Jacqueline
2015-Apr-09 13:23 UTC
[R] Using randtest in rlq works for one dataset, but not for the other....
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 -- View this message in context: http://r.789695.n4.nabble.com/Using-randtest-in-rlq-works-for-one-dataset-but-not-for-the-other-tp4705655.html Sent from the R help mailing list archive at Nabble.com.