# NOTE: Cada experimento avaliou apenas uma espécie. Juntar em uma única# tabela.tb <-bind_rows(tbs)tb$source <-factor(tb$source)# Doses usadas.tb |>distinct(dose) |>mutate(dose_log10 =log10(dose))
# Colocar um `_` após o número para separar número e origem.tb <- tb |>mutate(isolate =str_replace(isolate, "^(\\d+)\\s?(.*)","\\1_\\2"))tb |>count(source, species, isolate) |>print(n =Inf)
# A tibble: 71 × 4
source species isolate n
<fct> <fct> <chr> <int>
1 baseline C. crisophylum 132_PR 20
2 baseline C. crisophylum 133_PR 20
3 baseline C. crisophylum 48_PR 20
4 baseline C. crisophylum 53_PR 20
5 baseline C. crisophylum 55_PR 20
6 baseline C. crisophylum 71_PR 20
7 baseline C. melonis 29_SP 20
8 baseline C. melonis 34_SP 20
9 baseline C. melonis 35_SP 20
10 baseline C. melonis 9_PR 20
11 baseline C. nymphaea 142_PR 20
12 baseline C. nymphaea 14_PR 20
13 baseline C. nymphaea 15_PR 20
14 baseline C. nymphaea 179_PR 20
15 baseline C. nymphaea 181_PR 20
16 baseline C. nymphaea 19_PR 20
17 baseline C. nymphaea 20_PR 20
18 baseline C. nymphaea 20_RS 20
19 baseline C. nymphaea 21_PR 20
20 baseline C. nymphaea 26_RS 20
21 baseline C. nymphaea 47_RS 20
22 baseline C. nymphaea 65_PR 20
23 baseline C. nymphaea 66_PR 20
24 baseline C. nymphaea 69_PR 20
25 baseline C. nymphaea 70_PR 20
26 sensib C. crisophylum 14_SC 20
27 sensib C. crisophylum 19_SC 20
28 sensib C. crisophylum 27_SC 20
29 sensib C. crisophylum 39_SC 20
30 sensib C. crisophylum 60_SC 20
31 sensib C. crisophylum 62_SC 20
32 sensib C. crisophylum 69_SC 20
33 sensib C. crisophylum 70_SC 20
34 sensib C. crisophylum 74_SC 20
35 sensib C. crisophylum 75_SC 20
36 sensib C. crisophylum 76_SC 20
37 sensib C. crisophylum 79_SC 20
38 sensib C. crisophylum 87_SC 20
39 sensib C. melonis 107_J 20
40 sensib C. melonis 123_J 20
41 sensib C. melonis 128_J 20
42 sensib C. melonis 129_J 20
43 sensib C. melonis 133_J 20
44 sensib C. melonis 137_J 20
45 sensib C. melonis 21_J 20
46 sensib C. melonis 28_J 20
47 sensib C. melonis 32_J 20
48 sensib C. melonis 34_J 20
49 sensib C. melonis 38_J 20
50 sensib C. melonis 41_J 20
51 sensib C. melonis 52_J 20
52 sensib C. melonis 54_J 20
53 sensib C. melonis 56_J 20
54 sensib C. melonis 61_J 20
55 sensib C. melonis 64_J 20
56 sensib C. melonis 6_J 20
57 sensib C. melonis 9_J 20
58 sensib C. nymphaea 125_J 20
59 sensib C. nymphaea 134_J 20
60 sensib C. nymphaea 138_J 20
61 sensib C. nymphaea 144_J 20
62 sensib C. nymphaea 27_J 20
63 sensib C. nymphaea 29_J 20
64 sensib C. nymphaea 66_J 20
65 sensib C. nymphaea 69_J 20
66 sensib C. nymphaea 76_J 20
67 sensib C. nymphaea 78_J 20
68 sensib C. nymphaea 80_J 20
69 sensib C. nymphaea 82_J 20
70 sensib C. nymphaea 83_J 20
71 sensib C. nymphaea 89_J 20
# Ordena isolados dentro de espécie.tb <- tb |>arrange(source, species, isolate) |>mutate(isolate =factor(isolate, levels =unique(isolate)))
2.2 Transformação da escala da dose para ajuste dos modelos
Código
#-----------------------------------------------------------------------# Transformação na escala da dose para ajuste das curvas.# Experimenta transformações para ficar próximo de um equidistante pois# isso é interessante para o ajuste de modelos polinomiais.shift <-0.01tb |>distinct(dose) |>mutate(log_dose =log10(dose + shift) +2)
# IMPORTANT: Praticamente equidistante. O valor `0` é a testemunha. Os# valores de dose ficam positivos ao somar 2. Assim o intercepto será o# crescimento na dose 0. Sabendo isso, é resolver em x para saber qual# valor em x que retorna 0.5 * y(x = 0).# Cria uma nova coluna com a dose na escala logarítmica.tb <- tb |>mutate(log_dose =log10(dose + shift) +2)
Código
gg <-tb |>group_split(source) |>map(function(tbi) { title <-switch(as.character(tbi$source[1]),"baseline"="Antes da utilização do fungicida a campo (2010-2015)","sensib"="Após a utilização do fungicida a campo (2016-2017)") show_legend <-as.character(tbi$source[1]) =="sensib"ggplot(data = tbi,mapping =aes(x = log_dose,y = growth,color = species)) +facet_wrap(~isolate, scale ="free_y", ncol =7) +geom_point(pch =1, show.legend = show_legend) +# scale_y_log10() +# stat_summary(geom = "line", fun = "mean") +geom_smooth(method ="lm",formula = y ~poly(x, degree =3),se =FALSE,show.legend = show_legend,linewidth =0.5) +labs(title = title,x =expression("Dose · "* log[10](x +0.01) +2),y ="Crescimento (mm)",color ="Espécie") +# Sem valores no eixo y.theme(legend.position ="bottom",# axis.ticks.y = element_blank(),axis.text.y =element_blank(),axis.title.y =element_blank()) })gridExtra::grid.arrange(grobs = gg,ncol =1,heights =c(38, 62))
Figura 1. Análise exploratória para o crescimento de isolados de Colletotrichum em função da dose de fungicida. Curvas ajustadas considerando um polinômio de terceiro grau.
#-----------------------------------------------------------------------# Ajusta os modelos e calcula EC50.# Número de ajustes.tb |>distinct(source, species, isolate) |>nrow()
[1] 71
Código
# Calcula ec50 para cada isolado usando o modelo linear.k <-1tb_ec50_1 <- tb |>group_by(source, species, isolate) |>transmute(x = log_dose, y = growth) |>summarise(result =list(find_ec50_polyroot(tibble(x = x, y = y), k = k)),.groups ="drop") |>unnest(result)str(tb_ec50_1, max.level =2, give.attr =FALSE)
# Ajustes que não tiveram solução.tb_ec50_1 |>count(is.na(x))
# A tibble: 1 × 2
`is.na(x)` n
<lgl> <int>
1 FALSE 71
Código
# O polinômio de grau 1 tem apenas uma raíz.tb_ec50_1 |>filter(isolate == isolate[1])
# A tibble: 1 × 7
source species isolate x y y0 model
<fct> <fct> <fct> <dbl> <dbl> <dbl> <list>
1 baseline C. crisophylum 132_PR 1.71 0.408 0.408 <lm>
Código
# Calcula ec50 para cada isolado usando o modelo cúbico.k <-3tb_ec50_3 <- tb |>group_by(source, species, isolate) |>transmute(x = log_dose, y = growth) |>summarise(result =list(find_ec50_polyroot(tibble(x = x, y = y), k = k)),.groups ="drop") |>unnest(result)str(tb_ec50_3, max.level =2, give.attr =FALSE)
# O polinômio cúbico tem 3 raízes.tb_ec50_3 |>filter(isolate == isolate[1])
# A tibble: 3 × 7
source species isolate x y y0 model
<fct> <fct> <fct> <dbl> <dbl> <dbl> <list>
1 baseline C. crisophylum 132_PR 1.32 0.454 0.454 <lm>
2 baseline C. crisophylum 132_PR -3.10 0.454 0.454 <lm>
3 baseline C. crisophylum 132_PR 5.10 0.454 0.454 <lm>
Código
tb_ec50_3 |>count(is.na(x))
# A tibble: 1 × 2
`is.na(x)` n
<lgl> <int>
1 FALSE 147
Código
tb_ec50_3 |>count(is.finite(x))
# A tibble: 1 × 2
`is.finite(x)` n
<lgl> <int>
1 TRUE 147
Código
# Juntar os dois ajustes para fazer o restante dos processos.tb_ec50 <-bind_rows(list("1"= tb_ec50_1,"3"= tb_ec50_3),.id ="order")# str(tb_ec50, max.level = 2, give.attr = FALSE)#-----------------------------------------------------------------------# Elimina soluções inválidas para EC50.min(c(1, -Inf, NA), na.rm =TRUE)
[1] -Inf
Código
# Valida os valores de EC50 na escala em que foi estimada, ou seja, `x`.tb_ec50 <- tb_ec50 |># drop_na(x) |>group_by(order, source, species, isolate) |># y e y0 devem ser próximos e x deve estar entre 0 e 8.mutate(x =if_else(between(x, 0, 7.4), x, NA_real_)) |># Pega o menor valor quando houver vários válidos.filter(is.finite(x), !is.na(x)) |>mutate(x =if_else(x ==min(x, na.rm =TRUE), x, NA_real_)) |>ungroup()# Aplica a função inversa para obter a EC50.tb_ec50 <- tb_ec50 |>mutate(ec50 = x -2,ec50 =10^ec50 - shift)tb_ec50 |>count(order, source, species, isolate) |>pivot_wider(names_from = order,values_from = n) |>print(n =Inf)
# A tibble: 71 × 5
source species isolate `1` `3`
<fct> <fct> <fct> <int> <int>
1 baseline C. crisophylum 132_PR 1 2
2 baseline C. crisophylum 133_PR 1 1
3 baseline C. crisophylum 48_PR 1 2
4 baseline C. crisophylum 53_PR 1 2
5 baseline C. crisophylum 71_PR 1 2
6 baseline C. melonis 29_SP 1 1
7 baseline C. melonis 34_SP 1 1
8 baseline C. melonis 35_SP 1 2
9 baseline C. melonis 9_PR 1 1
10 baseline C. nymphaea 142_PR 1 1
11 baseline C. nymphaea 14_PR 1 1
12 baseline C. nymphaea 15_PR 1 1
13 baseline C. nymphaea 179_PR 1 2
14 baseline C. nymphaea 181_PR 1 1
15 baseline C. nymphaea 19_PR 1 1
16 baseline C. nymphaea 20_PR 1 1
17 baseline C. nymphaea 20_RS 1 NA
18 baseline C. nymphaea 21_PR 1 2
19 baseline C. nymphaea 26_RS 1 1
20 baseline C. nymphaea 47_RS 1 1
21 baseline C. nymphaea 65_PR 1 1
22 baseline C. nymphaea 66_PR 1 2
23 baseline C. nymphaea 69_PR 1 1
24 baseline C. nymphaea 70_PR 1 1
25 sensib C. crisophylum 14_SC 1 1
26 sensib C. crisophylum 19_SC 1 2
27 sensib C. crisophylum 27_SC 1 2
28 sensib C. crisophylum 39_SC 1 2
29 sensib C. crisophylum 60_SC 1 2
30 sensib C. crisophylum 62_SC 1 2
31 sensib C. crisophylum 69_SC 1 2
32 sensib C. crisophylum 70_SC 1 2
33 sensib C. crisophylum 74_SC 1 2
34 sensib C. crisophylum 75_SC 1 2
35 sensib C. crisophylum 76_SC 1 2
36 sensib C. crisophylum 79_SC 1 2
37 sensib C. crisophylum 87_SC 1 2
38 sensib C. melonis 107_J 1 1
39 sensib C. melonis 123_J 1 1
40 sensib C. melonis 128_J 1 NA
41 sensib C. melonis 129_J 1 2
42 sensib C. melonis 133_J 1 1
43 sensib C. melonis 137_J 1 NA
44 sensib C. melonis 21_J 1 2
45 sensib C. melonis 28_J 1 2
46 sensib C. melonis 32_J 1 1
47 sensib C. melonis 34_J 1 2
48 sensib C. melonis 38_J 1 1
49 sensib C. melonis 41_J 1 2
50 sensib C. melonis 52_J 1 1
51 sensib C. melonis 54_J 1 1
52 sensib C. melonis 56_J 1 1
53 sensib C. melonis 61_J 1 1
54 sensib C. melonis 64_J 1 2
55 sensib C. melonis 6_J 1 1
56 sensib C. melonis 9_J 1 1
57 sensib C. nymphaea 125_J 1 1
58 sensib C. nymphaea 134_J 1 2
59 sensib C. nymphaea 138_J 1 1
60 sensib C. nymphaea 144_J 1 1
61 sensib C. nymphaea 27_J 1 1
62 sensib C. nymphaea 29_J 1 2
63 sensib C. nymphaea 66_J 1 1
64 sensib C. nymphaea 69_J 1 2
65 sensib C. nymphaea 76_J 1 1
66 sensib C. nymphaea 78_J 1 1
67 sensib C. nymphaea 80_J 1 3
68 sensib C. nymphaea 82_J 1 1
69 sensib C. nymphaea 83_J 1 1
70 sensib C. nymphaea 89_J 1 1
71 baseline C. crisophylum 55_PR NA 1
Tabela 1. Medidas relacionadas ao ajuste dos modelos aos dados conforme a ordem do polinômio.
source
species
isolate
order
r_squared
adj_r_squared
sigma
statistic
p_value
df
log_lik
aic
bic
deviance
df_residual
nobs
baseline
C. crisophylum
132_PR
1
0.8792307
0.8725213
0.1313923
131.0444913
0.0000000
1
13.2661886
-20.5323771
-17.5451803
0.3107509
18
20
baseline
C. crisophylum
132_PR
3
0.9635134
0.9566722
0.0766010
140.8392232
0.0000000
3
25.2355714
-40.4711427
-35.4924814
0.0938834
16
20
baseline
C. crisophylum
133_PR
1
0.8755969
0.8686856
0.1897914
126.6908793
0.0000000
1
5.9114241
-5.8228483
-2.8356515
0.6483742
18
20
baseline
C. crisophylum
133_PR
3
0.9212259
0.9064558
0.1601876
62.3708365
0.0000000
3
10.4808591
-10.9617182
-5.9830568
0.4105610
16
20
baseline
C. crisophylum
48_PR
1
0.7926591
0.7811401
0.2234001
68.8135378
0.0000001
1
2.6506551
0.6986899
3.6858867
0.8983367
18
20
baseline
C. crisophylum
48_PR
3
0.9553595
0.9469894
0.1099465
114.1397093
0.0000000
3
18.0078902
-26.0157803
-21.0371190
0.1934118
16
20
baseline
C. crisophylum
53_PR
1
0.9465408
0.9435708
0.2225203
318.7052506
0.0000000
1
2.7295741
0.5408519
3.5280487
0.8912751
18
20
baseline
C. crisophylum
53_PR
3
0.9913941
0.9897804
0.0946965
614.3931161
0.0000000
3
20.9942298
-31.9884596
-27.0097982
0.1434788
16
20
baseline
C. crisophylum
55_PR
3
0.1160693
-0.0496677
0.1723844
0.7003224
0.5654778
3
9.0132376
-8.0264751
-3.0478138
0.4754619
16
20
baseline
C. crisophylum
71_PR
1
0.9371119
0.9336181
0.1733061
268.2226681
0.0000000
1
7.7287569
-9.4575138
-6.4703170
0.5406298
18
20
baseline
C. crisophylum
71_PR
3
0.9764729
0.9720615
0.1124321
221.3552707
0.0000000
3
17.5607823
-25.1215647
-20.1429033
0.2022556
16
20
baseline
C. melonis
29_SP
1
0.7876907
0.7758957
0.1116981
66.7819496
0.0000002
1
16.5139374
-27.0278748
-24.0406780
0.2245766
18
20
baseline
C. melonis
29_SP
3
0.8709697
0.8467766
0.0923599
36.0006420
0.0000002
3
21.4939121
-32.9878242
-28.0091628
0.1364856
16
20
baseline
C. melonis
34_SP
1
0.4749335
0.4457631
0.3810575
16.2813707
0.0007768
1
-8.0290652
22.0581303
25.0453271
2.6136866
18
20
baseline
C. melonis
34_SP
3
0.6505313
0.5850059
0.3297340
9.9279290
0.0006165
3
-3.9579553
17.9159105
22.8945719
1.7395922
16
20
baseline
C. melonis
35_SP
1
0.7031602
0.6866690
0.2125262
42.6387591
0.0000039
1
3.6486317
-1.2972634
1.6899334
0.8130132
18
20
baseline
C. melonis
35_SP
3
0.7683822
0.7249538
0.1991194
17.6931049
0.0000246
3
6.1296726
-2.2593453
2.7193161
0.6343769
16
20
baseline
C. melonis
9_PR
1
0.8333342
0.8240750
0.1097678
90.0005476
0.0000000
1
16.8625982
-27.7251965
-24.7379997
0.2168814
18
20
baseline
C. melonis
9_PR
3
0.9560467
0.9478054
0.0597893
116.0074670
0.0000000
3
30.1912197
-50.3824394
-45.4037780
0.0571963
16
20
baseline
C. nymphaea
142_PR
1
0.7999931
0.7888816
0.1749283
71.9968804
0.0000001
1
7.5424218
-9.0848437
-6.0976469
0.5507981
18
20
baseline
C. nymphaea
142_PR
3
0.8904075
0.8698589
0.1373422
43.3318110
0.0000001
3
13.5582555
-17.1165111
-12.1378497
0.3018062
16
20
baseline
C. nymphaea
14_PR
1
0.6908557
0.6736811
0.3862528
40.2252437
0.0000056
1
-8.2999009
22.5998019
25.5869987
2.6854419
18
20
baseline
C. nymphaea
14_PR
3
0.7042654
0.6488151
0.4006991
12.7008520
0.0001677
3
-7.8564463
25.7128926
30.6915540
2.5689566
16
20
baseline
C. nymphaea
15_PR
1
0.8905134
0.8844308
0.1471137
146.4036988
0.0000000
1
11.0058314
-16.0116628
-13.0244660
0.3895637
18
20
baseline
C. nymphaea
15_PR
3
0.8990809
0.8801585
0.1498082
47.5142635
0.0000000
3
11.8206572
-13.6413145
-8.6626531
0.3590798
16
20
baseline
C. nymphaea
179_PR
1
0.8711565
0.8639986
0.1910911
121.7044058
0.0000000
1
5.7749332
-5.5498664
-2.5626696
0.6572846
18
20
baseline
C. nymphaea
179_PR
3
0.8816411
0.8594488
0.1942612
39.7273436
0.0000001
3
6.6236980
-3.2473960
1.7312654
0.6037986
16
20
baseline
C. nymphaea
181_PR
1
0.7145123
0.6986519
0.1933502
45.0499991
0.0000027
1
5.5398793
-5.0797587
-2.0925618
0.6729174
18
20
baseline
C. nymphaea
181_PR
3
0.9002382
0.8815328
0.1212298
48.1273257
0.0000000
3
16.0540125
-22.1080249
-17.1293636
0.2351466
16
20
baseline
C. nymphaea
19_PR
1
0.6833351
0.6657426
0.2003745
38.8424143
0.0000070
1
4.8261757
-3.6523515
-0.6651546
0.7226991
18
20
baseline
C. nymphaea
19_PR
3
0.7369306
0.6876051
0.1937108
14.9401534
0.0000670
3
6.6804408
-3.3608816
1.6177797
0.6003822
16
20
baseline
C. nymphaea
20_PR
1
0.6790788
0.6612499
0.5726621
38.0885402
0.0000079
1
-16.1759760
38.3519519
41.3391488
5.9029534
18
20
baseline
C. nymphaea
20_PR
3
0.6809875
0.6211726
0.6055909
11.3849236
0.0003030
3
-16.1163244
42.2326488
47.2113101
5.8678462
16
20
baseline
C. nymphaea
20_RS
1
0.4005613
0.3672591
0.2426109
12.0280911
0.0027440
1
1.0007611
3.9984779
6.9856747
1.0594809
18
20
baseline
C. nymphaea
21_PR
1
0.7052544
0.6888797
0.3334203
43.0696238
0.0000036
1
-5.3581372
16.7162744
19.7034712
2.0010438
18
20
baseline
C. nymphaea
21_PR
3
0.7216492
0.6694585
0.3436694
13.8271433
0.0001043
3
-4.7858334
19.5716667
24.5503281
1.8897387
16
20
baseline
C. nymphaea
26_RS
1
0.8123544
0.8019296
0.1590586
77.9254999
0.0000001
1
9.4444881
-12.8889763
-9.9017795
0.4553934
18
20
baseline
C. nymphaea
26_RS
3
0.9717482
0.9664510
0.0654617
183.4453753
0.0000000
3
28.3784777
-46.7569553
-41.7782940
0.0685637
16
20
baseline
C. nymphaea
47_RS
1
0.8293102
0.8198274
0.1746821
87.4544351
0.0000000
1
7.5705830
-9.1411660
-6.1539692
0.5492492
18
20
baseline
C. nymphaea
47_RS
3
0.9236221
0.9093013
0.1239381
64.4948992
0.0000000
3
15.6121307
-21.2242613
-16.2456000
0.2457703
16
20
baseline
C. nymphaea
65_PR
1
0.9266418
0.9225664
0.1569508
227.3714760
0.0000000
1
9.7112865
-13.4225731
-10.4353762
0.4434042
18
20
baseline
C. nymphaea
65_PR
3
0.9416364
0.9306932
0.1484864
86.0478276
0.0000000
3
11.9979003
-13.9958005
-9.0171392
0.3527715
16
20
baseline
C. nymphaea
66_PR
1
0.7258399
0.7106087
0.2610165
47.6550571
0.0000019
1
-0.4617354
6.9234708
9.9106677
1.2263334
18
20
baseline
C. nymphaea
66_PR
3
0.9443840
0.9339560
0.1246930
90.5622826
0.0000000
3
15.4906742
-20.9813484
-16.0026870
0.2487736
16
20
baseline
C. nymphaea
69_PR
1
0.8473639
0.8388841
0.1568460
99.9275396
0.0000000
1
9.7246556
-13.4493112
-10.4621144
0.4428118
18
20
baseline
C. nymphaea
69_PR
3
0.8948860
0.8751772
0.1380547
45.4052447
0.0000000
3
13.4547705
-16.9095410
-11.9308797
0.3049456
16
20
baseline
C. nymphaea
70_PR
1
0.5623607
0.5380474
0.1953448
23.1297593
0.0001405
1
5.3346147
-4.6692294
-1.6820325
0.6868727
18
20
baseline
C. nymphaea
70_PR
3
0.6330492
0.5642459
0.1897247
9.2008587
0.0009006
3
7.0962880
-4.1925761
0.7860853
0.5759274
16
20
sensib
C. crisophylum
14_SC
1
0.7885340
0.7767859
0.4645032
67.1200842
0.0000002
1
-11.9894269
29.9788537
32.9660506
3.8837372
18
20
sensib
C. crisophylum
14_SC
3
0.9622813
0.9552091
0.2080765
136.0642967
0.0000000
3
5.2496562
-0.4993123
4.4793491
0.6927332
16
20
sensib
C. crisophylum
19_SC
1
0.8435049
0.8348108
0.2271885
97.0196086
0.0000000
1
2.3143349
1.3713302
4.3585270
0.9290634
18
20
sensib
C. crisophylum
19_SC
3
0.9666629
0.9604122
0.1112184
154.6484328
0.0000000
3
17.7778604
-25.5557208
-20.5770594
0.1979124
16
20
sensib
C. crisophylum
27_SC
1
0.7823089
0.7702149
0.2271238
64.6859584
0.0000002
1
2.3200314
1.3599371
4.3471339
0.9285343
18
20
sensib
C. crisophylum
27_SC
3
0.9726948
0.9675751
0.0853181
189.9899477
0.0000000
3
23.0800471
-36.1600942
-31.1814328
0.1164667
16
20
sensib
C. crisophylum
39_SC
1
0.7883332
0.7765740
0.2283874
67.0393317
0.0000002
1
2.2090738
1.5818525
4.5690493
0.9388945
18
20
sensib
C. crisophylum
39_SC
3
0.8748557
0.8513912
0.1862634
37.2841474
0.0000002
3
7.4645334
-4.9290668
0.0495945
0.5551049
16
20
sensib
C. crisophylum
60_SC
1
0.7799425
0.7677171
0.1766392
63.7967956
0.0000003
1
7.3477558
-8.6955116
-5.7083148
0.5616253
18
20
sensib
C. crisophylum
60_SC
3
0.9172283
0.9017087
0.1149042
59.1009649
0.0000000
3
17.1257891
-24.2515782
-19.2729168
0.2112477
16
20
sensib
C. crisophylum
62_SC
1
0.7732593
0.7606626
0.2301627
61.3858303
0.0000003
1
2.0542085
1.8915830
4.8787798
0.9535479
18
20
sensib
C. crisophylum
62_SC
3
0.9679403
0.9619291
0.0917966
161.0227677
0.0000000
3
21.6162696
-33.2325392
-28.2538778
0.1348258
16
20
sensib
C. crisophylum
69_SC
1
0.9067831
0.9016044
0.2139668
175.0981080
0.0000000
1
3.5135254
-1.0270509
1.9601459
0.8240720
18
20
sensib
C. crisophylum
69_SC
3
0.9746202
0.9698614
0.1184186
204.8072352
0.0000000
3
16.5232616
-23.0465232
-18.0678618
0.2243673
16
20
sensib
C. crisophylum
70_SC
1
0.8892212
0.8830668
0.1957989
144.4859313
0.0000000
1
5.2881813
-4.5763626
-1.5891657
0.6900695
18
20
sensib
C. crisophylum
70_SC
3
0.9009900
0.8824257
0.1963349
48.5333066
0.0000000
3
6.4113339
-2.8226678
2.1559935
0.6167582
16
20
sensib
C. crisophylum
74_SC
1
0.7852065
0.7732735
0.4856023
65.8014205
0.0000002
1
-12.8778575
31.7557149
34.7429117
4.2445718
18
20
sensib
C. crisophylum
74_SC
3
0.9234370
0.9090814
0.3075077
64.3260380
0.0000000
3
-2.5622314
15.1244628
20.1031242
1.5129757
16
20
sensib
C. crisophylum
75_SC
1
0.9018454
0.8963924
0.3829398
165.3841792
0.0000000
1
-8.1276136
22.2552272
25.2424240
2.6395714
18
20
sensib
C. crisophylum
75_SC
3
0.9821711
0.9788282
0.1731065
293.8069736
0.0000000
3
8.9296339
-7.8592678
-2.8806064
0.4794536
16
20
sensib
C. crisophylum
76_SC
1
0.8801869
0.8735307
0.4401472
132.2340391
0.0000000
1
-10.9122446
27.8244892
30.8116861
3.4871322
18
20
sensib
C. crisophylum
76_SC
3
0.9794019
0.9755398
0.1935689
253.5903469
0.0000000
3
6.6950987
-3.3901974
1.5884640
0.5995028
16
20
sensib
C. crisophylum
79_SC
1
0.8347934
0.8256153
0.4787166
90.9545104
0.0000000
1
-12.5922335
31.1844670
34.1716638
4.1250517
18
20
sensib
C. crisophylum
79_SC
3
0.9547490
0.9462645
0.2657388
112.5278470
0.0000000
3
0.3574901
9.2850198
14.2636812
1.1298741
16
20
sensib
C. crisophylum
87_SC
1
0.7976832
0.7864434
0.5499217
70.9693883
0.0000001
1
-15.3655789
36.7311578
39.7183547
5.4434503
18
20
sensib
C. crisophylum
87_SC
3
0.9478140
0.9380291
0.2962363
96.8651820
0.0000000
3
-1.8153778
13.6307555
18.6094169
1.4040950
16
20
sensib
C. melonis
107_J
1
0.3891686
0.3552335
0.2168420
11.4680324
0.0032887
1
3.2465622
-0.4931244
2.4940724
0.8463680
18
20
sensib
C. melonis
107_J
3
0.4549216
0.3527194
0.2172643
4.4511924
0.0186536
3
4.3854764
1.2290472
6.2077085
0.7552606
16
20
sensib
C. melonis
123_J
1
0.3570575
0.3213385
0.5113507
9.9962834
0.0053981
1
-13.9111727
33.8223454
36.8095422
4.7066315
18
20
sensib
C. melonis
123_J
3
0.3695139
0.2512978
0.5370897
3.1257488
0.0551371
3
-13.7155304
37.4310609
42.4097222
4.6154448
16
20
sensib
C. melonis
128_J
1
0.7317007
0.7167952
0.1668448
49.0892510
0.0000015
1
8.4886649
-10.9773298
-7.9901329
0.5010691
18
20
sensib
C. melonis
129_J
1
0.7052591
0.6888846
0.3189131
43.0705825
0.0000036
1
-4.4684360
14.9368720
17.9240688
1.8307007
18
20
sensib
C. melonis
129_J
3
0.7598354
0.7148045
0.3053395
16.8736572
0.0000328
3
-2.4207143
14.8414286
19.8200900
1.4917153
16
20
sensib
C. melonis
133_J
1
0.5932410
0.5706432
0.1630168
26.2522446
0.0000711
1
8.9528770
-11.9057540
-8.9185572
0.4783405
18
20
sensib
C. melonis
133_J
3
0.6407953
0.5734444
0.1624841
9.5142819
0.0007632
3
10.1961619
-10.3923239
-5.4136625
0.4224176
16
20
sensib
C. melonis
137_J
1
0.8375797
0.8285564
0.2756205
92.8236097
0.0000000
1
-1.5505589
9.1011179
12.0883147
1.3674000
18
20
sensib
C. melonis
21_J
1
0.4610781
0.4311380
0.3050826
15.4000163
0.0009941
1
-3.5817123
13.1634245
16.1506214
1.6753573
18
20
sensib
C. melonis
21_J
3
0.4804924
0.3830848
0.3177070
4.9327987
0.0129804
3
-3.2148197
16.4296393
21.4083007
1.6150036
16
20
sensib
C. melonis
28_J
1
0.7509240
0.7370864
0.1044368
54.2670936
0.0000008
1
17.8583038
-29.7166076
-26.7294107
0.1963267
18
20
sensib
C. melonis
28_J
3
0.8416138
0.8119164
0.0883329
28.3396305
0.0000012
3
22.3855207
-34.7710414
-29.7923800
0.1248432
16
20
sensib
C. melonis
32_J
1
0.7727707
0.7601468
0.1065887
61.2151297
0.0000003
1
17.4503906
-28.9007811
-25.9135843
0.2045007
18
20
sensib
C. melonis
32_J
3
0.8977128
0.8785340
0.0758518
46.8074514
0.0000000
3
25.4321453
-40.8642905
-35.8856292
0.0920559
16
20
sensib
C. melonis
34_J
1
0.7766213
0.7642113
0.3361725
62.5806338
0.0000003
1
-5.5225489
17.0450977
20.0322945
2.0342152
18
20
sensib
C. melonis
34_J
3
0.8611174
0.8350769
0.2811522
33.0684045
0.0000004
3
-0.7701519
11.5403038
16.5189652
1.2647448
16
20
sensib
C. melonis
38_J
1
0.6617608
0.6429697
0.0711730
35.2167789
0.0000129
1
25.5276582
-45.0553164
-42.0681196
0.0911808
18
20
sensib
C. melonis
38_J
3
0.6963859
0.6394582
0.0715222
12.2328226
0.0002059
3
26.6076150
-43.2152299
-38.2365686
0.0818468
16
20
sensib
C. melonis
41_J
1
0.6297443
0.6091745
0.1480289
30.6150510
0.0000297
1
10.8817895
-15.7635790
-12.7763822
0.3944260
18
20
sensib
C. melonis
41_J
3
0.6850325
0.6259761
0.1448121
11.5996308
0.0002743
3
12.4990336
-14.9980673
-10.0194059
0.3355286
16
20
sensib
C. melonis
52_J
1
0.5649349
0.5407647
0.4349736
23.3731230
0.0001330
1
-10.6757680
27.3515360
30.3387329
3.4056370
18
20
sensib
C. melonis
52_J
3
0.8573662
0.8306224
0.2641638
32.0584692
0.0000005
3
0.4763855
9.0472290
14.0258903
1.1165199
16
20
sensib
C. melonis
54_J
1
0.4204157
0.3882166
0.1041192
13.0567425
0.0019870
1
17.9192174
-29.8384348
-26.8512379
0.1951344
18
20
sensib
C. melonis
54_J
3
0.5484250
0.4637547
0.0974796
6.4771828
0.0044634
3
20.4149146
-30.8298292
-25.8511678
0.1520363
16
20
sensib
C. melonis
56_J
1
0.8524818
0.8442864
0.1031668
104.0188586
0.0000000
1
18.1029915
-30.2059829
-27.2187861
0.1915811
18
20
sensib
C. melonis
56_J
3
0.9457672
0.9355985
0.0663475
93.0080948
0.0000000
3
28.1096424
-46.2192849
-41.2406235
0.0704319
16
20
sensib
C. melonis
61_J
1
0.7749685
0.7624667
0.1412302
61.9887949
0.0000003
1
11.8221105
-17.6442210
-14.6570241
0.3590277
18
20
sensib
C. melonis
61_J
3
0.8560655
0.8290777
0.1198022
31.7205469
0.0000006
3
16.2909289
-22.5818577
-17.6031964
0.2296411
16
20
sensib
C. melonis
64_J
1
0.6420365
0.6221496
0.2508702
32.2844578
0.0000218
1
0.3312280
5.3375439
8.3247407
1.1328453
18
20
sensib
C. melonis
64_J
3
0.6908025
0.6328280
0.2472999
11.9156208
0.0002374
3
1.7957360
6.4085280
11.3871893
0.9785158
16
20
sensib
C. melonis
6_J
1
0.3720866
0.3372025
0.2089732
10.6663737
0.0042919
1
3.9858178
-1.9716357
1.0155611
0.7860565
18
20
sensib
C. melonis
6_J
3
0.3760623
0.2590740
0.2209468
3.2145289
0.0510589
3
4.0493355
1.9013291
6.8799905
0.7810795
16
20
sensib
C. melonis
9_J
1
0.8458240
0.8372586
0.1260035
98.7496653
0.0000000
1
14.1037500
-22.2074999
-19.2203031
0.2857838
18
20
sensib
C. melonis
9_J
3
0.8703056
0.8459879
0.1225776
35.7889831
0.0000003
3
15.8328924
-21.6657849
-16.6871235
0.2404041
16
20
sensib
C. nymphaea
125_J
1
0.4538095
0.4234656
0.1959613
14.9555340
0.0011289
1
5.2715954
-4.5431908
-1.5559940
0.6912150
18
20
sensib
C. nymphaea
125_J
3
0.5164420
0.4257749
0.1955685
5.6960230
0.0075296
3
6.4895615
-2.9791229
1.9995384
0.6119523
16
20
sensib
C. nymphaea
134_J
1
0.8583536
0.8504843
0.1583559
109.0769586
0.0000000
1
9.5330455
-13.0660910
-10.0788941
0.4513784
18
20
sensib
C. nymphaea
134_J
3
0.8743217
0.8507570
0.1582114
37.1030410
0.0000002
3
10.7291288
-11.4582575
-6.4795962
0.4004935
16
20
sensib
C. nymphaea
138_J
1
0.3411130
0.3045081
0.3050773
9.3187950
0.0068524
1
-3.5813667
13.1627334
16.1499302
1.6752994
18
20
sensib
C. nymphaea
138_J
3
0.4004543
0.2880394
0.3086682
3.5622903
0.0380185
3
-2.6375684
15.2751368
20.2537982
1.5244170
16
20
sensib
C. nymphaea
144_J
1
0.5685044
0.5445325
0.1513149
23.7153769
0.0001231
1
10.4426757
-14.8853515
-11.8981547
0.4121317
18
20
sensib
C. nymphaea
144_J
3
0.5774807
0.4982583
0.1588156
7.2893629
0.0026781
3
10.6528957
-11.3057914
-6.3271300
0.4035583
16
20
sensib
C. nymphaea
27_J
1
0.3084036
0.2699815
0.2985987
8.0267389
0.0110240
1
-3.1520712
12.3041425
15.2913393
1.6049014
18
20
sensib
C. nymphaea
27_J
3
0.3273480
0.2012258
0.3123439
2.5954819
0.0884413
3
-2.8743260
15.7486520
20.7273134
1.5609394
16
20
sensib
C. nymphaea
29_J
1
0.8754703
0.8685519
0.1322399
126.5437833
0.0000000
1
13.1375809
-20.2751618
-17.2879650
0.3147732
18
20
sensib
C. nymphaea
29_J
3
0.8867119
0.8654704
0.1337810
41.7442794
0.0000001
3
14.0836853
-18.1673706
-13.1887092
0.2863578
16
20
sensib
C. nymphaea
66_J
1
0.5236339
0.4971691
0.2095493
19.7860615
0.0003106
1
3.9307613
-1.8615226
1.1256742
0.7903962
18
20
sensib
C. nymphaea
66_J
3
0.5432291
0.4575846
0.2176412
6.3428345
0.0048721
3
4.3508099
1.2983803
6.2770416
0.7578834
16
20
sensib
C. nymphaea
69_J
1
0.7322562
0.7173815
0.1955758
49.2284443
0.0000015
1
5.3109828
-4.6219655
-1.6347687
0.6884979
18
20
sensib
C. nymphaea
69_J
3
0.8124605
0.7772968
0.1736113
23.1051206
0.0000047
3
8.8713941
-7.7427882
-2.7641268
0.4822541
16
20
sensib
C. nymphaea
76_J
1
0.2979542
0.2589517
0.1695265
7.6393534
0.0127862
1
8.1697601
-10.3395202
-7.3523234
0.5173059
18
20
sensib
C. nymphaea
76_J
3
0.3000835
0.1688492
0.1795371
2.2866235
0.1177339
3
8.2001363
-6.4002726
-1.4216112
0.5157370
16
20
sensib
C. nymphaea
78_J
1
0.3611349
0.3256423
0.2849865
10.1749601
0.0050748
1
-2.2188990
10.4377981
13.4249949
1.4619119
18
20
sensib
C. nymphaea
78_J
3
0.4145447
0.3047719
0.2893629
3.7763864
0.0318558
3
-1.3458628
12.6917256
17.6703870
1.3396943
16
20
sensib
C. nymphaea
80_J
1
0.6584325
0.6394566
0.2927405
34.6982276
0.0000141
1
-2.7557909
11.5115818
14.4987786
1.5425460
18
20
sensib
C. nymphaea
80_J
3
0.9504702
0.9411833
0.1182372
102.3458756
0.0000000
3
16.5539093
-23.1078186
-18.1291573
0.2236807
16
20
sensib
C. nymphaea
82_J
1
0.5999381
0.5777125
0.1887799
26.9930418
0.0000609
1
6.0183062
-6.0366125
-3.0494157
0.6414812
18
20
sensib
C. nymphaea
82_J
3
0.6630789
0.5999062
0.1837522
10.4962869
0.0004638
3
7.7360098
-5.4720196
-0.4933582
0.5402379
16
20
sensib
C. nymphaea
83_J
1
0.4690880
0.4395929
0.2615986
15.9039266
0.0008626
1
-0.5062833
7.0125666
9.9997634
1.2318086
18
20
sensib
C. nymphaea
83_J
3
0.5625220
0.4804949
0.2518712
6.8577561
0.0034995
3
1.4294147
7.1411707
12.1198320
1.0150255
16
20
sensib
C. nymphaea
89_J
1
0.6113711
0.5897806
0.2315317
28.3166767
0.0000466
1
1.9356032
2.1287937
5.1159905
0.9649248
18
20
sensib
C. nymphaea
89_J
3
0.6789685
0.6187751
0.2231994
11.2797802
0.0003183
3
3.8464591
2.3070818
7.2857432
0.7970876
16
20
3.3 Gráfico com ajustes
Código
#-----------------------------------------------------------------------# Faz o gráfico com as curvas.# Gera os valores preditos para fazer as curvas no gráfico.tb_ec50_pred <- tb_ec50 |>drop_na(ec50) |>rowwise() |>do(pred = {if (is.na(.$ec50)) {tibble(species = .$species,isolate = .$isolate,x =NA_real_,y =NA_real_) } else { s0 <- .$model er <-c(c(s0$model[[2]][, 1]), .$x) |>range(na.rm =TRUE) |>extendrange(f =0.025) x_seq <-seq(er[1], er[2], length.out =51) pred <-tibble(order = .$order,species = .$species,isolate = .$isolate,x = x_seq) pred$y <-predict(s0, newdata = pred) pred } }) |>unnest(pred)# tb_ec50_pred
Código
gg <-tb |>distinct(source, isolate) |>group_split(source) |>map(function(tbi) { title <-switch(as.character(tbi$source[1]),"baseline"="Antes da utilização do fungicida a campo (2010-2015)","sensib"="Após a utilização do fungicida a campo (2016-2017)") show_legend <-as.character(tbi$source[1]) =="sensib"ggplot(data =filter(tb, isolate %in% tbi$isolate),mapping =aes(x = log_dose,y = growth,color = species)) +facet_wrap(~isolate, scale ="free", ncol =6) +geom_point(pch =1, show.legend = show_legend) +geom_line(data =filter(tb_ec50_pred, isolate %in% tbi$isolate),show.legend = show_legend,mapping =aes(x = x, y = y, linetype = order),size =0.5) +geom_vline(data =filter(tb_ec50, isolate %in% tbi$isolate),mapping =aes(xintercept = x, # x = log10(ec50 + 0.01),linetype = order),color ="gray30",linewidth =0.25,show.legend =FALSE) +geom_hline(data =filter(tb_ec50, isolate %in% tbi$isolate),mapping =aes(yintercept = y0,linetype = order),color ="gray30",linewidth =0.25,show.legend =FALSE) +expand_limits(y =0) +theme(legend.position ="bottom",axis.text.x =element_blank(),# axis.title.x = element_blank(),# axis.ticks.y = element_blank(),# axis.title.y = element_blank(),axis.text.y =element_blank()) +labs(x =expression("Dose · "* log[10](x +0.01) +2),y ="Crescimento (mm)",color ="Espécie",linetype ="Modelo",title = title,subtitle =expression("Ajuste com os modelos linear e cúbico para determinação da"~ EC[50])) })gridExtra::grid.arrange(grobs = gg,ncol =1,heights =c(37, 63))
Figura 2. EC50 para os isolados de Colletotrichum a partir dos ajuste dos modelos polinomiais de order 1 (linear) e 3 (cúbico) aos dados de crescimento em função da dose de fungicida. Linhas verticais indicam o valor da EC50 e linhas horizontais indicam o crescimento estimado quando a dose é a EC50.
3.4 Ajustes finais
Código
#-----------------------------------------------------------------------# Pegar o ajuste mais apropriado.# Escolhe o ajuste.tb_ec50_final <- tb_ec50 |>drop_na(ec50) |>group_by(species, isolate) |>arrange(order) |>do({if (nrow(.) <=1) {# Se tem uma linha ou menos, retornar isso. . } else {# Usa o LRT para decidir qual modelo usar. p_value <-anova(.$model[[1]], .$model[[2]])[2, "Pr(>F)"]if (p_value <0.05) { .[2, ] } else { .[1, ] } } }) |>ungroup()tb_ec50_final
# A tibble: 71 × 9
order source species isolate x y y0 model ec50
<chr> <fct> <fct> <fct> <dbl> <dbl> <dbl> <list> <dbl>
1 3 baseline C. crisophylum 132_PR 1.32 0.454 0.454 <lm> 0.201
2 3 baseline C. crisophylum 133_PR 1.29 0.664 0.664 <lm> 0.186
3 3 baseline C. crisophylum 48_PR 1.07 0.572 0.572 <lm> 0.108
4 3 baseline C. crisophylum 53_PR 1.66 1.15 1.15 <lm> 0.452
5 3 baseline C. crisophylum 55_PR 7.25 1.11 1.11 <lm> 178799.
6 3 baseline C. crisophylum 71_PR 1.65 0.828 0.828 <lm> 0.438
7 3 sensib C. crisophylum 14_SC 0.750 1.28 1.28 <lm> 0.0463
8 3 sensib C. crisophylum 19_SC 1.11 0.695 0.695 <lm> 0.119
9 3 sensib C. crisophylum 27_SC 0.918 0.582 0.582 <lm> 0.0729
10 3 sensib C. crisophylum 39_SC 1.16 0.582 0.582 <lm> 0.133
# ℹ 61 more rows
Código
# Filtra os pontos.tb_ec50_pred <-left_join(tb_ec50_final[, c("order", "source", "species", "isolate")], tb_ec50_pred)# Exibir ordenando pela EC50 dentro de cada espécie.tb_ec50_final <- tb_ec50_final |>arrange(species, ec50) |>mutate(isolate =as.character(isolate),isolate =factor(isolate,levels = isolate))tb <- tb |>mutate(isolate =factor(isolate,levels = tb_ec50_final$isolate))# Cria os rótulos para exibir no gráfico.tb_ec50_final <- tb_ec50_final |>mutate(ec50_label =sprintf(fmt =case_when(ec50 <0.1~"EC[50] == %0.4f", ec50 <1~"EC[50] == %0.2f", ec50 <100~"EC[50] == %0.1f",TRUE~"EC[50] == %0.0f"), ec50))
Figura 3. EC50 para os isolados de Colletotrichum de acordo com o modelo polinomial mais apropriado quando ajustado aos dados de crescimento em função da dose de fungicida. Linhas verticais indicam o valor da EC50 e linhas horizontais indicam o crescimento estimado quando a dose é a EC50.
Figura 4. EC50 para os isolados de Colletotrichum de acordo com o modelo polinomial mais apropriado quando ajustado aos dados de crescimento em função da dose de fungicida. Linhas verticais indicam o valor da EC50 e linhas horizontais indicam o crescimento estimado quando a dose é a EC50.
Código
# Gráficos de densidade da ec50.# ggplot(data = tb_ec50_final,# mapping = aes(x = ec50)) +# facet_grid(species ~ source) +# geom_density(fill = "gray30", alpha = 0.3) +# geom_rug() +# scale_x_log10() +# labs(x = expression(EC[50] * "· escala" ~ log[10]),# y = "Densidade",# title = expression("Distribuição da" ~ EC[50] ~ "por espécie"))ggplot(data = tb_ec50_final,mapping =aes(x = ec50, color = source, fill = source)) +# facet_wrap(~species, ncol = 1, scale = "free") +facet_wrap(~species, ncol =1, scale ="free_y") +geom_density(alpha =0.3) +geom_rug() +scale_x_log10() +guides(fill ="none") +# Trocar o rótulo dos níveis de source na legenda das cores.scale_color_discrete(labels =c("2010-2015", "2016-2017")) +labs(x =expression(EC[50] *"· escala"~ log[10]),y ="Densidade",color ="Fonte",title =expression("Distribuição da"~ EC[50] ~"por espécie"))
Figura 5. Distribuição da EC50 para as espécies de isolados de Colletotrichum.
Tabela 2. Estimativas de EC50 para os isolados de Colletotrichum acompanhados de medidas de ajuste para os modelos ajustados aos dados de crescimento em função da dose de fungicida. ec50 é a estimativa da concentração para obter a metade do crescimento estimado na ausência de fungicida (EC50), growth50 é o crescimento estimado para a EC50, order corresponde a ordem do polinômio empregado (1 = linear, 3 = cúbico), r_squared é o coeficiente de determinação ajustado, p_value é o valor-p do teste F para o hipótese nula de que o modelo não difere do modelo trivial e stars são sinais gráficos para indicar a classe de significância.
