Row-col method associated with frequentist and Bayesian statistics in a passion fruit population

Souza, André Oliveira; Viana, Alexandre Pio; Silva, Fabyano Fonseca e; Azevedo, Camila Ferreira; Cavalcante, Natan Ramos; Silva, Flavia Alves

doi:10.1590/1984-70332023v23n3a34

André Oliveira Souza

Instituto Federal do Espírito Santo, Rodovia ES-482, km 72, Rive, 29500-000, Alegre, ES, Brazil

Alexandre Pio Viana

Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Parque California, 28013-602, Campos dos Goytacazes, RJ, Brazil.

Fabyano Fonseca e Silva

Universidade Federal do Espírito Santo, Avenida Fernando Ferrari, 514, Goiabeiras, 29060-900, Vitória, ES, Brazil

Camila Ferreira Azevedo

Universidade Federal de Viçosa, Avenida P H Rolfs, s/n, Campus Universitário, 36570-900, Viçosa, MG, Brazil

Natan Ramos Cavalcante

Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Parque California, 28013-602, Campos dos Goytacazes, RJ, Brazil.

Flavia Alves Silva ^** E-mail: flavia_uems@hotmail.com

Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Parque California, 28013-602, Campos dos Goytacazes, RJ, Brazil.

http://orcid.org/0000-0001-7818-707X

* E-mail: flavia_uems@hotmail.com

Variable	Model	Deviance	df	Statistics (D)	p-value
YLD	RCB + Row-Col	1289	2	33.50^*	<0.001
YLD	RCB	1322.5	2	33.50^*	<0.001
NF	RCB + Row-Col	2051	2	29.00^*	<0.001
NF	RCB	2022	2	29.00^*	<0.001
FW	RCB + Row-Col	1789.1	2	2.2	0.3343
FW	RCB	1791.3	2	2.2	0.3343
TVS	RCB + Row-Col	1212.1	2	5.1	0.0772
TVS	RCB	1217.2	2	5.1	0.0772
LGT	RCB + Row-Col	1075.8	2	1.79	0.6353
LGT	RCB	1074.01	2	1.79	0.6353
PT	RCB + Row-Col	618.07	2	0	1
PT	RCB	618.07	2	0	1
PP	RCB + Row-Col	1156.6	2	2.3	0.3257
PP	RCB	1158.9	2	2.3	0.3257
TSS	RCB + Row-Col	622.16	2	0.06	0.9722
TSS	RCB	622.22	2	0.06	0.9722

Variable		Model	DIC	W	Δ	Convergence
Variable		Model	DIC	W	Δ	Geweke (1992Geweke J1992 Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In Bernardo JM, Berger JO, Dawid AP and Smith AFM (eds) Bayesian statistics. Clarendon Press, Oxford, p. 169-193)	Heidenberg & Welch (1983)
	NF	RCB + Row-Col	2005.57	1.00	-48.30^d	Converged	Converged
	NF	RCB	2053.87	0.00	-48.30^d	Converged	Converged
	YLD	RCB + Row-Col	1271.72	1.00	-54.99^d	Converged	Converged
	YLD	RCB	1326.71	0.00	-54.99^d	Converged	Converged
	FW	RCB + Row-Col	1789.65	0.85	-3.43^d	Converged	Converged
	FW	RCB	1793.08	0.15	-3.43^d	Converged	Converged
	TVS	RCB + Row-Col	1206.08	0.98	-7.59^d	Converged	Converged
	TVS	RCB	1213.67	0.02	-7.59^d	Converged	Converged
	LGT	RCB + Row-Col	1073.92	0.60	0.76ⁱ	Converged	Converged
	LGT	RCB	1074.68	0.40	0.76ⁱ	Converged	Converged
	PP	RCB + Row-Col	1158.35	0.84	-3.37^d	Converged	Converged
	PP	RCB	1161.72	0.16	-3.37^d	Converged	Converged
	PT	RCB + Row-Col	609.87	0.17	3.22^d	Converged	Converged
	PT	RCB	606.65	0.83	3.22^d	Converged	Converged
	TSS	RCB + Row-Col	671.18	0.25	2.15^d	Converged	Converged
	TSS	RCB	669.03	0.75	2.15^d	Converged	Converged

Variable	${\hat{σ}}_{p r o g e n y}^{2}$	${\hat{σ}}_{b l o c k}^{2}$	${\hat{σ}}_{r o w}^{2}$	${\hat{σ}}_{c o l u m n}^{2}$	${\hat{σ}}_{e r r o r}^{2}$	${\hat{h}}^{2}$	Gs (%)	CI- ${\hat{h}}^{2}$ (95%)
YLD	4.50	1.26	24.01	0.00	34.21	0.07	5.89	-0.06; 0.20
NF	197.31	89.17	947.14	16.58	1486.45	0.07	6.06	-0.06; 0.20
FW	30.98	31.89	-	-	561.98	0.05	1.30	-0.14; 0.24
LGT	6.28	1.65	-	-	25.07	0.19	2.22	0.01; 0.38
TVS	1.26	1.73	-	-	13.45	0.08	0.73	-0.10; 0.26
PP	3.50	0.00	-	-	19.87	0.15	2.24	-0.04; 0.35
PT	0.41	0.13	-	-	1.04	0.26	-6.54	0.08; 0.44
TSS	0.28	0.00	-	-	1.57	0.15	2.41	-0.04; 0.35

Variable	${\hat{σ}}_{p r o g e n y}^{2}$	${\hat{σ}}_{b l o c k}^{2}$	${\hat{σ}}_{r o w}^{2}$	${\hat{σ}}_{c o l u m n}^{2}$	${\hat{σ}}_{e r r o r}^{2}$	${\hat{h}}^{2}$	Gs (%)	HPD-95%
NF	170.90	2593.00	1098.00	55.46	1533.00	0.05	6.06	0.00; 0.15
YLD	4.75	1332.00	32.68	0.93	34.51	0.04	5.67	0.00; 0.13
FW	51.89	2847.00	44.91	26.10	515.50	0.06	1.99	0.00; 0.18
TVS	6.39	986.30	1.14	3.08	23.37	0.11	2.22	0.00; 0.28
LGT	1.35	13.42	-	-	13.61	0.04	0.72	0.00; 0.15
PP	3.26	11.50	1.82	0.72	19.27	0.08	1.96	0.00; 0.24
PT	0.41	682.00	-	-	1.08	0.09	-6.27	0.00; 0.30
TSS	0.26	1034.00	-	-	1.63	0.07	2.04	0.00; 0.24

N	NF	NF	YLD	YLD	FW	FW	LGT	LGT	TVS	TVS	PP	PP	PT	PT	TSS	TSS
1	23	23	67	67	89	89	32	32	32	32	49	49	45	45	90	90
2	9	9	85	85	40	99	40	59	14	14	41	41	49	49	45	45
3	35	35	23	23	99	60	90	90	99	99	44	45	31	31	72	72
4	71	71	90	90	60	6	59	40	89	89	72	72	41	41	3	3
5	90	90	9	9	6	40	99	89	92	92	23	23	100	100	11	11
6	67	67	71	71	32	32	72	6	98	98	45	44	92	92	34	34
7	70	70	40	4	72	72	6	72	72	72	31	89	9	9	21	21
8	88	88	4	40	92	14	89	38	40	40	51	51	29	29	55	55
9	49	49	35	35	59	92	76	95	76	76	38	30	47	47	19	19
10	48	48	48	46	58	59	95	99	95	95	43	31	73	73	41	41
11	85	85	46	48	14	4	33	21	3	3	30	38	40	40	31	31
12	12	46	88	19	4	58	21	14	6	6	59	27	3	3	100	100
13	46	19	58	58	16	39	31	76	51	51	92	92	23	23	98	98
14	19	63	19	88	85	3	38	39	81	81	61	91	91	91	92	92
15	63	12	62	62	41	41	58	4	85	85	9	61	69	69	27	27
16	65	65	25	25	3	85	39	98	41	41	89	84	1	1	37	37
17	40	91	49	63	39	16	61	8	59	59	91	59	59	59	52	52
18	91	40	65	65	35	48	4	58	96	96	27	43	80	80	68	68
19	55	55	63	49	90	21	3	9	25	25	98	54	30	30	64	64
20	28	28	12	28	48	50	14	3	69	16	100	52	12	12	18	18
21	4	4	89	89	98	100	98	65	16	69	54	9	68	68	43	43
22	20	52	28	27	50	98	8	31	60	60	4	98	87	87	53	53
23	25	25	27	52	100	76	9	33	4	4	84	11	22	22	89	89
24	52	58	22	12	21	90	88	61	58	58	47	4	84	84	96	96
25	58	20	3	22	76	35	65	66	83	83	11	100	24	24	33	33
26	62	62	52	70	52	52	34	23	66	22	55	93	98	79	78	78
27	15	15	39	3	64	64	92	88	22	66	93	24	79	98	87	87
28	8	8	70	39	83	83	50	85	33	33	24	57	61	61	35	35
29	72	93	6	6	96	56	23	50	100	100	57	20	89	89	47	47
30	22	68	55	55	56	96	75	92	39	39	58	47	70	70	51	51
${\bar{X}}_{G}$	116.4	116.4	18.4	18.4	158.6	159.2	81.0	81.0	72.2	72.3	46.8	46.9	7.3	7.3	13.4	13.4
${\bar{X}}_{30}$	123.5	123.5	19.4	19.5	160.6	162.3	82.8	82.8	72.8	72.8	47.9	47.8	7.8	6.9	13.7	13.7

Variable	Model	PA_training	PA_validation	RMSE_training	RMSE_validation
Number of fruits	Mixed	0.74	0.39	33.44	44.43
Number of fruits	Bayesian	0.75	0.39	33.74	44.23
Yield	Mixed	0.74	0.40	5.15	6.69
Yield	Bayesian	0.75	0.40	5.07	6.68
Fruit weight	Mixed	0.70	0.07	20.43	24.85
Fruit weight	Bayesian	0.71	0.13	20.21	24.52
FLD/Length	Mixed	0.83	0.26	4.05	5.46
FLD/Length	Bayesian	0.82	0.26	4.10	5.46
FTD/Diameter	Mixed	0.51	0.20	3.50	3.82
FTD/Diameter	Bayesian	0.57	0.22	3.44	3.81
Peel thickness	Mixed	0.81	0.31	0.89	1.18
Peel thickness	Bayesian	0.78	0.31	0.92	1.18
Pulp percentage	Mixed	0.78	0.10	3.75	4.99
Pulp percentage	Bayesian	0.79	0.06	3.72	5.01
Total soluble solids	Mixed	0.79	0.14	1.19	1.35
Total soluble solids	Bayesian	0.78	0.12	1.19	1.34

[1] * E-mail: flavia_uems@hotmail.com

Brasil

Brasil

Row-col method associated with frequentist and Bayesian statistics in a passion fruit population

Abstract