variables:
$ Winglength : Factor w/ 25 levels "11","12","13",..: 13 10 20 22 11 19 7
16 14 15 ...
$ Eggs_pot : Factor w/ 52 levels "64","65","70",..: 51 33 20 22 28 33 37
32 30 25 ...
$ Generations: Factor w/ 5 levels "2","3","4","5",..: 2 1 1 1 1 1 4 1 1 1
...
$ Winterstage: Factor w/ 5 levels "adult","egg",..: 1 5 3 3 3 3 5 2 3 2 ...
$ Eggdevtime : Factor w/ 32 levels "3","4","5","6",..: 6 3 10 12 14 14 3 27
15 30 ...
$ Larvdevtime: Factor w/ 40 levels "16","17","18",..: 3 1 13 38 38 40 7 25
32 19 ...
$ Pupdevtime : Factor w/ 23 levels "8","10","11",..: 2 23 8 7 10 8 4 12 9 6
...
$ Imagotime : Factor w/ 14 levels "10","12","14",..: 12 3 8 8 7 1 3 10 7 5
...
$ r.K : Factor w/ 2 levels "K","r": 2 1 1 1 1 2 2 1 1 1 ...
$ Diet : Factor w/ 3 levels "m","o","p": 1 2 2 1 3 2 1 1 1 1 ...
$ Mobility : Factor w/ 8 levels "1","2","3","4",..: 6 4 4 3 3 5 5 4 3 3
...
summary(pres.rlq)
Eigenvalues decomposition:
eig covar sdR sdQ corr
1 0.6267516 0.7916764 1.724234 2.008068 0.2286511
2 0.2434880 0.4934451 1.153176 1.944874 0.2200148
Inertia & coinertia R:
inertia max ratio
1 2.972982 3.249839 0.9148091
12 4.302797 5.171932 0.8319515
Inertia & coinertia Q:
inertia max ratio
1 4.032335 7.669151 0.5257864
12 7.814868 13.948402 0.5602698
Correlation L:
corr max ratio
1 0.2286511 0.4422966 0.5169633
2 0.2200148 0.4046466 0.5437207
I have been trying to find the mistake for hours already and I just can´t
get a clue why the test works for one example but not for the other. I would
be happy about recommendations how to solve this problem.
Best wishes,
Jacqueline
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