Load Data
bat_pas<- read_excel("D:/BoxFiles/Box Sync/CodigoR/Murcielagos/data/Conteos_fases_xFabian.xls")
bat_pas_2 <- read_excel("D:/BoxFiles/Box Sync/CodigoR/Murcielagos/data/Conteos_fases_xFabian.xls",
sheet = "Sheet2")
bat_pas_3 <- read_excel("D:/BoxFiles/Box Sync/CodigoR/Murcielagos/data/Conteos_fases_xFabian.xls",
sheet = "Sheet3")
See data
kable(bat_pas) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = T,
font_size = 9)# %>%
|
Cobertura
|
Meses
|
Precipitación
|
FB_Eptesicus
|
FT_Eptesicus
|
FB_Molossus_rufus
|
FT_Molossus_rufus
|
FB_Myotis_nigricans
|
FT_Myotis_nigricans
|
FB_Saccopteryx_leptura
|
FT_Saccopteryx_leptura
|
FB_Peropteryx_macrotis
|
FT_Peropteryx_macrotis
|
FB_Cormura_brevirostris
|
FT_Cormura_brevirostris
|
|
Bosque
|
Febrero
|
41
|
0
|
0
|
0
|
0
|
1
|
0
|
1
|
0
|
0
|
0
|
0
|
0
|
|
Bosque
|
Febrero
|
100
|
33
|
0
|
6
|
0
|
0
|
0
|
7
|
0
|
0
|
0
|
1
|
0
|
|
Bosque
|
Febrero
|
10
|
51
|
1
|
1
|
0
|
0
|
0
|
26
|
0
|
2
|
0
|
5
|
0
|
|
Bosque
|
Junio
|
0
|
406
|
94
|
9
|
0
|
8
|
0
|
24
|
3
|
0
|
0
|
1
|
0
|
|
Bosque
|
Junio
|
5
|
2
|
0
|
0
|
0
|
0
|
0
|
4
|
0
|
30
|
0
|
3
|
1
|
|
Bosque
|
Junio
|
2
|
3
|
0
|
10
|
0
|
0
|
0
|
9
|
2
|
0
|
0
|
3
|
0
|
|
Bosque
|
Junio
|
27
|
0
|
0
|
5
|
0
|
0
|
0
|
5
|
1
|
0
|
0
|
2
|
0
|
|
Bosque
|
Octubre
|
0
|
228
|
42
|
24
|
1
|
95
|
3
|
8
|
2
|
19
|
3
|
6
|
0
|
|
SSP
|
Febrero
|
0
|
25
|
1
|
11
|
0
|
1
|
1
|
22
|
4
|
0
|
0
|
1
|
0
|
|
SSP
|
Febrero
|
5
|
42
|
0
|
90
|
12
|
0
|
0
|
7
|
0
|
2
|
0
|
3
|
0
|
|
SSP
|
Febrero
|
4
|
17
|
0
|
31
|
2
|
0
|
0
|
9
|
0
|
1
|
0
|
1
|
0
|
|
SSP
|
Junio
|
0
|
217
|
64
|
61
|
13
|
33
|
2
|
40
|
16
|
19
|
3
|
16
|
1
|
|
SSP
|
Junio
|
14
|
239
|
30
|
106
|
14
|
0
|
0
|
25
|
4
|
9
|
1
|
32
|
4
|
|
SSP
|
Junio
|
25
|
62
|
20
|
40
|
4
|
2
|
0
|
47
|
28
|
23
|
4
|
10
|
1
|
|
SSP
|
Octubre
|
0
|
128
|
8
|
37
|
3
|
32
|
1
|
32
|
0
|
10
|
0
|
3
|
0
|
|
SSP
|
Noviembre
|
11
|
131
|
9
|
11
|
2
|
119
|
4
|
116
|
35
|
0
|
0
|
7
|
0
|
|
SC
|
Febrero
|
35
|
239
|
16
|
59
|
4
|
51
|
3
|
1
|
0
|
36
|
4
|
3
|
0
|
|
SC
|
Febrero
|
3
|
347
|
26
|
108
|
29
|
62
|
2
|
11
|
0
|
21
|
5
|
2
|
0
|
|
SC
|
Febrero
|
9
|
263
|
5
|
71
|
11
|
114
|
5
|
8
|
2
|
24
|
0
|
0
|
0
|
|
SC
|
Junio
|
0
|
127
|
21
|
107
|
56
|
97
|
3
|
12
|
3
|
39
|
22
|
4
|
0
|
|
SC
|
Junio
|
44
|
328
|
16
|
84
|
15
|
84
|
6
|
37
|
6
|
62
|
18
|
7
|
0
|
|
SC
|
Junio
|
8
|
65
|
6
|
66
|
16
|
10
|
0
|
8
|
0
|
12
|
1
|
6
|
0
|
|
SC
|
Octubre
|
1
|
46
|
1
|
26
|
5
|
6
|
0
|
9
|
2
|
26
|
6
|
63
|
0
|
|
SC
|
Octubre
|
0
|
128
|
7
|
0
|
0
|
7
|
0
|
1
|
0
|
63
|
18
|
0
|
0
|
# scroll_box(height = "350px")
GLMs
GLM conteo todas las especies
Busqueda
# GLM_full <- glm(Busqueda~ Cobertura + Meses + Especie, family="poisson", data=bat_pas_2)
# anova(GLM_full_t, test = "Chi")
#
# dat <- ggpredict(GLM_full, terms = c("Meses", "Cobertura"))
# plot(dat)
GLM_full2 <- glm(Busqueda~ Cobertura + Meses, family="poisson", data=bat_pas_3)
anova(GLM_full2, test = "Chi")
dat <- ggpredict(GLM_full2, terms = c("Meses", "Cobertura"))
plot(dat, show.y.title = FALSE) + ylab("No. Fases de Busqueda")
Terminal
GLM_full3 <- glm(Terminal~ Cobertura + Meses, family="poisson", data=bat_pas_3)
anova(GLM_full3, test = "Chi")
dat <- ggpredict(GLM_full3, terms = c("Meses", "Cobertura"))
plot(dat, show.y.title = FALSE) + ylab("No. Fases Terminales")
Eptesicus
Fase Busqueda
GLM_E_FB <- glm(FB_Eptesicus~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
# opar <- par(no.readonly=TRUE)
# par(mfrow=c(1,2))
# attach(bat_pas)
# hist(FB_Eptesicus, breaks=20, xlab="Conteo Fases Busqueda",
# main="Distribución de fases")
# boxplot(FB_Eptesicus ~ Cobertura, xlab="Treatment", main="Group Comparisons")
# par(opar)
anova(GLM_E_FB, test = "Chisq")
##
## Call:
## glm(formula = FB_Eptesicus ~ Cobertura - 1 + Meses - 1, family = "poisson",
## data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -14.320 -10.102 -4.804 6.422 22.595
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque 4.34951 0.04564 95.305 < 2e-16 ***
## CoberturaSC 5.13843 0.03636 141.338 < 2e-16 ***
## CoberturaSSP 4.51208 0.04424 101.993 < 2e-16 ***
## MesesJunio 0.28063 0.04097 6.849 7.44e-12 ***
## MesesNoviembre 0.36312 0.09793 3.708 0.000209 ***
## MesesOctubre 0.03955 0.05393 0.733 0.463329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 27119.0 on 24 degrees of freedom
## Residual deviance: 2458.2 on 18 degrees of freedom
## AIC: 2606.4
##
## Number of Fisher Scoring iterations: 6
# summary(glht(GLM_E_FB, mcp(tension = "Tukey")))
ggpredict(GLM_E_FB) %>% plot(connect.lines = FALSE) # +
## $Cobertura
##
## $Meses
Fase Terminal
GLM_E_FT <- glm(FT_Eptesicus~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_E_FT, test = "Chisq")
##
## Call:
## glm(formula = FT_Eptesicus ~ Cobertura - 1 + Meses - 1, family = "poisson",
## data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.2152 -3.5451 -2.0838 0.2318 10.2696
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque 1.7351 0.1597 10.863 < 2e-16 ***
## CoberturaSC 1.4771 0.1677 8.808 < 2e-16 ***
## CoberturaSSP 1.8380 0.1585 11.599 < 2e-16 ***
## MesesJunio 1.5241 0.1564 9.748 < 2e-16 ***
## MesesNoviembre 0.3592 0.3691 0.973 0.33
## MesesOctubre 1.0297 0.1949 5.282 1.27e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1950.74 on 24 degrees of freedom
## Residual deviance: 495.56 on 18 degrees of freedom
## AIC: 579.78
##
## Number of Fisher Scoring iterations: 7
ggpredict(GLM_E_FT) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Molossus rufus
Fase Busqueda
GLM_Mr_FB <- glm(FB_Molossus_rufus~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Mr_FB, test = "Chisq")
##
## Call:
## glm(formula = FB_Molossus_rufus ~ Cobertura - 1 + Meses - 1,
## family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.7975 -2.7852 -0.4048 1.0047 7.6965
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque 1.86244 0.14162 13.151 < 2e-16 ***
## CoberturaSC 4.21208 0.05976 70.488 < 2e-16 ***
## CoberturaSSP 3.94604 0.06469 60.995 < 2e-16 ***
## MesesJunio 0.24113 0.06860 3.515 0.00044 ***
## MesesNoviembre -1.54815 0.30837 -5.020 5.16e-07 ***
## MesesOctubre -0.79763 0.11941 -6.680 2.39e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 6143.23 on 24 degrees of freedom
## Residual deviance: 310.63 on 18 degrees of freedom
## AIC: 431.02
##
## Number of Fisher Scoring iterations: 5
ggpredict(GLM_Mr_FB) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Fase Terminal
GLM_Mr_FT <- glm(FT_Molossus_rufus~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Mr_FT, test = "Chisq")
##
## Call:
## glm(formula = FT_Molossus_rufus ~ Cobertura - 1 + Meses - 1,
## family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2020 -1.7516 -0.5080 0.8422 4.5057
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque -2.4331 1.0067 -2.417 0.015651 *
## CoberturaSC 2.6475 0.1388 19.073 < 2e-16 ***
## CoberturaSSP 1.6344 0.1802 9.069 < 2e-16 ***
## MesesJunio 0.7087 0.1604 4.419 9.9e-06 ***
## MesesNoviembre -0.9413 0.7297 -1.290 0.197083
## MesesOctubre -1.3129 0.3588 -3.659 0.000253 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 793.49 on 24 degrees of freedom
## Residual deviance: 100.93 on 18 degrees of freedom
## AIC: 170.9
##
## Number of Fisher Scoring iterations: 6
ggpredict(GLM_Mr_FT) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Myotis nigricans
Fase Busqueda
GLM_Mn_FB <- glm(FB_Myotis_nigricans~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Mn_FB, test = "Chisq")
##
## Call:
## glm(formula = FB_Myotis_nigricans ~ Cobertura - 1 + Meses - 1,
## family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.730 -5.030 -3.969 1.879 14.159
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque 2.57189 0.11160 23.046 <2e-16 ***
## CoberturaSC 3.97964 0.07149 55.667 <2e-16 ***
## CoberturaSSP 2.27674 0.13052 17.443 <2e-16 ***
## MesesJunio -0.03399 0.09308 -0.365 0.715
## MesesNoviembre 2.50239 0.15950 15.689 <2e-16 ***
## MesesOctubre 0.07541 0.10787 0.699 0.485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 4814.62 on 24 degrees of freedom
## Residual deviance: 792.94 on 18 degrees of freedom
## AIC: 881.77
##
## Number of Fisher Scoring iterations: 7
ggpredict(GLM_Mn_FB) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Fase Terminal
GLM_Mn_FT <- glm(FT_Myotis_nigricans~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Mn_FT, test = "Chisq")
##
## Call:
## glm(formula = FT_Myotis_nigricans ~ Cobertura - 1 + Meses - 1,
## family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2629 -1.0874 -0.8790 0.3138 3.0553
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque -0.91528 0.62609 -1.462 0.14377
## CoberturaSC 0.97587 0.32567 2.997 0.00273 **
## CoberturaSSP -0.48973 0.54482 -0.899 0.36871
## MesesJunio -0.03575 0.42684 -0.084 0.93324
## MesesNoviembre 1.87603 0.73948 2.537 0.01118 *
## MesesOctubre -0.45745 0.58616 -0.780 0.43514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 62.006 on 24 degrees of freedom
## Residual deviance: 39.688 on 18 degrees of freedom
## AIC: 80.295
##
## Number of Fisher Scoring iterations: 7
ggpredict(GLM_Mn_FT) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Saccopteryx leptura
Fase Busqueda
GLM_Sl_FB <- glm(FB_Saccopteryx_leptura~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Sl_FB, test = "Chisq")
##
## Call:
## glm(formula = FB_Saccopteryx_leptura ~ Cobertura - 1 + Meses -
## 1, family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.5399 -2.2647 -0.1891 1.2087 5.7427
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque 1.8746 0.1436 13.053 < 2e-16 ***
## CoberturaSC 1.9756 0.1413 13.986 < 2e-16 ***
## CoberturaSSP 2.8295 0.1147 24.671 < 2e-16 ***
## MesesJunio 0.7616 0.1251 6.088 1.15e-09 ***
## MesesNoviembre 1.9240 0.1476 13.039 < 2e-16 ***
## MesesOctubre 0.2777 0.1766 1.572 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2390.31 on 24 degrees of freedom
## Residual deviance: 139.68 on 18 degrees of freedom
## AIC: 253.3
##
## Number of Fisher Scoring iterations: 5
ggpredict(GLM_Sl_FB) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Fase Terminal
GLM_Sl_FT <- glm(FT_Saccopteryx_leptura~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Sl_FT, test = "Chisq")
##
## Call:
## glm(formula = FT_Saccopteryx_leptura ~ Cobertura - 1 + Meses -
## 1, family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3857 -1.2490 -0.5930 0.9063 2.9848
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque -1.7384 0.5325 -3.264 0.0011 **
## CoberturaSC -1.0461 0.4803 -2.178 0.0294 *
## CoberturaSSP 0.3872 0.4139 0.935 0.3496
## MesesJunio 2.3225 0.4274 5.435 5.49e-08 ***
## MesesNoviembre 3.1681 0.4471 7.085 1.39e-12 ***
## MesesOctubre 0.5313 0.6466 0.822 0.4112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 424.156 on 24 degrees of freedom
## Residual deviance: 61.138 on 18 degrees of freedom
## AIC: 116.96
##
## Number of Fisher Scoring iterations: 6
ggpredict(GLM_Sl_FT) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Peropteryx macrotis
Fase Busqueda
GLM_Pm_FB <- glm(FB_Peropteryx_macrotis~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Pm_FB, test = "Chisq")
##
## Call:
## glm(formula = FB_Peropteryx_macrotis ~ Cobertura - 1 + Meses -
## 1, family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.551 -2.836 -0.844 2.003 6.021
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque 1.2857 0.1704 7.547 4.45e-14 ***
## CoberturaSC 2.9800 0.1134 26.277 < 2e-16 ***
## CoberturaSSP 1.6794 0.1550 10.834 < 2e-16 ***
## MesesJunio 0.7723 0.1297 5.957 2.58e-09 ***
## MesesNoviembre -16.9820 1275.7539 -0.013 0.989
## MesesOctubre 0.8921 0.1425 6.261 3.84e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2002.27 on 24 degrees of freedom
## Residual deviance: 207.31 on 18 degrees of freedom
## AIC: 296.79
##
## Number of Fisher Scoring iterations: 13
ggpredict(GLM_Pm_FB) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Fase Terminal
GLM_Pm_FT <- glm(FT_Peropteryx_macrotis~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Pm_FT, test = "Chisq")
##
## Call:
## glm(formula = FT_Peropteryx_macrotis ~ Cobertura - 1 + Meses -
## 1, family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.5125 -1.0484 -0.5497 0.8902 2.4078
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque -2.2819 0.6595 -3.460 0.00054 ***
## CoberturaSC 0.9418 0.3367 2.797 0.00515 **
## CoberturaSSP -1.0987 0.4699 -2.338 0.01939 *
## MesesJunio 1.6833 0.3627 4.641 3.47e-06 ***
## MesesNoviembre -15.2039 2103.3626 -0.007 0.99423
## MesesOctubre 1.5794 0.3855 4.097 4.19e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 295.072 on 24 degrees of freedom
## Residual deviance: 58.703 on 18 degrees of freedom
## AIC: 108.77
##
## Number of Fisher Scoring iterations: 14
ggpredict(GLM_Pm_FT) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Cormura brevirostris
Fase Busqueda
GLM_Cb_FB <- glm(FB_Cormura_brevirostris~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
anova(GLM_Cb_FB, test = "Chisq")
##
## Call:
## glm(formula = FB_Cormura_brevirostris ~ Cobertura - 1 + Meses -
## 1, family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6.5834 -1.4580 -0.4083 0.3855 7.1976
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque -0.4405 0.3239 -1.360 0.17384
## CoberturaSC 0.8313 0.2652 3.135 0.00172 **
## CoberturaSSP 0.8726 0.2647 3.296 0.00098 ***
## MesesJunio 1.6188 0.2729 5.932 2.99e-09 ***
## MesesNoviembre 1.0733 0.4615 2.326 0.02003 *
## MesesOctubre 2.2446 0.2777 8.084 6.27e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 731.79 on 24 degrees of freedom
## Residual deviance: 181.35 on 18 degrees of freedom
## AIC: 263.29
##
## Number of Fisher Scoring iterations: 6
ggpredict(GLM_Cb_FB) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
Fase Terminal
GLM_Cb_FT <- glm(FT_Cormura_brevirostris~ Cobertura-1 + Meses-1, family="poisson", data=bat_pas)
## Warning: glm.fit: fitted rates numerically 0 occurred
anova(GLM_Cb_FT, test = "Chisq")
##
## Call:
## glm(formula = FT_Cormura_brevirostris ~ Cobertura - 1 + Meses -
## 1, family = "poisson", data = bat_pas)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78339 -0.00005 -0.00003 0.00000 1.24305
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## CoberturaBosque -22.8774 10837.6758 -0.002 0.998
## CoberturaSC -41.9506 14548.2618 -0.003 0.998
## CoberturaSSP -20.7980 10837.6757 -0.002 0.998
## MesesJunio 21.4911 10837.6757 0.002 0.998
## MesesNoviembre -1.5046 43615.1146 0.000 1.000
## MesesOctubre 0.3733 18974.8369 0.000 1.000
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 45.0904 on 24 degrees of freedom
## Residual deviance: 5.5452 on 18 degrees of freedom
## AIC: 26.811
##
## Number of Fisher Scoring iterations: 20
ggpredict(GLM_Cb_FT) %>% plot(connect.lines = FALSE)# +
## $Cobertura
##
## $Meses
GLM Busqueda todas las especies
GLM_full_b <- glm(Busqueda~ Cobertura + Meses + Especie, family="poisson", data=bat_pas_2)
dat <- ggpredict(GLM_full_b, terms = c("Cobertura", "Especie"))
plot(dat, show.y.title = FALSE) + ylab("No. Fases de Busqueda")
plot(dat,
log.y = TRUE,
breaks = c(.1, 10, 100, 300, 600),
# limits = c(0, 800),
facet = TRUE, add.data = TRUE,
show.y.title = FALSE) + ylab("No. Fases de Busqueda")
## Warning: Transformation introduced infinite values in continuous y-axis
GLM Terminal todas las especies
GLM_full_t <- glm(Terminal~ Cobertura + Meses + Especie, family="poisson", data=bat_pas_2)
dat <- ggpredict(GLM_full_t, terms = c("Cobertura", "Especie"))
plot(dat, , show.y.title = FALSE) + ylab("No. Fases Terminales")
plot(dat,
log.y = TRUE,
breaks = c(.01, 1, 5, 15, 45, 100),
# limits = c(0, 800),
facet = TRUE, add.data = TRUE,
show.y.title = FALSE) + ylab("No. Fases de Terminales")
## Warning: Transformation introduced infinite values in continuous y-axis
