Yogi Honours Adult Reproduction
J Donelson
March 2022
R Markdown
library(ggplot2)
library(lme4)## Loading required package: Matrixlibrary(MuMIn)
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.5-7library(emmeans)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## stepsetwd("C:/Users/jc130714/OneDrive - James Cook University/Documents/Yogi Yasutake/Honours/Post-honours/Statistics and data/Final data")
getwd()## [1] "C:/Users/jc130714/OneDrive - James Cook University/Documents/Yogi Yasutake/Honours/Post-honours/Statistics and data/Final data"#Adult physical differences
Adults = read.csv('F1 adult fish_FINAL.csv', strip.white=TRUE)
head(Adults)## Tank.. Treatment Dev.treatment Repro.treatment Sex ID Family Family.number
## 1 17 CC-C C C F CCC8 C 3
## 2 17 CC-C C C M DCC D 4
## 3 38 CC-C C C F BCC8 B 2
## 4 38 CC-C C C M FCC F 6
## 5 84 CC-C C C F BCC4 B 2
## 6 84 CC-C C C M DCC8 D 4
## Reproduced SL.cm Weight.g Condition X X.1 X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9
## 1 N 9.534 33.90 3.911779 NA NA NA NA NA NA NA NA NA NA
## 2 N 8.710 28.35 4.290408 NA NA NA NA NA NA NA NA NA NA
## 3 N 9.874 43.38 4.506197 NA NA NA NA NA NA NA NA NA NA
## 4 N 8.448 27.63 4.582676 NA NA NA NA NA NA NA NA NA NA
## 5 N 9.504 42.30 4.927437 NA NA NA NA NA NA NA NA NA NA
## 6 N 9.804 40.20 4.265954 NA NA NA NA NA NA NA NA NA NA
## X.10 X.11 X.12 X.13 X.14 X.15 X.16 X.17 X.18 X.19 X.20 X.21 X.22 X.23 X.24
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## X.25 X.26 X.27 X.28 X.29 X.30 X.31 X.32 Notes
## 1 NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA#PCA building
AdultsPCA = read.csv('F1 adult fish_dataPCA.csv', strip.white=TRUE)
head(AdultsPCA)## SL.cm Weight.g Condition
## 1 9.534 33.90 3.911779
## 2 8.710 28.35 4.290408
## 3 9.874 43.38 4.506197
## 4 8.448 27.63 4.582676
## 5 9.504 42.30 4.927437
## 6 9.804 40.20 4.265954#standardising
Adults.std = stdize(AdultsPCA, centre=TRUE, scale = TRUE)
head(Adults.std)## z.SL.cm z.Weight.g z.Condition
## 1 0.9702193 0.5146636 -0.7918202
## 2 -0.4623866 -0.4018001 0.1814054
## 3 1.5613431 2.0800825 0.7360674
## 4 -0.9178996 -0.5206927 0.9326485
## 5 0.9180613 1.9017437 1.8188185
## 6 1.4396412 1.5549736 0.1185484Adults.rda = rda(Adults.std)
summary(Adults.rda, display=NULL)##
## Call:
## rda(X = Adults.std)
##
## Partitioning of variance:
## Inertia Proportion
## Total 3 1
## Unconstrained 3 1
##
## Eigenvalues, and their contribution to the variance
##
## Importance of components:
## PC1 PC2 PC3
## Eigenvalue 1.8877 1.1065 0.005842
## Proportion Explained 0.6292 0.3688 0.001947
## Cumulative Proportion 0.6292 0.9981 1.000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:screeplot(Adults.rda)
Adults.sites.score = (scores(Adults.rda, display='sites'))
head(Adults.sites.score)## PC1 PC2
## sit1 -0.3259455 0.35980439
## sit2 0.1915765 -0.08145263
## sit3 -0.8170804 -0.31441361
## sit4 0.3148028 -0.40937491
## sit5 -0.6408565 -0.78517034
## sit6 -0.6687076 -0.03975941Adults.species.score = as.data.frame(scores(Adults.rda, display='species'))
Adults.species.score$Species = rownames(Adults.species.score)
Adults.sites.score = data.frame(Adults.sites.score, AdultsPCA)#getting and adding groupings
AdultGroup = read.csv('F1 adults groupingsPCA.csv')
head(AdultGroup)## Treatment Dev.treatment Repro.treatment Sex Family
## 1 CC-C C C F C
## 2 CC-C C C M D
## 3 CC-C C C F B
## 4 CC-C C C M F
## 5 CC-C C C F B
## 6 CC-C C C M DAdults.sites.score2 = cbind(Adults.sites.score, AdultGroup)
head(Adults.sites.score2)## PC1 PC2 SL.cm Weight.g Condition Treatment Dev.treatment
## sit1 -0.3259455 0.35980439 9.534 33.90 3.911779 CC-C C
## sit2 0.1915765 -0.08145263 8.710 28.35 4.290408 CC-C C
## sit3 -0.8170804 -0.31441361 9.874 43.38 4.506197 CC-C C
## sit4 0.3148028 -0.40937491 8.448 27.63 4.582676 CC-C C
## sit5 -0.6408565 -0.78517034 9.504 42.30 4.927437 CC-C C
## sit6 -0.6687076 -0.03975941 9.804 40.20 4.265954 CC-C C
## Repro.treatment Sex Family
## sit1 C F C
## sit2 C M D
## sit3 C F B
## sit4 C M F
## sit5 C F B
## sit6 C M D#making a PCA figure
#putting the sites on the graph
g = ggplot() + geom_point(data=Adults.sites.score2, aes(y=PC2, x=PC1, shape=Treatment, colour= Treatment), show.legend = TRUE) + theme_classic()
#adding the arrows
g = g + geom_segment(data=Adults.species.score, aes(y=0, x=0, yend=PC2, xend=PC1), arrow = arrow(length=unit(0.3, 'lines')), show.legend = FALSE)
hjust = ifelse(Adults.species.score$PC1>0,0,1)
vjust = ifelse(Adults.species.score$PC2>0,0,1)
g = g + geom_text(data=Adults.species.score, aes(y=PC2, x=PC1, label = Species))
#now adding cross hairs and axis lines
g = g + geom_segment(data=NULL, aes(y=-Inf, x=-0, yend=Inf, xend=0))
g = g + geom_segment(data=NULL, aes(y=0, x=-Inf, yend=0, xend=Inf))
g
#lmer testing of PCA
lmer.PC1a = lmer(PC1 ~ Dev.treatment * Repro.treatment + (1|Family) , data = Adults.sites.score2)
summary(lmer.PC1a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PC1 ~ Dev.treatment * Repro.treatment + (1 | Family)
## Data: Adults.sites.score2
##
## REML criterion at convergence: 100.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8369 -0.4298 0.0159 0.6142 1.9782
##
## Random effects:
## Groups Name Variance Std.Dev.
## Family (Intercept) 0.03879 0.1970
## Residual 0.15702 0.3963
## Number of obs: 86, groups: Family, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03979 0.11258 9.40738 -0.353 0.7316
## Dev.treatmentH 0.23002 0.12796 78.46427 1.798 0.0761 .
## Repro.treatmentH -0.04558 0.11084 77.31546 -0.411 0.6821
## Dev.treatmentH:Repro.treatmentH -0.04218 0.17754 77.94350 -0.238 0.8129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dv.trH Rpr.tH
## Dev.trtmntH -0.422
## Rpr.trtmntH -0.488 0.444
## Dv.trtH:R.H 0.305 -0.722 -0.634anova(lmer.PC1a)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Dev.treatment 0.87177 0.87177 1 77.983 5.5520 0.02097 *
## Repro.treatment 0.09071 0.09071 1 76.547 0.5777 0.44956
## Dev.treatment:Repro.treatment 0.00886 0.00886 1 77.943 0.0564 0.81285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PC1EMM = (emmeans(lmer.PC1a, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
PC1EMM## Dev.treatment Repro.treatment emmean SE df lower.CL
## 1 C C -0.03978547 0.1127554 10.78477 -0.28856412
## 2 H C 0.19023873 0.1306568 17.54404 -0.08477335
## 3 C H -0.08536295 0.1133612 10.83946 -0.33532095
## 4 H H 0.10248618 0.1252105 15.50365 -0.16364046
## upper.CL
## 1 0.2089932
## 2 0.4652508
## 3 0.1645950
## 4 0.3686128#Plot
ggplot(PC1EMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean-SE, ymax = emmean+SE)) + theme_classic() 
lmer.PC2a = lmer(PC2 ~ Dev.treatment * Repro.treatment + (1|Family) , data = Adults.sites.score2)
summary(lmer.PC2a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PC2 ~ Dev.treatment * Repro.treatment + (1 | Family)
## Data: Adults.sites.score2
##
## REML criterion at convergence: 99.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3900 -0.5146 0.1451 0.6036 2.2319
##
## Random effects:
## Groups Name Variance Std.Dev.
## Family (Intercept) 0.01344 0.1159
## Residual 0.16278 0.4035
## Number of obs: 86, groups: Family, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13093 0.09289 17.58731 -1.410 0.17612
## Dev.treatmentH 0.08752 0.12947 79.86097 0.676 0.50099
## Repro.treatmentH 0.33757 0.11252 78.51255 3.000 0.00362
## Dev.treatmentH:Repro.treatmentH -0.36118 0.17987 79.49168 -2.008 0.04804
##
## (Intercept)
## Dev.treatmentH
## Repro.treatmentH **
## Dev.treatmentH:Repro.treatmentH *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dv.trH Rpr.tH
## Dev.trtmntH -0.523
## Rpr.trtmntH -0.603 0.441
## Dv.trtH:R.H 0.377 -0.721 -0.632anova(lmer.PC2a)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Dev.treatment 0.17467 0.17467 1 79.366 1.0730 0.30341
## Repro.treatment 0.50378 0.50378 1 77.416 3.0948 0.08249 .
## Dev.treatment:Repro.treatment 0.65634 0.65634 1 79.492 4.0320 0.04804 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PC2EMM = (emmeans(lmer.PC2a, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
PC2EMM## Dev.treatment Repro.treatment emmean SE df lower.CL
## 1 C C -0.13092748 0.09340086 18.25290 -0.326960709
## 2 H C -0.04340341 0.11441287 31.04538 -0.276736177
## 3 C H 0.20663943 0.09398270 17.96771 0.009163668
## 4 H H -0.06701742 0.10829319 27.98340 -0.288851884
## upper.CL
## 1 0.06510575
## 2 0.18992935
## 3 0.40411520
## 4 0.15481704#Plot
ggplot(PC2EMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean-SE, ymax = emmean+SE)) + theme_classic() 
##Individual models ##Standard length
lmer.SL = lmer(SL.cm ~ Dev.treatment * Repro.treatment + (1|Family) , data = Adults)
summary(lmer.SL)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SL.cm ~ Dev.treatment * Repro.treatment + (1 | Family)
## Data: Adults
##
## REML criterion at convergence: 144.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3610 -0.5419 -0.0211 0.4461 3.1791
##
## Random effects:
## Groups Name Variance Std.Dev.
## Family (Intercept) 0.05792 0.2407
## Residual 0.27223 0.5218
## Number of obs: 86, groups: Family, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.98408 0.14286 9.98394 62.886 2.62e-14
## Dev.treatmentH -0.26681 0.16836 78.59118 -1.585 0.117
## Repro.treatmentH 0.17805 0.14589 77.35559 1.220 0.226
## Dev.treatmentH:Repro.treatmentH -0.08937 0.23363 78.05481 -0.383 0.703
##
## (Intercept) ***
## Dev.treatmentH
## Repro.treatmentH
## Dev.treatmentH:Repro.treatmentH
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dv.trH Rpr.tH
## Dev.trtmntH -0.438
## Rpr.trtmntH -0.507 0.444
## Dv.trtH:R.H 0.317 -0.721 -0.634anova(lmer.SL)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Dev.treatment 1.94002 1.94002 1 78.080 7.1263 0.009237 **
## Repro.treatment 0.36308 0.36308 1 76.513 1.3337 0.251743
## Dev.treatment:Repro.treatment 0.03984 0.03984 1 78.055 0.1463 0.703106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lmer.SL), col="darkgray")
plot(fitted(lmer.SL), residuals(lmer.SL))
SLEMM = (emmeans(lmer.SL, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
SLEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 C C 8.984076 0.1431351 11.58062 8.670954 9.297198
## 2 H C 8.717266 0.1673804 19.16347 8.367137 9.067395
## 3 C H 9.162124 0.1439410 11.61816 8.847357 9.476892
## 4 H H 8.805941 0.1600452 16.92288 8.468158 9.143725#Plot
ggplot(SLEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean-SE, ymax = emmean+SE)) + theme_classic() 
##Weight
lmer.W = lmer(Weight.g~ Dev.treatment * Repro.treatment + (1|Family) , data = Adults)
summary(lmer.W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Weight.g ~ Dev.treatment * Repro.treatment + (1 | Family)
## Data: Adults
##
## REML criterion at convergence: 534.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8874 -0.6558 -0.0795 0.4815 4.2167
##
## Random effects:
## Groups Name Variance Std.Dev.
## Family (Intercept) 8.028 2.833
## Residual 31.099 5.577
## Number of obs: 86, groups: Family, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 31.7260 1.6025 9.3061 19.798 6.37e-09
## Dev.treatmentH -3.3799 1.8013 78.4514 -1.876 0.0643
## Repro.treatmentH -0.4865 1.5600 77.3355 -0.312 0.7560
## Dev.treatmentH:Repro.treatmentH 1.9090 2.4990 77.9396 0.764 0.4472
##
## (Intercept) ***
## Dev.treatmentH .
## Repro.treatmentH
## Dev.treatmentH:Repro.treatmentH
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dv.trH Rpr.tH
## Dev.trtmntH -0.417
## Rpr.trtmntH -0.483 0.444
## Dv.trtH:R.H 0.302 -0.722 -0.634anova(lmer.W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Dev.treatment 117.431 117.431 1 77.983 3.7760 0.0556 .
## Repro.treatment 4.470 4.470 1 76.593 0.1437 0.7056
## Dev.treatment:Repro.treatment 18.148 18.148 1 77.940 0.5835 0.4472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lmer.W), col="darkgray")
plot(fitted(lmer.W), residuals(lmer.W))
WEMM = (emmeans(lmer.W, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
WEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 C C 31.72604 1.604837 10.56602 28.17603 35.27604
## 2 H C 28.34613 1.854547 17.09140 24.43497 32.25729
## 3 C H 31.23953 1.613327 10.62448 27.67325 34.80581
## 4 H H 29.76864 1.778452 15.11047 25.98038 33.55691#Plot
ggplot(WEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean-SE, ymax = emmean+SE)) + theme_classic() 
##Condition
lmer.Cond = lmer(Condition~ Dev.treatment * Repro.treatment + (1|Family), data= Adults)
summary(lmer.Cond)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Condition ~ Dev.treatment * Repro.treatment + (1 | Family)
## Data: Adults
##
## REML criterion at convergence: 82.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2628 -0.6172 -0.1655 0.5387 3.4198
##
## Random effects:
## Groups Name Variance Std.Dev.
## Family (Intercept) 0.01099 0.1048
## Residual 0.13188 0.3632
## Number of obs: 86, groups: Family, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.33816 0.08371 17.51595 51.826 < 2e-16
## Dev.treatmentH -0.08360 0.11654 79.85392 -0.717 0.47527
## Repro.treatmentH -0.29878 0.10128 78.50611 -2.950 0.00418
## Dev.treatmentH:Repro.treatmentH 0.31607 0.16191 79.48247 1.952 0.05444
##
## (Intercept) ***
## Dev.treatmentH
## Repro.treatmentH **
## Dev.treatmentH:Repro.treatmentH .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dv.trH Rpr.tH
## Dev.trtmntH -0.523
## Rpr.trtmntH -0.602 0.441
## Dv.trtH:R.H 0.377 -0.721 -0.632anova(lmer.Cond)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Dev.treatment 0.11173 0.11173 1 79.359 0.8472 0.36014
## Repro.treatment 0.40499 0.40499 1 77.412 3.0709 0.08367 .
## Dev.treatment:Repro.treatment 0.50259 0.50259 1 79.482 3.8110 0.05444 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lmer.Cond), col="darkgray")
plot(fitted(lmer.Cond), residuals(lmer.Cond))
FKEMM = (emmeans(lmer.Cond, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
FKEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 C C 4.338163 0.08416424 18.18172 4.161467 4.514859
## 2 H C 4.254564 0.10306517 30.93334 4.044343 4.464785
## 3 C H 4.039382 0.08468873 17.90143 3.861387 4.217377
## 4 H H 4.271853 0.09755838 27.87370 4.071972 4.471733#Plot
ggplot(FKEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean-SE, ymax = emmean+SE)) + theme_classic() 
##Physical condition, weight for a given length
lmer.Cond = lmer(Weight.g ~ Dev.treatment * Repro.treatment + (1|Family) + SL.cm , data = Adults)
summary(lmer.Cond)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Weight.g ~ Dev.treatment * Repro.treatment + (1 | Family) + SL.cm
## Data: Adults
##
## REML criterion at convergence: 403.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21058 -0.62911 -0.09576 0.45871 2.92659
##
## Random effects:
## Groups Name Variance Std.Dev.
## Family (Intercept) 0.8586 0.9266
## Residual 6.6231 2.5735
## Number of obs: 86, groups: Family, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -54.5204 4.8786 80.0104 -11.175 < 2e-16
## Dev.treatmentH -0.8054 0.8396 79.3540 -0.959 0.34034
## Repro.treatmentH -2.1754 0.7255 76.7488 -2.998 0.00366
## SL.cm 9.6000 0.5384 80.5186 17.832 < 2e-16
## Dev.treatmentH:Repro.treatmentH 2.7506 1.1512 77.7467 2.389 0.01931
##
## (Intercept) ***
## Dev.treatmentH
## Repro.treatmentH **
## SL.cm ***
## Dev.treatmentH:Repro.treatmentH *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dv.trH Rpr.tH SL.cm
## Dev.trtmntH -0.226
## Rpr.trtmntH 0.064 0.409
## SL.cm -0.991 0.165 -0.137
## Dv.trtH:R.H -0.003 -0.702 -0.633 0.049anova(lmer.Cond)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Dev.treatment 6.01 6.01 1 78.787 0.9080 0.34355
## Repro.treatment 12.88 12.88 1 76.609 1.9441 0.16726
## SL.cm 2105.95 2105.95 1 80.519 317.9713 < 2e-16 ***
## Dev.treatment:Repro.treatment 37.81 37.81 1 77.747 5.7085 0.01931 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lmer.Cond), col="darkgray")
plot(fitted(lmer.Cond), residuals(lmer.Cond))
CondEMM = (emmeans(lmer.Cond, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
CondEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 C C 31.64888 0.6378392 14.39579 30.28437 33.01339
## 2 H C 30.84350 0.7805864 24.84596 29.23535 32.45165
## 3 C H 29.47351 0.6490274 15.25794 28.09217 30.85485
## 4 H H 31.41871 0.7342391 22.32097 29.89726 32.94016#Plot
ggplot(CondEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean-SE, ymax = emmean+SE)) + theme_classic() 
##Proportion of pairs breeding
Breeding = read.csv('Breeding proportions FINAL.csv', strip.white=TRUE)
head(Breeding)## Development Post.maturity Treatment.combo Reproduced
## 1 Control Control CC N
## 2 Control Control CC N
## 3 Control Control CC N
## 4 Control Control CC N
## 5 Control Control CC N
## 6 Control Control CC Ntable(Breeding$Treatment.combo, Breeding$Reproduced)##
## N Y
## CC 7 6
## CH 7 6
## HC 3 5
## HH 6 3chisq.test(table(Breeding$Treatment.combo, Breeding$Reproduced))## Warning in chisq.test(table(Breeding$Treatment.combo, Breeding$Reproduced)):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(Breeding$Treatment.combo, Breeding$Reproduced)
## X-squared = 1.4516, df = 3, p-value = 0.6935chisq.test(table(Breeding$Development, Breeding$Reproduced))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(Breeding$Development, Breeding$Reproduced)
## X-squared = 0, df = 1, p-value = 1chisq.test(table(Breeding$Post.maturity, Breeding$Reproduced))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(Breeding$Post.maturity, Breeding$Reproduced)
## X-squared = 0.20077, df = 1, p-value = 0.6541