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added more models
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@SamGurr
SamGurr committed Aug 15, 2024
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110 changes: 83 additions & 27 deletions RAnalysis/Scripts/Growth.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -956,13 +956,24 @@ cld(lsm_Exp2.all.AgeXOA, Letters=letters)
library(lsmeans)
library(emmeans)
library(multcomp)
lsm_Exp2.LMM.Age <- lsmeans(Exp2_LMM.lme4,list(pairwise~Age),adjust="tukey")
lsm_Exp2.LMM.Age <- lsmeans(Exp2_LMM.nlme,list(pairwise~Age),adjust="tukey") # does the Tukey Kramer
cld(lsm_Exp2.LMM.Age, Letters=letters)
# Age lsmean SE df lower.CL upper.CL .group
# 2 86.4 2.77 38.5 80.8 92 a
# 4 95.4 2.77 38.5 89.8 101 a
# 8 140.1 2.57 36.1 134.8 145 b
# 10 152.2 2.57 36.1 147.0 157 c
# Age lsmean SE df lower.CL upper.CL .group
# 2 86.4 2.76 12 80.4 92.4 a
# 4 95.4 2.76 12 89.4 101.4 a
# 8 140.1 2.57 12 134.5 145.7 b
# 10 152.2 2.57 12 146.6 157.8 c
emm_Exp2.LMM.Age <- emmeans(Exp2_LMM.nlme,
pairwise ~ "Age",
adjust = "tukey") # does the Tukey Kramer
cld(emm_Exp2.LMM.Age, Letters=letters)
# Age emmean SE df lower.CL upper.CL .group
# 2 86.4 2.76 12 80.4 92.4 a
# 4 95.4 2.76 12 89.4 101.4 a
# 8 140.1 2.57 12 134.5 145.7 b
# 10 152.2 2.57 12 146.6 157.8 c
lsm_Exp2.LMM.OA <- lsmeans(Exp2_LMM.lme4,list(pairwise~Larvae_pCO2),adjust="tukey")
cld(lsm_Exp2.LMM.OA, Letters=letters)
# Larvae_pCO2 lsmean SE df lower.CL upper.CL .group
Expand Down Expand Up @@ -1230,18 +1241,19 @@ capture.output(cld(lsm_Exp2.LowMod.Age, Letters=letters),file="Output/1_Survival
* all treatment low moderate and high
* moderate v. low

```{r ANOVA end length; F1 Experiment #2 all and low v mod}
# (2) Size at metamorphosis
```{r ANOVA and T-test metamorphosis; F1 Experiment #2 all and low v mod}
# Size at metamorphosis
# NOTE: one way test using three groups in the 2.1 ANOVA IS CORRECT
# Note: one way test using two groups the t test is more approporate - 2.2 T TEST IS CORRECT
# (2.1) All treatment Low, moderate, and high
Exp2_Anova <- Exp2 %>%
Exp2_metamorphosis <- Exp2 %>%
dplyr::filter(Age %in% 10) %>%
dplyr::select(ID, Larvae_pCO2, Age,Length) %>%
convert_as_factor(ID, Age, Larvae_pCO2) %>%
na.omit() # prGR is normmalized to the inital measurement, these are NAs, omit them
Exp2endsize_summ <- summarySE(Exp2_Anova, measurevar="Length", groupvars=c("Age", "Larvae_pCO2"))
Exp2endsize_summ <- summarySE(Exp2_metamorphosis, measurevar="Length", groupvars=c("Age", "Larvae_pCO2"))
# 10 Low pCO2 5 182.320 7.215404 3.226827 8.959107
# 10 Moderate pCO2 5 169.334 8.812167 3.940921 10.941750
Expand All @@ -1250,8 +1262,12 @@ Exp2endsize_summ <- summarySE(Exp2_Anova, measurevar="Length", groupvars=c("Age"
((182.320 - 169.334)/ 182.320) *100 # 7.122642 % larger under
# (2.1) All treatment Low, moderate, and high
# * run one way anova
Exp2_AOV_res.mod <- lm(Length~Larvae_pCO2,data=Exp2_Anova)
Exp2_AOV_res.mod <- lm(Length~Larvae_pCO2,data=Exp2_metamorphosis)
# * check model assumptions
shapiro.test(resid(Exp2_AOV_res.mod)) # 0.9314
Expand Down Expand Up @@ -1283,18 +1299,18 @@ capture.output(Exp2_AOV_res.means,
# (2.2) Trancate data to just Low and Moderate
Exp2_Anova <- Exp2 %>%
dplyr::filter(Age %in% 10) %>%
dplyr::filter(!Larvae_pCO2 %in% 'High pCO2') %>% # omit high run for low vs. mod
dplyr::select(ID, Larvae_pCO2, Age,Length) %>%
convert_as_factor(ID, Age, Larvae_pCO2) %>%
na.omit() # prGR is normmalized to the inital measurement, these are NAs, omit them
write.csv(Exp2_Anova,
Exp2_metamorphosis_LvM <- Exp2 %>%
dplyr::filter(Age %in% 10) %>%
dplyr::filter(!Larvae_pCO2 %in% 'High pCO2') %>% # omit high run for low vs. mod
dplyr::select(ID, Larvae_pCO2, Age,Length) %>%
convert_as_factor(ID, Age, Larvae_pCO2) %>%
na.omit() # prGR is normmalized to the inital measurement, these are NAs, omit them
write.csv(Exp2_metamorphosis_LvM,
file="Output/1_Survival_Growth/size_growth/Experiment2/Experiment2_MeanLength_metamorphosisLowvMod.csv")
# * run one way anova
Exp2_AOV_res.mod <- lm(Length~Larvae_pCO2,data=Exp2_Anova)
Exp2_AOV_res.mod <- lm(Length~Larvae_pCO2,data=Exp2_metamorphosis_LvM)
# * check model assumptions
shapiro.test(resid(Exp2_AOV_res.mod)) # 0.8102
Expand Down Expand Up @@ -1323,16 +1339,35 @@ capture.output(Exp2_AOV_res.means,
file="Output/1_Survival_Growth/size_growth/Experiment2/Experiment2_Length_metamorphosis_posthoc_LowvMod.doc")
# T test
# asusmptions , data is normal, model variance is equal
# data are norm dist ? YES
shapiro.test(Exp2_metamorphosis_LvM$Length)[[2]] # 0.8834981
# equal variance ? YES
var.test(Exp2_metamorphosis_LvM$Length~
as.numeric(as.factor(Exp2_metamorphosis_LvM$Larvae_pCO2)))[[3]] # 0.7078991
# run the model
Exp2_Ttest_mod <- t.test( as.numeric(as.factor(Exp2_metamorphosis_LvM$Larvae_pCO2)) ,
Exp2_metamorphosis_LvM$Length,
var.equal = TRUE) # because of tests above
# output important data
library(purrr)
purrr::map_df(list(Exp2_Ttest_mod), tidy)[4:6] # Tstatistic, p vaue, DF
# -53.85676 2.401214e-21 18
```

### Experiment 4 (F2s)

```{r ANOVA end length; F2 Experiment #4}
# (2) Size at metamorphosis
```{r ANOVA and T-test metamorphosis; F2 Experiment #4}
# Size at metamorphosis
# NOTE: one way test using two groups, the t test is more approporate here
# All treatment Low and moderate
Exp4_Anova <- Exp4 %>%
Exp4_metamorphosis <- Exp4 %>%
dplyr::filter(Age %in% 10) %>%
dplyr::select(ID, Larvae_pCO2, Age,Length) %>%
convert_as_factor(ID, Age, Larvae_pCO2) %>%
Expand All @@ -1342,9 +1377,8 @@ write.csv(Exp4_Anova,
file="Output/1_Survival_Growth/size_growth/Experiment4/Experiment4_MeanLength_metamorphosisLowvMod.csv")
# * run one way anova
Exp4_AOV_res.mod <- lm(Length~Larvae_pCO2,data=Exp4_Anova)
Exp4_AOV_res.mod <- lm(Length~Larvae_pCO2,data=Exp4_metamorphosis)
# * check model assumptions
shapiro.test(resid(Exp4_AOV_res.mod)) # 0.2569
Expand All @@ -1360,6 +1394,28 @@ capture.output(summary(aov(Exp4_AOV_res.mod)),
file="Output/1_Survival_Growth/size_growth/Experiment4_Length_metamorphosis_anova.doc")
# T test
# asusmptions , data is normal, model variance is equal
# data are norm dist ? YES
shapiro.test(Exp4_metamorphosis$Length)[[2]] # 0.1992734
# equal variance ? YES
var.test(Exp4_metamorphosis$Length~
as.numeric(as.factor(Exp4_metamorphosis$Larvae_pCO2)))[[3]] # 0.4875707
# run the model
Exp4_Ttest_mod <- t.test( as.numeric(as.factor(Exp4_metamorphosis$Larvae_pCO2)) ,
Exp4_metamorphosis$Length,
var.equal = TRUE) # because of tests above
# output important data
library(purrr)
purrr::map_df(list(Exp4_Ttest_mod), tidy)[4:6] # Tstatistic, p vaue, DF
# -43.55171 9.785468e-13 10
Rmisc::summarySE(Exp4_metamorphosis, measurevar="Length", groupvars="Larvae_pCO2")
((170.6000 - 162.0333) / 170.6000)*100 # 5.021512
```


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46 changes: 33 additions & 13 deletions RAnalysis/Scripts/Survival_embryo_metamorphosis.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -1257,10 +1257,9 @@ Exp2Exp4_PrePost.master$Generation <- factor(Exp2Exp4_PrePost.master$Generation)
TwoWay.Pre <- aov(lm(abs.delta.perc ~ Larvae_pCO2 * Generation,
data = (Exp2Exp4_PrePost.master %>% dplyr::filter(type %in% 'pre'))
))
shapiro.test(resid(TwoWay.Pre)) # 0.05353 - pass
leveneTest(TwoWay.Pre) # 0.7313 - pass
summary(TwoWay.Pre)
shapiro.test(resid(TwoWay.Pre)) # 0.2471 - pass
leveneTest(TwoWay.Pre) # 0.5528 - pass
summary(TwoWay.Pre) # NO EFFECT
# Df Sum Sq Mean Sq F value Pr(>F)
# Larvae_pCO2 1 0.001225 0.001225 0.644 0.438
# Generation 1 0.002470 0.002470 1.298 0.277
Expand All @@ -1269,25 +1268,46 @@ summary(TwoWay.Pre)
# delta post - test the effects of larvae pCO2 and generation
TwoWay.Post <- aov(lm(abs.delta.perc ~ Larvae_pCO2 * Generation,
data = (Exp2Exp4_PrePost.master %>% dplyr::filter(type %in% 'post'))
))
shapiro.test(resid(TwoWay.Post)) # 0.007293 - non-normal
leveneTest(TwoWay.Post) # 0.6631 - pass
shapiro.test(resid(TwoWay.Post)) # 0.05633 - pass
leveneTest(TwoWay.Post) # 0.2409 - pass
summary(TwoWay.Post)
# Df Sum Sq Mean Sq F value Pr(>F)
# Larvae_pCO2 1 0.009120 0.009120 13.583 0.00312 **
# Generation 1 0.003840 0.003840 5.719 0.03405 *
# Larvae_pCO2:Generation 1 0.009300 0.009300 13.851 0.00292 **
# Residuals 12 0.008058 0.000671
# * run non-parametric
SRH.Post <- scheirerRayHare(abs.delta.perc ~ Larvae_pCO2 * Generation,
data = (Exp2Exp4_PrePost.master %>% dplyr::filter(type %in% 'post')))
# Df Sum Sq H p.value
# Larvae_pCO2 1 132.250 5.8346 0.015714
# Generation 1 26.667 1.1765 0.278076
# Larvae_pCO2:Generation 1 36.817 1.6243 0.202498
# Residuals 12 144.267
# tukey-Kramer
TwoWay.Post.emmeans.pCO2 <- emmeans(TwoWay.Post, list(pairwise ~ Larvae_pCO2), adjust = "tukey")
TwoWay.Post.emmeans.pCO2.letters <- multcomp::cld(object = TwoWay.Post.emmeans.pCO2$emmeans,
Letters = letters)
# Larvae_pCO2 emmean SE df lower.CL upper.CL .group
# Low pCO2 0.0519 0.00946 12 0.0313 0.0725 a
# Moderate pCO2 0.1121 0.00946 12 0.0915 0.1327 b
TwoWay.Post.emmeans.pCO2.Gen <- emmeans(TwoWay.Post, list(pairwise ~ Larvae_pCO2:Generation), adjust = "tukey")
TwoWay.Post.emmeans.pCO2.Gen.letters <- multcomp::cld(object = TwoWay.Post.emmeans.pCO2.Gen$emmeans,
Letters = letters)
# Larvae_pCO2 Generation emmean SE df lower.CL upper.CL .group
# Low pCO2 2 0.0430 0.0150 12 0.0104 0.0756 a
# Low pCO2 1 0.0608 0.0116 12 0.0356 0.0860 a
# Moderate pCO2 1 0.0712 0.0116 12 0.0460 0.0964 a
# Moderate pCO2 2 0.1530 0.0150 12 0.1204 0.1856 b
```

```{r Plots for Pre and Post}
Exp2Exp4_PrePost.summ <- Exp2Exp4_PrePost.master %>%
dplyr::select(Generation,type,Larvae_pCO2,abs.delta.perc) %>%
na.omit() %>%
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