Pesticide application in a tomato greenhouse predicts bumble bee pollination and pathogen infection
Emily Runnion
June 2026
gh6 <- read_csv("gh6.csv", col_types = cols(gh = col_factor(levels = c("1",
"2", "3", "4", "5", "6")), year = col_factor(levels = c("2022",
"2024")), source = col_factor(levels = c("Koppert",
"Biobest"))))Crithidia
Crithidia probability per greenhouse
crith_prob <- glm(
cbind(crith_pos, crith_neg) ~
gh,
data = gh6,
family = binomial("logit"))
Anova(crith_prob)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## gh 242.42 5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(crith_prob)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ gh, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3246 0.3493 -6.656 2.82e-11 ***
## gh2 2.2846 0.4025 5.676 1.38e-08 ***
## gh3 3.2058 0.4132 7.758 8.62e-15 ***
## gh4 2.4046 0.3768 6.381 1.76e-10 ***
## gh5 2.2938 0.3775 6.076 1.23e-09 ***
## gh6 4.7560 0.4359 10.912 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2.4242e+02 on 5 degrees of freedom
## Residual deviance: 1.5765e-14 on 0 degrees of freedom
## AIC: 41.901
##
## Number of Fisher Scoring iterations: 3cr1 <- emmeans(crith_prob, pairwise ~ gh, type = "response")
cr1## $emmeans
## gh prob SE df asymp.LCL asymp.UCL
## 1 0.0891 0.0283 Inf 0.047 0.162
## 2 0.4900 0.0500 Inf 0.394 0.587
## 3 0.7071 0.0457 Inf 0.610 0.788
## 4 0.5200 0.0353 Inf 0.451 0.588
## 5 0.4923 0.0358 Inf 0.423 0.562
## 6 0.9192 0.0194 Inf 0.872 0.950
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## gh1 / gh2 0.1018 0.04100 Inf 1 -5.676 <0.0001
## gh1 / gh3 0.0405 0.01670 Inf 1 -7.758 <0.0001
## gh1 / gh4 0.0903 0.03400 Inf 1 -6.381 <0.0001
## gh1 / gh5 0.1009 0.03810 Inf 1 -6.076 <0.0001
## gh1 / gh6 0.0086 0.00375 Inf 1 -10.912 <0.0001
## gh2 / gh3 0.3980 0.11900 Inf 1 -3.092 0.0244
## gh2 / gh4 0.8869 0.21700 Inf 1 -0.490 0.9965
## gh2 / gh5 0.9908 0.24400 Inf 1 -0.038 1.0000
## gh2 / gh6 0.0845 0.02780 Inf 1 -7.520 <0.0001
## gh3 / gh4 2.2281 0.58400 Inf 1 3.054 0.0274
## gh3 / gh5 2.4892 0.65500 Inf 1 3.465 0.0070
## gh3 / gh6 0.2122 0.07250 Inf 1 -4.537 <0.0001
## gh4 / gh5 1.1172 0.22500 Inf 1 0.550 0.9940
## gh4 / gh6 0.0952 0.02830 Inf 1 -7.925 <0.0001
## gh5 / gh6 0.0852 0.02540 Inf 1 -8.276 <0.0001
##
## P value adjustment: tukey method for comparing a family of 6 estimates
## Tests are performed on the log odds ratio scalecr1.df <- as.data.frame(cr1$emmeans)
# Fantastic this matches what I calculated manually so this is a trustworthy method of comparing probabilities Crithidia vs. total pesticides applied (ml)
mod_1a <- glm(
cbind(crith_pos, crith_neg) ~ total_chemical,
data = gh6,
family = binomial("logit"))
Anova(mod_1a)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_chemical 87.04 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_1a)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_chemical, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.005e+00 1.607e-01 -6.254 4e-10 ***
## total_chemical 8.920e-06 9.932e-07 8.982 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 155.38 on 4 degrees of freedom
## AIC: 189.28
##
## Number of Fisher Scoring iterations: 4ggplot(gh6, aes(x = total_chemical, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
labs(
x = "Total Pesticides Applied (ml)",
y = "Probability of *Crithidia* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 45000,
y = 1,
label = "P < 0.001, χ² = 87.04",
size = 6
) ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'
Crithidia vs. insecticides
mod_2 <- glm(
cbind(crith_pos, crith_neg) ~
total_insecticide,
data = gh6,
family = binomial("logit"))
Anova(mod_2)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_insecticide 36.584 1 1.462e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_2)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_insecticide,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.799e-01 1.162e-01 -2.408 0.016 *
## total_insecticide 5.242e-06 8.761e-07 5.983 2.19e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 205.84 on 4 degrees of freedom
## AIC: 239.74
##
## Number of Fisher Scoring iterations: 4ggplot(gh6, aes(x = total_insecticide, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(3000, 200000)) +
labs(
x = "Total Insecticides Applied (ml)",
y = "Probability of *Crithidia* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 15000,
y = 1,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Crithidia vs. fungicides
mod_3 <- glm(
cbind(crith_pos, crith_neg) ~
total_fungicide,
data = gh6,
family = binomial("logit"))
Anova(mod_3)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_fungicide 70.956 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_3)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_fungicide,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.139e-01 1.316e-01 -4.664 3.10e-06 ***
## total_fungicide 2.596e-05 3.411e-06 7.612 2.69e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 171.46 on 4 degrees of freedom
## AIC: 205.36
##
## Number of Fisher Scoring iterations: 5ggplot(gh6, aes(x = total_fungicide, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(10000, 100000)) +
labs(
x = "Total Fungicides Applied (ml)",
y = "Probability of *Crithidia* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 15000,
y = 1,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Crithidia vs. synthetics
mod_4 <- glm(
cbind(crith_pos, crith_neg) ~
total_synthetic,
data = gh6,
family = binomial("logit"))
Anova(mod_4)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_synthetic 143.76 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_4)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_synthetic,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.480e+00 1.702e-01 -8.695 <2e-16 ***
## total_synthetic 6.137e-05 5.598e-06 10.962 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.420 on 5 degrees of freedom
## Residual deviance: 98.661 on 4 degrees of freedom
## AIC: 132.56
##
## Number of Fisher Scoring iterations: 4ggplot(gh6, aes(x = total_synthetic, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(13000, 55000)) +
labs(
x = "Total Synthetic Pesticdes Applied (ml)",
y = "Probability of *Crithidia* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 15000,
y = 1,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Crithidia vs. biologicals
mod_5 <- glm(
cbind(crith_pos, crith_neg) ~
total_biological,
data = gh6,
family = binomial("logit"))
Anova(mod_5)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_biological 56.695 1 5.089e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_5)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_biological,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.953e-01 1.381e-01 -4.309 1.64e-05 ***
## total_biological 7.686e-06 1.045e-06 7.354 1.92e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 185.72 on 4 degrees of freedom
## AIC: 219.63
##
## Number of Fisher Scoring iterations: 4ggplot(gh6, aes(x = total_biological, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(2500, 200000)) +
labs(
x = "Total Biological Pesticdes Applied (ml)",
y = "Probability of *Crithidia* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 10000,
y = 1,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Crithidia vs. average hives per greenhouse hectare
mod.cr_6 <- glm(
cbind(crith_pos, crith_neg) ~
avg_hives_ha,
data = gh6,
family = binomial("logit"))
Anova(mod.cr_6)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## avg_hives_ha 25.474 1 4.483e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.cr_6)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ avg_hives_ha, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.82141 0.31557 5.772 7.84e-09 ***
## avg_hives_ha -0.05385 0.01078 -4.995 5.90e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 216.95 on 4 degrees of freedom
## AIC: 250.85
##
## Number of Fisher Scoring iterations: 4ggplot(gh6, aes(x = avg_hives_ha, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(20, 40)) +
labs(
x = "Average Bumble Bee Colonies per Greenhouse Hectare",
y = "Probability of *Crithidia* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 22,
y = 1.1,
label = "β = -0.054 ± 0.011 SE,
P < 0.001",
size = 6
) ## `geom_smooth()` using formula = 'y ~ x'
Crithidia vs. commercial supplier of bumble bee colonies
mod.cr_7 <- glm(
cbind(crith_pos, crith_neg) ~
source,
data = gh6,
family = binomial("logit"))
Anova(mod.cr_7)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## source 71.093 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.cr_7)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ source, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5091 0.1190 -4.277 1.9e-05 ***
## sourceBiobest 1.2200 0.1477 8.260 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 171.33 on 4 degrees of freedom
## AIC: 205.23
##
## Number of Fisher Scoring iterations: 4cr.s <- emmeans(mod.cr_7, pairwise ~ source, type = "response")
cr.s## $emmeans
## source prob SE df asymp.LCL asymp.UCL
## Koppert 0.375 0.0279 Inf 0.322 0.431
## Biobest 0.671 0.0193 Inf 0.632 0.707
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## Koppert / Biobest 0.295 0.0436 Inf 1 -8.260 <0.0001
##
## Tests are performed on the log odds ratio scalecr.df <- as.data.frame(cr.s$emmeans)
cr.df## source prob SE df asymp.LCL asymp.UCL
## Koppert 0.3754153 0.02791054 Inf 0.3224898 0.4314948
## Biobest 0.6706081 0.01931654 Inf 0.6317060 0.7073050
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scaleggplot(cr.df, aes(x = source, y = prob, fill = source)) +
geom_pointrange(aes(ymin = asymp.LCL,
ymax = asymp.UCL),
shape = 21,
size = 1,
fatten = 4,
color = "black") +
geom_errorbar(aes(ymin = asymp.LCL,
ymax = asymp.UCL),
width = 0.1,
linewidth = 1) +
scale_fill_manual(values = c("Koppert" = "orchid4",
"Biobest" = "orchid1")) +
scale_y_continuous(
limits = c(0, 1),
expand = expansion(mult = c(0, 0.05))
) +
theme_cowplot() +
labs(
x = "",
y = "Predicted *Crithidia* detection probability",
caption = "Odds ratio = 0.30, P < 0.0001, N = 6"
) +
theme(axis.title.y = ggtext::element_markdown()) +
theme(
text = element_text(size = 16),
axis.text = element_text(size = 14),
legend.position = "none",
plot.caption = element_text(hjust = 0.5)
)## Warning: The `fatten` argument of `geom_pointrange()` is deprecated as of ggplot2 4.0.0.
## ℹ Please use the `size` aesthetic instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Anova(mod_1a)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_chemical 87.04 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_1a)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_chemical, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.005e+00 1.607e-01 -6.254 4e-10 ***
## total_chemical 8.920e-06 9.932e-07 8.982 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 155.38 on 4 degrees of freedom
## AIC: 189.28
##
## Number of Fisher Scoring iterations: 4Anova(mod_2)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_insecticide 36.584 1 1.462e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_2)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_insecticide,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.799e-01 1.162e-01 -2.408 0.016 *
## total_insecticide 5.242e-06 8.761e-07 5.983 2.19e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 205.84 on 4 degrees of freedom
## AIC: 239.74
##
## Number of Fisher Scoring iterations: 4Anova(mod_3)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_fungicide 70.956 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_3)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_fungicide,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.139e-01 1.316e-01 -4.664 3.10e-06 ***
## total_fungicide 2.596e-05 3.411e-06 7.612 2.69e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 171.46 on 4 degrees of freedom
## AIC: 205.36
##
## Number of Fisher Scoring iterations: 5Anova(mod_4)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_synthetic 143.76 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_4)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_synthetic,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.480e+00 1.702e-01 -8.695 <2e-16 ***
## total_synthetic 6.137e-05 5.598e-06 10.962 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.420 on 5 degrees of freedom
## Residual deviance: 98.661 on 4 degrees of freedom
## AIC: 132.56
##
## Number of Fisher Scoring iterations: 4Anova(mod_5)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## total_biological 56.695 1 5.089e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_5)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ total_biological,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.953e-01 1.381e-01 -4.309 1.64e-05 ***
## total_biological 7.686e-06 1.045e-06 7.354 1.92e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 185.72 on 4 degrees of freedom
## AIC: 219.63
##
## Number of Fisher Scoring iterations: 4Anova(mod.cr_6)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## avg_hives_ha 25.474 1 4.483e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.cr_6)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ avg_hives_ha, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.82141 0.31557 5.772 7.84e-09 ***
## avg_hives_ha -0.05385 0.01078 -4.995 5.90e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 216.95 on 4 degrees of freedom
## AIC: 250.85
##
## Number of Fisher Scoring iterations: 4Apicystis
Apicystis probability per greenhouse
api_prob <- glm(
cbind(api_pos, api_neg) ~
gh,
data = gh6,
family = binomial("logit"))
Anova(api_prob)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## gh 213.78 5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(api_prob)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ gh, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.399e+00 2.497e-01 -5.602 2.12e-08 ***
## gh2 1.242e-02 3.533e-01 0.035 0.971954
## gh3 -1.535e+00 5.225e-01 -2.938 0.003301 **
## gh4 9.932e-01 2.884e-01 3.444 0.000573 ***
## gh5 -2.657e+01 5.153e+04 -0.001 0.999589
## gh6 -2.659e+01 5.153e+04 -0.001 0.999588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2.1378e+02 on 5 degrees of freedom
## Residual deviance: 5.5431e-10 on 0 degrees of freedom
## AIC: 30.381
##
## Number of Fisher Scoring iterations: 22ap1 <- emmeans(api_prob, pairwise ~ gh, type = "response")
ap1## $emmeans
## gh prob SE df asymp.LCL asymp.UCL
## 1 0.1980 3.97e-02 Inf 0.1315 0.287
## 2 0.2000 4.00e-02 Inf 0.1328 0.290
## 3 0.0505 2.20e-02 Inf 0.0212 0.116
## 4 0.4000 3.46e-02 Inf 0.3344 0.469
## 5 0.0000 4.00e-08 Inf 0.0000 1.000
## 6 0.0000 4.00e-08 Inf 0.0000 1.000
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## gh1 / gh2 1.00e+00 0.00e+00 Inf 1 -0.035 1.0000
## gh1 / gh3 5.00e+00 2.00e+00 Inf 1 2.938 0.0388
## gh1 / gh4 0.00e+00 0.00e+00 Inf 1 -3.444 0.0076
## gh1 / gh5 3.48e+11 1.79e+16 Inf 1 0.001 1.0000
## gh1 / gh6 3.53e+11 1.82e+16 Inf 1 0.001 1.0000
## gh2 / gh3 5.00e+00 2.00e+00 Inf 1 2.961 0.0362
## gh2 / gh4 0.00e+00 0.00e+00 Inf 1 -3.398 0.0089
## gh2 / gh5 3.52e+11 1.81e+16 Inf 1 0.001 1.0000
## gh2 / gh6 3.57e+11 1.84e+16 Inf 1 0.001 1.0000
## gh3 / gh4 0.00e+00 0.00e+00 Inf 1 -5.255 <0.0001
## gh3 / gh5 7.49e+10 3.86e+15 Inf 1 0.000 1.0000
## gh3 / gh6 7.60e+10 3.92e+15 Inf 1 0.000 1.0000
## gh4 / gh5 9.38e+11 4.83e+16 Inf 1 0.001 1.0000
## gh4 / gh6 9.53e+11 4.91e+16 Inf 1 0.001 1.0000
## gh5 / gh6 1.00e+00 7.40e+04 Inf 1 0.000 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimates
## Tests are performed on the log odds ratio scaleap1.df <- as.data.frame(ap1$emmeans)
# Fantastic once again this matches what I calculated manually so this is a trustworthy method of comparing probabilities Apicystis vs. total pesticides applied (ml)
mod.ap_1a <- glm(
cbind(api_pos, api_neg) ~ total_chemical,
data = gh6,
family = binomial("logit"))
Anova(mod.ap_1a)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_chemical 17.948 1 2.27e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_1a)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_chemical, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.086e+00 1.848e-01 -5.876 4.20e-09 ***
## total_chemical -5.370e-06 1.254e-06 -4.281 1.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 195.83 on 4 degrees of freedom
## AIC: 218.22
##
## Number of Fisher Scoring iterations: 6ggplot(gh6, aes(x = total_chemical, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
labs(
x = "Total Pesticides Applied (ml)",
y = "Probability of *Apicystis* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 45000,
y = 0.5,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Apicystis vs. insecticides
mod.ap_2 <- glm(
cbind(api_pos, api_neg) ~
total_insecticide,
data = gh6,
family = binomial("logit"))
Anova(mod.ap_2)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_insecticide 13.797 1 0.0002037 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_2)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_insecticide, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.370e+00 1.451e-01 -9.440 < 2e-16 ***
## total_insecticide -4.467e-06 1.202e-06 -3.718 0.000201 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 199.99 on 4 degrees of freedom
## AIC: 222.37
##
## Number of Fisher Scoring iterations: 6qqnorm(resid(mod.ap_2));qqline(resid(mod.ap_2))
ggplot(gh6, aes(x = total_insecticide, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(3000, 200000)) +
labs(
x = "Total Insecticides Applied (ml)",
y = "Probability of *Apicystis* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 15000,
y = 0.5,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Apicystis vs. fungicides
mod.ap_3 <- glm(
cbind(api_pos, api_neg) ~
total_fungicide,
data = gh6,
family = binomial("logit"))
Anova(mod.ap_3)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_fungicide 0.71414 1 0.3981summary(mod.ap_3)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_fungicide, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.695e+00 1.712e-01 -9.899 <2e-16 ***
## total_fungicide -3.373e-06 4.043e-06 -0.834 0.404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.07 on 4 degrees of freedom
## AIC: 235.45
##
## Number of Fisher Scoring iterations: 5ggplot(gh6, aes(x = total_fungicide, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(10000, 100000)) +
labs(
x = "Total Fungicides Applied (ml)",
y = "Probability of *Apicystis* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 15000,
y = 0.5,
label = "P = 0.40",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Apicystis vs. synthetics
mod.ap_4 <- glm(
cbind(api_pos, api_neg) ~
total_synthetic,
data = gh6,
family = binomial("logit"))
Anova(mod.ap_4)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_synthetic 0.20155 1 0.6535summary(mod.ap_4)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_synthetic, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.905e+00 2.227e-01 -8.554 <2e-16 ***
## total_synthetic 2.967e-06 6.597e-06 0.450 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.58 on 4 degrees of freedom
## AIC: 235.96
##
## Number of Fisher Scoring iterations: 6ggplot(gh6, aes(x = total_synthetic, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(13000, 55000)) +
labs(
x = "Total Synthetic Pesticdes Applied (ml)",
y = "Probability of *Apicystis* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 15000,
y = 0.5,
label = "P = 0.65",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Apicystis vs. biologicals
mod.ap_5 <- glm(
cbind(api_pos, api_neg) ~
total_biological,
data = gh6,
family = binomial("logit"))
Anova(mod.ap_5)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_biological 21.977 1 2.76e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_5)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_biological, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.132e+00 1.630e-01 -6.942 3.86e-12 ***
## total_biological -6.481e-06 1.378e-06 -4.702 2.58e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 191.81 on 4 degrees of freedom
## AIC: 214.19
##
## Number of Fisher Scoring iterations: 6ggplot(gh6, aes(x = total_biological, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(2500, 200000)) +
labs(
x = "Total Biological Pesticdes Applied (ml)",
y = "Probability of *Apicystis* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 10000,
y = 0.5,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Apicystis vs. average hives per greenhouse hectare
mod.ap_6 <- glm(
cbind(api_pos, api_neg) ~
avg_hives_ha,
data = gh6,
family = binomial("logit"))
Anova(mod.ap_6)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## avg_hives_ha 118.05 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_6)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ avg_hives_ha, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.74826 0.52773 -12.79 <2e-16 ***
## avg_hives_ha 0.16031 0.01565 10.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.783 on 5 degrees of freedom
## Residual deviance: 95.729 on 4 degrees of freedom
## AIC: 118.11
##
## Number of Fisher Scoring iterations: 6ggplot(gh6, aes(x = avg_hives_ha, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(20, 40)) +
labs(
x = "Average bumble bee colonies per greenhouse hectare",
y = "Probability of *Apicystis* Detection"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 22,
y = 0.5,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Apicystis vs. commercial supplier of bumble bee colonies
mod.ap_7 <- glm(
cbind(api_pos, api_neg) ~
source,
data = gh6,
family = binomial("logit"))
Anova(mod.ap_7)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## source 133.31 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_7)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ source, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6981 0.1224 -5.705 1.16e-08 ***
## sourceBiobest -2.4233 0.2382 -10.174 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.783 on 5 degrees of freedom
## Residual deviance: 80.469 on 4 degrees of freedom
## AIC: 102.85
##
## Number of Fisher Scoring iterations: 6ap.s <- emmeans(mod.ap_7, pairwise ~ source, type = "response")
ap.s## $emmeans
## source prob SE df asymp.LCL asymp.UCL
## Koppert 0.3322 0.02710 Inf 0.2813 0.3874
## Biobest 0.0422 0.00827 Inf 0.0287 0.0617
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## Koppert / Biobest 11.3 2.69 Inf 1 10.174 <0.0001
##
## Tests are performed on the log odds ratio scaleap.df <- as.data.frame(ap.s$emmeans)
ap.df## source prob SE df asymp.LCL asymp.UCL
## Koppert 0.3322259 0.027148653 Inf 0.28130832 0.3873920
## Biobest 0.0422297 0.008265682 Inf 0.02869202 0.0617488
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scaleggplot(ap.df, aes(x = source, y = prob, fill = source)) +
geom_pointrange(aes(ymin = asymp.LCL,
ymax = asymp.UCL),
shape = 21,
size = 1,
fatten = 4,
color = "black") +
geom_errorbar(aes(ymin = asymp.LCL,
ymax = asymp.UCL),
width = 0.1,
linewidth = 1) +
scale_fill_manual(values = c("Koppert" = "orangered1",
"Biobest" = "orangered4")) +
scale_y_continuous(
limits = c(0, 0.45),
expand = expansion(mult = c(0, 0.05))
) +
theme_cowplot() +
labs(
x = "",
y = "Predicted *Apicystis* detection probability",
caption = "Odds ratio = 11.3, P < 0.0001, N = 6"
) +
theme(axis.title.y = ggtext::element_markdown()) +
theme(
text = element_text(size = 16),
axis.text = element_text(size = 14),
legend.position = "none",
plot.caption = element_text(hjust = 0.5)
)
Anova(mod.ap_1a)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_chemical 17.948 1 2.27e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_1a)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_chemical, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.086e+00 1.848e-01 -5.876 4.20e-09 ***
## total_chemical -5.370e-06 1.254e-06 -4.281 1.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 195.83 on 4 degrees of freedom
## AIC: 218.22
##
## Number of Fisher Scoring iterations: 6Anova(mod.ap_2)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_insecticide 13.797 1 0.0002037 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_2)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_insecticide, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.370e+00 1.451e-01 -9.440 < 2e-16 ***
## total_insecticide -4.467e-06 1.202e-06 -3.718 0.000201 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 199.99 on 4 degrees of freedom
## AIC: 222.37
##
## Number of Fisher Scoring iterations: 6Anova(mod.ap_3)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_fungicide 0.71414 1 0.3981summary(mod.ap_3)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_fungicide, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.695e+00 1.712e-01 -9.899 <2e-16 ***
## total_fungicide -3.373e-06 4.043e-06 -0.834 0.404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.07 on 4 degrees of freedom
## AIC: 235.45
##
## Number of Fisher Scoring iterations: 5Anova(mod.ap_4)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_synthetic 0.20155 1 0.6535summary(mod.ap_4)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_synthetic, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.905e+00 2.227e-01 -8.554 <2e-16 ***
## total_synthetic 2.967e-06 6.597e-06 0.450 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.58 on 4 degrees of freedom
## AIC: 235.96
##
## Number of Fisher Scoring iterations: 6Anova(mod.ap_5)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## total_biological 21.977 1 2.76e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_5)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ total_biological, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.132e+00 1.630e-01 -6.942 3.86e-12 ***
## total_biological -6.481e-06 1.378e-06 -4.702 2.58e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 191.81 on 4 degrees of freedom
## AIC: 214.19
##
## Number of Fisher Scoring iterations: 6Anova(mod.ap_6)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## avg_hives_ha 118.05 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod.ap_6)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ avg_hives_ha, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.74826 0.52773 -12.79 <2e-16 ***
## avg_hives_ha 0.16031 0.01565 10.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.783 on 5 degrees of freedom
## Residual deviance: 95.729 on 4 degrees of freedom
## AIC: 118.11
##
## Number of Fisher Scoring iterations: 6Anther bruising
hist(gh6$average_bruise)
gh6$logbruise <- log(gh6$average_bruise)
hist(gh6$logbruise)
shapiro.test(gh6$logbruise)##
## Shapiro-Wilk normality test
##
## data: gh6$logbruise
## W = 0.8978, p-value = 0.3611Anther bruising vs. crithidia
mod_b_1 <- glm(average_bruise ~ crith_prop, data = gh6)
Anova(mod_b_1)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## crith_prop 6.5964 1 0.01022 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_1)##
## Call:
## glm(formula = average_bruise ~ crith_prop, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.44224 0.09026 27.058 1.11e-05 ***
## crith_prop -0.39132 0.15236 -2.568 0.0621 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.008823438)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.035294 on 4 degrees of freedom
## AIC: -7.7876
##
## Number of Fisher Scoring iterations: 2mod_b_1 <- glm(logbruise ~ crith_prop, data = gh6)
Anova(mod_b_1)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## crith_prop 6.7171 1 0.009549 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_1)##
## Call:
## glm(formula = logbruise ~ crith_prop, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.89669 0.04058 22.097 2.48e-05 ***
## crith_prop -0.17753 0.06850 -2.592 0.0606 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.001783466)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0071339 on 4 degrees of freedom
## AIC: -17.381
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = crith_prop, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(0.08,1)) +
labs(
x = "Probability of *Crithidia* Detection",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 0.15,
y = 2.75,
label = "P = 0.01",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther bruising vs. apicystis
mod_b_2 <- glm(average_bruise ~ api_prop, data = gh6)
Anova(mod_b_2)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## api_prop 13.957 1 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_2)##
## Call:
## glm(formula = average_bruise ~ api_prop, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.12313 0.04151 51.149 8.74e-07 ***
## api_prop 0.77254 0.20679 3.736 0.0202 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.005206725)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.020827 on 4 degrees of freedom
## AIC: -10.952
##
## Number of Fisher Scoring iterations: 2mod_b_2 <- glm(logbruise ~ api_prop, data = gh6)
Anova(mod_b_2)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## api_prop 13.162 1 0.0002857 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_2)##
## Call:
## glm(formula = logbruise ~ api_prop, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.75242 0.01920 39.193 2.53e-06 ***
## api_prop 0.34697 0.09564 3.628 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.001113742)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.004455 on 4 degrees of freedom
## AIC: -20.206
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = api_prop, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(0,.41)) +
labs(
x = "Probability of *Apicystis* Detection",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 0.05,
y = 2.75,
label = "P < 0.001",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther brusing vs. total pesticides
mod_b_3 <- glm(average_bruise ~ total_chemical, data = gh6)
Anova(mod_b_3)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## total_chemical 2.7052 1 0.1summary(mod_b_3)##
## Call:
## glm(formula = average_bruise ~ total_chemical, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.359e+00 9.061e-02 26.029 1.29e-05 ***
## total_chemical -1.027e-06 6.244e-07 -1.645 0.175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01394399)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.055776 on 4 degrees of freedom
## AIC: -5.0418
##
## Number of Fisher Scoring iterations: 2mod_b_3 <- glm(logbruise ~ total_chemical, data = gh6)
Anova(mod_b_3)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_chemical 2.7706 1 0.09601 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_3)##
## Call:
## glm(formula = logbruise ~ total_chemical, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.590e-01 4.077e-02 21.067 3e-05 ***
## total_chemical -4.677e-07 2.810e-07 -1.665 0.171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002823047)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.011292 on 4 degrees of freedom
## AIC: -14.625
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = total_chemical, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limit = c(16000, 215000)) +
labs(
x = "Average total pesticides applied (ml)",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 25000,
y = 2.7,
label = "P = 0.11",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther bruising vs. insecticides
mod_b_4 <- glm(average_bruise ~ total_insecticide, data = gh6)
Anova(mod_b_4)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## total_insecticide 1.2021 1 0.2729summary(mod_b_4)##
## Call:
## glm(formula = average_bruise ~ total_insecticide, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.293e+00 7.805e-02 29.382 7.99e-06 ***
## total_insecticide -7.293e-07 6.652e-07 -1.096 0.334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.017973)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.071892 on 4 degrees of freedom
## AIC: -3.5188
##
## Number of Fisher Scoring iterations: 2mod_b_4 <- glm(logbruise ~ total_insecticide, data = gh6)
Anova(mod_b_4)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_insecticide 1.2407 1 0.2653summary(mod_b_4)##
## Call:
## glm(formula = logbruise ~ total_insecticide, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.294e-01 3.516e-02 23.589 1.91e-05 ***
## total_insecticide -3.338e-07 2.997e-07 -1.114 0.328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003647169)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.014589 on 4 degrees of freedom
## AIC: -13.088
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = total_insecticide, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(3000, 195000)) +
labs(
x = "Average total insecticides applied (ml)",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 10000,
y = 2.6,
label = "P = 0.28",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther brusing vs. fungicides
mod_b_5 <- glm(average_bruise ~ total_fungicide, data = gh6)
Anova(mod_b_5)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## total_fungicide 0.51373 1 0.4735summary(mod_b_5)##
## Call:
## glm(formula = average_bruise ~ total_fungicide, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.291e+00 1.008e-01 22.721 2.22e-05 ***
## total_fungicide -1.498e-06 2.090e-06 -0.717 0.513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.02071387)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.082855 on 4 degrees of freedom
## AIC: -2.6672
##
## Number of Fisher Scoring iterations: 2mod_b_5 <- glm(logbruise ~ total_fungicide, data = gh6)
Anova(mod_b_5)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_fungicide 0.49767 1 0.4805summary(mod_b_5)##
## Call:
## glm(formula = logbruise ~ total_fungicide, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.277e-01 4.567e-02 18.121 5.45e-05 ***
## total_fungicide -6.678e-07 9.466e-07 -0.705 0.519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.004249676)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016999 on 4 degrees of freedom
## AIC: -12.171
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = total_fungicide, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(10000, 100000)) +
labs(
x = "Average total fungicides applied (ml)",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 20000,
y = 2.6,
label = "P = 0.47",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther bruising vs. synthetics
mod_b_6 <- glm(average_bruise ~ total_synthetic, data = gh6)
Anova(mod_b_6)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## total_synthetic 0.97926 1 0.3224summary(mod_b_6)##
## Call:
## glm(formula = average_bruise ~ total_synthetic, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.347e+00 1.288e-01 18.22 5.33e-05 ***
## total_synthetic -4.127e-06 4.170e-06 -0.99 0.378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01877725)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.075109 on 4 degrees of freedom
## AIC: -3.2562
##
## Number of Fisher Scoring iterations: 2mod_b_6 <- glm(logbruise ~ total_synthetic, data = gh6)
Anova(mod_b_6)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_synthetic 1.0595 1 0.3033summary(mod_b_6)##
## Call:
## glm(formula = logbruise ~ total_synthetic, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.551e-01 5.777e-02 14.801 0.000121 ***
## total_synthetic -1.925e-06 1.871e-06 -1.029 0.361488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003777735)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.015111 on 4 degrees of freedom
## AIC: -12.877
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = total_synthetic, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(13000, 55000)) +
labs(
x = "Average total synthetic pesticides applied (ml)",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 20000,
y = 2.6,
label = "P = 0.32",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther bruising vs. biologicals
mod_b_7 <- glm(average_bruise ~ total_biological, data = gh6)
Anova(mod_b_7)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## total_biological 2.3194 1 0.1278summary(mod_b_7)##
## Call:
## glm(formula = average_bruise ~ total_biological, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.333e+00 8.289e-02 28.152 9.47e-06 ***
## total_biological -1.063e-06 6.982e-07 -1.523 0.202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01479514)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.059181 on 4 degrees of freedom
## AIC: -4.6863
##
## Number of Fisher Scoring iterations: 2mod_b_7 <- glm(logbruise ~ total_biological, data = gh6)
Anova(mod_b_7)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_biological 2.3467 1 0.1256summary(mod_b_7)##
## Call:
## glm(formula = logbruise ~ total_biological, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.474e-01 3.740e-02 22.659 2.25e-05 ***
## total_biological -4.826e-07 3.150e-07 -1.532 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003011599)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.012046 on 4 degrees of freedom
## AIC: -14.237
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = total_biological, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(3000, 195000)) +
labs(
x = "Average total biological pesticides applied (ml)",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 15000,
y = 2.6,
label = "P = 0.13",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther bruising vs. average hives per greenhouse hectare
mod_b_8 <- glm(average_bruise ~ avg_hives_ha, data = gh6)
Anova(mod_b_8)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## avg_hives_ha 2.1637 1 0.1413summary(mod_b_8)##
## Call:
## glm(formula = average_bruise ~ avg_hives_ha, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.866496 0.253771 7.355 0.00182 **
## avg_hives_ha 0.013182 0.008961 1.471 0.21526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01516884)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.060675 on 4 degrees of freedom
## AIC: -4.5366
##
## Number of Fisher Scoring iterations: 2mod_b_8 <- glm(logbruise ~ avg_hives_ha, data = gh6)
Anova(mod_b_8)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## avg_hives_ha 2.1692 1 0.1408summary(mod_b_8)##
## Call:
## glm(formula = logbruise ~ avg_hives_ha, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.635918 0.114689 5.545 0.00517 **
## avg_hives_ha 0.005965 0.004050 1.473 0.21479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003098209)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.012393 on 4 degrees of freedom
## AIC: -14.067
##
## Number of Fisher Scoring iterations: 2ggplot(gh6, aes(x = avg_hives_ha, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
se = TRUE,
color = "black",
size = 1.2) +
theme_cowplot() +
scale_x_continuous(limits = c(20, 40)) +
labs(
x = "Average total biologicals applied (ml)",
y = "Average anther bruising score"
) +
theme(
plot.title = element_text(size = 14, face = "bold")) +
theme(text = element_text(size = 20),
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)) +
theme(axis.title.x = ggtext::element_markdown()) +
annotate(
geom = "text",
x = 21,
y = 2.6,
label = "P = 0.14",
size = 9
) ## `geom_smooth()` using formula = 'y ~ x'
Anther bruising vs. commercial supplier of bumble bee colonies
mod_b_9 <- glm(average_bruise ~ source, data = gh6)
Anova(mod_b_9)## Analysis of Deviance Table (Type II tests)
##
## Response: average_bruise
## LR Chisq Df Pr(>Chisq)
## source 5.7858 1 0.01616 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_9)##
## Call:
## glm(formula = average_bruise ~ source, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.36813 0.06912 34.263 4.33e-06 ***
## sourceBiobest -0.20362 0.08465 -2.405 0.0739 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.009554299)
##
## Null deviance: 0.093497 on 5 degrees of freedom
## Residual deviance: 0.038217 on 4 degrees of freedom
## AIC: -7.3101
##
## Number of Fisher Scoring iterations: 2b.s <- emmeans(mod_b_9, pairwise ~ source, type = "response")
b.s## $emmeans
## source emmean SE df lower.CL upper.CL
## Koppert 2.37 0.0691 4 2.18 2.56
## Biobest 2.16 0.0489 4 2.03 2.30
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Koppert - Biobest 0.204 0.0847 4 2.405 0.0739bs.df <- as.data.frame(b.s$emmeans)
bs.df## source emmean SE df lower.CL upper.CL
## Koppert 2.368129 0.06911693 4 2.176229 2.560028
## Biobest 2.164512 0.04887305 4 2.028819 2.300206
##
## Confidence level used: 0.95mod_b_9 <- glm(logbruise ~ source, data = gh6)
Anova(mod_b_9)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## source 5.4475 1 0.0196 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_9)##
## Call:
## glm(formula = logbruise ~ source, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.86210 0.03181 27.106 1.1e-05 ***
## sourceBiobest -0.09092 0.03895 -2.334 0.0799 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002023148)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0080926 on 4 degrees of freedom
## AIC: -16.624
##
## Number of Fisher Scoring iterations: 2b.s <- emmeans(mod_b_9, pairwise ~ source, type = "response")
b.s## $emmeans
## source emmean SE df lower.CL upper.CL
## Koppert 0.862 0.0318 4 0.774 0.950
## Biobest 0.771 0.0225 4 0.709 0.834
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Koppert - Biobest 0.0909 0.039 4 2.334 0.0799bs.df <- as.data.frame(b.s$emmeans)
bs.df## source emmean SE df lower.CL upper.CL
## Koppert 0.8620982 0.03180525 4 0.7737926 0.9504037
## Biobest 0.7711819 0.02248971 4 0.7087404 0.8336233
##
## Confidence level used: 0.95ggplot(bs.df, aes(x = source, y = emmean, fill = source)) +
geom_pointrange(aes(ymin = lower.CL,
ymax = upper.CL),
shape = 21,
size = 1,
fatten = 4,
color = "black") +
geom_errorbar(aes(ymin = lower.CL,
ymax = upper.CL),
width = 0.1,
linewidth = 1) +
scale_fill_manual(values = c("Koppert" = "chartreuse4",
"Biobest" = "darkolivegreen")) +
scale_y_continuous(
limits = c(0, 3),
expand = expansion(mult = c(0, 0.05))
) +
theme_cowplot() +
labs(
x = "",
y = "Estimated mean anther bruise score",
caption = "Koppert − Biobest = 0.20; P = 0.074"
) +
theme(axis.title.y = ggtext::element_markdown()) +
theme(
text = element_text(size = 16),
axis.text = element_text(size = 14),
legend.position = "none",
plot.caption = element_text(hjust = 0.5)
)
Anova(mod_b_1)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## crith_prop 6.7171 1 0.009549 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_1)##
## Call:
## glm(formula = logbruise ~ crith_prop, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.89669 0.04058 22.097 2.48e-05 ***
## crith_prop -0.17753 0.06850 -2.592 0.0606 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.001783466)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0071339 on 4 degrees of freedom
## AIC: -17.381
##
## Number of Fisher Scoring iterations: 2Anova(mod_b_2)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## api_prop 13.162 1 0.0002857 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_2)##
## Call:
## glm(formula = logbruise ~ api_prop, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.75242 0.01920 39.193 2.53e-06 ***
## api_prop 0.34697 0.09564 3.628 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.001113742)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.004455 on 4 degrees of freedom
## AIC: -20.206
##
## Number of Fisher Scoring iterations: 2Anova(mod_b_3)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_chemical 2.7706 1 0.09601 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod_b_3)##
## Call:
## glm(formula = logbruise ~ total_chemical, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.590e-01 4.077e-02 21.067 3e-05 ***
## total_chemical -4.677e-07 2.810e-07 -1.665 0.171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002823047)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.011292 on 4 degrees of freedom
## AIC: -14.625
##
## Number of Fisher Scoring iterations: 2Anova(mod_b_4)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_insecticide 1.2407 1 0.2653summary(mod_b_4)##
## Call:
## glm(formula = logbruise ~ total_insecticide, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.294e-01 3.516e-02 23.589 1.91e-05 ***
## total_insecticide -3.338e-07 2.997e-07 -1.114 0.328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003647169)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.014589 on 4 degrees of freedom
## AIC: -13.088
##
## Number of Fisher Scoring iterations: 2Anova(mod_b_5)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_fungicide 0.49767 1 0.4805summary(mod_b_5)##
## Call:
## glm(formula = logbruise ~ total_fungicide, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.277e-01 4.567e-02 18.121 5.45e-05 ***
## total_fungicide -6.678e-07 9.466e-07 -0.705 0.519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.004249676)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016999 on 4 degrees of freedom
## AIC: -12.171
##
## Number of Fisher Scoring iterations: 2Anova(mod_b_6)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_synthetic 1.0595 1 0.3033summary(mod_b_6)##
## Call:
## glm(formula = logbruise ~ total_synthetic, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.551e-01 5.777e-02 14.801 0.000121 ***
## total_synthetic -1.925e-06 1.871e-06 -1.029 0.361488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003777735)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.015111 on 4 degrees of freedom
## AIC: -12.877
##
## Number of Fisher Scoring iterations: 2Anova(mod_b_7)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## total_biological 2.3467 1 0.1256summary(mod_b_7)##
## Call:
## glm(formula = logbruise ~ total_biological, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.474e-01 3.740e-02 22.659 2.25e-05 ***
## total_biological -4.826e-07 3.150e-07 -1.532 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003011599)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.012046 on 4 degrees of freedom
## AIC: -14.237
##
## Number of Fisher Scoring iterations: 2Anova(mod_b_8)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## avg_hives_ha 2.1692 1 0.1408summary(mod_b_8)##
## Call:
## glm(formula = logbruise ~ avg_hives_ha, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.635918 0.114689 5.545 0.00517 **
## avg_hives_ha 0.005965 0.004050 1.473 0.21479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003098209)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.012393 on 4 degrees of freedom
## AIC: -14.067
##
## Number of Fisher Scoring iterations: 2Applied pesticde comparisons
# List of your logical treatment variables
treat_vars <- c("azaguard", "botanigard_22wp", "botanigard_es", "captiva_prime", "nofly", "venerate_cg", "m_pede", "rootshield_plus", "lalstop_k61", "beleaf_50sg", "coragen", "entrust_sc", "pylon", "grotto", "luna_tranquility", "previcur_flex", "fontelis", "quadristop", "milstop")
# Reshape the data to long format
greenhouse_long <- gh6 %>%
pivot_longer(cols = all_of(treat_vars),
names_to = "treatment",
values_to = "applied")
greenhouse_long$applied_L <- ifelse(greenhouse_long$applied == 0, 0, 1)
greenhouse_long$applied_L <- as.logical(greenhouse_long$applied_L)
chem_cols <- c(
"azaguard","botanigard_22wp","botanigard_es","captiva_prime","nofly",
"venerate_cg","m_pede","rootshield_plus","lalstop_k61","beleaf_50sg",
"coragen","entrust_sc","pylon","grotto","luna_tranquility",
"previcur_flex","fontelis","quadristop","milstop"
)
gh6[paste0(chem_cols, "_L")] <-
lapply(gh6[chem_cols], function(x) x > 0)chem_info <- data.frame(
pesticide = c(
"azaguard_L",
"captiva_prime_L",
"nofly_L",
"venerate_cg_L",
"entrust_sc_L",
"m_pede_L",
"botanigard_22wp_L",
"botanigard_es_L",
"beleaf_50sg_L",
"coragen_L",
"pylon_L",
"rootshield_plus_L",
"lalstop_k61_L",
"milstop_L",
"grotto_L",
"luna_tranquility_L",
"previcur_flex_L",
"fontelis_L",
"quadristop_L"
),
type = c(
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"insecticide",
"fungicide",
"fungicide",
"fungicide",
"fungicide",
"fungicide",
"fungicide",
"fungicide",
"fungicide"
),
mode = c(
"biological",
"biological",
"biological",
"biological",
"biological",
"biological",
"biological",
"biological",
"synthetic",
"synthetic",
"synthetic",
"biological",
"biological",
"biological",
"synthetic",
"synthetic",
"synthetic",
"synthetic",
"synthetic"
)
)
chem_info$type <- factor(chem_info$type,
levels = c("insecticide", "fungicide"))
chem_info$mode <- factor(chem_info$mode,
levels = c("biological", "synthetic"))
chem_info <- chem_info[order(chem_info$type, chem_info$mode), ]
# Create combined classification
chem_info$class <- paste(chem_info$type, chem_info$mode, sep = "_")
chem_info <- chem_info[order(chem_info$mode), ]
# Four colors
chem_colors <- c(
"insecticide_synthetic" = "brown4",
"insecticide_biological" = "orchid1",
"fungicide_synthetic" = "deepskyblue4",
"fungicide_biological" = "lightskyblue"
)Crithidia vs. logical chemicals
Models
## ---------------- AZAGUARD ----------------
mod_azaguard <- glm(
cbind(crith_pos, crith_neg) ~ azaguard_L,
data = gh6,
family = binomial("logit")
)
summary(mod_azaguard)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ azaguard_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2955 0.1167 -2.531 0.0114 *
## azaguard_LTRUE 0.8890 0.1449 6.137 8.4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 204.05 on 4 degrees of freedom
## AIC: 237.95
##
## Number of Fisher Scoring iterations: 4Anova(mod_azaguard)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## azaguard_L 38.371 1 5.849e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma.s <- emmeans(mod_azaguard, pairwise ~ azaguard_L, type = "response")
ma.s.df <- as.data.frame(ma.s$emmeans)
ma.s## $emmeans
## azaguard_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- BOTANIGARD 22WP ----------------
mod_botanigard_22wp <- glm(
cbind(crith_pos, crith_neg) ~ botanigard_22wp_L,
data = gh6,
family = binomial("logit")
)
summary(mod_botanigard_22wp)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ botanigard_22wp_L,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2955 0.1167 -2.531 0.0114 *
## botanigard_22wp_LTRUE 0.8890 0.1449 6.137 8.4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 204.05 on 4 degrees of freedom
## AIC: 237.95
##
## Number of Fisher Scoring iterations: 4Anova(mod_botanigard_22wp)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## botanigard_22wp_L 38.371 1 5.849e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mb22.s <- emmeans(mod_botanigard_22wp, pairwise ~ botanigard_22wp_L, type = "response")
mb22.s.df <- as.data.frame(mb22.s$emmeans)
mb22.s ## $emmeans
## botanigard_22wp_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- BOTANIGARD ES ----------------
mod_botanigard_es <- glm(
cbind(crith_pos, crith_neg) ~ botanigard_es_L,
data = gh6,
family = binomial("logit")
)
summary(mod_botanigard_es)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ botanigard_es_L,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.50013 0.09244 5.410 6.29e-08 ***
## botanigard_es_LTRUE -0.47481 0.13665 -3.475 0.000511 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 230.29 on 4 degrees of freedom
## AIC: 264.19
##
## Number of Fisher Scoring iterations: 3Anova(mod_botanigard_es)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## botanigard_es_L 12.134 1 0.000495 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mbe.s <- emmeans(mod_botanigard_es, pairwise ~ botanigard_es_L, type = "response")
mbe.s.df <- as.data.frame(mbe.s$emmeans)
mbe.s## $emmeans
## botanigard_es_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.622 0.0217 Inf 0.579 0.664
## TRUE 0.506 0.0252 Inf 0.457 0.555
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.61 0.22 Inf 1 3.475 0.0005
##
## Tests are performed on the log odds ratio scale## ---------------- CAPTIVA PRIME ----------------
mod_captiva_prime <- glm(
cbind(crith_pos, crith_neg) ~ captiva_prime_L,
data = gh6,
family = binomial("logit")
)
summary(mod_captiva_prime)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ captiva_prime_L,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2955 0.1167 -2.531 0.0114 *
## captiva_prime_LTRUE 0.8890 0.1449 6.137 8.4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 204.05 on 4 degrees of freedom
## AIC: 237.95
##
## Number of Fisher Scoring iterations: 4Anova(mod_captiva_prime)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## captiva_prime_L 38.371 1 5.849e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mcp.s <- emmeans(mod_captiva_prime, pairwise ~ captiva_prime_L, type = "response")
mcp.s.df <- as.data.frame(mcp.s$emmeans)
mcp.s## $emmeans
## captiva_prime_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- NOFLY ----------------
mod_nofly <- glm(
cbind(crith_pos, crith_neg) ~ nofly_L,
data = gh6,
family = binomial("logit")
)
summary(mod_nofly)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ nofly_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2955 0.1167 -2.531 0.0114 *
## nofly_LTRUE 0.8890 0.1449 6.137 8.4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 204.05 on 4 degrees of freedom
## AIC: 237.95
##
## Number of Fisher Scoring iterations: 4Anova(mod_nofly)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## nofly_L 38.371 1 5.849e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mnf.s <- emmeans(mod_nofly, pairwise ~ nofly_L, type = "response")
mnf.s.df <- as.data.frame(mnf.s$emmeans)
mnf.s## $emmeans
## nofly_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- VENERATE CG ----------------
mod_venerate_cg <- glm(
cbind(crith_pos, crith_neg) ~ venerate_cg_L,
data = gh6,
family = binomial("logit")
)
summary(mod_venerate_cg)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ venerate_cg_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.14425 0.08968 -1.609 0.108
## venerate_cg_LTRUE 1.02694 0.14260 7.202 5.95e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 188.26 on 4 degrees of freedom
## AIC: 222.16
##
## Number of Fisher Scoring iterations: 4Anova(mod_venerate_cg)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## venerate_cg_L 54.162 1 1.846e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mvc.s <- emmeans(mod_venerate_cg, pairwise ~ venerate_cg_L, type = "response")
mvc.s.df <- as.data.frame(mvc.s$emmeans)
mvc.s## $emmeans
## venerate_cg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.464 0.0223 Inf 0.421 0.508
## TRUE 0.707 0.0229 Inf 0.660 0.750
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.358 0.0511 Inf 1 -7.202 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- M-PEDE ----------------
mod_m_pede <- glm(
cbind(crith_pos, crith_neg) ~ m_pede_L,
data = gh6,
family = binomial("logit")
)
summary(mod_m_pede)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ m_pede_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2955 0.1167 -2.531 0.0114 *
## m_pede_LTRUE 0.8890 0.1449 6.137 8.4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 204.05 on 4 degrees of freedom
## AIC: 237.95
##
## Number of Fisher Scoring iterations: 4Anova(mod_m_pede)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## m_pede_L 38.371 1 5.849e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mmp.s <- emmeans(mod_m_pede, pairwise ~ m_pede_L, type = "response")
mmp.s.df <- as.data.frame(mmp.s$emmeans)
mmp.s## $emmeans
## m_pede_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- ROOTSHIELD PLUS ----------------
mod_rootshield_plus <- glm(
cbind(crith_pos, crith_neg) ~ rootshield_plus_L,
data = gh6,
family = binomial("logit")
)
summary(mod_rootshield_plus)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ rootshield_plus_L,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.25497 0.07654 3.331 0.000864 ***
## rootshield_plus_LTRUE 0.14212 0.16359 0.869 0.384969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 241.66 on 4 degrees of freedom
## AIC: 275.56
##
## Number of Fisher Scoring iterations: 4Anova(mod_rootshield_plus)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## rootshield_plus_L 0.75854 1 0.3838mrs.s <- emmeans(mod_rootshield_plus, pairwise ~ rootshield_plus_L, type = "response")
mrs.s.df <- as.data.frame(mrs.s$emmeans)
mrs.s## $emmeans
## rootshield_plus_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.563 0.0188 Inf 0.526 0.600
## TRUE 0.598 0.0348 Inf 0.528 0.664
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.868 0.142 Inf 1 -0.869 0.3850
##
## Tests are performed on the log odds ratio scale## ---------------- LALSTOP K61 ----------------
mod_lalstop_k61 <- glm(
cbind(crith_pos, crith_neg) ~ lalstop_k61_L,
data = gh6,
family = binomial("logit")
)
summary(mod_lalstop_k61)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ lalstop_k61_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.63304 0.07987 7.926 2.26e-15 ***
## lalstop_k61_LTRUE -1.53544 0.17497 -8.776 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 157.44 on 4 degrees of freedom
## AIC: 191.34
##
## Number of Fisher Scoring iterations: 4Anova(mod_lalstop_k61)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## lalstop_k61_L 84.983 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mlk.s <- emmeans(mod_lalstop_k61, pairwise ~ lalstop_k61_L, type = "response")
mlk.s.df <- as.data.frame(mlk.s$emmeans)
mlk.s## $emmeans
## lalstop_k61_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.653 0.0181 Inf 0.617 0.688
## TRUE 0.289 0.0320 Inf 0.230 0.355
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 4.64 0.812 Inf 1 8.776 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- BELEAF 50SG ----------------
mod_beleaf_50sg <- glm(
cbind(crith_pos, crith_neg) ~ beleaf_50sg_L,
data = gh6,
family = binomial("logit")
)
summary(mod_beleaf_50sg)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ beleaf_50sg_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.11235 0.07598 -1.479 0.139
## beleaf_50sg_LTRUE 2.54377 0.27160 9.366 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.420 on 5 degrees of freedom
## Residual deviance: 95.039 on 4 degrees of freedom
## AIC: 128.94
##
## Number of Fisher Scoring iterations: 4Anova(mod_beleaf_50sg)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## beleaf_50sg_L 147.38 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mbf.s <- emmeans(mod_beleaf_50sg, pairwise ~ beleaf_50sg_L, type = "response")
mbf.s.df <- as.data.frame(mbf.s$emmeans)
mbf.s## $emmeans
## beleaf_50sg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.472 0.0189 Inf 0.435 0.509
## TRUE 0.919 0.0194 Inf 0.872 0.950
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.0786 0.0213 Inf 1 -9.366 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- CORAGEN ----------------
mod_coragen <- glm(
cbind(crith_pos, crith_neg) ~ coragen_L,
data = gh6,
family = binomial("logit")
)
summary(mod_coragen)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ coragen_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08004 0.14153 0.566 0.5717
## coragen_LTRUE 0.26683 0.16118 1.655 0.0978 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 239.69 on 4 degrees of freedom
## AIC: 273.59
##
## Number of Fisher Scoring iterations: 4Anova(mod_coragen)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## coragen_L 2.7331 1 0.09829 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mcg.s <- emmeans(mod_coragen, pairwise ~ coragen_L, type = "response")
mcg.s.df <- as.data.frame(mcg.s$emmeans)
mcg.s## $emmeans
## coragen_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.520 0.0353 Inf 0.451 0.588
## TRUE 0.586 0.0187 Inf 0.549 0.622
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.766 0.123 Inf 1 -1.655 0.0978
##
## Tests are performed on the log odds ratio scale## ---------------- ENTRUST SC ----------------
mod_entrust_sc <- glm(
cbind(crith_pos, crith_neg) ~ entrust_sc_L,
data = gh6,
family = binomial("logit")
)
summary(mod_entrust_sc)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ entrust_sc_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.11235 0.07598 -1.479 0.139
## entrust_sc_LTRUE 2.54377 0.27160 9.366 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.420 on 5 degrees of freedom
## Residual deviance: 95.039 on 4 degrees of freedom
## AIC: 128.94
##
## Number of Fisher Scoring iterations: 4Anova(mod_entrust_sc)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## entrust_sc_L 147.38 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mes.s <- emmeans(mod_entrust_sc, pairwise ~ entrust_sc_L, type = "response")
mes.s.df <- as.data.frame(mes.s$emmeans)
mes.s## $emmeans
## entrust_sc_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.472 0.0189 Inf 0.435 0.509
## TRUE 0.919 0.0194 Inf 0.872 0.950
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.0786 0.0213 Inf 1 -9.366 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- PYLON ----------------
mod_pylon <- glm(
cbind(crith_pos, crith_neg) ~ pylon_L,
data = gh6,
family = binomial("logit")
)
summary(mod_pylon)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ pylon_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.59356 0.08577 6.920 4.51e-12 ***
## pylon_LTRUE -0.88903 0.14486 -6.137 8.40e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 204.05 on 4 degrees of freedom
## AIC: 237.95
##
## Number of Fisher Scoring iterations: 4Anova(mod_pylon)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## pylon_L 38.371 1 5.849e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mpy.s <- emmeans(mod_pylon, pairwise ~ pylon_L, type = "response")
mpy.s.df <- as.data.frame(mpy.s$emmeans)
mpy.s ## $emmeans
## pylon_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.644 0.0197 Inf 0.605 0.682
## TRUE 0.427 0.0286 Inf 0.372 0.483
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 2.43 0.352 Inf 1 6.137 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- GROTTO ----------------
mod_grotto <- glm(
cbind(crith_pos, crith_neg) ~ grotto_L,
data = gh6,
family = binomial("logit")
)
summary(mod_grotto)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ grotto_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.11235 0.07598 -1.479 0.139
## grotto_LTRUE 2.54377 0.27160 9.366 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.420 on 5 degrees of freedom
## Residual deviance: 95.039 on 4 degrees of freedom
## AIC: 128.94
##
## Number of Fisher Scoring iterations: 4Anova(mod_grotto)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## grotto_L 147.38 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mgr.s <- emmeans(mod_grotto, pairwise ~ grotto_L, type = "response")
mgr.s.df <- as.data.frame(mgr.s$emmeans)
mgr.s## $emmeans
## grotto_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.472 0.0189 Inf 0.435 0.509
## TRUE 0.919 0.0194 Inf 0.872 0.950
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.0786 0.0213 Inf 1 -9.366 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- LUNA TRANQUILITY ----------------
mod_luna_tranquility <- glm(
cbind(crith_pos, crith_neg) ~ luna_tranquility_L,
data = gh6,
family = binomial("logit")
)
summary(mod_luna_tranquility)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ luna_tranquility_L,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.26415 0.08278 3.191 0.00142 **
## luna_tranquility_LTRUE 0.06659 0.14352 0.464 0.64267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 242.20 on 4 degrees of freedom
## AIC: 276.11
##
## Number of Fisher Scoring iterations: 4Anova(mod_luna_tranquility)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## luna_tranquility_L 0.21553 1 0.6425mlt.s <- emmeans(mod_luna_tranquility, pairwise ~ luna_tranquility_L, type = "response")
mlt.s.df <- as.data.frame(mlt.s$emmeans)
mlt.s ## $emmeans
## luna_tranquility_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.566 0.0203 Inf 0.525 0.605
## TRUE 0.582 0.0285 Inf 0.525 0.637
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.936 0.134 Inf 1 -0.464 0.6427
##
## Tests are performed on the log odds ratio scale## ---------------- PREVICUR FLEX ----------------
mod_previcur_flex <- glm(
cbind(crith_pos, crith_neg) ~ previcur_flex_L,
data = gh6,
family = binomial("logit")
)
summary(mod_previcur_flex)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ previcur_flex_L,
## family = binomial("logit"), data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.4314 0.2608 9.324 <2e-16 ***
## previcur_flex_LTRUE -2.5438 0.2716 -9.366 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.420 on 5 degrees of freedom
## Residual deviance: 95.039 on 4 degrees of freedom
## AIC: 128.94
##
## Number of Fisher Scoring iterations: 4Anova(mod_previcur_flex)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## previcur_flex_L 147.38 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mpf.s <- emmeans(mod_previcur_flex, pairwise ~ previcur_flex_L, type = "response")
mpf.s.df <- as.data.frame(mpf.s$emmeans)
mpf.s## $emmeans
## previcur_flex_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.919 0.0194 Inf 0.872 0.950
## TRUE 0.472 0.0189 Inf 0.435 0.509
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 12.7 3.46 Inf 1 9.366 <0.0001
##
## Tests are performed on the log odds ratio scale## ---------------- FONTELIS ----------------
mod_fontelis <- glm(
cbind(crith_pos, crith_neg) ~ fontelis_L,
data = gh6,
family = binomial("logit")
)
summary(mod_fontelis)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ fontelis_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.34687 0.07712 4.498 6.86e-06 ***
## fontelis_LTRUE -0.26683 0.16118 -1.655 0.0978 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 239.69 on 4 degrees of freedom
## AIC: 273.59
##
## Number of Fisher Scoring iterations: 4Anova(mod_fontelis)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## fontelis_L 2.7331 1 0.09829 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mfo.s <- emmeans(mod_fontelis, pairwise ~ fontelis_L, type = "response")
mfo.s.df <- as.data.frame(mfo.s$emmeans)
mfo.s## $emmeans
## fontelis_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.586 0.0187 Inf 0.549 0.622
## TRUE 0.520 0.0353 Inf 0.451 0.588
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.31 0.21 Inf 1 1.655 0.0978
##
## Tests are performed on the log odds ratio scale## ---------------- QUADRISTOP ----------------
mod_quadristop <- glm(
cbind(crith_pos, crith_neg) ~ quadristop_L,
data = gh6,
family = binomial("logit")
)
summary(mod_quadristop)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ quadristop_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2175 0.0714 3.046 0.00232 **
## quadristop_LTRUE 0.6637 0.2321 2.860 0.00424 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 233.71 on 4 degrees of freedom
## AIC: 267.61
##
## Number of Fisher Scoring iterations: 4Anova(mod_quadristop)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## quadristop_L 8.7121 1 0.003161 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mqs.s <- emmeans(mod_quadristop, pairwise ~ quadristop_L, type = "response")
mqs.s.df <- as.data.frame(mqs.s$emmeans)
mqs.s## $emmeans
## quadristop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.554 0.0176 Inf 0.519 0.588
## TRUE 0.707 0.0457 Inf 0.610 0.788
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.515 0.12 Inf 1 -2.860 0.0042
##
## Tests are performed on the log odds ratio scale## ---------------- MILSTOP ----------------
mod_milstop <- glm(
cbind(crith_pos, crith_neg) ~ milstop_L,
data = gh6,
family = binomial("logit")
)
summary(mod_milstop)##
## Call:
## glm(formula = cbind(crith_pos, crith_neg) ~ milstop_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2175 0.0714 3.046 0.00232 **
## milstop_LTRUE 0.6637 0.2321 2.860 0.00424 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 242.42 on 5 degrees of freedom
## Residual deviance: 233.71 on 4 degrees of freedom
## AIC: 267.61
##
## Number of Fisher Scoring iterations: 4Anova(mod_milstop)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(crith_pos, crith_neg)
## LR Chisq Df Pr(>Chisq)
## milstop_L 8.7121 1 0.003161 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mms.s <- emmeans(mod_milstop, pairwise ~ milstop_L, type = "response")
mms.s.df <- as.data.frame(mms.s$emmeans)
mms.s ## $emmeans
## milstop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.554 0.0176 Inf 0.519 0.588
## TRUE 0.707 0.0457 Inf 0.610 0.788
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.515 0.12 Inf 1 -2.860 0.0042
##
## Tests are performed on the log odds ratio scalema.s ## $emmeans
## azaguard_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scalemb22.s ## $emmeans
## botanigard_22wp_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scalembe.s ## $emmeans
## botanigard_es_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.622 0.0217 Inf 0.579 0.664
## TRUE 0.506 0.0252 Inf 0.457 0.555
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.61 0.22 Inf 1 3.475 0.0005
##
## Tests are performed on the log odds ratio scalemcp.s ## $emmeans
## captiva_prime_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scalemnf.s ## $emmeans
## nofly_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scalemvc.s ## $emmeans
## venerate_cg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.464 0.0223 Inf 0.421 0.508
## TRUE 0.707 0.0229 Inf 0.660 0.750
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.358 0.0511 Inf 1 -7.202 <0.0001
##
## Tests are performed on the log odds ratio scalemmp.s ## $emmeans
## m_pede_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.427 0.0286 Inf 0.372 0.483
## TRUE 0.644 0.0197 Inf 0.605 0.682
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.411 0.0595 Inf 1 -6.137 <0.0001
##
## Tests are performed on the log odds ratio scalemrs.s ## $emmeans
## rootshield_plus_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.563 0.0188 Inf 0.526 0.600
## TRUE 0.598 0.0348 Inf 0.528 0.664
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.868 0.142 Inf 1 -0.869 0.3850
##
## Tests are performed on the log odds ratio scalemlk.s ## $emmeans
## lalstop_k61_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.653 0.0181 Inf 0.617 0.688
## TRUE 0.289 0.0320 Inf 0.230 0.355
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 4.64 0.812 Inf 1 8.776 <0.0001
##
## Tests are performed on the log odds ratio scalembf.s ## $emmeans
## beleaf_50sg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.472 0.0189 Inf 0.435 0.509
## TRUE 0.919 0.0194 Inf 0.872 0.950
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.0786 0.0213 Inf 1 -9.366 <0.0001
##
## Tests are performed on the log odds ratio scalemcg.s ## $emmeans
## coragen_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.520 0.0353 Inf 0.451 0.588
## TRUE 0.586 0.0187 Inf 0.549 0.622
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.766 0.123 Inf 1 -1.655 0.0978
##
## Tests are performed on the log odds ratio scalemes.s ## $emmeans
## entrust_sc_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.472 0.0189 Inf 0.435 0.509
## TRUE 0.919 0.0194 Inf 0.872 0.950
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.0786 0.0213 Inf 1 -9.366 <0.0001
##
## Tests are performed on the log odds ratio scalempy.s ## $emmeans
## pylon_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.644 0.0197 Inf 0.605 0.682
## TRUE 0.427 0.0286 Inf 0.372 0.483
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 2.43 0.352 Inf 1 6.137 <0.0001
##
## Tests are performed on the log odds ratio scalemgr.s ## $emmeans
## grotto_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.472 0.0189 Inf 0.435 0.509
## TRUE 0.919 0.0194 Inf 0.872 0.950
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.0786 0.0213 Inf 1 -9.366 <0.0001
##
## Tests are performed on the log odds ratio scalemlt.s ## $emmeans
## luna_tranquility_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.566 0.0203 Inf 0.525 0.605
## TRUE 0.582 0.0285 Inf 0.525 0.637
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.936 0.134 Inf 1 -0.464 0.6427
##
## Tests are performed on the log odds ratio scalempf.s ## $emmeans
## previcur_flex_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.919 0.0194 Inf 0.872 0.950
## TRUE 0.472 0.0189 Inf 0.435 0.509
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 12.7 3.46 Inf 1 9.366 <0.0001
##
## Tests are performed on the log odds ratio scalemfo.s ## $emmeans
## fontelis_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.586 0.0187 Inf 0.549 0.622
## TRUE 0.520 0.0353 Inf 0.451 0.588
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.31 0.21 Inf 1 1.655 0.0978
##
## Tests are performed on the log odds ratio scalemqs.s ## $emmeans
## quadristop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.554 0.0176 Inf 0.519 0.588
## TRUE 0.707 0.0457 Inf 0.610 0.788
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.515 0.12 Inf 1 -2.860 0.0042
##
## Tests are performed on the log odds ratio scalemms.s ## $emmeans
## milstop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.554 0.0176 Inf 0.519 0.588
## TRUE 0.707 0.0457 Inf 0.610 0.788
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.515 0.12 Inf 1 -2.860 0.0042
##
## Tests are performed on the log odds ratio scaleSummary Table
mod.crith <- list(
azaguard = mod_azaguard,
botanigard_22wp = mod_botanigard_22wp,
botanigard_es = mod_botanigard_es,
captiva_prime = mod_captiva_prime,
nofly = mod_nofly,
venerate_cg = mod_venerate_cg,
m_pede = mod_m_pede,
rootshield_plus = mod_rootshield_plus,
lalstop_k61 = mod_lalstop_k61,
beleaf_50sg = mod_beleaf_50sg,
coragen = mod_coragen,
entrust_sc = mod_entrust_sc,
pylon = mod_pylon,
grotto = mod_grotto,
luna_tranquility = mod_luna_tranquility,
previcur_flex = mod_previcur_flex,
fontelis = mod_fontelis,
quadristop = mod_quadristop,
milstop = mod_milstop
)
summary_table.crith <- do.call(rbind, lapply(names(mod.crith), function(name){
m <- mod.crith[[name]]
s <- summary(m)
coef_row <- s$coefficients[2, ]
lr <- Anova(m)$`LR Chisq`[1]
LRT <- Anova(m)$'Pr(>Chisq)'[1]
data.frame(
Pesticide = name,
Estimate = coef_row["Estimate"],
SE = coef_row["Std. Error"],
z = coef_row["z value"],
p = coef_row["Pr(>|z|)"],
LR_ChiSq = lr,
LRT = LRT,
AIC = AIC(m),
Residual_Deviance = m$deviance
)
}))
summary_table.crith$Estimate <- signif(summary_table.crith$Estimate,3)
summary_table.crith$SE <- signif(summary_table.crith$SE,3)
summary_table.crith$z <- round(summary_table.crith$z,2)
summary_table.crith$p <- signif(summary_table.crith$p,3)
summary_table.crith$LR_ChiSq <- round(summary_table.crith$LR_ChiSq,2)
summary_table.crith$AIC <- round(summary_table.crith$AIC,2)
summary_table.crith$Residual_Deviance <- round(summary_table.crith$Residual_Deviance,2)
summary_table.crith$LRT <- signif(summary_table.crith$LRT,2)
summary_table.crith## Pesticide Estimate SE z p LR_ChiSq LRT
## Estimate azaguard 0.8890 0.145 6.14 8.40e-10 38.37 5.8e-10
## Estimate1 botanigard_22wp 0.8890 0.145 6.14 8.40e-10 38.37 5.8e-10
## Estimate2 botanigard_es -0.4750 0.137 -3.47 5.11e-04 12.13 5.0e-04
## Estimate3 captiva_prime 0.8890 0.145 6.14 8.40e-10 38.37 5.8e-10
## Estimate4 nofly 0.8890 0.145 6.14 8.40e-10 38.37 5.8e-10
## Estimate5 venerate_cg 1.0300 0.143 7.20 5.95e-13 54.16 1.8e-13
## Estimate6 m_pede 0.8890 0.145 6.14 8.40e-10 38.37 5.8e-10
## Estimate7 rootshield_plus 0.1420 0.164 0.87 3.85e-01 0.76 3.8e-01
## Estimate8 lalstop_k61 -1.5400 0.175 -8.78 1.70e-18 84.98 3.0e-20
## Estimate9 beleaf_50sg 2.5400 0.272 9.37 7.55e-21 147.38 6.5e-34
## Estimate10 coragen 0.2670 0.161 1.66 9.78e-02 2.73 9.8e-02
## Estimate11 entrust_sc 2.5400 0.272 9.37 7.55e-21 147.38 6.5e-34
## Estimate12 pylon -0.8890 0.145 -6.14 8.40e-10 38.37 5.8e-10
## Estimate13 grotto 2.5400 0.272 9.37 7.55e-21 147.38 6.5e-34
## Estimate14 luna_tranquility 0.0666 0.144 0.46 6.43e-01 0.22 6.4e-01
## Estimate15 previcur_flex -2.5400 0.272 -9.37 7.55e-21 147.38 6.5e-34
## Estimate16 fontelis -0.2670 0.161 -1.66 9.78e-02 2.73 9.8e-02
## Estimate17 quadristop 0.6640 0.232 2.86 4.24e-03 8.71 3.2e-03
## Estimate18 milstop 0.6640 0.232 2.86 4.24e-03 8.71 3.2e-03
## AIC Residual_Deviance
## Estimate 237.95 204.05
## Estimate1 237.95 204.05
## Estimate2 264.19 230.29
## Estimate3 237.95 204.05
## Estimate4 237.95 204.05
## Estimate5 222.16 188.26
## Estimate6 237.95 204.05
## Estimate7 275.56 241.66
## Estimate8 191.34 157.44
## Estimate9 128.94 95.04
## Estimate10 273.59 239.69
## Estimate11 128.94 95.04
## Estimate12 237.95 204.05
## Estimate13 128.94 95.04
## Estimate14 276.11 242.20
## Estimate15 128.94 95.04
## Estimate16 273.59 239.69
## Estimate17 267.61 233.71
## Estimate18 267.61 233.71csdf <- as.data.frame(summary_table.crith)Plots
basic plot
predictors <- c(
"azaguard_L",
"botanigard_22wp_L",
"botanigard_es_L",
"captiva_prime_L",
"nofly_L",
"venerate_cg_L",
"m_pede_L",
"rootshield_plus_L",
"lalstop_k61_L",
"beleaf_50sg_L",
"coragen_L",
"entrust_sc_L",
"pylon_L",
"grotto_L",
"luna_tranquility_L",
"previcur_flex_L",
"fontelis_L",
"quadristop_L",
"milstop_L"
)
gh6$crith_prop <- gh6$crith_pos /
(gh6$crith_pos + gh6$crith_neg)
gh6$crith_se <- sqrt(
(gh6$crith_prop * (1 - gh6$crith_prop)) /
(gh6$crith_pos + gh6$crith_neg)
)
plots <- lapply(predictors, function(var){
ggplot(gh6, aes_string(x = var, y = "crith_prop")) +
geom_boxplot(width = 0.5, alpha = 0.6, outlier.shape = NA) +
geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
labs(
x = var,
y = "Crithidia infection proportion"
) +
theme_classic(base_size = 14) +
scale_x_discrete(labels = c("FALSE" = "Not applied", "TRUE" = "Applied"))
})## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.wrap_plots(plots, ncol = 4)
updated crithidia vs. logical chems
plots <- lapply(1:nrow(chem_info), function(i){
chem_type <- chem_info$type[i]
chem_mode <- chem_info$mode[i]
var <- chem_info$pesticide[i]
chem_class <- chem_info$class[i]
var2 <- gsub("_L$", "", chem_info$pesticide[i]) # remove _L at end
stats_row <- summary_table.crith[summary_table.crith$Pesticide == var2, ]
annot_text <- paste0(
"LRT χ² = ", round(stats_row$LR_ChiSq, 2),
", p = ", signif(stats_row$LRT, 3))
ggplot(gh6, aes_string(x = var, y = "crith_prop")) +
geom_boxplot(fill = chem_colors[chem_class],
alpha = 0.6,
width = 0.5,
outlier.shape = NA) +
geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
labs(
title = gsub("_L","",var),
subtitle = paste(chem_type, "-", chem_mode),
x = "",
y = "Probability of *Crithidia* Detection"
) +
scale_x_discrete(labels = c("FALSE"="Not applied","TRUE"="Applied")) +
theme_classic(base_size = 16) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
"text",
x = 2, y = 1.05, # top-right
label = annot_text,
hjust = 1, vjust = 1, size = 4
) +
coord_cartesian(ylim = c(0, 1))
})
wrap_plots(plots, ncol = 4)
Apicystis vs. logical chemicals
mod.a_azaguard <- glm(
cbind(api_pos, api_neg) ~ azaguard_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_azaguard)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ azaguard_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7346 0.1617 -10.728 <2e-16 ***
## azaguard_LTRUE -0.1236 0.2015 -0.614 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.41 on 4 degrees of freedom
## AIC: 235.79
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_azaguard)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## azaguard_L 0.37344 1 0.5411ma.a <- emmeans(mod.a_azaguard, pairwise ~ azaguard_L, type = "response")
ma.a.df <- as.data.frame(ma.a$emmeans)
ma.a## $emmeans
## azaguard_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemod.a_botanigard_22wp <- glm(
cbind(api_pos, api_neg) ~ botanigard_22wp_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_botanigard_22wp)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ botanigard_22wp_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7346 0.1617 -10.728 <2e-16 ***
## botanigard_22wp_LTRUE -0.1236 0.2015 -0.614 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.41 on 4 degrees of freedom
## AIC: 235.79
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_botanigard_22wp)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## botanigard_22wp_L 0.37344 1 0.5411mb22.a <- emmeans(mod.a_botanigard_22wp, pairwise ~ botanigard_22wp_L, type = "response")
mb22.a.df <- as.data.frame(mb22.a$emmeans)
mb22.a## $emmeans
## botanigard_22wp_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemod.a_botanigard_es <- glm(
cbind(api_pos, api_neg) ~ botanigard_es_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_botanigard_es)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ botanigard_es_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3092 0.1563 -14.774 < 2e-16 ***
## botanigard_es_LTRUE 0.9387 0.2003 4.687 2.77e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 190.83 on 4 degrees of freedom
## AIC: 213.21
##
## Number of Fisher Scoring iterations: 5Anova(mod.a_botanigard_es)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## botanigard_es_L 22.958 1 1.656e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mbe.a <- emmeans(mod.a_botanigard_es, pairwise ~ botanigard_es_L, type = "response")
mbe.a.df <- as.data.frame(mbe.a$emmeans)
mbe.a## $emmeans
## botanigard_es_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.0904 0.0128 Inf 0.0681 0.119
## TRUE 0.2025 0.0202 Inf 0.1658 0.245
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.391 0.0783 Inf 1 -4.687 <0.0001
##
## Tests are performed on the log odds ratio scalemod.a_captiva_prime <- glm(
cbind(api_pos, api_neg) ~ captiva_prime_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_captiva_prime)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ captiva_prime_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7346 0.1617 -10.728 <2e-16 ***
## captiva_prime_LTRUE -0.1236 0.2015 -0.614 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.41 on 4 degrees of freedom
## AIC: 235.79
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_captiva_prime)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## captiva_prime_L 0.37344 1 0.5411mcp.a <- emmeans(mod.a_captiva_prime, pairwise ~ captiva_prime_L, type = "response")
mcp.a.df <- as.data.frame(mcp.a$emmeans)
mcp.a## $emmeans
## captiva_prime_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemod.a_nofly <- glm(
cbind(api_pos, api_neg) ~ nofly_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_nofly)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ nofly_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7346 0.1617 -10.728 <2e-16 ***
## nofly_LTRUE -0.1236 0.2015 -0.614 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.41 on 4 degrees of freedom
## AIC: 235.79
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_nofly)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## nofly_L 0.37344 1 0.5411mnf.a <- emmeans(mod.a_nofly, pairwise ~ nofly_L, type = "response")
mnf.a.df <- as.data.frame(mnf.a$emmeans)
mnf.a## $emmeans
## nofly_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemod.a_venerate_cg <- glm(
cbind(api_pos, api_neg) ~ venerate_cg_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_venerate_cg)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ venerate_cg_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0986 0.1033 -10.637 <2e-16 ***
## venerate_cg_LTRUE -20.8817 1813.9497 -0.012 0.991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.783 on 5 degrees of freedom
## Residual deviance: 52.926 on 4 degrees of freedom
## AIC: 75.307
##
## Number of Fisher Scoring iterations: 16Anova(mod.a_venerate_cg)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## venerate_cg_L 160.86 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mvc.a <- emmeans(mod.a_venerate_cg, pairwise ~ venerate_cg_L, type = "response")
mvc.a.df <- as.data.frame(mvc.a$emmeans)
mvc.a## $emmeans
## venerate_cg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.25 1.94e-02 Inf 0.214 0.29
## TRUE 0.00 5.16e-07 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.17e+09 2.13e+12 Inf 1 0.012 0.9908
##
## Tests are performed on the log odds ratio scalemod.a_m_pede <- glm(
cbind(api_pos, api_neg) ~ m_pede_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_m_pede)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ m_pede_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7346 0.1617 -10.728 <2e-16 ***
## m_pede_LTRUE -0.1236 0.2015 -0.614 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.41 on 4 degrees of freedom
## AIC: 235.79
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_m_pede)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## m_pede_L 0.37344 1 0.5411mmp.a <- emmeans(mod.a_m_pede, pairwise ~ m_pede_L, type = "response")
mmp.a.df <- as.data.frame(mmp.a$emmeans)
mmp.a## $emmeans
## m_pede_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemod.a_rootshield_plus <- glm(
cbind(api_pos, api_neg) ~ rootshield_plus_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_rootshield_plus)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ rootshield_plus_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7817 0.1081 -16.484 <2e-16 ***
## rootshield_plus_LTRUE -0.1585 0.2396 -0.661 0.508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.34 on 4 degrees of freedom
## AIC: 235.72
##
## Number of Fisher Scoring iterations: 5Anova(mod.a_rootshield_plus)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## rootshield_plus_L 0.44747 1 0.5035mrs.a <- emmeans(mod.a_rootshield_plus, pairwise ~ rootshield_plus_L, type = "response")
mrs.a.df <- as.data.frame(mrs.a$emmeans)
mrs.a## $emmeans
## rootshield_plus_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.144 0.0133 Inf 0.1199 0.172
## TRUE 0.126 0.0235 Inf 0.0863 0.179
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.17 0.281 Inf 1 0.661 0.5084
##
## Tests are performed on the log odds ratio scalemod.a_lalstop_k61 <- glm(
cbind(api_pos, api_neg) ~ lalstop_k61_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_lalstop_k61)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ lalstop_k61_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9659 0.1158 -16.975 < 2e-16 ***
## lalstop_k61_LTRUE 0.5734 0.2112 2.714 0.00664 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 206.78 on 4 degrees of freedom
## AIC: 229.16
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_lalstop_k61)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## lalstop_k61_L 7.0039 1 0.008133 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mlk.a <- emmeans(mod.a_lalstop_k61, pairwise ~ lalstop_k61_L, type = "response")
mlk.a.df <- as.data.frame(mlk.a$emmeans)
mlk.a## $emmeans
## lalstop_k61_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.123 0.0125 Inf 0.100 0.149
## TRUE 0.199 0.0282 Inf 0.149 0.260
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.564 0.119 Inf 1 -2.714 0.0066
##
## Tests are performed on the log odds ratio scalemod.a_beleaf_50sg <- glm(
cbind(api_pos, api_neg) ~ beleaf_50sg_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_beleaf_50sg)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ beleaf_50sg_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.51732 0.09876 -15.36 <2e-16 ***
## beleaf_50sg_LTRUE -18.47059 943.71566 -0.02 0.984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 145.52 on 4 degrees of freedom
## AIC: 167.91
##
## Number of Fisher Scoring iterations: 14Anova(mod.a_beleaf_50sg)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## beleaf_50sg_L 68.259 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mbf.a <- emmeans(mod.a_beleaf_50sg, pairwise ~ beleaf_50sg_L, type = "response")
mbf.a.df <- as.data.frame(mbf.a$emmeans)
mbf.a## $emmeans
## beleaf_50sg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.18 1.46e-02 Inf 0.153 0.21
## TRUE 0.00 1.97e-06 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.05e+08 9.92e+10 Inf 1 0.020 0.9844
##
## Tests are performed on the log odds ratio scalemod.a_coragen <- glm(
cbind(api_pos, api_neg) ~ coragen_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_coragen)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ coragen_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4055 0.1443 -2.809 0.00497 **
## coragen_LTRUE -2.2618 0.2112 -10.710 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.783 on 5 degrees of freedom
## Residual deviance: 92.901 on 4 degrees of freedom
## AIC: 115.28
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_coragen)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## coragen_L 120.88 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mcg.a <- emmeans(mod.a_coragen, pairwise ~ coragen_L, type = "response")
mcg.a.df <- as.data.frame(mcg.a$emmeans)
mcg.a## $emmeans
## coragen_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.4000 0.03460 Inf 0.3344 0.4694
## TRUE 0.0649 0.00936 Inf 0.0488 0.0859
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 9.6 2.03 Inf 1 10.710 <0.0001
##
## Tests are performed on the log odds ratio scalemod.a_entrust_sc <- glm(
cbind(api_pos, api_neg) ~ entrust_sc_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_entrust_sc)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ entrust_sc_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.51732 0.09876 -15.36 <2e-16 ***
## entrust_sc_LTRUE -18.47059 943.71566 -0.02 0.984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 145.52 on 4 degrees of freedom
## AIC: 167.91
##
## Number of Fisher Scoring iterations: 14Anova(mod.a_entrust_sc)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## entrust_sc_L 68.259 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mes.a <- emmeans(mod.a_entrust_sc, pairwise ~ entrust_sc_L, type = "response")
mes.a.df <- as.data.frame(mes.a$emmeans)
mes.a## $emmeans
## entrust_sc_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.18 1.46e-02 Inf 0.153 0.21
## TRUE 0.00 1.97e-06 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.05e+08 9.92e+10 Inf 1 0.020 0.9844
##
## Tests are performed on the log odds ratio scalemod.a_pylon <- glm(
cbind(api_pos, api_neg) ~ pylon_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_pylon)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ pylon_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8582 0.1202 -15.459 <2e-16 ***
## pylon_LTRUE 0.1236 0.2015 0.614 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 213.41 on 4 degrees of freedom
## AIC: 235.79
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_pylon)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## pylon_L 0.37344 1 0.5411mpy.a <- emmeans(mod.a_pylon, pairwise ~ pylon_L, type = "response")
mpy.a.df <- as.data.frame(mpy.a$emmeans)
mpy.a## $emmeans
## pylon_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.135 0.0140 Inf 0.110 0.165
## TRUE 0.150 0.0206 Inf 0.114 0.195
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.884 0.178 Inf 1 -0.614 0.5394
##
## Tests are performed on the log odds ratio scalemod.a_grotto <- glm(
cbind(api_pos, api_neg) ~ grotto_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_grotto)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ grotto_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.51732 0.09876 -15.36 <2e-16 ***
## grotto_LTRUE -18.47059 943.71566 -0.02 0.984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 145.52 on 4 degrees of freedom
## AIC: 167.91
##
## Number of Fisher Scoring iterations: 14Anova(mod.a_grotto)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## grotto_L 68.259 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mgr.a <- emmeans(mod.a_grotto, pairwise ~ grotto_L, type = "response")
mgr.a.df <- as.data.frame(mgr.a$emmeans)
mgr.a## $emmeans
## grotto_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.18 1.46e-02 Inf 0.153 0.21
## TRUE 0.00 1.97e-06 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.05e+08 9.92e+10 Inf 1 0.020 0.9844
##
## Tests are performed on the log odds ratio scalemod.a_luna_tranquility <- glm(
cbind(api_pos, api_neg) ~ luna_tranquility_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_luna_tranquility)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ luna_tranquility_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6283 0.1637 -16.053 < 2e-16 ***
## luna_tranquility_LTRUE 1.7050 0.2079 8.199 2.42e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 140.65 on 4 degrees of freedom
## AIC: 163.03
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_luna_tranquility)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## luna_tranquility_L 73.132 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mlt.a <- emmeans(mod.a_luna_tranquility, pairwise ~ luna_tranquility_L, type = "response")
mlt.a.df <- as.data.frame(mlt.a$emmeans)
mlt.a## $emmeans
## luna_tranquility_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.0673 0.0103 Inf 0.0498 0.0905
## TRUE 0.2843 0.0261 Inf 0.2360 0.3380
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.182 0.0378 Inf 1 -8.199 <0.0001
##
## Tests are performed on the log odds ratio scalemod.a_previcur_flex <- glm(
cbind(api_pos, api_neg) ~ previcur_flex_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_previcur_flex)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ previcur_flex_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -19.99 943.72 -0.021 0.983
## previcur_flex_LTRUE 18.47 943.72 0.020 0.984
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 145.52 on 4 degrees of freedom
## AIC: 167.91
##
## Number of Fisher Scoring iterations: 14Anova(mod.a_previcur_flex)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## previcur_flex_L 68.259 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mpf.a <- emmeans(mod.a_previcur_flex, pairwise ~ previcur_flex_L, type = "response")
mpf.a.df <- as.data.frame(mpf.a$emmeans)
mpf.a## $emmeans
## previcur_flex_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.00 1.97e-06 Inf 0.000 1.00
## TRUE 0.18 1.46e-02 Inf 0.153 0.21
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 9.51e-09 8.98e-06 Inf 1 -0.020 0.9844
##
## Tests are performed on the log odds ratio scalemod.a_fontelis <- glm(
cbind(api_pos, api_neg) ~ fontelis_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_fontelis)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ fontelis_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6672 0.1542 -17.30 <2e-16 ***
## fontelis_LTRUE 2.2618 0.2112 10.71 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.783 on 5 degrees of freedom
## Residual deviance: 92.901 on 4 degrees of freedom
## AIC: 115.28
##
## Number of Fisher Scoring iterations: 6Anova(mod.a_fontelis)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## fontelis_L 120.88 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mfo.a <- emmeans(mod.a_fontelis, pairwise ~ fontelis_L, type = "response")
mfo.a.df <- as.data.frame(mfo.a$emmeans)
mfo.a## $emmeans
## fontelis_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.0649 0.00936 Inf 0.0488 0.0859
## TRUE 0.4000 0.03460 Inf 0.3344 0.4694
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.104 0.022 Inf 1 -10.710 <0.0001
##
## Tests are performed on the log odds ratio scalemod.a_quadristop <- glm(
cbind(api_pos, api_neg) ~ quadristop_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_quadristop)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ quadristop_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.72574 0.09908 -17.418 <2e-16 ***
## quadristop_LTRUE -1.20812 0.46953 -2.573 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 204.57 on 4 degrees of freedom
## AIC: 226.95
##
## Number of Fisher Scoring iterations: 5Anova(mod.a_quadristop)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## quadristop_L 9.2159 1 0.002399 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mqs.a <- emmeans(mod.a_quadristop, pairwise ~ quadristop_L, type = "response")
mqs.a.df <- as.data.frame(mqs.a$emmeans)
mqs.a## $emmeans
## quadristop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.1511 0.0127 Inf 0.1279 0.178
## TRUE 0.0505 0.0220 Inf 0.0212 0.116
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 3.35 1.57 Inf 1 2.573 0.0101
##
## Tests are performed on the log odds ratio scalemod.a_milstop <- glm(
cbind(api_pos, api_neg) ~ milstop_L,
data = gh6,
family = binomial("logit"))
summary(mod.a_milstop)##
## Call:
## glm(formula = cbind(api_pos, api_neg) ~ milstop_L, family = binomial("logit"),
## data = gh6)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.72574 0.09908 -17.418 <2e-16 ***
## milstop_LTRUE -1.20812 0.46953 -2.573 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 213.78 on 5 degrees of freedom
## Residual deviance: 204.57 on 4 degrees of freedom
## AIC: 226.95
##
## Number of Fisher Scoring iterations: 5Anova(mod.a_milstop)## Analysis of Deviance Table (Type II tests)
##
## Response: cbind(api_pos, api_neg)
## LR Chisq Df Pr(>Chisq)
## milstop_L 9.2159 1 0.002399 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mms.a <- emmeans(mod.a_milstop, pairwise ~ milstop_L, type = "response")
mms.a.df <- as.data.frame(mms.a$emmeans)
mms.a## $emmeans
## milstop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.1511 0.0127 Inf 0.1279 0.178
## TRUE 0.0505 0.0220 Inf 0.0212 0.116
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 3.35 1.57 Inf 1 2.573 0.0101
##
## Tests are performed on the log odds ratio scalema.a ## $emmeans
## azaguard_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemb22.a ## $emmeans
## botanigard_22wp_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalembe.a ## $emmeans
## botanigard_es_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.0904 0.0128 Inf 0.0681 0.119
## TRUE 0.2025 0.0202 Inf 0.1658 0.245
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.391 0.0783 Inf 1 -4.687 <0.0001
##
## Tests are performed on the log odds ratio scalemcp.a ## $emmeans
## captiva_prime_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemnf.a ## $emmeans
## nofly_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemvc.a ## $emmeans
## venerate_cg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.25 1.94e-02 Inf 0.214 0.29
## TRUE 0.00 5.16e-07 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.17e+09 2.13e+12 Inf 1 0.012 0.9908
##
## Tests are performed on the log odds ratio scalemmp.a ## $emmeans
## m_pede_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.150 0.0206 Inf 0.114 0.195
## TRUE 0.135 0.0140 Inf 0.110 0.165
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.13 0.228 Inf 1 0.614 0.5394
##
## Tests are performed on the log odds ratio scalemrs.a ## $emmeans
## rootshield_plus_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.144 0.0133 Inf 0.1199 0.172
## TRUE 0.126 0.0235 Inf 0.0863 0.179
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.17 0.281 Inf 1 0.661 0.5084
##
## Tests are performed on the log odds ratio scalemlk.a ## $emmeans
## lalstop_k61_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.123 0.0125 Inf 0.100 0.149
## TRUE 0.199 0.0282 Inf 0.149 0.260
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.564 0.119 Inf 1 -2.714 0.0066
##
## Tests are performed on the log odds ratio scalembf.a ## $emmeans
## beleaf_50sg_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.18 1.46e-02 Inf 0.153 0.21
## TRUE 0.00 1.97e-06 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.05e+08 9.92e+10 Inf 1 0.020 0.9844
##
## Tests are performed on the log odds ratio scalemcg.a ## $emmeans
## coragen_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.4000 0.03460 Inf 0.3344 0.4694
## TRUE 0.0649 0.00936 Inf 0.0488 0.0859
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 9.6 2.03 Inf 1 10.710 <0.0001
##
## Tests are performed on the log odds ratio scalemes.a ## $emmeans
## entrust_sc_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.18 1.46e-02 Inf 0.153 0.21
## TRUE 0.00 1.97e-06 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.05e+08 9.92e+10 Inf 1 0.020 0.9844
##
## Tests are performed on the log odds ratio scalempy.a ## $emmeans
## pylon_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.135 0.0140 Inf 0.110 0.165
## TRUE 0.150 0.0206 Inf 0.114 0.195
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.884 0.178 Inf 1 -0.614 0.5394
##
## Tests are performed on the log odds ratio scalemgr.a ## $emmeans
## grotto_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.18 1.46e-02 Inf 0.153 0.21
## TRUE 0.00 1.97e-06 Inf 0.000 1.00
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 1.05e+08 9.92e+10 Inf 1 0.020 0.9844
##
## Tests are performed on the log odds ratio scalemlt.a ## $emmeans
## luna_tranquility_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.0673 0.0103 Inf 0.0498 0.0905
## TRUE 0.2843 0.0261 Inf 0.2360 0.3380
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.182 0.0378 Inf 1 -8.199 <0.0001
##
## Tests are performed on the log odds ratio scalempf.a ## $emmeans
## previcur_flex_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.00 1.97e-06 Inf 0.000 1.00
## TRUE 0.18 1.46e-02 Inf 0.153 0.21
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 9.51e-09 8.98e-06 Inf 1 -0.020 0.9844
##
## Tests are performed on the log odds ratio scalemfo.a ## $emmeans
## fontelis_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.0649 0.00936 Inf 0.0488 0.0859
## TRUE 0.4000 0.03460 Inf 0.3344 0.4694
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 0.104 0.022 Inf 1 -10.710 <0.0001
##
## Tests are performed on the log odds ratio scalemqs.a ## $emmeans
## quadristop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.1511 0.0127 Inf 0.1279 0.178
## TRUE 0.0505 0.0220 Inf 0.0212 0.116
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 3.35 1.57 Inf 1 2.573 0.0101
##
## Tests are performed on the log odds ratio scalemms.a ## $emmeans
## milstop_L prob SE df asymp.LCL asymp.UCL
## FALSE 0.1511 0.0127 Inf 0.1279 0.178
## TRUE 0.0505 0.0220 Inf 0.0212 0.116
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## FALSE / TRUE 3.35 1.57 Inf 1 2.573 0.0101
##
## Tests are performed on the log odds ratio scalesummary table
mod.a.api <- list(
azaguard = mod.a_azaguard,
botanigard_22wp = mod.a_botanigard_22wp,
botanigard_es = mod.a_botanigard_es,
captiva_prime = mod.a_captiva_prime,
nofly = mod.a_nofly,
venerate_cg = mod.a_venerate_cg,
m_pede = mod.a_m_pede,
rootshield_plus = mod.a_rootshield_plus,
lalstop_k61 = mod.a_lalstop_k61,
beleaf_50sg = mod.a_beleaf_50sg,
coragen = mod.a_coragen,
entrust_sc = mod.a_entrust_sc,
pylon = mod.a_pylon,
grotto = mod.a_grotto,
luna_tranquility = mod.a_luna_tranquility,
previcur_flex = mod.a_previcur_flex,
fontelis = mod.a_fontelis,
quadristop = mod.a_quadristop,
milstop = mod.a_milstop
)
summary_table.api <- do.call(rbind, lapply(names(mod.a.api), function(name){
m <- mod.a.api[[name]]
s <- summary(m)
coef_row <- s$coefficients[2, ]
lr <- Anova(m)$`LR Chisq`[1]
LRT <- Anova(m)$'Pr(>Chisq)'[1]
data.frame(
Pesticide = name,
Estimate = coef_row["Estimate"],
SE = coef_row["Std. Error"],
z = coef_row["z value"],
p = coef_row["Pr(>|z|)"],
LR_ChiSq = lr,
LRT = LRT,
AIC = AIC(m),
Residual_Deviance = m$deviance
)
}))
summary_table.api$Estimate <- signif(summary_table.api$Estimate,3)
summary_table.api$SE <- signif(summary_table.api$SE,3)
summary_table.api$z <- round(summary_table.api$z,2)
summary_table.api$p <- signif(summary_table.api$p,3)
summary_table.api$LR_ChiSq <- round(summary_table.api$LR_ChiSq,2)
summary_table.api$AIC <- round(summary_table.api$AIC,2)
summary_table.api$Residual_Deviance <- round(summary_table.api$Residual_Deviance,2)
summary_table.api$LRT <- signif(summary_table.api$LRT,2)
summary_table.api## Pesticide Estimate SE z p LR_ChiSq LRT
## Estimate azaguard -0.124 0.201 -0.61 5.39e-01 0.37 5.4e-01
## Estimate1 botanigard_22wp -0.124 0.201 -0.61 5.39e-01 0.37 5.4e-01
## Estimate2 botanigard_es 0.939 0.200 4.69 2.77e-06 22.96 1.7e-06
## Estimate3 captiva_prime -0.124 0.201 -0.61 5.39e-01 0.37 5.4e-01
## Estimate4 nofly -0.124 0.201 -0.61 5.39e-01 0.37 5.4e-01
## Estimate5 venerate_cg -20.900 1810.000 -0.01 9.91e-01 160.86 7.4e-37
## Estimate6 m_pede -0.124 0.201 -0.61 5.39e-01 0.37 5.4e-01
## Estimate7 rootshield_plus -0.158 0.240 -0.66 5.08e-01 0.45 5.0e-01
## Estimate8 lalstop_k61 0.573 0.211 2.71 6.64e-03 7.00 8.1e-03
## Estimate9 beleaf_50sg -18.500 944.000 -0.02 9.84e-01 68.26 1.4e-16
## Estimate10 coragen -2.260 0.211 -10.71 9.15e-27 120.88 4.1e-28
## Estimate11 entrust_sc -18.500 944.000 -0.02 9.84e-01 68.26 1.4e-16
## Estimate12 pylon 0.124 0.201 0.61 5.39e-01 0.37 5.4e-01
## Estimate13 grotto -18.500 944.000 -0.02 9.84e-01 68.26 1.4e-16
## Estimate14 luna_tranquility 1.700 0.208 8.20 2.42e-16 73.13 1.2e-17
## Estimate15 previcur_flex 18.500 944.000 0.02 9.84e-01 68.26 1.4e-16
## Estimate16 fontelis 2.260 0.211 10.71 9.15e-27 120.88 4.1e-28
## Estimate17 quadristop -1.210 0.470 -2.57 1.01e-02 9.22 2.4e-03
## Estimate18 milstop -1.210 0.470 -2.57 1.01e-02 9.22 2.4e-03
## AIC Residual_Deviance
## Estimate 235.79 213.41
## Estimate1 235.79 213.41
## Estimate2 213.21 190.83
## Estimate3 235.79 213.41
## Estimate4 235.79 213.41
## Estimate5 75.31 52.93
## Estimate6 235.79 213.41
## Estimate7 235.72 213.34
## Estimate8 229.16 206.78
## Estimate9 167.91 145.52
## Estimate10 115.28 92.90
## Estimate11 167.91 145.52
## Estimate12 235.79 213.41
## Estimate13 167.91 145.52
## Estimate14 163.03 140.65
## Estimate15 167.91 145.52
## Estimate16 115.28 92.90
## Estimate17 226.95 204.57
## Estimate18 226.95 204.57asdf <- as.data.frame(summary_table.api)plots
plots <- lapply(1:nrow(chem_info), function(i){
chem_type <- chem_info$type[i]
chem_mode <- chem_info$mode[i]
chem_class <- chem_info$class[i]
var <- chem_info$pesticide[i]
var2 <- gsub("_L$", "", chem_info$pesticide[i]) # remove _L at end
stats_row <- summary_table.api[summary_table.api$Pesticide == var2, ]
annot_text <- paste0(
"LRT χ² = ", round(stats_row$LR_ChiSq, 2),
", p = ", signif(stats_row$LRT, 3))
ggplot(gh6, aes_string(x = var, y = "api_prop")) +
geom_boxplot(fill = chem_colors[chem_class],
alpha = 0.6,
width = 0.5,
outlier.shape = NA) +
geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
labs(
title = gsub("_L","",var),
subtitle = paste(chem_type, "-", chem_mode),
x = "",
y = "Probability of *Apicystis* detection"
) +
scale_x_discrete(labels = c("FALSE"="Not applied","TRUE"="Applied")) +
theme_classic(base_size = 16) +
theme(axis.title.y = ggtext::element_markdown()) +
annotate(
"text",
x = 2, y = 1.05, # top-right
label = annot_text,
hjust = 1, vjust = 1, size = 4
) +
coord_cartesian(ylim = c(0, 1))
})
wrap_plots(plots, ncol = 4)
anther bruising vs. specific chemicals presence absence
anth_chem_m1 <- glm(logbruise ~ azaguard_L, data = gh6)
Anova(anth_chem_m1)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## azaguard_L 0.65877 1 0.417summary(anth_chem_m1)##
## Call:
## glm(formula = logbruise ~ azaguard_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82271 0.03698 22.247 2.42e-05 ***
## azaguard_LTRUE -0.04245 0.05230 -0.812 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.00410272)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016411 on 4 degrees of freedom
## AIC: -12.382
##
## Number of Fisher Scoring iterations: 2anth_chem_m2 <- glm(logbruise ~ botanigard_22wp_L, data = gh6)
Anova(anth_chem_m2)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## botanigard_22wp_L 0.65877 1 0.417summary(anth_chem_m2)##
## Call:
## glm(formula = logbruise ~ botanigard_22wp_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82271 0.03698 22.247 2.42e-05 ***
## botanigard_22wp_LTRUE -0.04245 0.05230 -0.812 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.00410272)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016411 on 4 degrees of freedom
## AIC: -12.382
##
## Number of Fisher Scoring iterations: 2anth_chem_m3 <- glm(logbruise ~ botanigard_es_L, data = gh6)
Anova(anth_chem_m3)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## botanigard_es_L 0.12131 1 0.7276summary(anth_chem_m3)##
## Call:
## glm(formula = logbruise ~ botanigard_es_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.79464 0.03405 23.337 2e-05 ***
## botanigard_es_LTRUE 0.02054 0.05898 0.348 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.004637755)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.018551 on 4 degrees of freedom
## AIC: -11.647
##
## Number of Fisher Scoring iterations: 2anth_chem_m4 <- glm(logbruise ~ captiva_prime_L, data = gh6)
Anova(anth_chem_m4)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## captiva_prime_L 0.65877 1 0.417summary(anth_chem_m4)##
## Call:
## glm(formula = logbruise ~ captiva_prime_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82271 0.03698 22.247 2.42e-05 ***
## captiva_prime_LTRUE -0.04245 0.05230 -0.812 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.00410272)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016411 on 4 degrees of freedom
## AIC: -12.382
##
## Number of Fisher Scoring iterations: 2anth_chem_m5 <- glm(logbruise ~ nofly_L, data = gh6)
Anova(anth_chem_m5)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## nofly_L 0.65877 1 0.417summary(anth_chem_m5)##
## Call:
## glm(formula = logbruise ~ nofly_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82271 0.03698 22.247 2.42e-05 ***
## nofly_LTRUE -0.04245 0.05230 -0.812 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.00410272)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016411 on 4 degrees of freedom
## AIC: -12.382
##
## Number of Fisher Scoring iterations: 2anth_chem_m6 <- glm(logbruise ~ venerate_cg_L, data = gh6)
Anova(anth_chem_m6)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## venerate_cg_L 6.6774 1 0.009765 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anth_chem_m6)##
## Call:
## glm(formula = logbruise ~ venerate_cg_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.83305 0.02115 39.379 2.48e-06 ***
## venerate_cg_LTRUE -0.09468 0.03664 -2.584 0.0611 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.001790108)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0071604 on 4 degrees of freedom
## AIC: -17.358
##
## Number of Fisher Scoring iterations: 2anth_chem_m7 <- glm(logbruise ~ m_pede_L, data = gh6)
Anova(anth_chem_m7)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## m_pede_L 0.65877 1 0.417summary(anth_chem_m7)##
## Call:
## glm(formula = logbruise ~ m_pede_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82271 0.03698 22.247 2.42e-05 ***
## m_pede_LTRUE -0.04245 0.05230 -0.812 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.00410272)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016411 on 4 degrees of freedom
## AIC: -12.382
##
## Number of Fisher Scoring iterations: 2anth_chem_m8 <- glm(logbruise ~ rootshield_plus_L, data = gh6)
Anova(anth_chem_m8)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## rootshield_plus_L 0.0039631 1 0.9498summary(anth_chem_m8)##
## Call:
## glm(formula = logbruise ~ rootshield_plus_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.800232 0.034546 23.164 2.06e-05 ***
## rootshield_plus_LTRUE 0.003767 0.059835 0.063 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.004773677)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.019095 on 4 degrees of freedom
## AIC: -11.473
##
## Number of Fisher Scoring iterations: 2anth_chem_m9 <- glm(logbruise ~ lalstop_k61_L, data = gh6)
Anova(anth_chem_m9)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## lalstop_k61_L 2.1541 1 0.1422summary(anth_chem_m9)##
## Call:
## glm(formula = logbruise ~ lalstop_k61_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.77788 0.02787 27.916 9.8e-06 ***
## lalstop_k61_LTRUE 0.07084 0.04826 1.468 0.216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003105838)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.012423 on 4 degrees of freedom
## AIC: -14.052
##
## Number of Fisher Scoring iterations: 2anth_chem_m10 <- glm(logbruise ~ beleaf_50sg_L, data = gh6)
Anova(anth_chem_m10)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## beleaf_50sg_L 4.3437 1 0.03715 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anth_chem_m10)##
## Call:
## glm(formula = logbruise ~ beleaf_50sg_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.81970 0.02140 38.295 2.78e-06 ***
## beleaf_50sg_LTRUE -0.10927 0.05243 -2.084 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002290794)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0091632 on 4 degrees of freedom
## AIC: -15.879
##
## Number of Fisher Scoring iterations: 2anth_chem_m11 <- glm(logbruise ~ coragen_L, data = gh6)
Anova(anth_chem_m11)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## coragen_L 1.3037 1 0.2535summary(anth_chem_m11)##
## Call:
## glm(formula = logbruise ~ coragen_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.86406 0.06003 14.393 0.000135 ***
## coragen_LTRUE -0.07509 0.06576 -1.142 0.317246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003603822)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.014415 on 4 degrees of freedom
## AIC: -13.16
##
## Number of Fisher Scoring iterations: 2anth_chem_m12 <- glm(logbruise ~ entrust_sc_L, data = gh6)
Anova(anth_chem_m12)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## entrust_sc_L 4.3437 1 0.03715 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anth_chem_m12)##
## Call:
## glm(formula = logbruise ~ entrust_sc_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.81970 0.02140 38.295 2.78e-06 ***
## entrust_sc_LTRUE -0.10927 0.05243 -2.084 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002290794)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0091632 on 4 degrees of freedom
## AIC: -15.879
##
## Number of Fisher Scoring iterations: 2anth_chem_m13 <- glm(logbruise ~ pylon_L, data = gh6)
Anova(anth_chem_m13)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## pylon_L 0.65877 1 0.417summary(anth_chem_m13)##
## Call:
## glm(formula = logbruise ~ pylon_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.78026 0.03698 21.099 2.98e-05 ***
## pylon_LTRUE 0.04245 0.05230 0.812 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.00410272)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.016411 on 4 degrees of freedom
## AIC: -12.382
##
## Number of Fisher Scoring iterations: 2anth_chem_m14 <- glm(logbruise ~ grotto_L, data = gh6)
Anova(anth_chem_m14)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## grotto_L 4.3437 1 0.03715 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anth_chem_m14)##
## Call:
## glm(formula = logbruise ~ grotto_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.81970 0.02140 38.295 2.78e-06 ***
## grotto_LTRUE -0.10927 0.05243 -2.084 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002290794)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0091632 on 4 degrees of freedom
## AIC: -15.879
##
## Number of Fisher Scoring iterations: 2anth_chem_m15 <- glm(logbruise ~ luna_tranquility_L, data = gh6)
Anova(anth_chem_m15)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## luna_tranquility_L 0.16524 1 0.6844summary(anth_chem_m15)##
## Call:
## glm(formula = logbruise ~ luna_tranquility_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.79354 0.03387 23.429 1.97e-05 ***
## luna_tranquility_LTRUE 0.02385 0.05867 0.406 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.004588841)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.018355 on 4 degrees of freedom
## AIC: -11.71
##
## Number of Fisher Scoring iterations: 2anth_chem_m16 <- glm(logbruise ~ previcur_flex_L, data = gh6)
Anova(anth_chem_m16)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## previcur_flex_L 4.3437 1 0.03715 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anth_chem_m16)##
## Call:
## glm(formula = logbruise ~ previcur_flex_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.71043 0.04786 14.843 0.00012 ***
## previcur_flex_LTRUE 0.10927 0.05243 2.084 0.10553
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002290794)
##
## Null deviance: 0.0191136 on 5 degrees of freedom
## Residual deviance: 0.0091632 on 4 degrees of freedom
## AIC: -15.879
##
## Number of Fisher Scoring iterations: 2anth_chem_m17 <- glm(logbruise ~ fontelis_L, data = gh6)
Anova(anth_chem_m17)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## fontelis_L 1.3037 1 0.2535summary(anth_chem_m17)##
## Call:
## glm(formula = logbruise ~ fontelis_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.78897 0.02685 29.388 7.98e-06 ***
## fontelis_LTRUE 0.07509 0.06576 1.142 0.317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.003603822)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.014415 on 4 degrees of freedom
## AIC: -13.16
##
## Number of Fisher Scoring iterations: 2anth_chem_m18 <- glm(logbruise ~ quadristop_L, data = gh6)
Anova(anth_chem_m18)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## quadristop_L 0.25289 1 0.615summary(anth_chem_m18)##
## Call:
## glm(formula = logbruise ~ quadristop_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.80764 0.02998 26.939 1.13e-05 ***
## quadristop_LTRUE -0.03693 0.07344 -0.503 0.641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.004494262)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.017977 on 4 degrees of freedom
## AIC: -11.835
##
## Number of Fisher Scoring iterations: 2anth_chem_m19 <- glm(logbruise ~ milstop_L, data = gh6)
Anova(anth_chem_m19)## Analysis of Deviance Table (Type II tests)
##
## Response: logbruise
## LR Chisq Df Pr(>Chisq)
## milstop_L 0.25289 1 0.615summary(anth_chem_m19)##
## Call:
## glm(formula = logbruise ~ milstop_L, data = gh6)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.80764 0.02998 26.939 1.13e-05 ***
## milstop_LTRUE -0.03693 0.07344 -0.503 0.641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.004494262)
##
## Null deviance: 0.019114 on 5 degrees of freedom
## Residual deviance: 0.017977 on 4 degrees of freedom
## AIC: -11.835
##
## Number of Fisher Scoring iterations: 2summary table
anth_models <- list(
azaguard_L = anth_chem_m1,
botanigard_22wp_L = anth_chem_m2,
botanigard_es_L = anth_chem_m3,
captiva_prime_L = anth_chem_m4,
nofly_L = anth_chem_m5,
venerate_cg_L = anth_chem_m6,
m_pede_L = anth_chem_m7,
rootshield_plus_L = anth_chem_m8,
lalstop_k61_L = anth_chem_m9,
beleaf_50sg_L = anth_chem_m10,
coragen_L = anth_chem_m11,
entrust_sc_L = anth_chem_m12,
pylon_L = anth_chem_m13,
grotto_L = anth_chem_m14,
luna_tranquility_L = anth_chem_m15,
previcur_flex_L = anth_chem_m16,
fontelis_L = anth_chem_m17,
quadristop_L = anth_chem_m18,
milstop_L = anth_chem_m19
)
summary_table.anth <- do.call(rbind, lapply(names(anth_models), function(name){
m <- anth_models[[name]]
s <- summary(m)
coef_row <- s$coefficients[2, ]
lr <- Anova(m)$`LR Chisq`[1]
LRT <- Anova(m)$`Pr(>Chisq)`[1]
data.frame(
Predictor = name,
Estimate = coef_row["Estimate"],
SE = coef_row["Std. Error"],
z = coef_row["t value"],
p = coef_row["Pr(>|t|)"],
LR_ChiSq = lr,
AIC = AIC(m),
LRT = LRT,
Residual_Deviance = m$deviance
)
}))
summary_table.anth$SE <- signif(summary_table.anth$SE, 3)
summary_table.anth$z <- round(summary_table.anth$z, 2)
summary_table.anth$p <- signif(summary_table.anth$p, 3)
summary_table.anth$LR_ChiSq <- round(summary_table.anth$LR_ChiSq, 2)
summary_table.anth$AIC <- round(summary_table.anth$AIC, 2)
summary_table.anth$Residual_Deviance <- round(summary_table.anth$Residual_Deviance, 2)
summary_table.anth$LRT <- signif(summary_table.anth$LRT, 3)
anth_table <- as.data.frame(summary_table.anth)
anth_table## Predictor Estimate SE z p LR_ChiSq AIC
## Estimate azaguard_L -0.04244796 0.0523 -0.81 0.4630 0.66 -12.38
## Estimate1 botanigard_22wp_L -0.04244796 0.0523 -0.81 0.4630 0.66 -12.38
## Estimate2 botanigard_es_L 0.02054152 0.0590 0.35 0.7450 0.12 -11.65
## Estimate3 captiva_prime_L -0.04244796 0.0523 -0.81 0.4630 0.66 -12.38
## Estimate4 nofly_L -0.04244796 0.0523 -0.81 0.4630 0.66 -12.38
## Estimate5 venerate_cg_L -0.09468312 0.0366 -2.58 0.0611 6.68 -17.36
## Estimate6 m_pede_L -0.04244796 0.0523 -0.81 0.4630 0.66 -12.38
## Estimate7 rootshield_plus_L 0.00376682 0.0598 0.06 0.9530 0.00 -11.47
## Estimate8 lalstop_k61_L 0.07083577 0.0483 1.47 0.2160 2.15 -14.05
## Estimate9 beleaf_50sg_L -0.10927277 0.0524 -2.08 0.1060 4.34 -15.88
## Estimate10 coragen_L -0.07508666 0.0658 -1.14 0.3170 1.30 -13.16
## Estimate11 entrust_sc_L -0.10927277 0.0524 -2.08 0.1060 4.34 -15.88
## Estimate12 pylon_L 0.04244796 0.0523 0.81 0.4630 0.66 -12.38
## Estimate13 grotto_L -0.10927277 0.0524 -2.08 0.1060 4.34 -15.88
## Estimate14 luna_tranquility_L 0.02384735 0.0587 0.41 0.7050 0.17 -11.71
## Estimate15 previcur_flex_L 0.10927277 0.0524 2.08 0.1060 4.34 -15.88
## Estimate16 fontelis_L 0.07508666 0.0658 1.14 0.3170 1.30 -13.16
## Estimate17 quadristop_L -0.03693090 0.0734 -0.50 0.6410 0.25 -11.84
## Estimate18 milstop_L -0.03693090 0.0734 -0.50 0.6410 0.25 -11.84
## LRT Residual_Deviance
## Estimate 0.41700 0.02
## Estimate1 0.41700 0.02
## Estimate2 0.72800 0.02
## Estimate3 0.41700 0.02
## Estimate4 0.41700 0.02
## Estimate5 0.00976 0.01
## Estimate6 0.41700 0.02
## Estimate7 0.95000 0.02
## Estimate8 0.14200 0.01
## Estimate9 0.03710 0.01
## Estimate10 0.25400 0.01
## Estimate11 0.03710 0.01
## Estimate12 0.41700 0.02
## Estimate13 0.03710 0.01
## Estimate14 0.68400 0.02
## Estimate15 0.03710 0.01
## Estimate16 0.25400 0.01
## Estimate17 0.61500 0.02
## Estimate18 0.61500 0.02plot of anther bruise vs pesticides applied/not applied
plots <- lapply(1:nrow(chem_info), function(i){
chem_type <- chem_info$type[i]
chem_mode <- chem_info$mode[i]
chem_class <- chem_info$class[i]
var <- chem_info$pesticide[i]
var2 <- gsub("_L$", "", chem_info$pesticide[i]) # remove _L at end
ggplot(gh6, aes_string(x = var, y = "average_bruise")) +
geom_boxplot(fill = chem_colors[chem_class],
alpha = 0.6,
width = 0.5,
outlier.shape = NA) +
geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
labs(
title = gsub("_L","",var),
subtitle = paste(chem_type, "-", chem_mode),
x = "",
y = "Average Anther Score"
) +
scale_x_discrete(labels = c("FALSE"="Not applied","TRUE"="Applied")) +
theme_classic(base_size = 16)
})
wrap_plots(plots, ncol = 4)
---
title: Pesticide application in a tomato greenhouse predicts bumble bee pollination
  and pathogen infection
author: "Emily Runnion"
date: "June 2026"
output:
  html_document:
    toc: true
    toc_depth: 4
    number_sections: false
    toc_float: true
    theme: journal
    code_download: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


```{r load libraries, include=FALSE}
library(viridisLite)
library(stats)
library(ggplot2)
library(car)
library(emmeans)
library(MASS)
library(lme4)
library(tidyverse)
library(dplyr)
library(readr)
library(viridisLite)
library(stats)
library(DHARMa)
library(ggpattern)
library(kableExtra)
library(blmeco)

library(cowplot)
library(plotly)
library(agricolae) 
library(ggpubr)
library(glue)
library(multcomp)
library(multcompView)
library(glmmTMB)
library(rstatix)
library(fitdistrplus)
library(logspline)
library(GGally)
library(patchwork)
library(data.table)
library(ggtext)
library(ggeffects)

```

```{r}
gh6 <- read_csv("gh6.csv", col_types = cols(gh = col_factor(levels = c("1", 
    "2", "3", "4", "5", "6")), year = col_factor(levels = c("2022", 
    "2024")), source = col_factor(levels = c("Koppert", 
    "Biobest"))))
```

# Crithidia

## Crithidia probability per greenhouse 

```{r}
crith_prob <- glm(
  cbind(crith_pos, crith_neg) ~ 
gh,
  data = gh6,
  family = binomial("logit"))

Anova(crith_prob)
summary(crith_prob)

cr1 <- emmeans(crith_prob, pairwise ~ gh, type = "response")
cr1

cr1.df <- as.data.frame(cr1$emmeans)

# Fantastic this matches what I calculated manually so this is a trustworthy method of comparing probabilities 

```

## Crithidia vs. total pesticides applied (ml)

```{r}
mod_1a <- glm(
  cbind(crith_pos, crith_neg) ~ total_chemical,
  data = gh6,
  family = binomial("logit"))
Anova(mod_1a)
summary(mod_1a)

```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_chemical, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
  labs(
    x = "Total Pesticides Applied (ml)",
    y = "Probability of *Crithidia* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 45000,
    y = 1,
    label = "P < 0.001, χ² = 87.04",
    size = 6
  ) 

```

## Crithidia vs. insecticides 

```{r}
mod_2 <- glm(
  cbind(crith_pos, crith_neg) ~ 
total_insecticide,
  data = gh6,
  family = binomial("logit"))
Anova(mod_2)
summary(mod_2)


```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_insecticide, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(3000, 200000)) +
  labs(
    x = "Total Insecticides Applied (ml)",
    y = "Probability of *Crithidia* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 15000,
    y = 1,
    label = "P < 0.001",
    size = 9
  ) 

```

## Crithidia vs. fungicides 

```{r}
mod_3 <- glm(
  cbind(crith_pos, crith_neg) ~ 
total_fungicide,
  data = gh6,
  family = binomial("logit"))

Anova(mod_3)
summary(mod_3)

```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_fungicide, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(10000, 100000)) +
  labs(
    x = "Total Fungicides Applied (ml)",
    y = "Probability of *Crithidia* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 15000,
    y = 1,
    label = "P < 0.001",
    size = 9
  ) 

```

## Crithidia vs. synthetics 

```{r}
mod_4 <- glm(
  cbind(crith_pos, crith_neg) ~ 
total_synthetic,
  data = gh6,
  family = binomial("logit"))

Anova(mod_4)
summary(mod_4)

```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_synthetic, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(13000, 55000)) +
  labs(
    x = "Total Synthetic Pesticdes Applied (ml)",
    y = "Probability of *Crithidia* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 15000,
    y = 1,
    label = "P < 0.001",
    size = 9
  ) 

```

## Crithidia vs. biologicals 

```{r}
mod_5 <- glm(
  cbind(crith_pos, crith_neg) ~ 
total_biological,
  data = gh6,
  family = binomial("logit"))

Anova(mod_5)
summary(mod_5)

```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_biological, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orchid4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(2500, 200000)) +
  labs(
    x = "Total Biological Pesticdes Applied (ml)",
    y = "Probability of *Crithidia* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 10000,
    y = 1,
    label = "P < 0.001",
    size = 9
  ) 

```


## Crithidia vs. average hives per greenhouse hectare

```{r}
mod.cr_6 <- glm(
  cbind(crith_pos, crith_neg) ~ 
avg_hives_ha,
  data = gh6,
  family = binomial("logit"))

Anova(mod.cr_6)
summary(mod.cr_6)

```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = avg_hives_ha, y = crith_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(20, 40)) +
  labs(
    x = "Average Bumble Bee Colonies per Greenhouse Hectare",
    y = "Probability of *Crithidia* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 22,
    y = 1.1,
    label = "β = -0.054 ± 0.011 SE, 
    P < 0.001",
    size = 6
  ) 

```

## Crithidia vs. commercial supplier of bumble bee colonies

```{r}
mod.cr_7 <- glm(
  cbind(crith_pos, crith_neg) ~ 
source,
  data = gh6,
  family = binomial("logit"))

Anova(mod.cr_7)

summary(mod.cr_7)

cr.s <- emmeans(mod.cr_7, pairwise ~ source, type = "response")
cr.s

cr.df <- as.data.frame(cr.s$emmeans)
cr.df

```

```{r, fig.width=6, fig.height=6, dpi=600}

ggplot(cr.df, aes(x = source, y = prob, fill = source)) +

  geom_pointrange(aes(ymin = asymp.LCL,
                    ymax = asymp.UCL),
                shape = 21,
                size = 1,
                fatten = 4,
                color = "black") +

  geom_errorbar(aes(ymin = asymp.LCL,
                    ymax = asymp.UCL),
                width = 0.1,
                linewidth = 1) +

  scale_fill_manual(values = c("Koppert" = "orchid4",
                               "Biobest" = "orchid1")) +

  scale_y_continuous(
    limits = c(0, 1),
    expand = expansion(mult = c(0, 0.05))
  ) +

  theme_cowplot() +

  labs(
    x = "",
    y = "Predicted *Crithidia* detection probability",
    caption = "Odds ratio = 0.30, P < 0.0001, N = 6"
  ) +
    theme(axis.title.y = ggtext::element_markdown()) + 

  theme(
    text = element_text(size = 16),
    axis.text = element_text(size = 14),
    legend.position = "none",
    plot.caption = element_text(hjust = 0.5)
  )

```

```{r}
Anova(mod_1a)
summary(mod_1a)
Anova(mod_2)
summary(mod_2)
Anova(mod_3)
summary(mod_3)
Anova(mod_4)
summary(mod_4)
Anova(mod_5)
summary(mod_5)
Anova(mod.cr_6)
summary(mod.cr_6)
```



# Apicystis

## Apicystis probability per greenhouse 

```{r}
api_prob <- glm(
  cbind(api_pos, api_neg) ~ 
gh,
  data = gh6,
  family = binomial("logit"))

Anova(api_prob)
summary(api_prob)

ap1 <- emmeans(api_prob, pairwise ~ gh, type = "response")
ap1

ap1.df <- as.data.frame(ap1$emmeans)

# Fantastic once again this matches what I calculated manually so this is a trustworthy method of comparing probabilities 

```

## Apicystis vs. total pesticides applied (ml)

```{r}
mod.ap_1a <- glm(
  cbind(api_pos, api_neg) ~ total_chemical,
  data = gh6,
  family = binomial("logit"))
Anova(mod.ap_1a)
summary(mod.ap_1a)

```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_chemical, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
  labs(
    x = "Total Pesticides Applied (ml)",
    y = "Probability of *Apicystis* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 45000,
    y = 0.5,
    label = "P < 0.001",
    size = 9
  ) 

```

## Apicystis vs. insecticides 

```{r}
mod.ap_2 <- glm(
  cbind(api_pos, api_neg) ~ 
total_insecticide,
  data = gh6,
  family = binomial("logit"))
Anova(mod.ap_2)
summary(mod.ap_2)

qqnorm(resid(mod.ap_2));qqline(resid(mod.ap_2))


```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_insecticide, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(3000, 200000)) +
  labs(
    x = "Total Insecticides Applied (ml)",
    y = "Probability of *Apicystis* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 15000,
    y = 0.5,
    label = "P < 0.001",
    size = 9
  ) 

```

## Apicystis vs. fungicides 

```{r}
mod.ap_3 <- glm(
  cbind(api_pos, api_neg) ~ 
total_fungicide,
  data = gh6,
  family = binomial("logit"))

Anova(mod.ap_3)
summary(mod.ap_3)
```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_fungicide, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(10000, 100000)) +
  labs(
    x = "Total Fungicides Applied (ml)",
    y = "Probability of *Apicystis* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 15000,
    y = 0.5,
    label = "P = 0.40",
    size = 9
  ) 

```

## Apicystis vs. synthetics 

```{r}
mod.ap_4 <- glm(
  cbind(api_pos, api_neg) ~ 
total_synthetic,
  data = gh6,
  family = binomial("logit"))

Anova(mod.ap_4)
summary(mod.ap_4)

```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_synthetic, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(13000, 55000)) +
  labs(
    x = "Total Synthetic Pesticdes Applied (ml)",
    y = "Probability of *Apicystis* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 15000,
    y = 0.5,
    label = "P = 0.65",
    size = 9
  ) 

```

## Apicystis vs. biologicals 

```{r}
mod.ap_5 <- glm(
  cbind(api_pos, api_neg) ~ 
total_biological,
  data = gh6,
  family = binomial("logit"))

Anova(mod.ap_5)
summary(mod.ap_5)

```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_biological, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(2500, 200000)) +
  labs(
    x = "Total Biological Pesticdes Applied (ml)",
    y = "Probability of *Apicystis* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 10000,
    y = 0.5,
    label = "P < 0.001",
    size = 9
  ) 

```

## Apicystis vs. average hives per greenhouse hectare

```{r}
mod.ap_6 <- glm(
  cbind(api_pos, api_neg) ~ 
avg_hives_ha,
  data = gh6,
  family = binomial("logit"))

Anova(mod.ap_6)
summary(mod.ap_6)

```

```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = avg_hives_ha, y = api_prop)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "orangered") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(20, 40)) +
  labs(
    x = "Average bumble bee colonies per greenhouse hectare",
    y = "Probability of *Apicystis* Detection"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 22,
    y = 0.5,
    label = "P < 0.001",
    size = 9
  ) 

```


## Apicystis vs. commercial supplier of bumble bee colonies

```{r}
mod.ap_7 <- glm(
  cbind(api_pos, api_neg) ~ 
source,
  data = gh6,
  family = binomial("logit"))

Anova(mod.ap_7)
summary(mod.ap_7)

ap.s <- emmeans(mod.ap_7, pairwise ~ source, type = "response")
ap.s

ap.df <- as.data.frame(ap.s$emmeans)
ap.df
```

```{r, fig.width=6, fig.height=6, dpi=600}

ggplot(ap.df, aes(x = source, y = prob, fill = source)) +

  geom_pointrange(aes(ymin = asymp.LCL,
                    ymax = asymp.UCL),
                shape = 21,
                size = 1,
                fatten = 4,
                color = "black") +

  geom_errorbar(aes(ymin = asymp.LCL,
                    ymax = asymp.UCL),
                width = 0.1,
                linewidth = 1) +

  scale_fill_manual(values = c("Koppert" = "orangered1",
                               "Biobest" = "orangered4")) +

  scale_y_continuous(
    limits = c(0, 0.45),
    expand = expansion(mult = c(0, 0.05))
  ) +

  theme_cowplot() +

  labs(
    x = "",
    y = "Predicted *Apicystis* detection probability",
    caption = "Odds ratio = 11.3, P < 0.0001, N = 6"
  ) +
    theme(axis.title.y = ggtext::element_markdown()) + 

  theme(
    text = element_text(size = 16),
    axis.text = element_text(size = 14),
    legend.position = "none",
    plot.caption = element_text(hjust = 0.5)
  )

```

```{r}
Anova(mod.ap_1a)
summary(mod.ap_1a)
Anova(mod.ap_2)
summary(mod.ap_2)
Anova(mod.ap_3)
summary(mod.ap_3)
Anova(mod.ap_4)
summary(mod.ap_4)
Anova(mod.ap_5)
summary(mod.ap_5)
Anova(mod.ap_6)
summary(mod.ap_6)

```


# Anther bruising

```{r}
hist(gh6$average_bruise)
gh6$logbruise <- log(gh6$average_bruise)
hist(gh6$logbruise)
shapiro.test(gh6$logbruise)

```


## Anther bruising vs. crithidia 

```{r}
mod_b_1 <- glm(average_bruise ~ crith_prop, data = gh6)
Anova(mod_b_1)
summary(mod_b_1)

mod_b_1 <- glm(logbruise ~ crith_prop, data = gh6)
Anova(mod_b_1)
summary(mod_b_1)
```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = crith_prop, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(0.08,1)) +
  labs(
    x = "Probability of *Crithidia* Detection",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 0.15,
    y = 2.75,
    label = "P = 0.01",
    size = 9
  ) 

```

## Anther bruising vs. apicystis 

```{r}
mod_b_2 <- glm(average_bruise ~ api_prop, data = gh6)
Anova(mod_b_2)
summary(mod_b_2)

mod_b_2 <- glm(logbruise ~ api_prop, data = gh6)
Anova(mod_b_2)
summary(mod_b_2)
```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = api_prop, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(0,.41)) +
  labs(
    x = "Probability of *Apicystis* Detection",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 0.05,
    y = 2.75,
    label = "P < 0.001",
    size = 9
  ) 

```

## Anther brusing vs. total pesticides 

```{r}
mod_b_3 <- glm(average_bruise ~ total_chemical, data = gh6)
Anova(mod_b_3)
summary(mod_b_3)

mod_b_3 <- glm(logbruise ~ total_chemical, data = gh6)
Anova(mod_b_3)
summary(mod_b_3)

```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_chemical, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limit = c(16000, 215000)) +
  labs(
    x = "Average total pesticides applied (ml)",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 25000,
    y = 2.7,
    label = "P = 0.11",
    size = 9
  ) 

```

## Anther bruising vs. insecticides 

```{r}
mod_b_4 <- glm(average_bruise ~ total_insecticide, data = gh6)
Anova(mod_b_4)
summary(mod_b_4)

mod_b_4 <- glm(logbruise ~ total_insecticide, data = gh6)
Anova(mod_b_4)
summary(mod_b_4)
```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_insecticide, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(3000, 195000)) +
  labs(
    x = "Average total insecticides applied (ml)",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 10000,
    y = 2.6,
    label = "P = 0.28",
    size = 9
  ) 

```

## Anther brusing vs. fungicides 

```{r}
mod_b_5 <- glm(average_bruise ~ total_fungicide, data = gh6)
Anova(mod_b_5)
summary(mod_b_5)

mod_b_5 <- glm(logbruise ~ total_fungicide, data = gh6)
Anova(mod_b_5)
summary(mod_b_5)

```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_fungicide, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(10000, 100000)) +
  labs(
    x = "Average total fungicides applied (ml)",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 20000,
    y = 2.6,
    label = "P = 0.47",
    size = 9
  ) 

```

## Anther bruising vs. synthetics 

```{r}
mod_b_6 <- glm(average_bruise ~ total_synthetic, data = gh6)
Anova(mod_b_6)
summary(mod_b_6)

mod_b_6 <- glm(logbruise ~ total_synthetic, data = gh6)
Anova(mod_b_6)
summary(mod_b_6)
```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_synthetic, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(13000, 55000)) +
  labs(
    x = "Average total synthetic pesticides applied (ml)",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 20000,
    y = 2.6,
    label = "P = 0.32",
    size = 9
  ) 

```

## Anther bruising vs. biologicals 

```{r}
mod_b_7 <- glm(average_bruise ~ total_biological, data = gh6)
Anova(mod_b_7)
summary(mod_b_7)

mod_b_7 <- glm(logbruise ~ total_biological, data = gh6)
Anova(mod_b_7)
summary(mod_b_7)
```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = total_biological, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(3000, 195000)) +
  labs(
    x = "Average total biological pesticides applied (ml)",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 15000,
    y = 2.6,
    label = "P = 0.13",
    size = 9
  ) 

```

## Anther bruising vs. average hives per greenhouse hectare 

```{r}
mod_b_8 <- glm(average_bruise ~ avg_hives_ha, data = gh6)
Anova(mod_b_8)
summary(mod_b_8)

mod_b_8 <- glm(logbruise ~ avg_hives_ha, data = gh6)
Anova(mod_b_8)
summary(mod_b_8)

```


```{r, fig.width=12, fig.height=8, dpi=600}

ggplot(gh6, aes(x = avg_hives_ha, y = average_bruise)) +
geom_point(size = 5, stroke = 0.8, alpha = 0.8, color = "chartreuse4") +
geom_smooth(method = "glm",
              se = TRUE,
              color = "black",
              size = 1.2) +
  theme_cowplot() +
    scale_x_continuous(limits = c(20, 40)) +
  labs(
    x = "Average total biologicals applied (ml)",
    y = "Average anther bruising score"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold")) +
  theme(text = element_text(size = 20),
        axis.text.x = element_text(size = 20),
        axis.text.y = element_text(size = 20)) +
    theme(axis.title.x = ggtext::element_markdown()) + 
  annotate(
    geom = "text",
    x = 21,
    y = 2.6,
    label = "P = 0.14",
    size = 9
  ) 

```

## Anther bruising vs. commercial supplier of bumble bee colonies 

```{r}

mod_b_9 <- glm(average_bruise ~ source, data = gh6)
Anova(mod_b_9)
summary(mod_b_9)

b.s <- emmeans(mod_b_9, pairwise ~ source, type = "response")
b.s

bs.df <- as.data.frame(b.s$emmeans)
bs.df




mod_b_9 <- glm(logbruise ~ source, data = gh6)
Anova(mod_b_9)
summary(mod_b_9)

b.s <- emmeans(mod_b_9, pairwise ~ source, type = "response")
b.s
bs.df <- as.data.frame(b.s$emmeans)
bs.df
```

```{r, fig.width=6, fig.height=6, dpi=600}

ggplot(bs.df, aes(x = source, y = emmean, fill = source)) +

  geom_pointrange(aes(ymin = lower.CL,
                    ymax = upper.CL),
                shape = 21,
                size = 1,
                fatten = 4,
                color = "black") +

  geom_errorbar(aes(ymin = lower.CL,
                    ymax = upper.CL),
                width = 0.1,
                linewidth = 1) +

  scale_fill_manual(values = c("Koppert" = "chartreuse4",
                               "Biobest" = "darkolivegreen")) +

  scale_y_continuous(
    limits = c(0, 3),
    expand = expansion(mult = c(0, 0.05))
  ) +

  theme_cowplot() +

  labs(
    x = "",
    y = "Estimated mean anther bruise score",
    caption = "Koppert − Biobest = 0.20; P = 0.074"
  ) +
    theme(axis.title.y = ggtext::element_markdown()) + 

  theme(
    text = element_text(size = 16),
    axis.text = element_text(size = 14),
    legend.position = "none",
    plot.caption = element_text(hjust = 0.5)
  )

```


```{r}
Anova(mod_b_1)
summary(mod_b_1)
Anova(mod_b_2)
summary(mod_b_2)
Anova(mod_b_3)
summary(mod_b_3)
Anova(mod_b_4)
summary(mod_b_4)
Anova(mod_b_5)
summary(mod_b_5)
Anova(mod_b_6)
summary(mod_b_6)
Anova(mod_b_7)
summary(mod_b_7)
Anova(mod_b_8)
summary(mod_b_8)

```


# Applied pesticde comparisons


```{r}

# List of your logical treatment variables
treat_vars <- c("azaguard",	"botanigard_22wp",	"botanigard_es",	"captiva_prime",	"nofly",	"venerate_cg",	"m_pede",	"rootshield_plus",	"lalstop_k61",	"beleaf_50sg",	"coragen",	"entrust_sc",	"pylon",	"grotto",	"luna_tranquility",	"previcur_flex",	"fontelis",	"quadristop",	"milstop")


# Reshape the data to long format
greenhouse_long <- gh6 %>%
  pivot_longer(cols = all_of(treat_vars),
               names_to = "treatment",
               values_to = "applied")

greenhouse_long$applied_L <- ifelse(greenhouse_long$applied == 0, 0, 1)
greenhouse_long$applied_L <- as.logical(greenhouse_long$applied_L)

chem_cols <- c(
"azaguard","botanigard_22wp","botanigard_es","captiva_prime","nofly",
"venerate_cg","m_pede","rootshield_plus","lalstop_k61","beleaf_50sg",
"coragen","entrust_sc","pylon","grotto","luna_tranquility",
"previcur_flex","fontelis","quadristop","milstop"
)

gh6[paste0(chem_cols, "_L")] <-
  lapply(gh6[chem_cols], function(x) x > 0)

```



```{r}
chem_info <- data.frame(
  pesticide = c(
    "azaguard_L",
    "captiva_prime_L",
    "nofly_L",
    "venerate_cg_L",
    "entrust_sc_L",
    "m_pede_L",
    "botanigard_22wp_L",
    "botanigard_es_L",
    "beleaf_50sg_L",
    "coragen_L",
    "pylon_L",
    "rootshield_plus_L",
    "lalstop_k61_L",
    "milstop_L",
    "grotto_L",
    "luna_tranquility_L",
    "previcur_flex_L",
    "fontelis_L",
    "quadristop_L"
  ),

  type = c(
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "insecticide",
    "fungicide",
    "fungicide",
    "fungicide",
    "fungicide",
    "fungicide",
    "fungicide",
    "fungicide",
    "fungicide"
  ),

  mode = c(
    "biological",
    "biological",
    "biological",
    "biological",
    "biological",
    "biological",
    "biological",
    "biological",
    "synthetic",
    "synthetic",
    "synthetic",
    "biological",
    "biological",
    "biological",
    "synthetic",
    "synthetic",
    "synthetic",
    "synthetic",
    "synthetic"
  )
)

chem_info$type <- factor(chem_info$type,
                         levels = c("insecticide", "fungicide"))

chem_info$mode <- factor(chem_info$mode,
                         levels = c("biological", "synthetic"))

chem_info <- chem_info[order(chem_info$type, chem_info$mode), ]

# Create combined classification
chem_info$class <- paste(chem_info$type, chem_info$mode, sep = "_")

chem_info <- chem_info[order(chem_info$mode), ]

# Four colors
chem_colors <- c(
"insecticide_synthetic" = "brown4",
"insecticide_biological" = "orchid1",
"fungicide_synthetic" = "deepskyblue4",
"fungicide_biological" = "lightskyblue"
)
```

## Crithidia vs. logical chemicals 

### Models 

```{r}

## ---------------- AZAGUARD ----------------
mod_azaguard <- glm(
  cbind(crith_pos, crith_neg) ~ azaguard_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_azaguard)
Anova(mod_azaguard)

ma.s <- emmeans(mod_azaguard, pairwise ~ azaguard_L, type = "response")
ma.s.df <- as.data.frame(ma.s$emmeans)
ma.s

## ---------------- BOTANIGARD 22WP ----------------
mod_botanigard_22wp <- glm(
  cbind(crith_pos, crith_neg) ~ botanigard_22wp_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_botanigard_22wp)
Anova(mod_botanigard_22wp)

mb22.s <- emmeans(mod_botanigard_22wp, pairwise ~ botanigard_22wp_L, type = "response")
mb22.s.df <- as.data.frame(mb22.s$emmeans)
mb22.s 

## ---------------- BOTANIGARD ES ----------------
mod_botanigard_es <- glm(
  cbind(crith_pos, crith_neg) ~ botanigard_es_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_botanigard_es)
Anova(mod_botanigard_es)

mbe.s <- emmeans(mod_botanigard_es, pairwise ~ botanigard_es_L, type = "response")
mbe.s.df <- as.data.frame(mbe.s$emmeans)
mbe.s

## ---------------- CAPTIVA PRIME ----------------
mod_captiva_prime <- glm(
  cbind(crith_pos, crith_neg) ~ captiva_prime_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_captiva_prime)
Anova(mod_captiva_prime)

mcp.s <- emmeans(mod_captiva_prime, pairwise ~ captiva_prime_L, type = "response")
mcp.s.df <- as.data.frame(mcp.s$emmeans)
mcp.s

## ---------------- NOFLY ----------------
mod_nofly <- glm(
  cbind(crith_pos, crith_neg) ~ nofly_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_nofly)
Anova(mod_nofly)

mnf.s <- emmeans(mod_nofly, pairwise ~ nofly_L, type = "response")
mnf.s.df <- as.data.frame(mnf.s$emmeans)
mnf.s

## ---------------- VENERATE CG ----------------
mod_venerate_cg <- glm(
  cbind(crith_pos, crith_neg) ~ venerate_cg_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_venerate_cg)
Anova(mod_venerate_cg)

mvc.s <- emmeans(mod_venerate_cg, pairwise ~ venerate_cg_L, type = "response")
mvc.s.df <- as.data.frame(mvc.s$emmeans)
mvc.s

## ---------------- M-PEDE ----------------
mod_m_pede <- glm(
  cbind(crith_pos, crith_neg) ~ m_pede_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_m_pede)
Anova(mod_m_pede)

mmp.s <- emmeans(mod_m_pede, pairwise ~ m_pede_L, type = "response")
mmp.s.df <- as.data.frame(mmp.s$emmeans)
mmp.s

## ---------------- ROOTSHIELD PLUS ----------------
mod_rootshield_plus <- glm(
  cbind(crith_pos, crith_neg) ~ rootshield_plus_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_rootshield_plus)
Anova(mod_rootshield_plus)

mrs.s <- emmeans(mod_rootshield_plus, pairwise ~ rootshield_plus_L, type = "response")
mrs.s.df <- as.data.frame(mrs.s$emmeans)
mrs.s

## ---------------- LALSTOP K61 ----------------
mod_lalstop_k61 <- glm(
  cbind(crith_pos, crith_neg) ~ lalstop_k61_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_lalstop_k61)
Anova(mod_lalstop_k61)

mlk.s <- emmeans(mod_lalstop_k61, pairwise ~ lalstop_k61_L, type = "response")
mlk.s.df <- as.data.frame(mlk.s$emmeans)
mlk.s

## ---------------- BELEAF 50SG ----------------
mod_beleaf_50sg <- glm(
  cbind(crith_pos, crith_neg) ~ beleaf_50sg_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_beleaf_50sg)
Anova(mod_beleaf_50sg)

mbf.s <- emmeans(mod_beleaf_50sg, pairwise ~ beleaf_50sg_L, type = "response")
mbf.s.df <- as.data.frame(mbf.s$emmeans)
mbf.s

## ---------------- CORAGEN ----------------
mod_coragen <- glm(
  cbind(crith_pos, crith_neg) ~ coragen_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_coragen)
Anova(mod_coragen)

mcg.s <- emmeans(mod_coragen, pairwise ~ coragen_L, type = "response")
mcg.s.df <- as.data.frame(mcg.s$emmeans)
mcg.s

## ---------------- ENTRUST SC ----------------
mod_entrust_sc <- glm(
  cbind(crith_pos, crith_neg) ~ entrust_sc_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_entrust_sc)
Anova(mod_entrust_sc)

mes.s <- emmeans(mod_entrust_sc, pairwise ~ entrust_sc_L, type = "response")
mes.s.df <- as.data.frame(mes.s$emmeans)
mes.s

## ---------------- PYLON ----------------
mod_pylon <- glm(
  cbind(crith_pos, crith_neg) ~ pylon_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_pylon)
Anova(mod_pylon)

mpy.s <- emmeans(mod_pylon, pairwise ~ pylon_L, type = "response")
mpy.s.df <- as.data.frame(mpy.s$emmeans)
mpy.s 

## ---------------- GROTTO ----------------
mod_grotto <- glm(
  cbind(crith_pos, crith_neg) ~ grotto_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_grotto)
Anova(mod_grotto)

mgr.s <- emmeans(mod_grotto, pairwise ~ grotto_L, type = "response")
mgr.s.df <- as.data.frame(mgr.s$emmeans)
mgr.s

## ---------------- LUNA TRANQUILITY ----------------
mod_luna_tranquility <- glm(
  cbind(crith_pos, crith_neg) ~ luna_tranquility_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_luna_tranquility)
Anova(mod_luna_tranquility)

mlt.s <- emmeans(mod_luna_tranquility, pairwise ~ luna_tranquility_L, type = "response")
mlt.s.df <- as.data.frame(mlt.s$emmeans)
mlt.s 

## ---------------- PREVICUR FLEX ----------------
mod_previcur_flex <- glm(
  cbind(crith_pos, crith_neg) ~ previcur_flex_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_previcur_flex)
Anova(mod_previcur_flex)

mpf.s <- emmeans(mod_previcur_flex, pairwise ~ previcur_flex_L, type = "response")
mpf.s.df <- as.data.frame(mpf.s$emmeans)
mpf.s

## ---------------- FONTELIS ----------------
mod_fontelis <- glm(
  cbind(crith_pos, crith_neg) ~ fontelis_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_fontelis)
Anova(mod_fontelis)

mfo.s <- emmeans(mod_fontelis, pairwise ~ fontelis_L, type = "response")
mfo.s.df <- as.data.frame(mfo.s$emmeans)
mfo.s

## ---------------- QUADRISTOP ----------------
mod_quadristop <- glm(
  cbind(crith_pos, crith_neg) ~ quadristop_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_quadristop)
Anova(mod_quadristop)

mqs.s <- emmeans(mod_quadristop, pairwise ~ quadristop_L, type = "response")
mqs.s.df <- as.data.frame(mqs.s$emmeans)
mqs.s

## ---------------- MILSTOP ----------------
mod_milstop <- glm(
  cbind(crith_pos, crith_neg) ~ milstop_L,
  data = gh6,
  family = binomial("logit")
)

summary(mod_milstop)
Anova(mod_milstop)

mms.s <- emmeans(mod_milstop, pairwise ~ milstop_L, type = "response")
mms.s.df <- as.data.frame(mms.s$emmeans)
mms.s 
```

```{r}
ma.s 
mb22.s 
mbe.s 
mcp.s 
mnf.s 
mvc.s 
mmp.s 
mrs.s 
mlk.s 
mbf.s 
mcg.s 
mes.s 
mpy.s 
mgr.s 
mlt.s  
mpf.s 
mfo.s 
mqs.s 
mms.s 
```


### Summary Table

```{r}
mod.crith <- list(
azaguard = mod_azaguard,
botanigard_22wp = mod_botanigard_22wp,
botanigard_es = mod_botanigard_es,
captiva_prime = mod_captiva_prime,
nofly = mod_nofly,
venerate_cg = mod_venerate_cg,
m_pede = mod_m_pede,
rootshield_plus = mod_rootshield_plus,
lalstop_k61 = mod_lalstop_k61,
beleaf_50sg = mod_beleaf_50sg,
coragen = mod_coragen,
entrust_sc = mod_entrust_sc,
pylon = mod_pylon,
grotto = mod_grotto,
luna_tranquility = mod_luna_tranquility,
previcur_flex = mod_previcur_flex,
fontelis = mod_fontelis,
quadristop = mod_quadristop,
milstop = mod_milstop
)


summary_table.crith <- do.call(rbind, lapply(names(mod.crith), function(name){

  m <- mod.crith[[name]]
  s <- summary(m)

  coef_row <- s$coefficients[2, ]
  lr <- Anova(m)$`LR Chisq`[1]
  LRT <- Anova(m)$'Pr(>Chisq)'[1]

  data.frame(
    Pesticide = name,
    Estimate = coef_row["Estimate"],
    SE = coef_row["Std. Error"],
    z = coef_row["z value"],
    p = coef_row["Pr(>|z|)"],
    LR_ChiSq = lr,
    LRT = LRT,
    AIC = AIC(m),
    Residual_Deviance = m$deviance
  )
}))


summary_table.crith$Estimate <- signif(summary_table.crith$Estimate,3)
summary_table.crith$SE <- signif(summary_table.crith$SE,3)
summary_table.crith$z <- round(summary_table.crith$z,2)
summary_table.crith$p <- signif(summary_table.crith$p,3)
summary_table.crith$LR_ChiSq <- round(summary_table.crith$LR_ChiSq,2)
summary_table.crith$AIC <- round(summary_table.crith$AIC,2)
summary_table.crith$Residual_Deviance <- round(summary_table.crith$Residual_Deviance,2)
summary_table.crith$LRT <- signif(summary_table.crith$LRT,2)

summary_table.crith

csdf <- as.data.frame(summary_table.crith)

```

#### Plots

##### basic plot 

```{r, fig.height=20, fig.width=12, dpi=600}
predictors <- c(
"azaguard_L",
"botanigard_22wp_L",
"botanigard_es_L",
"captiva_prime_L",
"nofly_L",
"venerate_cg_L",
"m_pede_L",
"rootshield_plus_L",
"lalstop_k61_L",
"beleaf_50sg_L",
"coragen_L",
"entrust_sc_L",
"pylon_L",
"grotto_L",
"luna_tranquility_L",
"previcur_flex_L",
"fontelis_L",
"quadristop_L",
"milstop_L"
)

gh6$crith_prop <- gh6$crith_pos /
  (gh6$crith_pos + gh6$crith_neg)

gh6$crith_se <- sqrt(
  (gh6$crith_prop * (1 - gh6$crith_prop)) /
  (gh6$crith_pos + gh6$crith_neg)
)


plots <- lapply(predictors, function(var){

  ggplot(gh6, aes_string(x = var, y = "crith_prop")) +
    geom_boxplot(width = 0.5, alpha = 0.6, outlier.shape = NA) +
    geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
    labs(
      x = var,
      y = "Crithidia infection proportion"
    ) +
    theme_classic(base_size = 14) +
    scale_x_discrete(labels = c("FALSE" = "Not applied", "TRUE" = "Applied"))
})

wrap_plots(plots, ncol = 4)

```


##### updated crithidia vs. logical chems 

```{r, fig.height=22, fig.width=18}
plots <- lapply(1:nrow(chem_info), function(i){


chem_type <- chem_info$type[i]
chem_mode <- chem_info$mode[i]
var <- chem_info$pesticide[i]
chem_class <- chem_info$class[i]
var2 <- gsub("_L$", "", chem_info$pesticide[i])  # remove _L at end


stats_row <- summary_table.crith[summary_table.crith$Pesticide == var2, ]

annot_text <- paste0(
    "LRT χ² = ", round(stats_row$LR_ChiSq, 2), 
    ", p = ", signif(stats_row$LRT, 3))

ggplot(gh6, aes_string(x = var, y = "crith_prop")) +
  geom_boxplot(fill = chem_colors[chem_class],
               alpha = 0.6,
               width = 0.5,
               outlier.shape = NA) +
  geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
  labs(
    title = gsub("_L","",var),
    subtitle = paste(chem_type, "-", chem_mode),
    x = "",
    y = "Probability of *Crithidia* Detection"
  ) +
  scale_x_discrete(labels = c("FALSE"="Not applied","TRUE"="Applied")) +
  theme_classic(base_size = 16) +
  theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
      "text",
      x = 2, y = 1.05, # top-right
      label = annot_text,
      hjust = 1, vjust = 1, size = 4
    ) +
  coord_cartesian(ylim = c(0, 1))
})


wrap_plots(plots, ncol = 4)
```

## Apicystis vs. logical chemicals 

```{r}
mod.a_azaguard <- glm(
  cbind(api_pos, api_neg) ~ azaguard_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_azaguard)
Anova(mod.a_azaguard)

ma.a <- emmeans(mod.a_azaguard, pairwise ~ azaguard_L, type = "response")
ma.a.df <- as.data.frame(ma.a$emmeans)
ma.a


mod.a_botanigard_22wp <- glm(
  cbind(api_pos, api_neg) ~ botanigard_22wp_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_botanigard_22wp)
Anova(mod.a_botanigard_22wp)

mb22.a <- emmeans(mod.a_botanigard_22wp, pairwise ~ botanigard_22wp_L, type = "response")
mb22.a.df <- as.data.frame(mb22.a$emmeans)
mb22.a


mod.a_botanigard_es <- glm(
  cbind(api_pos, api_neg) ~ botanigard_es_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_botanigard_es)
Anova(mod.a_botanigard_es)

mbe.a <- emmeans(mod.a_botanigard_es, pairwise ~ botanigard_es_L, type = "response")
mbe.a.df <- as.data.frame(mbe.a$emmeans)
mbe.a


mod.a_captiva_prime <- glm(
  cbind(api_pos, api_neg) ~ captiva_prime_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_captiva_prime)
Anova(mod.a_captiva_prime)

mcp.a <- emmeans(mod.a_captiva_prime, pairwise ~ captiva_prime_L, type = "response")
mcp.a.df <- as.data.frame(mcp.a$emmeans)
mcp.a


mod.a_nofly <- glm(
  cbind(api_pos, api_neg) ~ nofly_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_nofly)
Anova(mod.a_nofly)

mnf.a <- emmeans(mod.a_nofly, pairwise ~ nofly_L, type = "response")
mnf.a.df <- as.data.frame(mnf.a$emmeans)
mnf.a


mod.a_venerate_cg <- glm(
  cbind(api_pos, api_neg) ~ venerate_cg_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_venerate_cg)
Anova(mod.a_venerate_cg)

mvc.a <- emmeans(mod.a_venerate_cg, pairwise ~ venerate_cg_L, type = "response")
mvc.a.df <- as.data.frame(mvc.a$emmeans)
mvc.a


mod.a_m_pede <- glm(
  cbind(api_pos, api_neg) ~ m_pede_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_m_pede)
Anova(mod.a_m_pede)

mmp.a <- emmeans(mod.a_m_pede, pairwise ~ m_pede_L, type = "response")
mmp.a.df <- as.data.frame(mmp.a$emmeans)
mmp.a


mod.a_rootshield_plus <- glm(
  cbind(api_pos, api_neg) ~ rootshield_plus_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_rootshield_plus)
Anova(mod.a_rootshield_plus)

mrs.a <- emmeans(mod.a_rootshield_plus, pairwise ~ rootshield_plus_L, type = "response")
mrs.a.df <- as.data.frame(mrs.a$emmeans)
mrs.a


mod.a_lalstop_k61 <- glm(
  cbind(api_pos, api_neg) ~ lalstop_k61_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_lalstop_k61)
Anova(mod.a_lalstop_k61)

mlk.a <- emmeans(mod.a_lalstop_k61, pairwise ~ lalstop_k61_L, type = "response")
mlk.a.df <- as.data.frame(mlk.a$emmeans)
mlk.a


mod.a_beleaf_50sg <- glm(
  cbind(api_pos, api_neg) ~ beleaf_50sg_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_beleaf_50sg)
Anova(mod.a_beleaf_50sg)

mbf.a <- emmeans(mod.a_beleaf_50sg, pairwise ~ beleaf_50sg_L, type = "response")
mbf.a.df <- as.data.frame(mbf.a$emmeans)
mbf.a


mod.a_coragen <- glm(
  cbind(api_pos, api_neg) ~ coragen_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_coragen)
Anova(mod.a_coragen)

mcg.a <- emmeans(mod.a_coragen, pairwise ~ coragen_L, type = "response")
mcg.a.df <- as.data.frame(mcg.a$emmeans)
mcg.a


mod.a_entrust_sc <- glm(
  cbind(api_pos, api_neg) ~ entrust_sc_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_entrust_sc)
Anova(mod.a_entrust_sc)

mes.a <- emmeans(mod.a_entrust_sc, pairwise ~ entrust_sc_L, type = "response")
mes.a.df <- as.data.frame(mes.a$emmeans)
mes.a


mod.a_pylon <- glm(
  cbind(api_pos, api_neg) ~ pylon_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_pylon)
Anova(mod.a_pylon)

mpy.a <- emmeans(mod.a_pylon, pairwise ~ pylon_L, type = "response")
mpy.a.df <- as.data.frame(mpy.a$emmeans)
mpy.a


mod.a_grotto <- glm(
  cbind(api_pos, api_neg) ~ grotto_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_grotto)
Anova(mod.a_grotto)

mgr.a <- emmeans(mod.a_grotto, pairwise ~ grotto_L, type = "response")
mgr.a.df <- as.data.frame(mgr.a$emmeans)
mgr.a


mod.a_luna_tranquility <- glm(
  cbind(api_pos, api_neg) ~ luna_tranquility_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_luna_tranquility)
Anova(mod.a_luna_tranquility)

mlt.a <- emmeans(mod.a_luna_tranquility, pairwise ~ luna_tranquility_L, type = "response")
mlt.a.df <- as.data.frame(mlt.a$emmeans)
mlt.a


mod.a_previcur_flex <- glm(
  cbind(api_pos, api_neg) ~ previcur_flex_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_previcur_flex)
Anova(mod.a_previcur_flex)

mpf.a <- emmeans(mod.a_previcur_flex, pairwise ~ previcur_flex_L, type = "response")
mpf.a.df <- as.data.frame(mpf.a$emmeans)
mpf.a


mod.a_fontelis <- glm(
  cbind(api_pos, api_neg) ~ fontelis_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_fontelis)
Anova(mod.a_fontelis)

mfo.a <- emmeans(mod.a_fontelis, pairwise ~ fontelis_L, type = "response")
mfo.a.df <- as.data.frame(mfo.a$emmeans)
mfo.a


mod.a_quadristop <- glm(
  cbind(api_pos, api_neg) ~ quadristop_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_quadristop)
Anova(mod.a_quadristop)

mqs.a <- emmeans(mod.a_quadristop, pairwise ~ quadristop_L, type = "response")
mqs.a.df <- as.data.frame(mqs.a$emmeans)
mqs.a


mod.a_milstop <- glm(
  cbind(api_pos, api_neg) ~ milstop_L,
  data = gh6,
  family = binomial("logit"))
summary(mod.a_milstop)
Anova(mod.a_milstop)

mms.a <- emmeans(mod.a_milstop, pairwise ~ milstop_L, type = "response")
mms.a.df <- as.data.frame(mms.a$emmeans)
mms.a

```

```{r}
ma.a 
mb22.a 
mbe.a 
mcp.a 
mnf.a 
mvc.a 
mmp.a 
mrs.a 
mlk.a 
mbf.a 
mcg.a 
mes.a 
mpy.a 
mgr.a 
mlt.a  
mpf.a 
mfo.a 
mqs.a 
mms.a 
```


### summary table 

```{r}
mod.a.api <- list(
azaguard = mod.a_azaguard,
botanigard_22wp = mod.a_botanigard_22wp,
botanigard_es = mod.a_botanigard_es,
captiva_prime = mod.a_captiva_prime,
nofly = mod.a_nofly,
venerate_cg = mod.a_venerate_cg,
m_pede = mod.a_m_pede,
rootshield_plus = mod.a_rootshield_plus,
lalstop_k61 = mod.a_lalstop_k61,
beleaf_50sg = mod.a_beleaf_50sg,
coragen = mod.a_coragen,
entrust_sc = mod.a_entrust_sc,
pylon = mod.a_pylon,
grotto = mod.a_grotto,
luna_tranquility = mod.a_luna_tranquility,
previcur_flex = mod.a_previcur_flex,
fontelis = mod.a_fontelis,
quadristop = mod.a_quadristop,
milstop = mod.a_milstop
)


summary_table.api <- do.call(rbind, lapply(names(mod.a.api), function(name){

  m <- mod.a.api[[name]]
  s <- summary(m)

  coef_row <- s$coefficients[2, ]
  lr <- Anova(m)$`LR Chisq`[1]
  LRT <- Anova(m)$'Pr(>Chisq)'[1]

  data.frame(
    Pesticide = name,
    Estimate = coef_row["Estimate"],
    SE = coef_row["Std. Error"],
    z = coef_row["z value"],
    p = coef_row["Pr(>|z|)"],
    LR_ChiSq = lr,
    LRT = LRT,
    AIC = AIC(m),
    Residual_Deviance = m$deviance
  )
}))


summary_table.api$Estimate <- signif(summary_table.api$Estimate,3)
summary_table.api$SE <- signif(summary_table.api$SE,3)
summary_table.api$z <- round(summary_table.api$z,2)
summary_table.api$p <- signif(summary_table.api$p,3)
summary_table.api$LR_ChiSq <- round(summary_table.api$LR_ChiSq,2)
summary_table.api$AIC <- round(summary_table.api$AIC,2)
summary_table.api$Residual_Deviance <- round(summary_table.api$Residual_Deviance,2)
summary_table.api$LRT <- signif(summary_table.api$LRT,2)

summary_table.api

asdf <- as.data.frame(summary_table.api)

```

### plots

```{r, fig.height=22, fig.width=18}
plots <- lapply(1:nrow(chem_info), function(i){


chem_type <- chem_info$type[i]
chem_mode <- chem_info$mode[i]
chem_class <- chem_info$class[i]
var <- chem_info$pesticide[i]
var2 <- gsub("_L$", "", chem_info$pesticide[i])  # remove _L at end


stats_row <- summary_table.api[summary_table.api$Pesticide == var2, ]

annot_text <- paste0(
    "LRT χ² = ", round(stats_row$LR_ChiSq, 2), 
    ", p = ", signif(stats_row$LRT, 3))

ggplot(gh6, aes_string(x = var, y = "api_prop")) +
  geom_boxplot(fill = chem_colors[chem_class],
               alpha = 0.6,
               width = 0.5,
               outlier.shape = NA) +
  geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
  labs(
    title = gsub("_L","",var),
    subtitle = paste(chem_type, "-", chem_mode),
    x = "",
    y = "Probability of *Apicystis* detection"
  ) +
  scale_x_discrete(labels = c("FALSE"="Not applied","TRUE"="Applied")) +
  theme_classic(base_size = 16) +
   theme(axis.title.y = ggtext::element_markdown()) + 
  annotate(
      "text",
      x = 2, y = 1.05, # top-right
      label = annot_text,
      hjust = 1, vjust = 1, size = 4
    ) +
  coord_cartesian(ylim = c(0, 1))
})


wrap_plots(plots, ncol = 4)
```

### anther bruising vs. specific chemicals presence absence 

```{r}
anth_chem_m1 <- glm(logbruise ~ azaguard_L, data = gh6)
Anova(anth_chem_m1)
summary(anth_chem_m1)

anth_chem_m2 <- glm(logbruise ~ botanigard_22wp_L, data = gh6)
Anova(anth_chem_m2)
summary(anth_chem_m2)

anth_chem_m3 <- glm(logbruise ~ botanigard_es_L, data = gh6)
Anova(anth_chem_m3)
summary(anth_chem_m3)

anth_chem_m4 <- glm(logbruise ~ captiva_prime_L, data = gh6)
Anova(anth_chem_m4)
summary(anth_chem_m4)

anth_chem_m5 <- glm(logbruise ~ nofly_L, data = gh6)
Anova(anth_chem_m5)
summary(anth_chem_m5)

anth_chem_m6 <- glm(logbruise ~ venerate_cg_L, data = gh6)
Anova(anth_chem_m6)
summary(anth_chem_m6)

anth_chem_m7 <- glm(logbruise ~ m_pede_L, data = gh6)
Anova(anth_chem_m7)
summary(anth_chem_m7)

anth_chem_m8 <- glm(logbruise ~ rootshield_plus_L, data = gh6)
Anova(anth_chem_m8)
summary(anth_chem_m8)

anth_chem_m9 <- glm(logbruise ~ lalstop_k61_L, data = gh6)
Anova(anth_chem_m9)
summary(anth_chem_m9)

anth_chem_m10 <- glm(logbruise ~ beleaf_50sg_L, data = gh6)
Anova(anth_chem_m10)
summary(anth_chem_m10)

anth_chem_m11 <- glm(logbruise ~ coragen_L, data = gh6)
Anova(anth_chem_m11)
summary(anth_chem_m11)

anth_chem_m12 <- glm(logbruise ~ entrust_sc_L, data = gh6)
Anova(anth_chem_m12)
summary(anth_chem_m12)

anth_chem_m13 <- glm(logbruise ~ pylon_L, data = gh6)
Anova(anth_chem_m13)
summary(anth_chem_m13)

anth_chem_m14 <- glm(logbruise ~ grotto_L, data = gh6)
Anova(anth_chem_m14)
summary(anth_chem_m14)

anth_chem_m15 <- glm(logbruise ~ luna_tranquility_L, data = gh6)
Anova(anth_chem_m15)
summary(anth_chem_m15)

anth_chem_m16 <- glm(logbruise ~ previcur_flex_L, data = gh6)
Anova(anth_chem_m16)
summary(anth_chem_m16)

anth_chem_m17 <- glm(logbruise ~ fontelis_L, data = gh6)
Anova(anth_chem_m17)
summary(anth_chem_m17)

anth_chem_m18 <- glm(logbruise ~ quadristop_L, data = gh6)
Anova(anth_chem_m18)
summary(anth_chem_m18)

anth_chem_m19 <- glm(logbruise ~ milstop_L, data = gh6)
Anova(anth_chem_m19)
summary(anth_chem_m19)
```

### summary table 

```{r}
anth_models <- list(
  azaguard_L = anth_chem_m1,
  botanigard_22wp_L = anth_chem_m2,
  botanigard_es_L = anth_chem_m3,
  captiva_prime_L = anth_chem_m4,
  nofly_L = anth_chem_m5,
  venerate_cg_L = anth_chem_m6,
  m_pede_L = anth_chem_m7,
  rootshield_plus_L = anth_chem_m8,
  lalstop_k61_L = anth_chem_m9,
  beleaf_50sg_L = anth_chem_m10,
  coragen_L = anth_chem_m11,
  entrust_sc_L = anth_chem_m12,
  pylon_L = anth_chem_m13,
  grotto_L = anth_chem_m14,
  luna_tranquility_L = anth_chem_m15,
  previcur_flex_L = anth_chem_m16,
  fontelis_L = anth_chem_m17,
  quadristop_L = anth_chem_m18,
  milstop_L = anth_chem_m19
)

summary_table.anth <- do.call(rbind, lapply(names(anth_models), function(name){

  m <- anth_models[[name]]
  s <- summary(m)

  coef_row <- s$coefficients[2, ]

  lr <- Anova(m)$`LR Chisq`[1]
  LRT <- Anova(m)$`Pr(>Chisq)`[1]

  data.frame(
    Predictor = name,
    Estimate = coef_row["Estimate"],
    SE = coef_row["Std. Error"],
    z = coef_row["t value"],
    p = coef_row["Pr(>|t|)"],
    LR_ChiSq = lr,
    AIC = AIC(m),
    LRT = LRT,
    Residual_Deviance = m$deviance
  )

}))

summary_table.anth$SE <- signif(summary_table.anth$SE, 3)
summary_table.anth$z <- round(summary_table.anth$z, 2)
summary_table.anth$p <- signif(summary_table.anth$p, 3)
summary_table.anth$LR_ChiSq <- round(summary_table.anth$LR_ChiSq, 2)
summary_table.anth$AIC <- round(summary_table.anth$AIC, 2)
summary_table.anth$Residual_Deviance <- round(summary_table.anth$Residual_Deviance, 2)
summary_table.anth$LRT <- signif(summary_table.anth$LRT, 3)

anth_table <- as.data.frame(summary_table.anth)
anth_table

```


### plot of anther bruise vs pesticides applied/not applied 


```{r, fig.height=22, fig.width=18}
plots <- lapply(1:nrow(chem_info), function(i){


chem_type <- chem_info$type[i]
chem_mode <- chem_info$mode[i]
chem_class <- chem_info$class[i]
var <- chem_info$pesticide[i]
var2 <- gsub("_L$", "", chem_info$pesticide[i])  # remove _L at end


ggplot(gh6, aes_string(x = var, y = "average_bruise")) +
  geom_boxplot(fill = chem_colors[chem_class],
               alpha = 0.6,
               width = 0.5,
               outlier.shape = NA) +
  geom_jitter(width = 0.1, size = 3, alpha = 0.8) +
  labs(
    title = gsub("_L","",var),
    subtitle = paste(chem_type, "-", chem_mode),
    x = "",
    y = "Average Anther Score"
  ) +
  scale_x_discrete(labels = c("FALSE"="Not applied","TRUE"="Applied")) +
  theme_classic(base_size = 16) 
})


wrap_plots(plots, ncol = 4)
```