Pipiens competence for Usutu
2024-04-15
Vector competence : Efficiency parameters :
Data
M<-subset(data_full,tissu=="z_midguts")
X<-subset(data_full,tissu=="thorax")
S<-subset(data_full,tissu=="saliva")Efficiency :
##All tissues together
glm_all<-glmmTMB(results~lineage*bloodmeal_titer*tissu*wing_size+(1|replicate),family="binomial",data=data_full,na.action = na.omit)
car::Anova(glm_all,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0645 1 0.7995
## lineage 0.0001 1 0.9919
## bloodmeal_titer 1.6801 1 0.1949
## tissu 0.0498 2 0.9754
## wing_size 0.0253 1 0.8736
## lineage:bloodmeal_titer 1.5139 1 0.2185
## lineage:tissu 0.1067 2 0.9481
## bloodmeal_titer:tissu 0.2202 2 0.8957
## lineage:wing_size 0.0132 1 0.9085
## bloodmeal_titer:wing_size 1.2072 1 0.2719
## tissu:wing_size 0.0686 2 0.9663
## lineage:bloodmeal_titer:tissu 0.2462 2 0.8842
## lineage:bloodmeal_titer:wing_size 1.6844 1 0.1943
## lineage:tissu:wing_size 0.1054 2 0.9487
## bloodmeal_titer:tissu:wing_size 0.1445 2 0.9303
## lineage:bloodmeal_titer:tissu:wing_size 0.3576 2 0.8363glm_all1<-glmmTMB(results~(lineage+bloodmeal_titer+tissu+wing_size)^3+(1|replicate),family="binomial",data=data_full,na.action = na.omit)
anova(glm_all1,glm_all)## Data: data_full
## Models:
## glm_all1: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^3 + , zi=~0, disp=~1
## glm_all1: (1 | replicate), zi=~0, disp=~1
## glm_all: results ~ lineage * bloodmeal_titer * tissu * wing_size + (1 | , zi=~0, disp=~1
## glm_all: replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_all1 23 699.96 806.44 -326.98 653.96
## glm_all 25 703.60 819.33 -326.80 653.60 0.3674 2 0.8322car::Anova(glm_all,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0645 1 0.7995
## lineage 0.0001 1 0.9919
## bloodmeal_titer 1.6801 1 0.1949
## tissu 0.0498 2 0.9754
## wing_size 0.0253 1 0.8736
## lineage:bloodmeal_titer 1.5139 1 0.2185
## lineage:tissu 0.1067 2 0.9481
## bloodmeal_titer:tissu 0.2202 2 0.8957
## lineage:wing_size 0.0132 1 0.9085
## bloodmeal_titer:wing_size 1.2072 1 0.2719
## tissu:wing_size 0.0686 2 0.9663
## lineage:bloodmeal_titer:tissu 0.2462 2 0.8842
## lineage:bloodmeal_titer:wing_size 1.6844 1 0.1943
## lineage:tissu:wing_size 0.1054 2 0.9487
## bloodmeal_titer:tissu:wing_size 0.1445 2 0.9303
## lineage:bloodmeal_titer:tissu:wing_size 0.3576 2 0.8363glm_all2<-glmmTMB(results~(lineage+bloodmeal_titer+tissu+wing_size)^2+(lineage:tissu:wing_size+lineage:bloodmeal_titer:wing_size+lineage:bloodmeal_titer:tissu)+(1|replicate),family="binomial",data=data_full,na.action = na.omit)
anova(glm_all1,glm_all2)## Data: data_full
## Models:
## glm_all2: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^2 + , zi=~0, disp=~1
## glm_all2: (lineage:tissu:wing_size + lineage:bloodmeal_titer:wing_size + , zi=~0, disp=~1
## glm_all2: lineage:bloodmeal_titer:tissu) + (1 | replicate), zi=~0, disp=~1
## glm_all1: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^3 + , zi=~0, disp=~1
## glm_all1: (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_all2 21 696.12 793.34 -327.06 654.12
## glm_all1 23 699.96 806.44 -326.98 653.96 0.1582 2 0.9239car::Anova(glm_all2,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.1175 1 0.73181
## lineage 0.0415 1 0.83852
## bloodmeal_titer 6.1060 1 0.01347 *
## tissu 0.0637 2 0.96866
## wing_size 0.0128 1 0.90980
## lineage:bloodmeal_titer 4.4337 1 0.03524 *
## lineage:tissu 0.0960 2 0.95312
## lineage:wing_size 0.0084 1 0.92702
## bloodmeal_titer:tissu 2.0740 2 0.35452
## bloodmeal_titer:wing_size 4.6372 1 0.03129 *
## tissu:wing_size 0.0198 2 0.99014
## lineage:tissu:wing_size 0.0749 2 0.96326
## lineage:bloodmeal_titer:wing_size 5.6772 1 0.01719 *
## lineage:bloodmeal_titer:tissu 3.0493 2 0.21769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1glm_all3<-glmmTMB(results~(lineage+bloodmeal_titer+tissu+wing_size)^2+(lineage:tissu:wing_size+lineage:bloodmeal_titer:wing_size)+(1|replicate),family="binomial",data=data_full,na.action = na.omit)
anova(glm_all3,glm_all2)## Data: data_full
## Models:
## glm_all3: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^2 + , zi=~0, disp=~1
## glm_all3: (lineage:tissu:wing_size + lineage:bloodmeal_titer:wing_size) + , zi=~0, disp=~1
## glm_all3: (1 | replicate), zi=~0, disp=~1
## glm_all2: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^2 + , zi=~0, disp=~1
## glm_all2: (lineage:tissu:wing_size + lineage:bloodmeal_titer:wing_size + , zi=~0, disp=~1
## glm_all2: lineage:bloodmeal_titer:tissu) + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_all3 19 695.27 783.23 -328.64 657.27
## glm_all2 21 696.12 793.34 -327.06 654.12 3.15 2 0.207car::Anova(glm_all3,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.2195 1 0.639386
## lineage 0.0638 1 0.800534
## bloodmeal_titer 7.6202 1 0.005772 **
## tissu 0.1392 2 0.932761
## wing_size 0.0021 1 0.963066
## lineage:bloodmeal_titer 6.1602 1 0.013065 *
## lineage:tissu 0.1298 2 0.937176
## lineage:wing_size 0.0432 1 0.835324
## bloodmeal_titer:tissu 10.4878 2 0.005280 **
## bloodmeal_titer:wing_size 5.2966 1 0.021367 *
## tissu:wing_size 0.0035 2 0.998232
## lineage:tissu:wing_size 0.1143 2 0.944452
## lineage:bloodmeal_titer:wing_size 6.0271 1 0.014088 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1glm_all4<-glmmTMB(results~(lineage+bloodmeal_titer+tissu+wing_size)^2+(lineage:bloodmeal_titer:wing_size)+(1|replicate),family="binomial",data=data_full,na.action = na.omit)
anova(glm_all4,glm_all3)## Data: data_full
## Models:
## glm_all4: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^2 + , zi=~0, disp=~1
## glm_all4: (lineage:bloodmeal_titer:wing_size) + (1 | replicate), zi=~0, disp=~1
## glm_all3: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^2 + , zi=~0, disp=~1
## glm_all3: (lineage:tissu:wing_size + lineage:bloodmeal_titer:wing_size) + , zi=~0, disp=~1
## glm_all3: (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_all4 17 691.39 770.08 -328.69 657.39
## glm_all3 19 695.27 783.23 -328.64 657.27 0.1143 2 0.9444car::Anova(glm_all4,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.2069 1 0.649175
## lineage 0.0654 1 0.798217
## bloodmeal_titer 7.6073 1 0.005813 **
## tissu 0.1913 2 0.908760
## wing_size 0.0016 1 0.968380
## lineage:bloodmeal_titer 6.2238 1 0.012604 *
## lineage:tissu 0.4666 2 0.791926
## lineage:wing_size 0.0236 1 0.878020
## bloodmeal_titer:tissu 10.5376 2 0.005150 **
## bloodmeal_titer:wing_size 5.2856 1 0.021502 *
## tissu:wing_size 0.0487 2 0.975920
## lineage:bloodmeal_titer:wing_size 6.0910 1 0.013587 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1glm_all4a<-glmmTMB(results~(lineage+bloodmeal_titer+tissu+wing_size)^2+(lineage:bloodmeal_titer:wing_size)+(replicate),family="binomial",data=data_full,na.action = na.omit)## dropping columns from rank-deficient conditional model: replicateFanova(glm_all4,glm_all4a)## Data: data_full
## Models:
## glm_all4: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^2 + , zi=~0, disp=~1
## glm_all4: (lineage:bloodmeal_titer:wing_size) + (1 | replicate), zi=~0, disp=~1
## glm_all4a: results ~ (lineage + bloodmeal_titer + tissu + wing_size)^2 + , zi=~0, disp=~1
## glm_all4a: (lineage:bloodmeal_titer:wing_size) + (replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_all4 17 691.39 770.08 -328.69 657.39
## glm_all4a 20 668.80 761.39 -314.40 628.80 28.582 3 2.741e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Plot by replicate
summarySE(data_full,measurevar="results",groupvars=c("lineage","bloodmeal_titer","tissu","replicate"),na.rm = TRUE)## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:plotly':
##
## arrange, mutate, rename, summarise## The following objects are masked from 'package:rstatix':
##
## desc, mutate## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compact## The following object is masked from 'package:ggpubr':
##
## mutate## lineage bloodmeal_titer tissu replicate N results sd
## 1 EU2 high saliva D 8 0.25000000 0.4629100
## 2 EU2 high saliva E 38 0.10526316 0.3110117
## 3 EU2 high saliva F 26 0.07692308 0.2717465
## 4 EU2 high thorax D 10 0.50000000 0.5270463
## 5 EU2 high thorax E 38 0.36842105 0.4888515
## 6 EU2 high thorax F 26 0.42307692 0.5038315
## 7 EU2 high z_midguts D 11 0.45454545 0.5222330
## 8 EU2 high z_midguts E 39 0.41025641 0.4983102
## 9 EU2 high z_midguts F 26 0.50000000 0.5099020
## 10 EU2 low saliva A 18 0.00000000 0.0000000
## 11 EU2 low saliva B 21 0.00000000 0.0000000
## 12 EU2 low saliva C 40 0.22500000 0.4229021
## 13 EU2 low thorax A 18 0.00000000 0.0000000
## 14 EU2 low thorax B 21 0.00000000 0.0000000
## 15 EU2 low thorax C 44 0.72727273 0.4505106
## 16 EU2 low z_midguts A 18 0.11111111 0.3233808
## 17 EU2 low z_midguts B 21 0.00000000 0.0000000
## 18 EU2 low z_midguts C 45 0.88888889 0.3178209
## 19 EU3 high saliva D 16 0.12500000 0.3415650
## 20 EU3 high saliva E 30 0.16666667 0.3790490
## 21 EU3 high saliva F 21 0.47619048 0.5117663
## 22 EU3 high thorax D 19 0.26315789 0.4524139
## 23 EU3 high thorax E 30 0.46666667 0.5074163
## 24 EU3 high thorax F 21 0.80952381 0.4023739
## 25 EU3 high z_midguts D 19 0.52631579 0.5129892
## 26 EU3 high z_midguts E 30 0.50000000 0.5085476
## 27 EU3 high z_midguts F 21 0.80952381 0.4023739
## 28 EU3 low saliva A 39 0.02564103 0.1601282
## 29 EU3 low saliva B 43 0.00000000 0.0000000
## 30 EU3 low saliva C 26 0.07692308 0.2717465
## 31 EU3 low thorax A 38 0.18421053 0.3928595
## 32 EU3 low thorax B 43 0.02325581 0.1524986
## 33 EU3 low thorax C 28 0.71428571 0.4600437
## 34 EU3 low z_midguts A 40 0.32500000 0.4743416
## 35 EU3 low z_midguts B 39 0.07692308 0.2699528
## 36 EU3 low z_midguts C 28 0.92857143 0.2622653
## se ci
## 1 0.16366342 0.38700249
## 2 0.05045277 0.10222702
## 3 0.05329387 0.10976078
## 4 0.16666667 0.37702619
## 5 0.07930219 0.16068150
## 6 0.09880948 0.20350194
## 7 0.15745916 0.35084088
## 8 0.07979350 0.16153349
## 9 0.10000000 0.20595386
## 10 0.00000000 0.00000000
## 11 0.00000000 0.00000000
## 12 0.06686669 0.13525064
## 13 0.00000000 0.00000000
## 14 0.00000000 0.00000000
## 15 0.06791703 0.13696775
## 16 0.07622159 0.16081351
## 17 0.00000000 0.00000000
## 18 0.04737794 0.09548396
## 19 0.08539126 0.18200715
## 20 0.06920457 0.14153923
## 21 0.11167657 0.23295323
## 22 0.10379087 0.21805653
## 23 0.09264111 0.18947235
## 24 0.08780519 0.18315841
## 25 0.11768779 0.24725287
## 26 0.09284767 0.18989481
## 27 0.08780519 0.18315841
## 28 0.02564103 0.05190754
## 29 0.00000000 0.00000000
## 30 0.05329387 0.10976078
## 31 0.06373022 0.12912969
## 32 0.02325581 0.04693213
## 33 0.08694009 0.17838633
## 34 0.07500000 0.15170182
## 35 0.04322704 0.08750856
## 36 0.04956348 0.10169585summarySE(data_full,measurevar="results",groupvars=c("lineage","bloodmeal_titer","tissu"),na.rm = TRUE)## lineage bloodmeal_titer tissu N results sd se
## 1 EU2 high saliva 72 0.11111111 0.3164751 0.03729695
## 2 EU2 high thorax 74 0.40540541 0.4943217 0.05746373
## 3 EU2 high z_midguts 76 0.44736842 0.5005260 0.05741427
## 4 EU2 low saliva 79 0.11392405 0.3197492 0.03597459
## 5 EU2 low thorax 83 0.38554217 0.4896820 0.05374958
## 6 EU2 low z_midguts 84 0.50000000 0.5030030 0.05488213
## 7 EU3 high saliva 67 0.25373134 0.4384298 0.05356273
## 8 EU3 high thorax 70 0.51428571 0.5034046 0.06016835
## 9 EU3 high z_midguts 70 0.60000000 0.4934352 0.05897678
## 10 EU3 low saliva 108 0.02777778 0.1651017 0.01588692
## 11 EU3 low thorax 109 0.25688073 0.4389311 0.04204198
## 12 EU3 low z_midguts 107 0.39252336 0.4906101 0.04742907
## ci
## 1 0.07436800
## 2 0.11452505
## 3 0.11437510
## 4 0.07161991
## 5 0.10692503
## 6 0.10915835
## 7 0.10694144
## 8 0.12003258
## 9 0.11765546
## 10 0.03149396
## 11 0.08333450
## 12 0.09403275est <- read_excel("data_full_15042024.xlsx", sheet = "PrevEstim",
col_types = c("text", "text",
"text", "text", "numeric",
"numeric", "numeric","numeric", "numeric", "numeric", "numeric"))
png("prev_300dpiTot.png", units = 'in', width = 12, height = 8, res = 300) # res = dpi (nombre pixels par image)
fig2<-ggplot(est) +
aes(
x = factor(bloodmeal_titer,levels = c('low','high')),
y = results,
colour = lineage,
size = N
) +
geom_point(aes(fill=lineage),shape = 21, color="black",position = position_jitterdodge(jitter.width=0.2,dodge.width=.75)) +
scale_color_brewer(palette = "Dark2", direction = 1) +
labs(y = "Prevalence",
x = "Blodmeal titer") +
facet_wrap(. ~ factor (tissu,levels=c('z_midguts','thorax','saliva')))+
theme_classic() +
scale_y_continuous(labels = scales::percent, limits=c(0,1))+
geom_jitter(data = est, aes(x = bloodmeal_titer, y = prev,fill = lineage), size = 15,position = position_dodge(.75,preserve = 'total'),color="black",shape="_")
plot(fig2)
dev.off()## quartz_off_screen
## 2plot(fig2)
Midgut analysis :
# Maximum model :
glm_mid<-glmmTMB(results~lineage*bloodmeal_titer*wing_size+(1|replicate),family="binomial",data=M,na.action = na.omit)
summary(glm_mid)## Family: binomial ( logit )
## Formula:
## results ~ lineage * bloodmeal_titer * wing_size + (1 | replicate)
## Data: M
##
## AIC BIC logLik deviance df.resid
## 297.7 330.2 -139.9 279.7 263
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 3.549 1.884
## Number of obs: 272, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.16270 3.70932 -0.044 0.965
## lineageEU3 1.70551 5.06378 0.337 0.736
## bloodmeal_titerlow -10.89853 8.28725 -1.315 0.188
## wing_size 0.00798 10.19341 0.001 0.999
## lineageEU3:bloodmeal_titerlow 15.77150 10.34135 1.525 0.127
## lineageEU3:wing_size -3.31276 14.62401 -0.226 0.821
## bloodmeal_titerlow:wing_size 27.45379 23.43399 1.172 0.241
## lineageEU3:bloodmeal_titerlow:wing_size -43.18206 29.80173 -1.449 0.147car::Anova(glm_mid,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0019 1 0.9650
## lineage 0.1134 1 0.7363
## bloodmeal_titer 1.7295 1 0.1885
## wing_size 0.0000 1 0.9994
## lineage:bloodmeal_titer 2.3259 1 0.1272
## lineage:wing_size 0.0513 1 0.8208
## bloodmeal_titer:wing_size 1.3725 1 0.2414
## lineage:bloodmeal_titer:wing_size 2.0995 1 0.1473# Minimum model
glm_mid2<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)^2+(1|replicate),family="binomial",data=M,na.action = na.omit)
summary(glm_mid2)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate)
## Data: M
##
## AIC BIC logLik deviance df.resid
## 297.9 326.8 -141.0 281.9 264
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 3.294 1.815
## Number of obs: 272, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8376 3.5608 -0.516 0.606
## lineageEU3 5.5022 4.3955 1.252 0.211
## bloodmeal_titerlow -1.6512 5.1899 -0.318 0.750
## wing_size 4.8227 9.7757 0.493 0.622
## lineageEU3:bloodmeal_titerlow 0.8531 0.7806 1.093 0.274
## lineageEU3:wing_size -14.2994 12.6621 -1.129 0.259
## bloodmeal_titerlow:wing_size 0.8439 14.3087 0.059 0.953anova(glm_mid2,glm_mid)## Data: M
## Models:
## glm_mid2: results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate), zi=~0, disp=~1
## glm_mid: results ~ lineage * bloodmeal_titer * wing_size + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_mid2 8 297.92 326.76 -140.96 281.92
## glm_mid 9 297.75 330.20 -139.87 279.75 2.1694 1 0.1408car::Anova(glm_mid2,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.2663 1 0.6058
## lineage 1.5670 1 0.2106
## bloodmeal_titer 0.1012 1 0.7504
## wing_size 0.2434 1 0.6218
## lineage:bloodmeal_titer 1.1943 1 0.2745
## lineage:wing_size 1.2753 1 0.2588
## bloodmeal_titer:wing_size 0.0035 1 0.9530glm_mid3<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)+(lineage:wing_size+lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=M,na.action = na.omit)
anova(glm_mid2,glm_mid3)## Data: M
## Models:
## glm_mid3: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size + , zi=~0, disp=~1
## glm_mid3: lineage:bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## glm_mid2: results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_mid3 7 295.92 321.16 -140.96 281.92
## glm_mid2 8 297.92 326.76 -140.96 281.92 0.0035 1 0.953summary(glm_mid3)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size +
## lineage:bloodmeal_titer) + (1 | replicate)
## Data: M
##
## AIC BIC logLik deviance df.resid
## 295.9 321.2 -141.0 281.9 265
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 3.297 1.816
## Number of obs: 272, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9055 3.3710 -0.565 0.572
## lineageEU3 5.4465 4.2943 1.268 0.205
## bloodmeal_titerlow -1.3600 1.6018 -0.849 0.396
## wing_size 5.0189 9.1962 0.546 0.585
## lineageEU3:wing_size -14.1378 12.3673 -1.143 0.253
## lineageEU3:bloodmeal_titerlow 0.8508 0.7797 1.091 0.275car::Anova(glm_mid3,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.3195 1 0.5719
## lineage 1.6086 1 0.2047
## bloodmeal_titer 0.7208 1 0.3959
## wing_size 0.2978 1 0.5852
## lineage:wing_size 1.3068 1 0.2530
## lineage:bloodmeal_titer 1.1908 1 0.2752glm_mid4<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=M,na.action = na.omit)
anova(glm_mid4,glm_mid3)## Data: M
## Models:
## glm_mid4: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:bloodmeal_titer) + , zi=~0, disp=~1
## glm_mid4: (1 | replicate), zi=~0, disp=~1
## glm_mid3: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size + , zi=~0, disp=~1
## glm_mid3: lineage:bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_mid4 6 295.27 316.90 -141.63 283.27
## glm_mid3 7 295.92 321.16 -140.96 281.92 1.3468 1 0.2458summary(glm_mid4)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:bloodmeal_titer) +
## (1 | replicate)
## Data: M
##
## AIC BIC logLik deviance df.resid
## 295.3 316.9 -141.6 283.3 266
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 3.291 1.814
## Number of obs: 272, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7607 2.3006 0.331 0.741
## lineageEU3 0.5608 0.3758 1.492 0.136
## bloodmeal_titerlow -1.3621 1.5993 -0.852 0.394
## wing_size -2.6527 5.8661 -0.452 0.651
## lineageEU3:bloodmeal_titerlow 0.8759 0.7779 1.126 0.260car::Anova(glm_mid4,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.1093 1 0.7409
## lineage 2.2276 1 0.1356
## bloodmeal_titer 0.7254 1 0.3944
## wing_size 0.2045 1 0.6511
## lineage:bloodmeal_titer 1.2677 1 0.2602glm_mid5<-glmmTMB(results~(lineage+bloodmeal_titer)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=M,na.action = na.omit)
summary(glm_mid5)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer) + (lineage:bloodmeal_titer) +
## (1 | replicate)
## Data: M
##
## AIC BIC logLik deviance df.resid
## 360.8 379.9 -175.4 350.8 332
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.642 1.625
## Number of obs: 337, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2106 0.9713 -0.217 0.8284
## lineageEU3 0.6697 0.3493 1.917 0.0552 .
## bloodmeal_titerlow -0.9218 1.4116 -0.653 0.5137
## lineageEU3:bloodmeal_titerlow 0.2850 0.6381 0.447 0.6551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_mid5,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0470 1 0.82836
## lineage 3.6764 1 0.05519 .
## bloodmeal_titer 0.4265 1 0.51373
## lineage:bloodmeal_titer 0.1995 1 0.65512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1glm_mid6<-glmmTMB(results~(lineage+bloodmeal_titer)+(1|replicate),family="binomial",data=M,na.action = na.omit)
anova(glm_mid6,glm_mid5)## Data: M
## Models:
## glm_mid6: results ~ (lineage + bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## glm_mid5: results ~ (lineage + bloodmeal_titer) + (lineage:bloodmeal_titer) + , zi=~0, disp=~1
## glm_mid5: (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_mid6 4 358.98 374.26 -175.49 350.98
## glm_mid5 5 360.78 379.88 -175.39 350.78 0.2038 1 0.6517summary(glm_mid6)## Family: binomial ( logit )
## Formula: results ~ (lineage + bloodmeal_titer) + (1 | replicate)
## Data: M
##
## AIC BIC logLik deviance df.resid
## 359.0 374.3 -175.5 351.0 333
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.527 1.59
## Number of obs: 337, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2542 0.9466 -0.269 0.78826
## lineageEU3 0.7580 0.2893 2.620 0.00879 **
## bloodmeal_titerlow -0.7578 1.3339 -0.568 0.56996
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_mid6,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0721 1 0.788265
## lineage 6.8642 1 0.008794 **
## bloodmeal_titer 0.3228 1 0.569958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Minimum model
glm_mid7<-glmmTMB(results~(lineage)+(1|replicate),family="binomial",data=M,na.action = na.omit)
anova(glm_mid6,glm_mid7)## Data: M
## Models:
## glm_mid7: results ~ (lineage) + (1 | replicate), zi=~0, disp=~1
## glm_mid6: results ~ (lineage + bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_mid7 3 357.30 368.76 -175.65 351.30
## glm_mid6 4 358.98 374.26 -175.49 350.98 0.316 1 0.574summary(glm_mid7)## Family: binomial ( logit )
## Formula: results ~ (lineage) + (1 | replicate)
## Data: M
##
## AIC BIC logLik deviance df.resid
## 357.3 368.8 -175.6 351.3 334
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.663 1.632
## Number of obs: 337, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6283 0.7018 -0.895 0.37063
## lineageEU3 0.7569 0.2893 2.616 0.00889 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_mid7,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.8016 1 0.370629
## lineage 6.8452 1 0.008888 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Model for calculating means (emmeans)
glm_mid5<-glmmTMB(results~(lineage+bloodmeal_titer)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=M,na.action = na.omit)
car::Anova(glm_mid5,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0470 1 0.82836
## lineage 3.6764 1 0.05519 .
## bloodmeal_titer 0.4265 1 0.51373
## lineage:bloodmeal_titer 0.1995 1 0.65512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_mid5,pairwise~lineage|bloodmeal_titer,type="response",p.adjust.methods="bonf") ## $emmeans
## bloodmeal_titer = high:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.448 0.240 Inf 0.1077 0.845
## EU3 0.613 0.231 Inf 0.1906 0.914
##
## bloodmeal_titer = low:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.244 0.189 Inf 0.0415 0.706
## EU3 0.456 0.246 Inf 0.1070 0.854
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## bloodmeal_titer = high:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 0.512 0.179 Inf 1 -1.917 0.0552
##
## bloodmeal_titer = low:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 0.385 0.206 Inf 1 -1.786 0.0740
##
## Tests are performed on the log odds ratio scaleemmeans(glm_mid5,pairwise~bloodmeal_titer|lineage,type="response",p.adjust.methods="bonf")## $emmeans
## lineage = EU2:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.448 0.240 Inf 0.1077 0.845
## low 0.244 0.189 Inf 0.0415 0.706
##
## lineage = EU3:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.613 0.231 Inf 0.1906 0.914
## low 0.456 0.246 Inf 0.1070 0.854
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## lineage = EU2:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 2.51 3.55 Inf 1 0.653 0.5137
##
## lineage = EU3:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 1.89 2.62 Inf 1 0.459 0.6465
##
## Tests are performed on the log odds ratio scaleemmeans Extract data from model
glm_mid_data<-emmeans(glm_mid5,pairwise~bloodmeal_titer*lineage,type="response",p.adjust.methods="bonf")
ok_midgut<-summary(glm_mid_data$emmeans)
ok_midgut## bloodmeal_titer lineage prob SE df asymp.LCL asymp.UCL
## high EU2 0.448 0.240 Inf 0.1077 0.845
## low EU2 0.244 0.189 Inf 0.0415 0.706
## high EU3 0.613 0.231 Inf 0.1906 0.914
## low EU3 0.456 0.246 Inf 0.1070 0.854
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scaleThorax analysis :
glm_tho<-glmmTMB(results~lineage*bloodmeal_titer*wing_size+(1|replicate),family="binomial",data=X,na.action = na.omit)
summary(glm_tho)## Family: binomial ( logit )
## Formula:
## results ~ lineage * bloodmeal_titer * wing_size + (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 295.6 328.1 -138.8 277.6 265
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.253 1.501
## Number of obs: 274, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.45657 3.65433 0.125 0.9006
## lineageEU3 -0.04367 5.03249 -0.009 0.9931
## bloodmeal_titerlow -13.14311 7.39725 -1.777 0.0756 .
## wing_size -2.55748 10.20553 -0.251 0.8021
## lineageEU3:bloodmeal_titerlow 13.88269 9.82696 1.413 0.1577
## lineageEU3:wing_size 1.39973 14.52836 0.096 0.9232
## bloodmeal_titerlow:wing_size 33.42946 20.87814 1.601 0.1093
## lineageEU3:bloodmeal_titerlow:wing_size -39.38509 28.33260 -1.390 0.1645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_tho,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0156 1 0.90057
## lineage 0.0001 1 0.99308
## bloodmeal_titer 3.1569 1 0.07561 .
## wing_size 0.0628 1 0.80213
## lineage:bloodmeal_titer 1.9958 1 0.15774
## lineage:wing_size 0.0093 1 0.92325
## bloodmeal_titer:wing_size 2.5637 1 0.10934
## lineage:bloodmeal_titer:wing_size 1.9324 1 0.16450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1glm_tho2<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)^2+(1|replicate),family="binomial",data=X,na.action = na.omit)
summary(glm_tho2)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 295.8 324.7 -139.9 279.8 266
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.169 1.473
## Number of obs: 274, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5631 3.4052 -0.459 0.646
## lineageEU3 4.4413 3.9544 1.123 0.261
## bloodmeal_titerlow -4.5652 4.1093 -1.111 0.267
## wing_size 3.2439 9.4662 0.343 0.732
## lineageEU3:bloodmeal_titerlow 0.2442 0.6794 0.359 0.719
## lineageEU3:wing_size -11.5773 11.3827 -1.017 0.309
## bloodmeal_titerlow:wing_size 8.8090 11.1790 0.788 0.431anova(glm_tho2,glm_tho)## Data: X
## Models:
## glm_tho2: results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate), zi=~0, disp=~1
## glm_tho: results ~ lineage * bloodmeal_titer * wing_size + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_tho2 8 295.82 324.73 -139.91 279.82
## glm_tho 9 295.58 328.10 -138.79 277.58 2.2441 1 0.1341car::Anova(glm_tho2,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.2107 1 0.6462
## lineage 1.2614 1 0.2614
## bloodmeal_titer 1.2342 1 0.2666
## wing_size 0.1174 1 0.7318
## lineage:bloodmeal_titer 0.1292 1 0.7193
## lineage:wing_size 1.0345 1 0.3091
## bloodmeal_titer:wing_size 0.6209 1 0.4307glm_tho3<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)+(lineage:wing_size+lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=X,na.action = na.omit)
anova(glm_tho2,glm_tho3)## Data: X
## Models:
## glm_tho3: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size + , zi=~0, disp=~1
## glm_tho3: lineage:bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## glm_tho2: results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_tho3 7 294.40 319.69 -140.20 280.40
## glm_tho2 8 295.82 324.73 -139.91 279.82 0.5797 1 0.4464summary(glm_tho3)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size +
## lineage:bloodmeal_titer) + (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 294.4 319.7 -140.2 280.4 267
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.147 1.465
## Number of obs: 274, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.5075 3.1882 -0.786 0.432
## lineageEU3 3.4906 4.0762 0.856 0.392
## bloodmeal_titerlow -1.5085 1.3272 -1.137 0.256
## wing_size 5.9738 8.8089 0.678 0.498
## lineageEU3:wing_size -8.8110 11.7302 -0.751 0.453
## lineageEU3:bloodmeal_titerlow 0.2253 0.6777 0.332 0.740car::Anova(glm_tho3,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.6185 1 0.4316
## lineage 0.7333 1 0.3918
## bloodmeal_titer 1.2918 1 0.2557
## wing_size 0.4599 1 0.4977
## lineage:wing_size 0.5642 1 0.4526
## lineage:bloodmeal_titer 0.1105 1 0.7395glm_tho4<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=X,na.action = na.omit)
anova(glm_tho4,glm_tho3)## Data: X
## Models:
## glm_tho4: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:bloodmeal_titer) + , zi=~0, disp=~1
## glm_tho4: (1 | replicate), zi=~0, disp=~1
## glm_tho3: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size + , zi=~0, disp=~1
## glm_tho3: lineage:bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_tho4 6 293.02 314.70 -140.51 281.02
## glm_tho3 7 294.40 319.69 -140.20 280.40 0.6195 1 0.4312summary(glm_tho4)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:bloodmeal_titer) +
## (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 293.0 314.7 -140.5 281.0 268
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.163 1.471
## Number of obs: 274, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6160 1.9000 -0.324 0.746
## lineageEU3 0.4424 0.3756 1.178 0.239
## bloodmeal_titerlow -1.5146 1.3306 -1.138 0.255
## wing_size 0.5369 4.8442 0.111 0.912
## lineageEU3:bloodmeal_titerlow 0.2247 0.6768 0.332 0.740car::Anova(glm_tho4,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.1051 1 0.7458
## lineage 1.3874 1 0.2388
## bloodmeal_titer 1.2957 1 0.2550
## wing_size 0.0123 1 0.9117
## lineage:bloodmeal_titer 0.1103 1 0.7399glm_tho5<-glmmTMB(results~(lineage+bloodmeal_titer)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=X,na.action = na.omit)
summary(glm_tho5)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer) + (lineage:bloodmeal_titer) +
## (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 359.9 379.0 -175.0 349.9 331
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.117 1.455
## Number of obs: 336, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4957 0.8801 -0.563 0.573
## lineageEU3 0.5585 0.3538 1.579 0.114
## bloodmeal_titerlow -1.3125 1.2884 -1.019 0.308
## lineageEU3:bloodmeal_titerlow -0.1631 0.5752 -0.284 0.777car::Anova(glm_tho5,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.3173 1 0.5733
## lineage 2.4923 1 0.1144
## bloodmeal_titer 1.0378 1 0.3083
## lineage:bloodmeal_titer 0.0804 1 0.7768glm_tho6<-glmmTMB(results~(lineage+bloodmeal_titer)+(1|replicate),family="binomial",data=X,na.action = na.omit)
anova(glm_tho6,glm_tho5)## Data: X
## Models:
## glm_tho6: results ~ (lineage + bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## glm_tho5: results ~ (lineage + bloodmeal_titer) + (lineage:bloodmeal_titer) + , zi=~0, disp=~1
## glm_tho5: (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_tho6 4 358.01 373.28 -175.00 350.01
## glm_tho5 5 359.93 379.01 -174.97 349.93 0.08 1 0.7773summary(glm_tho6)## Family: binomial ( logit )
## Formula: results ~ (lineage + bloodmeal_titer) + (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 358.0 373.3 -175.0 350.0 332
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.155 1.468
## Number of obs: 336, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4639 0.8800 -0.527 0.5981
## lineageEU3 0.4976 0.2804 1.775 0.0759 .
## bloodmeal_titerlow -1.4090 1.2535 -1.124 0.2610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_tho6,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.2779 1 0.59811
## lineage 3.1498 1 0.07594 .
## bloodmeal_titer 1.2635 1 0.26099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1glm_tho7<-glmmTMB(results~(lineage)+(1|replicate),family="binomial",data=X,na.action = na.omit)
anova(glm_tho6,glm_tho7)## Data: X
## Models:
## glm_tho7: results ~ (lineage) + (1 | replicate), zi=~0, disp=~1
## glm_tho6: results ~ (lineage + bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_tho7 3 357.20 368.65 -175.6 351.20
## glm_tho6 4 358.01 373.28 -175.0 350.01 1.1925 1 0.2748summary(glm_tho7)## Family: binomial ( logit )
## Formula: results ~ (lineage) + (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 357.2 368.7 -175.6 351.2 333
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.602 1.613
## Number of obs: 336, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1644 0.7027 -1.657 0.0975 .
## lineageEU3 0.4996 0.2807 1.780 0.0751 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_tho7,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 2.7455 1 0.09753 .
## lineage 3.1684 1 0.07508 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Minimum model
glm_tho8<-glmmTMB(results~(1)+(1|replicate),family="binomial",data=X,na.action = na.omit)
summary(glm_tho8)## Family: binomial ( logit )
## Formula: results ~ (1) + (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 358.4 366.1 -177.2 354.4 334
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.368 1.539
## Number of obs: 336, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8792 0.6536 -1.345 0.179anova(glm_tho8,glm_tho7)## Data: X
## Models:
## glm_tho8: results ~ (1) + (1 | replicate), zi=~0, disp=~1
## glm_tho7: results ~ (lineage) + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_tho8 2 358.45 366.08 -177.22 354.45
## glm_tho7 3 357.20 368.65 -175.60 351.20 3.2432 1 0.07172 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_tho8,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 1.8097 1 0.1785# Model for calculating means (emmeans)
glm_tho5<-glmmTMB(results~(lineage+bloodmeal_titer)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=X,na.action = na.omit)
summary(glm_tho5)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer) + (lineage:bloodmeal_titer) +
## (1 | replicate)
## Data: X
##
## AIC BIC logLik deviance df.resid
## 359.9 379.0 -175.0 349.9 331
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.117 1.455
## Number of obs: 336, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4957 0.8801 -0.563 0.573
## lineageEU3 0.5585 0.3538 1.579 0.114
## bloodmeal_titerlow -1.3125 1.2884 -1.019 0.308
## lineageEU3:bloodmeal_titerlow -0.1631 0.5752 -0.284 0.777car::Anova(glm_tho5,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.3173 1 0.5733
## lineage 2.4923 1 0.1144
## bloodmeal_titer 1.0378 1 0.3083
## lineage:bloodmeal_titer 0.0804 1 0.7768emmeans(glm_tho5,pairwise~lineage|bloodmeal_titer,type="response",p.adjust.methods="bonf") ## $emmeans
## bloodmeal_titer = high:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.379 0.207 Inf 0.0979 0.774
## EU3 0.516 0.219 Inf 0.1604 0.856
##
## bloodmeal_titer = low:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.141 0.114 Inf 0.0252 0.509
## EU3 0.196 0.144 Inf 0.0391 0.593
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## bloodmeal_titer = high:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 0.572 0.202 Inf 1 -1.579 0.1144
##
## bloodmeal_titer = low:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 0.673 0.306 Inf 1 -0.871 0.3840
##
## Tests are performed on the log odds ratio scaleemmeans(glm_tho5,pairwise~bloodmeal_titer|lineage,type="response",p.adjust.methods="bonf")## $emmeans
## lineage = EU2:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.379 0.207 Inf 0.0979 0.774
## low 0.141 0.114 Inf 0.0252 0.509
##
## lineage = EU3:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.516 0.219 Inf 0.1604 0.856
## low 0.196 0.144 Inf 0.0391 0.593
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## lineage = EU2:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 3.72 4.79 Inf 1 1.019 0.3083
##
## lineage = EU3:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 4.37 5.53 Inf 1 1.166 0.2435
##
## Tests are performed on the log odds ratio scaleextract data from model
glm_tho_data<-emmeans(glm_tho5,pairwise~bloodmeal_titer*lineage,type="response",p.adjust.methods="bonf")
ok_thorax<-summary(glm_tho_data$emmeans)
ok_thorax## bloodmeal_titer lineage prob SE df asymp.LCL asymp.UCL
## high EU2 0.379 0.207 Inf 0.0979 0.774
## low EU2 0.141 0.114 Inf 0.0252 0.509
## high EU3 0.516 0.219 Inf 0.1604 0.856
## low EU3 0.196 0.144 Inf 0.0391 0.593
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scaleSaliva analysis :
glm_salive<-glmmTMB(results~lineage*bloodmeal_titer*wing_size+(1|replicate),family="binomial",data=S,na.action=na.omit)
summary(glm_salive)## Family: binomial ( logit )
## Formula:
## results ~ lineage * bloodmeal_titer * wing_size + (1 | replicate)
## Data: S
##
## AIC BIC logLik deviance df.resid
## 140.3 170.5 -61.2 122.3 202
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 0.5231 0.7233
## Number of obs: 211, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.970 5.711 -0.870 0.384
## lineageEU3 3.604 8.445 0.427 0.670
## bloodmeal_titerlow -9.981 11.056 -0.903 0.367
## wing_size 7.922 16.205 0.489 0.625
## lineageEU3:bloodmeal_titerlow 17.317 17.567 0.986 0.324
## lineageEU3:wing_size -8.946 24.710 -0.362 0.717
## bloodmeal_titerlow:wing_size 26.183 31.077 0.843 0.399
## lineageEU3:bloodmeal_titerlow:wing_size -53.599 51.266 -1.046 0.296car::Anova(glm_salive,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 0.7572 1 0.3842
## lineage 0.1821 1 0.6696
## bloodmeal_titer 0.8150 1 0.3666
## wing_size 0.2390 1 0.6249
## lineage:bloodmeal_titer 0.9717 1 0.3242
## lineage:wing_size 0.1311 1 0.7173
## bloodmeal_titer:wing_size 0.7099 1 0.3995
## lineage:bloodmeal_titer:wing_size 1.0931 1 0.2958#Model reduction
glm_salive2<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)^2+(1|replicate),family="binomial",data=S,na.action = na.omit)
summary(glm_salive2)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate)
## Data: S
##
## AIC BIC logLik deviance df.resid
## 139.4 166.2 -61.7 123.4 203
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 0.5571 0.7464
## Number of obs: 211, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.812 5.496 -1.240 0.215
## lineageEU3 7.753 7.456 1.040 0.298
## bloodmeal_titerlow -3.706 8.587 -0.432 0.666
## wing_size 13.178 15.487 0.851 0.395
## lineageEU3:bloodmeal_titerlow -1.073 1.162 -0.923 0.356
## lineageEU3:wing_size -21.119 21.824 -0.968 0.333
## bloodmeal_titerlow:wing_size 8.502 24.281 0.350 0.726anova(glm_salive2,glm_salive)## Data: S
## Models:
## glm_salive2: results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate), zi=~0, disp=~1
## glm_salive: results ~ lineage * bloodmeal_titer * wing_size + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_salive2 8 139.43 166.25 -61.717 123.43
## glm_salive 9 140.35 170.52 -61.175 122.35 1.0835 1 0.2979car::Anova(glm_salive2,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 1.5363 1 0.2152
## lineage 1.0811 1 0.2984
## bloodmeal_titer 0.1862 1 0.6661
## wing_size 0.7240 1 0.3948
## lineage:bloodmeal_titer 0.8526 1 0.3558
## lineage:wing_size 0.9364 1 0.3332
## bloodmeal_titer:wing_size 0.1226 1 0.7262glm_salive3<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)+(lineage:wing_size+lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=S,na.action = na.omit)
anova(glm_salive2,glm_salive3)## Data: S
## Models:
## glm_salive3: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size + , zi=~0, disp=~1
## glm_salive3: lineage:bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## glm_salive2: results ~ (lineage + bloodmeal_titer + wing_size)^2 + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_salive3 7 137.56 161.02 -61.778 123.56
## glm_salive2 8 139.43 166.25 -61.717 123.43 0.1236 1 0.7252summary(glm_salive3)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size +
## lineage:bloodmeal_titer) + (1 | replicate)
## Data: S
##
## AIC BIC logLik deviance df.resid
## 137.6 161.0 -61.8 123.6 204
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 0.6154 0.7845
## Number of obs: 211, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.8119 4.7399 -1.648 0.0993 .
## lineageEU3 8.2253 7.3514 1.119 0.2632
## bloodmeal_titerlow -0.7284 1.0706 -0.680 0.4963
## wing_size 16.0024 13.2903 1.204 0.2286
## lineageEU3:wing_size -22.4585 21.5029 -1.044 0.2963
## lineageEU3:bloodmeal_titerlow -1.1724 1.1225 -1.044 0.2963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_salive3,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 2.7163 1 0.09933 .
## lineage 1.2519 1 0.26319
## bloodmeal_titer 0.4629 1 0.49626
## wing_size 1.4498 1 0.22857
## lineage:wing_size 1.0909 1 0.29628
## lineage:bloodmeal_titer 1.0907 1 0.29631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1glm_salive4<-glmmTMB(results~(lineage+bloodmeal_titer+wing_size)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=S,na.action = na.omit)
anova(glm_salive4,glm_salive3)## Data: S
## Models:
## glm_salive4: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:bloodmeal_titer) + , zi=~0, disp=~1
## glm_salive4: (1 | replicate), zi=~0, disp=~1
## glm_salive3: results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:wing_size + , zi=~0, disp=~1
## glm_salive3: lineage:bloodmeal_titer) + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glm_salive4 6 136.66 156.77 -62.329 124.66
## glm_salive3 7 137.56 161.02 -61.778 123.56 1.1008 1 0.2941summary(glm_salive4)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer + wing_size) + (lineage:bloodmeal_titer) +
## (1 | replicate)
## Data: S
##
## AIC BIC logLik deviance df.resid
## 136.7 156.8 -62.3 124.7 205
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 0.5276 0.7264
## Number of obs: 211, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.8050 3.6469 -1.317 0.188
## lineageEU3 0.5575 0.6293 0.886 0.376
## bloodmeal_titerlow -0.6731 1.0183 -0.661 0.509
## wing_size 7.4396 10.2659 0.725 0.469
## lineageEU3:bloodmeal_titerlow -1.2688 1.1103 -1.143 0.253car::Anova(glm_salive4,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 1.7359 1 0.1877
## lineage 0.7848 1 0.3757
## bloodmeal_titer 0.4369 1 0.5086
## wing_size 0.5252 1 0.4686
## lineage:bloodmeal_titer 1.3057 1 0.2532# Minimum model
glm_salive5<-glmmTMB(results~(lineage+bloodmeal_titer)+(lineage:bloodmeal_titer)+(1|replicate),family="binomial",data=S,na.action = na.omit)
summary(glm_salive5)## Family: binomial ( logit )
## Formula:
## results ~ (lineage + bloodmeal_titer) + (lineage:bloodmeal_titer) +
## (1 | replicate)
## Data: S
##
## AIC BIC logLik deviance df.resid
## 215.2 234.1 -102.6 205.2 321
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 0.5546 0.7447
## Number of obs: 326, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1682 0.5886 -3.684 0.00023 ***
## lineageEU3 1.0567 0.4807 2.198 0.02792 *
## bloodmeal_titerlow -0.4802 0.9148 -0.525 0.59960
## lineageEU3:bloodmeal_titerlow -2.1861 0.8603 -2.541 0.01105 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(glm_salive5,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: results
## Chisq Df Pr(>Chisq)
## (Intercept) 13.5712 1 0.0002297 ***
## lineage 4.8332 1 0.0279162 *
## bloodmeal_titer 0.2756 1 0.5996032
## lineage:bloodmeal_titer 6.4568 1 0.0110527 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_salive5,pairwise~lineage|bloodmeal_titer,type="response",p.adjust.methods="bonf") ## $emmeans
## bloodmeal_titer = high:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.1026 0.0542 Inf 0.03483 0.2661
## EU3 0.2476 0.0971 Inf 0.10596 0.4774
##
## bloodmeal_titer = low:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.0661 0.0444 Inf 0.01696 0.2250
## EU3 0.0224 0.0172 Inf 0.00487 0.0966
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## bloodmeal_titer = high:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 0.348 0.167 Inf 1 -2.198 0.0279
##
## bloodmeal_titer = low:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 3.094 2.218 Inf 1 1.575 0.1153
##
## Tests are performed on the log odds ratio scaleemmeans(glm_salive5,pairwise~bloodmeal_titer|lineage,type="response",p.adjust.methods="bonf") ## $emmeans
## lineage = EU2:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.1026 0.0542 Inf 0.03483 0.2661
## low 0.0661 0.0444 Inf 0.01696 0.2250
##
## lineage = EU3:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.2476 0.0971 Inf 0.10596 0.4774
## low 0.0224 0.0172 Inf 0.00487 0.0966
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## lineage = EU2:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 1.62 1.48 Inf 1 0.525 0.5996
##
## lineage = EU3:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 14.39 13.51 Inf 1 2.839 0.0045
##
## Tests are performed on the log odds ratio scaleextract data from model
glm_sal_data<-emmeans(glm_salive5,pairwise~bloodmeal_titer|lineage,type="response",p.adjust.methods="bonf")
ok_sal<-summary(glm_sal_data$emmeans)
ok_sal## lineage = EU2:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.1026 0.0542 Inf 0.03483 0.2661
## low 0.0661 0.0444 Inf 0.01696 0.2250
##
## lineage = EU3:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.2476 0.0971 Inf 0.10596 0.4774
## low 0.0224 0.0172 Inf 0.00487 0.0966
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scaleRate :
##Data
##Dissemination
dr_glm<-glmmTMB(rate~lineage*bloodmeal_titer+(1|replicate),family=binomial,data=DR)
summary(dr_glm)## Family: binomial ( logit )
## Formula: rate ~ lineage * bloodmeal_titer + (1 | replicate)
## Data: DR
##
## AIC BIC logLik deviance df.resid
## 79.2 81.6 -34.6 69.2 7
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.021 1.422
## Number of obs: 12, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5384 0.8609 -0.625 0.532
## lineageEU3 0.6007 0.3521 1.706 0.088 .
## bloodmeal_titerlow -1.2730 1.2604 -1.010 0.312
## lineageEU3:bloodmeal_titerlow -0.2691 0.5635 -0.478 0.633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(dr_glm,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: rate
## Chisq Df Pr(>Chisq)
## (Intercept) 0.3911 1 0.53174
## lineage 2.9099 1 0.08804 .
## bloodmeal_titer 1.0201 1 0.31249
## lineage:bloodmeal_titer 0.2280 1 0.63302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Model reduction
dr_glm2<-glmmTMB(rate~lineage+bloodmeal_titer+(1|replicate),family=binomial,data=DR)
anova(dr_glm2,dr_glm) ## Data: DR
## Models:
## dr_glm2: rate ~ lineage + bloodmeal_titer + (1 | replicate), zi=~0, disp=~1
## dr_glm: rate ~ lineage * bloodmeal_titer + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dr_glm2 4 77.378 79.317 -34.689 69.378
## dr_glm 5 79.151 81.576 -34.576 69.151 0.2264 1 0.6342car::Anova(dr_glm2,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: rate
## Chisq Df Pr(>Chisq)
## (Intercept) 0.3148 1 0.57474
## lineage 3.2295 1 0.07232 .
## bloodmeal_titer 1.3487 1 0.24551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dr_glm3<-glmmTMB(rate~lineage+(1|replicate),family=binomial,data=DR)
anova(dr_glm3,dr_glm2) ## Data: DR
## Models:
## dr_glm3: rate ~ lineage + (1 | replicate), zi=~0, disp=~1
## dr_glm2: rate ~ lineage + bloodmeal_titer + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dr_glm3 3 76.646 78.100 -35.323 70.646
## dr_glm2 4 77.378 79.317 -34.689 69.378 1.2678 1 0.2602car::Anova(dr_glm3,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: rate
## Chisq Df Pr(>Chisq)
## (Intercept) 2.9683 1 0.08491 .
## lineage 3.2397 1 0.07187 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dr_glm4<-glmmTMB(rate~1+(1|replicate),family=binomial,data=DR)
anova(dr_glm3,dr_glm4) ## Data: DR
## Models:
## dr_glm4: rate ~ 1 + (1 | replicate), zi=~0, disp=~1
## dr_glm3: rate ~ lineage + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dr_glm4 2 77.960 78.929 -36.980 73.960
## dr_glm3 3 76.646 78.100 -35.323 70.646 3.314 1 0.06869 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(dr_glm4,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: rate
## Chisq Df Pr(>Chisq)
## (Intercept) 1.9996 1 0.1573drglm<-emmeans(dr_glm,pairwise~bloodmeal_titer|lineage,type="response",p.adjust.methods="bonf")
data_glm_dr<-summary(drglm$emmeans)
drglm## $emmeans
## lineage = EU2:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.369 0.200 Inf 0.0975 0.759
## low 0.140 0.111 Inf 0.0261 0.499
##
## lineage = EU3:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.516 0.214 Inf 0.1652 0.851
## low 0.185 0.135 Inf 0.0380 0.567
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## lineage = EU2:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 3.57 4.50 Inf 1 1.010 0.3125
##
## lineage = EU3:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 4.67 5.79 Inf 1 1.245 0.2132
##
## Tests are performed on the log odds ratio scale## quartz_off_screen
## 2##Transmission
tr_glm<-glmmTMB(rate~lineage*bloodmeal_titer+(1|replicate),family=binomial,data=TR)
summary(tr_glm)## Family: binomial ( logit )
## Formula: rate ~ lineage * bloodmeal_titer + (1 | replicate)
## Data: TR
##
## AIC BIC logLik deviance df.resid
## 57.3 59.7 -23.6 47.3 7
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 0.5078 0.7126
## Number of obs: 12, groups: replicate, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2524 0.5727 -3.933 8.39e-05 ***
## lineageEU3 1.0742 0.4783 2.246 0.0247 *
## bloodmeal_titerlow -0.4442 0.8884 -0.500 0.6171
## lineageEU3:bloodmeal_titerlow -2.1911 0.8564 -2.559 0.0105 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(tr_glm,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: rate
## Chisq Df Pr(>Chisq)
## (Intercept) 15.4693 1 8.386e-05 ***
## lineage 5.0436 1 0.02472 *
## bloodmeal_titer 0.2500 1 0.61709
## lineage:bloodmeal_titer 6.5462 1 0.01051 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(tr_glm,pairwise~bloodmeal_titer|lineage,type="response",p.adjust.methods="bonf") ## $emmeans
## lineage = EU2:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.0951 0.0493 Inf 0.03309 0.2442
## low 0.0632 0.0418 Inf 0.01664 0.2119
##
## lineage = EU3:
## bloodmeal_titer prob SE df asymp.LCL asymp.UCL
## high 0.2354 0.0907 Inf 0.10284 0.4526
## low 0.0216 0.0163 Inf 0.00483 0.0912
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## lineage = EU2:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 1.56 1.39 Inf 1 0.500 0.6171
##
## lineage = EU3:
## contrast odds.ratio SE df null z.ratio p.value
## high / low 13.95 12.78 Inf 1 2.875 0.0040
##
## Tests are performed on the log odds ratio scaleemmeans(tr_glm,pairwise~lineage|bloodmeal_titer,type="response",p.adjust.methods="bonf")## $emmeans
## bloodmeal_titer = high:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.0951 0.0493 Inf 0.03309 0.2442
## EU3 0.2354 0.0907 Inf 0.10284 0.4526
##
## bloodmeal_titer = low:
## lineage prob SE df asymp.LCL asymp.UCL
## EU2 0.0632 0.0418 Inf 0.01664 0.2119
## EU3 0.0216 0.0163 Inf 0.00483 0.0912
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## bloodmeal_titer = high:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 0.342 0.163 Inf 1 -2.246 0.0247
##
## bloodmeal_titer = low:
## contrast odds.ratio SE df null z.ratio p.value
## EU2 / EU3 3.055 2.185 Inf 1 1.562 0.1184
##
## Tests are performed on the log odds ratio scaletrglm<-emmeans(tr_glm,pairwise~bloodmeal_titer|lineage,type="response",p.adjust.methods="bonf")
data_glm_tr<-summary(trglm$emmeans)## quartz_off_screen
## 2
## quartz_off_screen
## 2
Saliva analysis :
Maximum model :
CVS<-glmmTMB(c_virus~lineage*bloodmeal_titer+(1|replicate),family=nbinom2,data=S_ent,na.action = na.omit)## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; false convergence (8). See vignette('troubleshooting'),
## help('diagnose')summary(CVS)## Family: nbinom2 ( log )
## Formula: c_virus ~ lineage * bloodmeal_titer + (1 | replicate)
## Data: S_ent
##
## AIC BIC logLik deviance df.resid
## 1197.0 1206.7 -592.5 1185.0 31
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 2.909e-06 0.001706
## Number of obs: 37, groups: replicate, 5
##
## Dispersion parameter for nbinom2 family (): 0.226
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 18.0759 0.7436 24.309 <2e-16 ***
## lineageEU3 -1.5889 0.9017 -1.762 0.0781 .
## bloodmeal_titerlow -2.4785 1.0220 -2.425 0.0153 *
## lineageEU3:bloodmeal_titerlow 1.4188 1.6671 0.851 0.3947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(CVS,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: c_virus
## Chisq Df Pr(>Chisq)
## (Intercept) 590.9122 1 < 2e-16 ***
## lineage 3.1049 1 0.07806 .
## bloodmeal_titer 5.8814 1 0.01530 *
## lineage:bloodmeal_titer 0.7243 1 0.39474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Reduced model :
CVS1<-glmmTMB(c_virus~lineage+bloodmeal_titer+(1|replicate),family=nbinom2,data=S_ent,na.action = na.omit)## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; false convergence (8). See vignette('troubleshooting'),
## help('diagnose')summary(CVS1)## Family: nbinom2 ( log )
## Formula: c_virus ~ lineage + bloodmeal_titer + (1 | replicate)
## Data: S_ent
##
## AIC BIC logLik deviance df.resid
## 1195.8 1203.9 -592.9 1185.8 32
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 1.05e-06 0.001024
## Number of obs: 37, groups: replicate, 5
##
## Dispersion parameter for nbinom2 family (): 0.223
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 17.7933 0.5852 30.406 <2e-16 ***
## lineageEU3 -1.1395 0.7019 -1.623 0.1045
## bloodmeal_titerlow -1.8530 0.7473 -2.480 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(CVS1,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: c_virus
## Chisq Df Pr(>Chisq)
## (Intercept) 924.5204 1 < 2e-16 ***
## lineage 2.6351 1 0.10452
## bloodmeal_titer 6.1489 1 0.01315 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(CVS1,CVS)## Data: S_ent
## Models:
## CVS1: c_virus ~ lineage + bloodmeal_titer + (1 | replicate), zi=~0, disp=~1
## CVS: c_virus ~ lineage * bloodmeal_titer + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## CVS1 5 1195.8 1203.9 -592.9 1185.8
## CVS 6 1197.0 1206.7 -592.5 1185.0 0.8008 1 0.3709CVS2<-glmmTMB(c_virus~bloodmeal_titer+(1|replicate),family=nbinom2,data=S_ent,na.action = na.omit)## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; false convergence (8). See vignette('troubleshooting'),
## help('diagnose')summary(CVS2)## Family: nbinom2 ( log )
## Formula: c_virus ~ bloodmeal_titer + (1 | replicate)
## Data: S_ent
##
## AIC BIC logLik deviance df.resid
## 1196.3 1202.8 -594.2 1188.3 33
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## replicate (Intercept) 6.139e-06 0.002478
## Number of obs: 37, groups: replicate, 5
##
## Dispersion parameter for nbinom2 family (): 0.214
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 17.2968 0.4321 40.03 <2e-16 ***
## bloodmeal_titerlow -1.7393 0.7587 -2.29 0.0219 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(CVS2,type="III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: c_virus
## Chisq Df Pr(>Chisq)
## (Intercept) 1602.5220 1 < 2e-16 ***
## bloodmeal_titer 5.2556 1 0.02188 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(CVS1,CVS2)## Data: S_ent
## Models:
## CVS2: c_virus ~ bloodmeal_titer + (1 | replicate), zi=~0, disp=~1
## CVS1: c_virus ~ lineage + bloodmeal_titer + (1 | replicate), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## CVS2 4 1196.3 1202.8 -594.17 1188.3
## CVS1 5 1195.8 1203.9 -592.90 1185.8 2.529 1 0.1118##Viral load Estimates
## $emmeans
## lineage bloodmeal_titer response SE df asymp.LCL asymp.UCL
## EU2 high 2.64e+10 1.38e+10 Inf 9.51e+09 7.33e+10
## EU3 high 2.88e+10 1.37e+10 Inf 1.13e+10 7.31e+10
## EU2 low 1.15e+10 5.16e+09 Inf 4.74e+09 2.77e+10
## EU3 low 2.79e+04 1.36e+04 Inf 1.07e+04 7.27e+04
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale
##
## $contrasts
## contrast ratio SE df null z.ratio p.value
## EU2 high / EU3 high 9.00e-01 0.6 Inf 1 -0.121 0.9994
## EU2 high / EU2 low 2.30e+00 1.6 Inf 1 1.213 0.6188
## EU2 high / EU3 low 9.47e+05 678200.2 Inf 1 19.226 <.0001
## EU3 high / EU2 low 2.50e+00 1.6 Inf 1 1.405 0.4962
## EU3 high / EU3 low 1.03e+06 704011.0 Inf 1 20.292 <.0001
## EU2 low / EU3 low 4.11e+05 273167.5 Inf 1 19.456 <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimates
## Tests are performed on the log scale## $emmeans
## lineage bloodmeal_titer response SE df asymp.LCL asymp.UCL
## EU2 high 3.07e+10 1.56e+10 Inf 1.14e+10 8.30e+10
## EU3 high 1.75e+10 8.36e+09 Inf 6.86e+09 4.46e+10
## EU2 low 8.62e+10 4.24e+10 Inf 3.29e+10 2.26e+11
## EU3 low 3.34e+05 1.79e+05 Inf 1.17e+05 9.54e+05
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale
##
## $contrasts
## contrast ratio SE df null z.ratio p.value
## EU2 high / EU3 high 1.75e+00 1.22e+00 Inf 1 0.807 0.8510
## EU2 high / EU2 low 3.60e-01 2.50e-01 Inf 1 -1.461 0.4615
## EU2 high / EU3 low 9.18e+04 6.77e+04 Inf 1 15.505 <.0001
## EU3 high / EU2 low 2.00e-01 1.40e-01 Inf 1 -2.325 0.0923
## EU3 high / EU3 low 5.23e+04 3.75e+04 Inf 1 15.154 <.0001
## EU2 low / EU3 low 2.58e+05 1.87e+05 Inf 1 17.145 <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimates
## Tests are performed on the log scale## $emmeans
## lineage bloodmeal_titer response SE df asymp.LCL asymp.UCL
## EU2 high 70835538 52673112 Inf 16493015 3.04e+08
## EU3 high 14460466 7376423 Inf 5320790 3.93e+07
## EU2 low 5941042 4165168 Inf 1503485 2.35e+07
## EU3 low 5011373 6085302 Inf 463814 5.41e+07
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale
##
## $contrasts
## contrast ratio SE df null z.ratio p.value
## EU2 high / EU3 high 4.90 4.42 Inf 1 1.762 0.2918
## EU2 high / EU2 low 11.92 12.19 Inf 1 2.425 0.0724
## EU2 high / EU3 low 14.13 20.13 Inf 1 1.860 0.2454
## EU3 high / EU2 low 2.43 2.11 Inf 1 1.026 0.7342
## EU3 high / EU3 low 2.89 3.80 Inf 1 0.805 0.8523
## EU2 low / EU3 low 1.19 1.66 Inf 1 0.121 0.9994
##
## P value adjustment: tukey method for comparing a family of 4 estimates
## Tests are performed on the log scaleGraphical representation :
## Warning: Removed 48 rows containing missing values or values outside the scale range
## (`stat_bindot()`).## quartz_off_screen
## 2## Warning: Removed 48 rows containing missing values or values outside the scale range
## (`stat_bindot()`).