I am working with multiple regression models. After running the dredge function, I got approximately 54 000 different combinations. I selected the first 300 models and ran this code:
par(mar=c(1,4,10,3))
> plot(fitt, labels = c("Intercept",
+ "YOFE",
+ "'RW Closeness'",
+ "'LW Closeness'",
+ "Age",
+ "SES",
+ "'GAD-7 Score'",
+ "Fantasy",
+ "'Personal Distress'",
+ "'Empathic Concern'",
+ "'Perspective Taking'",
+ "'PHQ-9 Score'",
+ "'Religioius Affinity'",
+ "'Agreement with IH'",
+ "'Moral Judgement of IH'",
+ "'Harm Assessment of IH'",
+ "'Agreement with IB'",
+ "'Moral Judgement of IB'",
+ "RMET",
+ "Sex"),ylab = expression("Cumulative" ~italic(w[i]*(AICc))),col = c(colfunc(1)), border = "gray30",labAsExpr = TRUE)
10 minutes later, I got this error:
Error in (function (text, side = 3, line = 0, outer = FALSE, at = NA, : zero-length 'text' specified In addition: Warning message: In max(strwidth(arg[["text"]], cex = arg$cex, units = "in")) : no non-missing arguments to max; returning -Inf
And this is the output plot:
I've tried plotting only the first model and the same error appears:
This also happens when using the whole model selection table (54 000 combinations).
What is a solution to this?
I'm running the latest version of R and RStudio on my 2016 12 inch Macbook.
Note: I've tried increasing the plot-window size manually by dragging the edges without any improvement.
This is what I'd like my plot to look like:
EDIT: Here is the data file data and the code.
modeloglobal<-lm(PROMEDIO_CREENCIA_NFALSA_CORONAVIRUS~Edad+Sex+
AnEdu+
Estrato_1+
GAD_TOTAL+
PHQ_TOTAL+
PracticRel_2+
CercanPolDer_1+
CercanPolIz_1+
RMET_TOTAL+
IRI_PREOCUPACIÓN_EMPATICA+
IRI_FANTASÍA+
IRI_MALESTAR_PERSONAL+
IRI_TOMA_DE_PERSPECTIVA+
PROMEDIO_DILEMAS_BI_ACTUARIGUAL_CORONAVIRUS+
PROMEDIO_DILEMAS_BI_BIENOMAL_CORONAVIRUS+
PROMEDIO_DI_SINPOL_ACTUARIGUAL+
PROMEDIO_DI_SINPOL_BIENOMAL+
PROMEDIO_DI_SINPOL_DANO, data=fake_news,na.action="na.fail")
library(MuMIn)
fitt<-dredge(modeloglobal,trace=2)
m.sel <- model.sel(fitt)
m.sel2 <- m.sel[1:300,]
library(binovisualfields)
And the code that runs the error (using a subset of the first 300 rows):
par(mar=c(1,4,10,3))
> plot(m.sel2, labels = c("Intercept",
+ "YOFE",
+ "'RW Closeness'",
+ "'LW Closeness'",
+ "Age",
+ "SES",
+ "'GAD-7 Score'",
+ "Fantasy",
+ "'Personal Distress'",
+ "'Empathic Concern'",
+ "'Perspective Taking'",
+ "'PHQ-9 Score'",
+ "'Religioius Affinity'",
+ "'Agreement with IH'",
+ "'Moral Judgement of IH'",
+ "'Harm Assessment of IH'",
+ "'Agreement with IB'",
+ "'Moral Judgement of IB'",
+ "RMET",
+ "Sex"),ylab = expression("Cumulative" ~italic(w[i]*(AICc))),col = c(colfunc(1)), border = "gray30",labAsExpr = TRUE)
EDIT 2: Here's the data frame I got from dput().
ResponseId Edad Sex Genero Nacion Resid Estrato_1 Gastos salud
1 R_25GEak825Ohmb9G 18 Female Femenino Colombia Colombia 7 Seguro privado
2 R_1kT7u0PALDHV8H6 20 Female Femenino Colombia Colombia 5 Seguro privado
3 R_2cpBb5Ifzj7lVGs 21 Female Femenino Colombia Colombia 6 Seguro privado
4 R_sGqNUMTXTJzwC09 20 Male Masculino Colombia Colombia 5 Seguro del Estado
5 R_2Cpixt9Z5FJkhg1 36 Male Masculino Colombia Colombia 6 Otro (especifique)
6 R_3QFq50SZNs6CePA 18 Female Femenino Colombia Colombia 7 Seguro privado
Relig PracticRel_2 AnEdu Q161 Ecron Epsiq Q183 Eneu Q184
1 Ninguna 0 15 Estudiante 1 0 <NA> 0 <NA>
2 Cristianismo (Catolicismo) 2 15 Estudiante 0 0 <NA> 0 <NA>
3 Cristianismo (Catolicismo) 2 19 Estudiante 0 0 <NA> 0 <NA>
4 Cristianismo (Catolicismo) 2 15 Estudiante 0 0 <NA> 0 <NA>
5 Cristianismo (Catolicismo) 1 17 Empleado de tiempo completo 0 0 <NA> 0 <NA>
6 Cristianismo (Catolicismo) 4 15 Estudiante 0 0 <NA> 0 <NA>
NPviven Sustancias Pviven AdhAS LevantarAS_1 CumplimAS_1 HorasFuera
1 1 1 Padres 1 5 6 Menos de una hora
2 3 0 Padres,Hermanos 1 1 6 Menos de una hora
3 4 0 Padres,Hermanos 1 2 6 Menos de una hora
4 4 0 Padres,Hermanos 1 2 6 Menos de una hora
5 3 0 Pareja,Hijos 1 2 3 Entre cuatro y seis horas
6 3 0 Padres,Hermanos 1 2 6 Entre una y tres horas
Apoyo CV19_1 ContagUd ContagEC Prob_1_contagio Prob_2_familiar_contagio
1 1 No 0 81 100
2 4 No 0 81 35
3 6 No 0 60 80
4 4 No 0 4 15
5 5 No 0 40 40
6 6 No 0 79 86
Prob_3_contagio_poblaciongeneral Caract_1 Caract_2 Inv_3 Caract_3 Caract_4 Caract_5 Caract_6 Caract_8
1 87 4 2 1 6 4 5 4 5
2 81 5 4 3 4 4 5 2 3
3 80 4 4 1 6 6 6 1 2
4 20 6 5 5 2 1 5 1 5
5 60 2 1 2 5 4 3 2 3
6 70 5 4 2 5 6 2 5 6
Caract_9 Caract_11 Caract_14 INV_15 Caract_15 Caract_16 Caract_17 CompPan_1 CompPan_2 CompPan_3
1 5 3 2 4 3 5 5 1 6 1
2 4 5 4 5 2 3 3 4 5 8
3 6 1 6 6 1 6 6 1 1 1
4 5 5 2 6 1 3 1 1 3 2
5 4 1 1 5 2 2 2 2 2 2
6 6 2 3 5 2 6 5 2 7 3
CompPan_4 CompPan_5 CompPan_6 CercanPolDer_1 CercanPolIz_1 IDpol_1 PHQ_TOTAL GAD_TOTAL
1 5 5 7 8 2 5 8 6
2 8 8 8 7 3 5 4 3
3 3 2 4 6 3 4 2 3
4 4 3 3 5 5 4 3 3
5 3 3 2 5 5 4 2 2
6 6 2 7 3 8 3 7 7
INTEROCEPCION_TOTAL BIS BAS_FUN_SEEKING BAS_REWARD_RESPONSIVENESS BAS_DRIVE BAS_TOTAL
1 45 19 14 19 11 44
2 44 20 10 17 14 41
3 24 17 10 19 13 42
4 17 17 9 14 8 31
5 36 21 10 17 11 38
6 41 25 6 17 13 36
IRI_TOMA_DE_PERSPECTIVA IRI_MALESTAR_PERSONAL IRI_FANTASÍA IRI_PREOCUPACIÓN_EMPATICA RMET_TOTAL
1 14 13 14 19 7
2 18 11 14 20 4
3 17 4 10 20 10
4 16 9 11 12 7
5 10 11 7 10 10
6 16 11 16 18 8
PROMEDIO_TIEMPO_REACCION_RMET PROMEDIO_CREENCIA_NFALSA_TODAS PROMEDIO_CREENCIA_NFALSA_CORONAVIRUS
1 2.411750 2.8 2.666667
2 3.348500 2.8 2.333333
3 3.261083 2.4 2.000000
4 6.390500 2.2 1.666667
5 13.212667 1.8 1.333333
6 4.218583 3.6 2.666667
PROMEDIO_CREENCIA_NFALSA_OTRO PROMEDIO_TIEMPOREACCION_NFALSA PROMEDIO_CREENCIA_NVERDADERA_TODAS
1 3.0 4.3438 3.333333
2 3.5 9.4222 3.000000
3 3.0 5.9734 3.666667
4 3.0 10.1448 2.666667
5 2.5 16.3196 1.333333
6 5.0 7.1954 3.333333
PROMEDIO_CREENCIA_NVERDADERA_CORONAVIRUS PROMEDIO_CREENCIA_NVERDADERA_OTRO
1 5 5
2 4 5
3 6 5
4 5 3
5 1 3
6 6 4
PROMEDIO_TIEMPOREACCION_NVERDADERA PROMEDIO_CREENCIA_NMISLEADING_TODAS
1 5.6440 2.666667
2 7.0430 2.666667
3 8.0265 3.666667
4 4.0495 3.000000
5 32.2400 1.666667
6 9.5830 4.333333
PROMEDIO_TIEMPOREACCION_NMISLEADING PROMEDIO_DILEMAS_BI_BIENOMAL_CORONAVIRUS
1 5.726667 1.000000
2 12.012333 4.000000
3 5.753000 4.333333
4 4.969667 1.333333
5 15.233000 0.000000
6 30.045667 3.666667
PROMEDIO_DILEMAS_BI_ACTUARIGUAL_CORONAVIRUS DILEMA_BI_CONTROL_BIENOMAL DILEMA_BI_CONTROL_ACTUARIGUAL
1 5.666667 4 7
2 7.666667 5 4
3 9.666667 2 6
4 4.333333 0 2
5 3.666667 -3 2
6 9.333333 4 10
PROMEDIO_DILEMAS_BI_BIENOMAL_JUNTOS PROMEDIO_DILEMAS_BI_ACTUARIGUAL_JUNTOS
1 1.75 6.00
2 4.25 6.75
3 3.75 8.75
4 1.00 3.75
5 -0.75 3.25
6 3.75 9.50
PROMEDIO_DILEMAS_DI_BIENOMAL PROMEDIO_DILEMAS_DI_ACTUARIGUAL PROMEDIO_DILEMAS_DI_DANO
1 0.5000000 6.666667 5.666667
2 1.8333333 7.666667 6.166667
3 0.5000000 5.666667 5.333333
4 1.6666667 5.000000 5.500000
5 0.8333333 4.833333 5.666667
6 0.1666667 5.166667 7.000000
TIEMPOREACCION_DILEMAS_DI TIEMPOREACCION_DILEMAS_BI PROMEDIO_DI_SINPOL_BIENOMAL
1 12.140500 7.89900 0.2
2 9.130667 9.99550 1.2
3 6.998333 9.25175 -1.0
4 1.857833 2.84125 0.4
5 19.014333 32.82850 0.8
6 11.633667 16.92000 0.2
PROMEDIO_DI_SINPOL_ACTUARIGUAL PROMEDIO_DI_SINPOL_DANO COMPRAS_COVID19 PERCEPCION_RIESGO_TOTAL
1 7.00 7.25 4.166667 39
2 8.00 6.75 6.833333 37
3 4.25 7.25 2.000000 42
4 4.50 7.00 2.666667 38
5 5.00 7.75 2.333333 26
6 5.50 7.75 4.500000 46
PERCEPCION_RIESGO_INDICE PROB_CONTAGIO_TOTAL PROMEDIO_DILEMASPOLITICOS_BIENOMAL
1 3.9 89.33333 1.0
2 3.7 65.66667 2.5
3 4.2 73.33333 4.0
4 3.8 13.00000 4.0
5 2.6 46.66667 0.5
6 4.6 78.33333 0.0
PROMEDIO_DILEMASPOLITICOS_ACTUARIGUAL PROMEDIO_DILEMASPOLITICOS_DANO D31_1_DI D32_2_DI D33_3_DI
1 6.0 2.5 -2 4 9
2 7.0 5.0 3 9 7
3 8.5 1.5 -3 3 8
4 6.0 2.5 0 3 8
5 4.5 1.5 -2 4 8
6 4.5 5.5 4 9 7
D41_1_DI D42_2_DI D43_3_DI D51_1_DI D52_2_DI D53_3_DI D61_1_DI D62_2_DI D63_3_DI D71_1_DIP D72_2_DIP
1 -1 7 7 5 10 4 -1 7 9 0 4
2 1 8 9 0 7 4 2 8 7 3 7
3 0 6 7 1 5 6 -3 3 8 3 7
4 0 5 8 4 7 3 -2 3 9 4 3
5 3 7 9 1 3 7 2 6 7 -2 2
6 1 8 6 0 4 9 -4 1 9 -4 1
D73_3_DIP D81_1_DIP D82_2_DIP D83_3_DIP D91_1_BI D92_2_BI D101_1_BI D102_2_BI D111_1_BI D112_2_BI
1 3 2 8 2 -3 4 3 9 3 4
2 6 2 7 4 3 8 5 8 4 7
3 2 5 10 1 5 10 5 10 3 9
4 2 4 9 3 4 9 0 2 0 2
5 2 3 7 1 -1 3 3 6 -2 2
6 8 4 8 3 4 9 5 10 2 9
D121_1_BI D122_2_BI total_iri promedio_falsaymisleading prediccioncompraspercprob
1 4 7 60 2.750 4.249759
2 5 4 63 2.750 4.404450
3 2 6 51 2.875 4.431635
4 0 2 48 2.500 5.143974
5 -3 2 38 1.750 3.765907
6 4 10 61 3.875 4.893797
prediccioncomprasperc
1 4.474456
2 4.439994
3 4.521980
4 4.689385
5 3.762449
6 4.967286
Here is the raw dput() output:
structure(list(ResponseId = c("R_25GEak825Ohmb9G", "R_1kT7u0PALDHV8H6",
"R_2cpBb5Ifzj7lVGs", "R_sGqNUMTXTJzwC09", "R_2Cpixt9Z5FJkhg1",
"R_3QFq50SZNs6CePA"), Edad = c(18, 20, 21, 20, 36, 18), Sex = structure(c(2L,
2L, 2L, 1L, 1L, 2L), .Label = c("Male", "Female"), class = "factor"),
Genero = c("Femenino", "Femenino", "Femenino", "Masculino",
"Masculino", "Femenino"), Nacion = c("Colombia", "Colombia",
"Colombia", "Colombia", "Colombia", "Colombia"), Resid = c("Colombia",
"Colombia", "Colombia", "Colombia", "Colombia", "Colombia"
), Estrato_1 = c(7, 5, 6, 5, 6, 7), `Gastos salud` = c("Seguro privado",
"Seguro privado", "Seguro privado", "Seguro del Estado",
"Otro (especifique)", "Seguro privado"), Relig = c("Ninguna",
"Cristianismo (Catolicismo)", "Cristianismo (Catolicismo)",
"Cristianismo (Catolicismo)", "Cristianismo (Catolicismo)",
"Cristianismo (Catolicismo)"), PracticRel_2 = c(0, 2, 2,
2, 1, 4), AnEdu = c(15, 15, 19, 15, 17, 15), Q161 = c("Estudiante",
"Estudiante", "Estudiante", "Estudiante", "Empleado de tiempo completo",
"Estudiante"), Ecron = c(1, 0, 0, 0, 0, 0), Epsiq = c(0,
0, 0, 0, 0, 0), Q183 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_), Eneu = c(0,
0, 0, 0, 0, 0), Q184 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_), NPviven = c("1",
"3", "4", "4", "3", "3"), Sustancias = c(1, 0, 0, 0, 0, 0
), Pviven = c("Padres", "Padres,Hermanos", "Padres,Hermanos",
"Padres,Hermanos", "Pareja,Hijos", "Padres,Hermanos"), AdhAS = c(1,
1, 1, 1, 1, 1), LevantarAS_1 = c(5, 1, 2, 2, 2, 2), CumplimAS_1 = c(6,
6, 6, 6, 3, 6), HorasFuera = c("Menos de una hora", "Menos de una hora",
"Menos de una hora", "Menos de una hora", "Entre cuatro y seis horas",
"Entre una y tres horas"), `Apoyo CV19_1` = c(1, 4, 6, 4,
5, 6), ContagUd = c("No", "No", "No", "No", "No", "No"),
ContagEC = c(0, 0, 0, 0, 0, 0), Prob_1_contagio = c(81, 81,
60, 4, 40, 79), Prob_2_familiar_contagio = c(100, 35, 80,
15, 40, 86), Prob_3_contagio_poblaciongeneral = c(87, 81,
80, 20, 60, 70), Caract_1 = c(4, 5, 4, 6, 2, 5), Caract_2 = c(2,
4, 4, 5, 1, 4), Inv_3 = c(1, 3, 1, 5, 2, 2), Caract_3 = c(6,
4, 6, 2, 5, 5), Caract_4 = c(4, 4, 6, 1, 4, 6), Caract_5 = c(5,
5, 6, 5, 3, 2), Caract_6 = c(4, 2, 1, 1, 2, 5), Caract_8 = c(5,
3, 2, 5, 3, 6), Caract_9 = c(5, 4, 6, 5, 4, 6), Caract_11 = c(3,
5, 1, 5, 1, 2), Caract_14 = c(2, 4, 6, 2, 1, 3), INV_15 = c(4,
5, 6, 6, 5, 5), Caract_15 = c(3, 2, 1, 1, 2, 2), Caract_16 = c(5,
3, 6, 3, 2, 6), Caract_17 = c(5, 3, 6, 1, 2, 5), CompPan_1 = c(1,
4, 1, 1, 2, 2), CompPan_2 = c(6, 5, 1, 3, 2, 7), CompPan_3 = c(1,
8, 1, 2, 2, 3), CompPan_4 = c(5, 8, 3, 4, 3, 6), CompPan_5 = c(5,
8, 2, 3, 3, 2), CompPan_6 = c(7, 8, 4, 3, 2, 7), CercanPolDer_1 = c(8,
7, 6, 5, 5, 3), CercanPolIz_1 = c(2, 3, 3, 5, 5, 8), IDpol_1 = c(5,
5, 4, 4, 4, 3), PHQ_TOTAL = c(8, 4, 2, 3, 2, 7), GAD_TOTAL = c(6,
3, 3, 3, 2, 7), INTEROCEPCION_TOTAL = c(45, 44, 24, 17, 36,
41), BIS = c(19, 20, 17, 17, 21, 25), BAS_FUN_SEEKING = c(14,
10, 10, 9, 10, 6), BAS_REWARD_RESPONSIVENESS = c(19, 17,
19, 14, 17, 17), BAS_DRIVE = c(11, 14, 13, 8, 11, 13), BAS_TOTAL = c(44,
41, 42, 31, 38, 36), IRI_TOMA_DE_PERSPECTIVA = c(14, 18,
17, 16, 10, 16), IRI_MALESTAR_PERSONAL = c(13, 11, 4, 9,
11, 11), IRI_FANTASÍA = c(14, 14, 10, 11, 7, 16), IRI_PREOCUPACIÓN_EMPATICA = c(19,
20, 20, 12, 10, 18), RMET_TOTAL = c(7, 4, 10, 7, 10, 8),
PROMEDIO_TIEMPO_REACCION_RMET = c(2.41175, 3.3485, 3.26108333333333,
6.3905, 13.2126666666667, 4.21858333333333), PROMEDIO_CREENCIA_NFALSA_TODAS = c(2.8,
2.8, 2.4, 2.2, 1.8, 3.6), PROMEDIO_CREENCIA_NFALSA_CORONAVIRUS = c(2.66666666666667,
2.33333333333333, 2, 1.66666666666667, 1.33333333333333,
2.66666666666667), PROMEDIO_CREENCIA_NFALSA_OTRO = c(3, 3.5,
3, 3, 2.5, 5), PROMEDIO_TIEMPOREACCION_NFALSA = c(4.3438,
9.4222, 5.9734, 10.1448, 16.3196, 7.1954), PROMEDIO_CREENCIA_NVERDADERA_TODAS = c(3.33333333333333,
3, 3.66666666666667, 2.66666666666667, 1.33333333333333,
3.33333333333333), PROMEDIO_CREENCIA_NVERDADERA_CORONAVIRUS = c(5,
4, 6, 5, 1, 6), PROMEDIO_CREENCIA_NVERDADERA_OTRO = c(5,
5, 5, 3, 3, 4), PROMEDIO_TIEMPOREACCION_NVERDADERA = c(5.644,
7.043, 8.0265, 4.0495, 32.24, 9.583), PROMEDIO_CREENCIA_NMISLEADING_TODAS = c(2.66666666666667,
2.66666666666667, 3.66666666666667, 3, 1.66666666666667,
4.33333333333333), PROMEDIO_TIEMPOREACCION_NMISLEADING = c(5.72666666666667,
12.0123333333333, 5.753, 4.96966666666667, 15.233, 30.0456666666667
), PROMEDIO_DILEMAS_BI_BIENOMAL_CORONAVIRUS = c(1, 4, 4.33333333333333,
1.33333333333333, 0, 3.66666666666667), PROMEDIO_DILEMAS_BI_ACTUARIGUAL_CORONAVIRUS = c(5.66666666666667,
7.66666666666667, 9.66666666666667, 4.33333333333333, 3.66666666666667,
9.33333333333333), DILEMA_BI_CONTROL_BIENOMAL = c(4, 5, 2,
0, -3, 4), DILEMA_BI_CONTROL_ACTUARIGUAL = c(7, 4, 6, 2,
2, 10), PROMEDIO_DILEMAS_BI_BIENOMAL_JUNTOS = c(1.75, 4.25,
3.75, 1, -0.75, 3.75), PROMEDIO_DILEMAS_BI_ACTUARIGUAL_JUNTOS = c(6,
6.75, 8.75, 3.75, 3.25, 9.5), PROMEDIO_DILEMAS_DI_BIENOMAL = c(0.5,
1.83333333333333, 0.5, 1.66666666666667, 0.833333333333333,
0.166666666666667), PROMEDIO_DILEMAS_DI_ACTUARIGUAL = c(6.66666666666667,
7.66666666666667, 5.66666666666667, 5, 4.83333333333333,
5.16666666666667), PROMEDIO_DILEMAS_DI_DANO = c(5.66666666666667,
6.16666666666667, 5.33333333333333, 5.5, 5.66666666666667,
7), TIEMPOREACCION_DILEMAS_DI = c(12.1405, 9.13066666666666,
6.99833333333333, 1.85783333333333, 19.0143333333333, 11.6336666666667
), TIEMPOREACCION_DILEMAS_BI = c(7.899, 9.9955, 9.25175,
2.84125, 32.8285, 16.92), PROMEDIO_DI_SINPOL_BIENOMAL = c(0.2,
1.2, -1, 0.4, 0.8, 0.2), PROMEDIO_DI_SINPOL_ACTUARIGUAL = c(7,
8, 4.25, 4.5, 5, 5.5), PROMEDIO_DI_SINPOL_DANO = c(7.25,
6.75, 7.25, 7, 7.75, 7.75), COMPRAS_COVID19 = c(4.16666666666667,
6.83333333333333, 2, 2.66666666666667, 2.33333333333333,
4.5), PERCEPCION_RIESGO_TOTAL = c(39, 37, 42, 38, 26, 46),
PERCEPCION_RIESGO_INDICE = c(3.9, 3.7, 4.2, 3.8, 2.6, 4.6
), PROB_CONTAGIO_TOTAL = c(89.3333333333333, 65.6666666666667,
73.3333333333333, 13, 46.6666666666667, 78.3333333333333),
PROMEDIO_DILEMASPOLITICOS_BIENOMAL = c(1, 2.5, 4, 4, 0.5,
0), PROMEDIO_DILEMASPOLITICOS_ACTUARIGUAL = c(6, 7, 8.5,
6, 4.5, 4.5), PROMEDIO_DILEMASPOLITICOS_DANO = c(2.5, 5,
1.5, 2.5, 1.5, 5.5), D31_1_DI = c(-2, 3, -3, 0, -2, 4), D32_2_DI = c(4,
9, 3, 3, 4, 9), D33_3_DI = c(9, 7, 8, 8, 8, 7), D41_1_DI = c(-1,
1, 0, 0, 3, 1), D42_2_DI = c(7, 8, 6, 5, 7, 8), D43_3_DI = c(7,
9, 7, 8, 9, 6), D51_1_DI = c(5, 0, 1, 4, 1, 0), D52_2_DI = c(10,
7, 5, 7, 3, 4), D53_3_DI = c(4, 4, 6, 3, 7, 9), D61_1_DI = c(-1,
2, -3, -2, 2, -4), D62_2_DI = c(7, 8, 3, 3, 6, 1), D63_3_DI = c(9,
7, 8, 9, 7, 9), D71_1_DIP = c(0, 3, 3, 4, -2, -4), D72_2_DIP = c(4,
7, 7, 3, 2, 1), D73_3_DIP = c(3, 6, 2, 2, 2, 8), D81_1_DIP = c(2,
2, 5, 4, 3, 4), D82_2_DIP = c(8, 7, 10, 9, 7, 8), D83_3_DIP = c(2,
4, 1, 3, 1, 3), D91_1_BI = c(-3, 3, 5, 4, -1, 4), D92_2_BI = c(4,
8, 10, 9, 3, 9), D101_1_BI = c(3, 5, 5, 0, 3, 5), D102_2_BI = c(9,
8, 10, 2, 6, 10), D111_1_BI = c(3, 4, 3, 0, -2, 2), D112_2_BI = c(4,
7, 9, 2, 2, 9), D121_1_BI = c(4, 5, 2, 0, -3, 4), D122_2_BI = c(7,
4, 6, 2, 2, 10), total_iri = c(60, 63, 51, 48, 38, 61), promedio_falsaymisleading = c(2.75,
2.75, 2.875, 2.5, 1.75, 3.875), prediccioncompraspercprob = c(`1` = 4.24975892576113,
`2` = 4.40445037029013, `3` = 4.43163539588384, `4` = 5.14397435590305,
`5` = 3.76590707825915, `6` = 4.8937968160894), prediccioncomprasperc = c(`1` = 4.47445595202732,
`2` = 4.4399943212902, `3` = 4.52198006754018, `4` = 4.68938453833302,
`5` = 3.7624488758014, `6` = 4.96728571465517)), row.names = c(NA,
6L), class = c("tbl_df", "tbl", "data.frame"))
