A-Maine-Tank-2022_All_Color_V8
McMahon
2024-05-14
this script will be the same as 6 except with changes/additions based on discussion in new draft for figures. Brittany mentioned wanting the temperatures to also be floored at 0 for the intercept interpretation but I will not add that here yet. Main goal: UPDATE PLOTS for figure 4 to include ad arctica and scallops.
Set up
read in relevant files
I took the sheet Diana sent me and saved a csv with each tab of that sheet then read those in here.
remove dead and lost
We had 59 removed. This is lower than height because some dead were not measured.
## [1] 389 18## [1] 332 18look at data
Looking for trends
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'add a pH where minimum pH is 0 (to avoid weird intercepts later on)
Not totally sure if this is necessary for temperature too, but it makes sense to me.
## [1] 7.39## [1] 6.14
Plot
## ------------------------------------------------------------------------------## 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:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:ggpubr':
##
## mutate## temp pH N mean sd se ci
## 1 6 7.4 23 123.8135 31.52645 6.573718 11.288024
## 2 6 7.6 23 114.8844 28.20548 5.881249 10.098954
## 3 6 7.8 21 104.8837 25.91503 5.655123 9.753494
## 4 6 8 24 108.1858 23.46086 4.788928 8.207608
## 5 9 7.4 42 127.5220 35.29663 5.446388 9.165607
## 6 9 7.6 41 116.8224 28.96624 4.523767 7.617350
## 7 9 7.8 38 109.2931 26.96463 4.374241 7.379753
## 8 9 8 46 106.3783 27.62087 4.072475 6.839427
## 9 12 7.4 15 125.7987 32.07403 8.281480 14.586254
## 10 12 7.6 22 122.2714 35.53425 7.575928 13.036225
## 11 12 7.8 19 101.1314 26.68662 6.122329 10.616509
## 12 12 8 18 110.2446 29.29434 6.904743 12.011537
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Individual Species Groups
mercenaria
check structure
Looks pretty normal to me!
## 'data.frame': 116 obs. of 20 variables:
## $ Tank : Factor w/ 16 levels "H1","H10","H11",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ File path : chr NA "C:\\Users\\thatc\\OneDrive - Iowa State University\\Desktop\\jpg\\mercenaria\\4th round\\b41.jpeg" "C:\\Users\\thatc\\OneDrive - Iowa State University\\Desktop\\jpg\\mercenaria\\b38.jpeg" NA ...
## $ file name : chr "b38.jpeg" "b42" "b39" "b47" ...
## $ shell : chr "b38" "b42" "b39" "b47" ...
## $ N : num 456633 274746 316471 329233 343592 ...
## $ mean : num 146 140 142 126 138 ...
## $ stdev : num 24.1 16.8 22.7 20.9 21.1 ...
## $ mode : num 139 144 128 134 145 126 133 137 130 127 ...
## $ pH : Factor w/ 4 levels "7.4","7.6","7.8",..: 2 2 2 2 2 2 2 2 2 4 ...
## $ temp : Factor w/ 3 levels "6","9","12": 3 3 3 3 3 3 3 3 3 2 ...
## $ dead? : chr NA NA NA NA ...
## $ species : chr "mercenaria" "mercenaria" "mercenaria" "mercenaria" ...
## $ temp.average : num 12.1 12.1 12.1 12.1 12.1 ...
## $ temp.stdev : num 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.63 ...
## $ pH.average.YSI : num 7.57 7.57 7.57 7.57 7.57 7.57 7.57 7.57 7.57 8 ...
## $ pH.stdev.YSI : num 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.07 ...
## $ pH.average : num 7.59 7.59 7.59 7.59 7.59 7.59 7.59 7.59 7.59 8 ...
## $ pH.stdev : num 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.05 ...
## $ pH.normalized : num 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.61 ...
## $ temp.normalized: num 5.92 5.92 5.92 5.92 5.92 5.92 5.92 5.92 5.92 3.05 ...
##
## Shapiro-Wilk normality test
##
## data: mercenaria$mean
## W = 0.9899, p-value = 0.5504## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 116 121.68 11.03 122.41 121.71 12.29 96.69 146.08 49.39 -0.04 -0.67
## se
## X1 1.02linear model
‘normal’ linear mixed effect model
first we will try this and see how it looks as per Brittany’s suggestion. This will not specify a family, most similar to what I did way back when with CCA.
check the normality assumptions of full model
looks good
color.1 <- lmer(mean~temp.average*pH.normalized+(1|Tank), data = mercenaria, na.action = na.fail)
simulationOutput1<-simulateResiduals(color.1)
plot(simulationOutput1)
shapiro.test(resid(color.1))##
## Shapiro-Wilk normality test
##
## data: resid(color.1)
## W = 0.99444, p-value = 0.9282plot(color.1)
#plot(top_model1)
testZeroInflation(color.1)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedplotResiduals(color.1, mercenaria$temp)
plotResiduals(color.1, mercenaria$pH)
#plotResiduals(top_model1, mercenaria$temp)make models and likelihood ratio test
when talking with Brittany we decided to compare w and w o interaction and then from there test significance of main effects
full.m<-lmer(mean~temp.average*pH.normalized+(1|Tank), data = mercenaria, na.action = na.fail, REML=FALSE)
main.m<-lmer(mean~temp.average+pH.normalized+(1|Tank), data = mercenaria, na.action = na.fail, REML=FALSE)
full.v.main<-anova(full.m, main.m, test="Chisq")
full.v.main # no dif - drops AIC, drop interaction ## Data: mercenaria
## Models:
## main.m: mean ~ temp.average + pH.normalized + (1 | Tank)
## full.m: mean ~ temp.average * pH.normalized + (1 | Tank)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## main.m 5 795.51 809.28 -392.75 785.51
## full.m 6 797.02 813.54 -392.51 785.02 0.4926 1 0.4828summary(main.m)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: mean ~ temp.average + pH.normalized + (1 | Tank)
## Data: mercenaria
##
## AIC BIC logLik deviance df.resid
## 795.5 809.3 -392.8 785.5 111
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26256 -0.65541 0.00644 0.59647 2.40795
##
## Random effects:
## Groups Name Variance Std.Dev.
## Tank (Intercept) 20.35 4.511
## Residual 41.62 6.451
## Number of obs: 116, groups: Tank, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 102.8706 6.3897 14.1990 16.099 1.62e-10 ***
## temp.average 2.7868 0.6435 14.4332 4.331 0.000646 ***
## pH.normalized -19.5916 5.6590 14.7095 -3.462 0.003574 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmp.vr
## temp.averag -0.935
## pH.normalzd -0.374 0.091confint(main.m)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 2.778928 7.279378
## .sigma 5.648080 7.463544
## (Intercept) 89.489723 116.307519
## temp.average 1.429197 4.127774
## pH.normalized -31.489772 -7.787176drop1(main.m)## Single term deletions using Satterthwaite's method:
##
## Model:
## mean ~ temp.average + pH.normalized + (1 | Tank)
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## temp.average 780.47 780.47 1 14.433 18.754 0.0006464 ***
## pH.normalized 498.80 498.80 1 14.710 11.986 0.0035737 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(main.m)| Â | mean | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 102.87 | 90.21 – 115.53 | <0.001 |
| temp average | 2.79 | 1.51 – 4.06 | <0.001 |
| pH normalized | -19.59 | -30.81 – -8.38 | 0.001 |
| Random Effects | |||
| σ2 | 41.62 | ||
| Ï„00 Tank | 20.35 | ||
| ICC | 0.33 | ||
| N Tank | 16 | ||
| Observations | 116 | ||
| Marginal R2 / Conditional R2 | 0.482 / 0.652 | ||
formal plot
i think we want three lines here because temp and pH slopes - this seems pretty good but I would eventually like to be able to have just 4 lines for our 4 groups and to label them based on their non normalized numbers but this is tricky to do because pH in the model is CONTINUOUS so hard to plot effect size at discrete numbers?
plot(predictorEffects(full.m, xlevels=list(pH.normalized=seq(0, 0.6, 0.2))),
lines=list(multiline=TRUE))
plot(predictorEffects(main.m, xlevels=list(pH.normalized=seq(0, 0.6, 0.2))),
lines=list(multiline=TRUE))
#effect_top_model.both<-as.data.frame(effect_top_model.both)
#effect_top_model.both
#custom_labels <- c("7.4", "7.6", "7.8", "8.0")
#effect_temp <- ggplot() +
# geom_line(data = effect_top_model.both, aes(x = temp.average, y = fit, group = pH.normalized, color = pH.normalized), alpha = 0.7) +
#geom_ribbon(data = effect_top_model.both, aes(x = temp.average, ymin = lower, ymax = upper, group = pH.normalized, fill = pH.normalized), alpha = 0.3) +
#geom_point(data = mercenaria, mapping = aes(x = temp.average, y = mean, color = pH.normalized), size = 3) +
#labs(x = "Temperature (C)", y = "Mean Shell Coloration", color = "pH") +
#theme_classic() +
#scale_color_viridis(direction = -1, option = "cividis", breaks = c(0, 0.2, 0.4, 0.6), labels = custom_labels) +
#scale_fill_viridis(direction = -1, option = "cividis", breaks = c(0, 0.2, 0.4, 0.6), labels = custom_labels, guide="none")
#effect_temp
#mercenaria.model.plot<-ggarrange(effect_temp,
# labels = c("A"),
#ncol = 1, nrow = 1)
#ggsave("02_output/02_plots/color_mercenaria_linear.png", effect_temp, width = 7, height = 4, dpi = 300)formal plot both
values_of_temp <- data.frame(temp.average = c(6, 9, 12))
effect_interact.both<-effects::effect(term= c("temp.average", "pH.normalized"), mod=full.m, xlevels=list(temp.average=c(6, 9, 12), pH.normalized=c(0, 0.2, 0.4, 0.6)))## Warning in term == terms: longer object length is not a multiple of shorter
## object length## Warning in term == names: longer object length is not a multiple of shorter
## object length## NOTE: temp.averagepH.normalized is not a high-order term in the modeleffect_interact.both<-as.data.frame(effect_interact.both)
effect_interact.both## temp.average pH.normalized fit se lower upper
## 1 6 0.0 121.4134 3.985039 113.51759 129.3093
## 2 9 0.0 127.8479 2.234808 123.41993 132.2759
## 3 12 0.0 134.2824 4.025759 126.30588 142.2589
## 4 6 0.2 116.4220 2.673760 111.12431 121.7197
## 5 9 0.2 124.0483 1.458269 121.15888 126.9376
## 6 12 0.2 131.6745 2.548673 126.62461 136.7244
## 7 6 0.4 111.4306 2.347622 106.77911 116.0821
## 8 9 0.4 120.2486 1.339659 117.59423 122.9030
## 9 12 0.4 129.0666 2.473412 124.16581 133.9673
## 10 6 0.6 106.4392 3.311728 99.87746 113.0010
## 11 9 0.6 116.4489 1.999697 112.48679 120.4111
## 12 12 0.6 126.4586 3.882374 118.76621 134.1511original_values <- c(0, 0.2, 0.4, 0.6)
custom_labels_x <- c("7.4", "7.6", "7.8", "8.0")
effect_temp_swap <- ggplot() +
#geom_point(data=effect_interact.both, aes(x=temp.average, y=fit), color="black") +
geom_line(data = effect_interact.both, aes(x = temp.average, y = fit, group = pH.normalized, color = pH.normalized), alpha = 0.7) +
geom_ribbon(data= effect_interact.both, aes(x=temp.average, ymin=lower, ymax=upper, group = pH.normalized, fill=pH.normalized), alpha= 0.3) +
geom_point(data=mercenaria, mapping = aes(x=temp.average, y=mean, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Mean Shell Coloration", color = "pH",title = "Juvenile *M. mercenaria*")+
theme_classic()+
scale_color_viridis( option = "cividis", breaks = original_values, labels = custom_labels_x, direction = -1)+
scale_fill_viridis( option= "cividis", guide = "none", direction = -1)+
theme(
axis.text.x = element_text(size= 8, family = "Arial"),
axis.text.y = element_text(size= 8, family = "Arial"),
axis.title.x = element_text(size=10,family = "Arial"),
axis.title.y = element_text(size=10,family = "Arial", margin = margin(r = 50)),
legend.title = element_text(size = 10,family = "Arial"),
legend.text = element_text(size = 8,family = "Arial"),
plot.title = element_text(size = 12,family = "Arial", hjust = 0.5))+
theme(plot.title = ggtext::element_markdown())
effect_temp_swap
mercenaria.model.plot<-ggarrange(effect_temp_swap,
labels = c("A"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
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## family 'Arial' not found in PostScript font databaseggsave("02_output/02_plots/color_mercenaria_linear.png", mercenaria.model.plot, width = 6, height = 4, dpi = 300, bg = 'white')
effect_temp_swap1 <- ggplot() +
#geom_point(data=effect_interact.both, aes(x=temp.average, y=fit), color="black") +
geom_line(data = effect_interact.both, aes(x = temp.average, y = fit, group = pH.normalized, color = pH.normalized), alpha = 0.7) +
geom_ribbon(data= effect_interact.both, aes(x=temp.average, ymin=lower, ymax=upper, group = pH.normalized, fill=pH.normalized), alpha= 0.3) +
geom_point(data=mercenaria, mapping = aes(x=temp.average, y=mean, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Mean Shell Coloration", color = "pH",title = "Juvenile *M. mercenaria*")+
theme_classic()+
scale_color_viridis( option = "cividis", breaks = original_values, labels = custom_labels_x, direction = -1)+
scale_fill_viridis( option= "cividis", guide = "none", direction = -1)+
theme(
axis.text.x = element_text(size= 8, family = "Arial"),
axis.text.y = element_text(size= 8, family = "Arial"),
axis.title.x = element_text(size=10,family = "Arial"),
axis.title.y = element_text(size=10,family = "Arial", margin = margin(r = 50)),
legend.title = element_text(size = 10,family = "Arial"),
legend.text = element_text(size = 8,family = "Arial"),
plot.title = element_text(size = 12,family = "Arial", hjust = 0.5))+
theme(plot.title = ggtext::element_markdown())
effect_temp_swap1
mercenaria.model.plot1<-ggarrange(effect_temp_swap1,
labels = c("A"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
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This plot illustrates the trend in both pH and temperature for Mercenaria shell coloration.
## temp pH N mean sd se ci
## 1 6 7.4 9 120.3091 5.836794 1.945598 3.617933
## 2 6 7.6 9 117.7914 5.050849 1.683616 3.130766
## 3 6 7.8 8 104.6461 4.402510 1.556522 2.948954
## 4 6 8 9 108.4222 7.452191 2.484064 4.619235
## 5 9 7.4 14 132.0149 8.297423 2.217580 3.927186
## 6 9 7.6 14 122.6619 8.737814 2.335279 4.135624
## 7 9 7.8 10 115.9668 5.505253 1.740914 3.191292
## 8 9 8 16 120.6646 10.680070 2.670018 4.680675
## 9 12 7.4 5 129.4374 3.401533 1.521212 3.242990
## 10 12 7.6 9 135.2718 6.861502 2.287167 4.253098
## 11 12 7.8 6 124.2305 7.934959 3.239433 6.527615
## 12 12 8 7 127.6544 5.246114 1.982845 3.853025
mya
check structure
Appears pretty normal.
## 'data.frame': 150 obs. of 20 variables:
## $ Tank : Factor w/ 16 levels "H1","H10","H11",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ File path : chr "C:\\Users\\thatc\\OneDrive - Iowa State University\\Tank Experiment_Boron Isotopes Paper\\Mya arenaria pics\\jpg\\r41.jpeg" "C:\\Users\\thatc\\OneDrive - Iowa State University\\Tank Experiment_Boron Isotopes Paper\\Mya arenaria pics\\jpg\\r40.jpeg" "C:\\Users\\thatc\\OneDrive - Iowa State University\\Tank Experiment_Boron Isotopes Paper\\Mya arenaria pics\\jpg\\r37.jpeg" "C:\\Users\\thatc\\OneDrive - Iowa State University\\Tank Experiment_Boron Isotopes Paper\\Mya arenaria pics\\jpg\\r42.jpeg" ...
## $ file name : chr "r41.jpeg" "r40.jpeg" "r37.jpeg" "r42.jpeg" ...
## $ shell : chr "r41" "r40" "r37" "r42" ...
## $ N : num 661697 587852 417483 603232 286395 ...
## $ mean : num 130 140 158 139 118 ...
## $ stdev : num 45.9 46.1 39.6 46.9 51.9 ...
## $ mode : num 127 171 174 96 180 172 117 181 170 112 ...
## $ pH : Factor w/ 4 levels "7.4","7.6","7.8",..: 2 2 2 2 2 2 2 2 2 4 ...
## $ temp : Factor w/ 3 levels "6","9","12": 3 3 3 3 3 3 3 3 3 2 ...
## $ dead? : chr NA NA NA NA ...
## $ species : chr "mya" "mya" "mya" "mya" ...
## $ temp.average : num 12.1 12.1 12.1 12.1 12.1 ...
## $ temp.stdev : num 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.63 ...
## $ pH.average.YSI : num 7.57 7.57 7.57 7.57 7.57 7.57 7.57 7.57 7.57 8 ...
## $ pH.stdev.YSI : num 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.07 ...
## $ pH.average : num 7.59 7.59 7.59 7.59 7.59 7.59 7.59 7.59 7.59 8 ...
## $ pH.stdev : num 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.05 ...
## $ pH.normalized : num 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.61 ...
## $ temp.normalized: num 5.92 5.92 5.92 5.92 5.92 5.92 5.92 5.92 5.92 3.05 ...
##
## Shapiro-Wilk normality test
##
## data: mya$mean
## W = 0.98704, p-value = 0.1759## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 150 132.61 15.32 129.99 132.29 15.1 96.47 172.74 76.27 0.21 -0.38
## se
## X1 1.25linear model
‘normal’ linear mixed effect model
first we will try this and see how it looks as per Brittany’s suggestion. This will not specify a family, most similar to what I did way back when with CCA.
check the normality assumptions of full model
looks good
color.1 <- lmer(mean~temp.average*pH.normalized+(1|Tank), data = mya, na.action = na.fail)
simulationOutput1<-simulateResiduals(color.1)
plot(simulationOutput1)
shapiro.test(resid(color.1))##
## Shapiro-Wilk normality test
##
## data: resid(color.1)
## W = 0.98572, p-value = 0.1249plot(color.1)
#plot(top_model1)
testZeroInflation(color.1)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedplotResiduals(color.1, mya$temp)
plotResiduals(color.1, mya$pH)
#plotResiduals(top_model1, mercenaria$temp)make models and likelihood ratio test
full.m<-lmer(mean~temp.average*pH.normalized+(1|Tank), data = mya, na.action = na.fail, REML=FALSE)
main.m<-lmer(mean~temp.average+pH.normalized+(1|Tank), data = mya, na.action = na.fail, REML=FALSE)
full.v.main<-anova(full.m, main.m, test="Chisq")
full.v.main # no dif - drops AIC, drop interaction ## Data: mya
## Models:
## main.m: mean ~ temp.average + pH.normalized + (1 | Tank)
## full.m: mean ~ temp.average * pH.normalized + (1 | Tank)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## main.m 5 1110.5 1125.5 -550.23 1100.5
## full.m 6 1110.3 1128.3 -549.14 1098.3 2.1824 1 0.1396summary(main.m)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: mean ~ temp.average + pH.normalized + (1 | Tank)
## Data: mya
##
## AIC BIC logLik deviance df.resid
## 1110.5 1125.5 -550.2 1100.5 145
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.97320 -0.56490 0.03388 0.74850 2.71712
##
## Random effects:
## Groups Name Variance Std.Dev.
## Tank (Intercept) 26.58 5.156
## Residual 77.10 8.781
## Number of obs: 150, groups: Tank, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 162.3229 7.4692 15.8188 21.732 3.33e-13 ***
## temp.average -1.4706 0.7521 16.0762 -1.955 0.0682 .
## pH.normalized -49.9321 6.5427 15.7431 -7.632 1.13e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmp.vr
## temp.averag -0.937
## pH.normalzd -0.376 0.097confint(main.m)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 3.179168 8.2698850
## .sigma 7.825115 9.9480662
## (Intercept) 146.706365 177.8530462
## temp.average -3.031654 0.1021498
## pH.normalized -63.582793 -36.2936394drop1(main.m)## Single term deletions using Satterthwaite's method:
##
## Model:
## mean ~ temp.average + pH.normalized + (1 | Tank)
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## temp.average 294.7 294.7 1 16.076 3.8228 0.06817 .
## pH.normalized 4490.7 4490.7 1 15.743 58.2439 1.127e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(main.m)| Â | mean | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 162.32 | 147.56 – 177.09 | <0.001 |
| temp average | -1.47 | -2.96 – 0.02 | 0.052 |
| pH normalized | -49.93 | -62.86 – -37.00 | <0.001 |
| Random Effects | |||
| σ2 | 77.10 | ||
| Ï„00 Tank | 26.58 | ||
| ICC | 0.26 | ||
| N Tank | 16 | ||
| Observations | 150 | ||
| Marginal R2 / Conditional R2 | 0.559 / 0.672 | ||
formal plot
i think we want three lines here because temp and pH slopes - this seems pretty good but I would eventually like to be able to have just 4 lines for our 4 groups and to label them based on their non normalized numbers but this is tricky to do because pH in the model is CONTINUOUS so hard to plot effect size at discrete numbers?
plot(predictorEffects(full.m, xlevels=list(pH.normalized=seq(0, 0.6, 0.2))),
lines=list(multiline=TRUE))
plot(predictorEffects(main.m, xlevels=list(pH.normalized=seq(0, 0.6, 0.2))),
lines=list(multiline=TRUE))
formal plot both
values_of_temp <- data.frame(temp.average = c(6, 9, 12))
effect_interact.both<-effects::effect(term= c("temp.average", "pH.normalized"), mod=full.m, xlevels=list(temp.average=c(6, 9, 12), pH.normalized=c(0, 0.2, 0.4, 0.6)))## Warning in term == terms: longer object length is not a multiple of shorter
## object length## Warning in term == names: longer object length is not a multiple of shorter
## object length## NOTE: temp.averagepH.normalized is not a high-order term in the modeleffect_interact.both<-as.data.frame(effect_interact.both)
effect_interact.both## temp.average pH.normalized fit se lower upper
## 1 6 0.0 149.1485 4.384107 140.4839 157.8130
## 2 9 0.0 149.3366 2.433678 144.5268 154.1464
## 3 12 0.0 149.5247 4.410052 140.8090 158.2405
## 4 6 0.2 141.7378 2.939691 135.9279 147.5476
## 5 9 0.2 139.0711 1.582096 135.9443 142.1978
## 6 12 0.2 136.4044 2.790119 130.8901 141.9186
## 7 6 0.4 134.3271 2.575520 129.2369 139.4172
## 8 9 0.4 128.8055 1.450958 125.9380 131.6731
## 9 12 0.4 123.2840 2.720211 117.9080 128.6601
## 10 6 0.6 126.9164 3.632485 119.7373 134.0954
## 11 9 0.6 118.5400 2.174752 114.2420 122.8381
## 12 12 0.6 110.1637 4.277022 101.7108 118.6166original_values <- c(0, 0.2, 0.4, 0.6)
custom_labels_x <- c("7.4", "7.6", "7.8", "8.0")
effect_temp_swap <- ggplot() +
#geom_point(data=effect_interact.both, aes(x=temp.average, y=fit), color="black") +
geom_line(data = effect_interact.both, aes(x = temp.average, y = fit, group = pH.normalized, color = pH.normalized), alpha = 0.7) +
geom_ribbon(data= effect_interact.both, aes(x=temp.average, ymin=lower, ymax=upper, group = pH.normalized, fill=pH.normalized), alpha= 0.3) +
geom_point(data=mya, mapping = aes(x=temp.average, y=mean, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Mean Shell Coloration", color = "pH", title = "Juvenile *M. arenaria*")+
theme_classic()+
scale_color_viridis( option = "cividis", breaks = original_values, labels = custom_labels_x, direction = -1)+
scale_fill_viridis( option= "cividis", guide = "none", direction = -1)+
theme(
axis.text.x = element_text(size= 8, family = "Arial"),
axis.text.y = element_text(size= 8, family = "Arial"),
axis.title.x = element_text(size=10,family = "Arial"),
axis.title.y = element_text(size=10,family = "Arial", margin = margin(r = 50)),
legend.title = element_text(size = 10,family = "Arial"),
legend.text = element_text(size = 8,family = "Arial"),
plot.title = element_text(size = 12,family = "Arial", hjust = 0.5))+
theme(plot.title = ggtext::element_markdown())
effect_temp_swap
mya.model.plot<-ggarrange(effect_temp_swap,
labels = c("B"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
ncol = 1, nrow = 1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## family 'Arial' not found in PostScript font databaseggsave("02_output/02_plots/color_mya_linear.png", mya.model.plot, width = 14, height = 4, dpi = 300, bg = 'white')
effect_temp_swap1 <- ggplot() +
#geom_point(data=effect_interact.both, aes(x=temp.average, y=fit), color="black") +
geom_line(data = effect_interact.both, aes(x = temp.average, y = fit, group = pH.normalized, color = pH.normalized), alpha = 0.7) +
geom_ribbon(data= effect_interact.both, aes(x=temp.average, ymin=lower, ymax=upper, group = pH.normalized, fill=pH.normalized), alpha= 0.3) +
geom_point(data=mya, mapping = aes(x=temp.average, y=mean, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Mean Shell Coloration", color = "pH", title = "Juvenile *M. arenaria*")+
theme_classic()+
scale_color_viridis( option = "cividis", breaks = original_values, labels = custom_labels_x, direction = -1)+
scale_fill_viridis( option= "cividis", guide = "none", direction = -1)+
theme(
axis.text.x = element_text(size= 8, family = "Arial"),
axis.text.y = element_text(size= 8, family = "Arial"),
axis.title.x = element_text(size=10,family = "Arial"),
axis.title.y = element_text(size=10,family = "Arial", margin = margin(r = 50)),
legend.title = element_text(size = 10,family = "Arial"),
legend.text = element_text(size = 8,family = "Arial"),
plot.title = element_text(size = 12,family = "Arial", hjust = 0.5))+
theme(plot.title = ggtext::element_markdown())
effect_temp_swap1
mya.model.plot1<-ggarrange(effect_temp_swap1,
labels = c("B"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
ncol = 1, nrow = 1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## family 'Arial' not found in PostScript font databaseggsave("02_output/02_plots/color_mya_linear1.png", mya.model.plot1, width = 14, height = 4, dpi = 300, bg = 'white')Plot
This plot depicts the strong relationship between pH and shell color in Mya, seemingly regardless of temperature.
## temp pH N mean sd se ci
## 1 6 7.4 10 150.5219 5.412784 1.711673 3.137689
## 2 6 7.6 10 134.9325 5.662082 1.790508 3.282203
## 3 6 7.8 9 125.8381 6.966905 2.322302 4.318432
## 4 6 8 11 125.0058 5.317460 1.603274 2.905873
## 5 9 7.4 20 150.8367 14.428803 3.226378 5.578837
## 6 9 7.6 19 135.3885 9.583922 2.198703 3.812690
## 7 9 7.8 20 125.7649 7.166237 1.602419 2.770796
## 8 9 8 19 121.1604 8.098192 1.857853 3.221635
## 9 12 7.4 7 148.4751 6.220035 2.350952 4.568324
## 10 12 7.6 9 140.8783 11.784180 3.928060 7.304416
## 11 12 7.8 9 106.1281 8.849082 2.949694 5.485098
## 12 12 8 7 122.8366 4.856334 1.835522 3.566750
juv arctica
check structure
Appears pretty normal. Lower than others.
## 'data.frame': 66 obs. of 20 variables:
## $ Tank : Factor w/ 16 levels "H1","H10","H11",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ File path : chr "C:\\Users\\thatc\\OneDrive - Iowa State University\\Desktop\\jpg\\juv Arctica\\g29.jpeg" NA NA NA ...
## $ file name : chr "g29.jpeg" NA NA NA ...
## $ shell : chr "g29" "g30" "g31" "g32" ...
## $ N : num 381123 339242 764948 602176 970122 ...
## $ mean : num 52.5 41 58.5 52.7 68.8 ...
## $ stdev : num 9.96 17.64 19.74 16.74 24.64 ...
## $ mode : num 47 28 46 51 68 47 46 50 47 58 ...
## $ pH : Factor w/ 4 levels "7.4","7.6","7.8",..: 2 2 2 2 4 4 4 4 4 4 ...
## $ temp : Factor w/ 3 levels "6","9","12": 3 3 3 3 2 2 2 2 1 1 ...
## $ dead? : chr NA NA NA NA ...
## $ species : chr "juv.arctica" "juv.arctica" "juv.arctica" "juv.arctica" ...
## $ temp.average : num 12.06 12.06 12.06 12.06 9.19 ...
## $ temp.stdev : num 0.42 0.42 0.42 0.42 0.63 0.63 0.63 0.63 0.78 0.78 ...
## $ pH.average.YSI : num 7.57 7.57 7.57 7.57 8 8 8 8 8.02 8.02 ...
## $ pH.stdev.YSI : num 0.04 0.04 0.04 0.04 0.07 0.07 0.07 0.07 0.06 0.06 ...
## $ pH.average : num 7.59 7.59 7.59 7.59 8 8 8 8 8.05 8.05 ...
## $ pH.stdev : num 0.07 0.07 0.07 0.07 0.05 0.05 0.05 0.05 0.05 0.05 ...
## $ pH.normalized : num 0.2 0.2 0.2 0.2 0.61 ...
## $ temp.normalized: num 5.92 5.92 5.92 5.92 3.05 ...
##
## Shapiro-Wilk normality test
##
## data: juv.arctica$mean
## W = 0.9902, p-value = 0.8847## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 66 59.97 6.25 59.67 59.92 5.99 40.98 75.74 34.75 -0.03 0.43 0.77linear model
‘normal’ linear mixed effect model
first we will try this and see how it looks as per Brittany’s suggestion. This will not specify a family, most similar to what I did way back when with CCA.
check the normality assumptions of full model
looks good
color.1 <- lmer(mean~temp.average*pH.normalized+(1|Tank), data = juv.arctica, na.action = na.fail)
simulationOutput1<-simulateResiduals(color.1)
plot(simulationOutput1)
shapiro.test(resid(color.1))##
## Shapiro-Wilk normality test
##
## data: resid(color.1)
## W = 0.98992, p-value = 0.8723plot(color.1)
#plot(top_model1)
testZeroInflation(color.1)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedplotResiduals(color.1, juv.arctica$temp)
plotResiduals(color.1, juv.arctica$pH)
#plotResiduals(top_model1, mercenaria$temp)make models and likelihood ratio test
full.m<-lmer(mean~temp.average*pH.normalized+(1|Tank), data = juv.arctica, na.action = na.fail, REML=FALSE)## boundary (singular) fit: see help('isSingular')main.m<-lmer(mean~temp.average+pH.normalized+(1|Tank), data = juv.arctica, na.action = na.fail, REML=FALSE)## boundary (singular) fit: see help('isSingular')full.v.main<-anova(full.m, main.m, test="Chisq")
full.v.main # no dif - drops AIC, drop interaction ## Data: juv.arctica
## Models:
## main.m: mean ~ temp.average + pH.normalized + (1 | Tank)
## full.m: mean ~ temp.average * pH.normalized + (1 | Tank)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## main.m 5 432.70 443.65 -211.35 422.70
## full.m 6 434.68 447.81 -211.34 422.68 0.0247 1 0.8752summary(main.m)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: mean ~ temp.average + pH.normalized + (1 | Tank)
## Data: juv.arctica
##
## AIC BIC logLik deviance df.resid
## 432.7 443.6 -211.4 422.7 61
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94750 -0.70080 -0.03117 0.62721 2.35086
##
## Random effects:
## Groups Name Variance Std.Dev.
## Tank (Intercept) 0.0 0.00
## Residual 35.4 5.95
## Number of obs: 66, groups: Tank, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 68.3168 3.7643 66.0000 18.149 <2e-16 ***
## temp.average -0.7258 0.3809 66.0000 -1.905 0.0611 .
## pH.normalized -5.2104 3.2252 66.0000 -1.616 0.1110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmp.vr
## temp.averag -0.935
## pH.normalzd -0.368 0.078
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')confint(main.m)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 0.000000 3.21096076
## .sigma 4.978291 7.12927818
## (Intercept) 60.619899 75.94393333
## temp.average -1.491953 0.05864277
## pH.normalized -11.978240 1.27929098drop1(main.m)## Single term deletions using Satterthwaite's method:
##
## Model:
## mean ~ temp.average + pH.normalized + (1 | Tank)
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## temp.average 128.495 128.495 1 66 3.6299 0.06111 .
## pH.normalized 92.391 92.391 1 66 2.6100 0.11096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(main.m)| Â | mean | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 68.32 | 60.79 – 75.84 | <0.001 |
| temp average | -0.73 | -1.49 – 0.04 | 0.061 |
| pH normalized | -5.21 | -11.66 – 1.24 | 0.111 |
| Random Effects | |||
| σ2 | 35.40 | ||
| Ï„00 Tank | 0.00 | ||
| N Tank | 16 | ||
| Observations | 66 | ||
| Marginal R2 / Conditional R2 | 0.082 / NA | ||
formal plot
i think we want three lines here because temp and pH slopes - this seems pretty good but I would eventually like to be able to have just 4 lines for our 4 groups and to label them based on their non normalized numbers but this is tricky to do because pH in the model is CONTINUOUS so hard to plot effect size at discrete numbers?
plot(predictorEffects(full.m, xlevels=list(pH.normalized=seq(0, 0.6, 0.2))),
lines=list(multiline=TRUE))
plot(predictorEffects(main.m, xlevels=list(pH.normalized=seq(0, 0.6, 0.2))),
lines=list(multiline=TRUE))
formal plot both
values_of_temp <- data.frame(temp.average = c(6, 9, 12))
effect_interact.both<-effects::effect(term= c("temp.average", "pH.normalized"), mod=full.m, xlevels=list(temp.average=c(6, 9, 12), pH.normalized=c(0, 0.2, 0.4, 0.6)))## Warning in term == terms: longer object length is not a multiple of shorter
## object length## Warning in term == names: longer object length is not a multiple of shorter
## object length## NOTE: temp.averagepH.normalized is not a high-order term in the modeleffect_interact.both<-as.data.frame(effect_interact.both)
effect_interact.both## temp.average pH.normalized fit se lower upper
## 1 6 0.0 63.71382 2.4083082 58.89968 68.52796
## 2 9 0.0 61.80431 1.3428174 59.12006 64.48857
## 3 12 0.0 59.89480 2.4967872 54.90380 64.88581
## 4 6 0.2 62.81662 1.6103072 59.59766 66.03558
## 5 9 0.2 60.74562 0.8723342 59.00185 62.48940
## 6 12 0.2 58.67463 1.5675285 55.54118 61.80807
## 7 6 0.4 61.91942 1.3855884 59.14966 64.68917
## 8 9 0.4 59.68693 0.7569363 58.17384 61.20003
## 9 12 0.4 57.45445 1.4434514 54.56903 60.33987
## 10 6 0.6 61.02221 1.9442855 57.13564 64.90878
## 11 9 0.6 58.62824 1.1131555 56.40308 60.85341
## 12 12 0.6 56.23428 2.2612310 51.71414 60.75441original_values <- c(0, 0.2, 0.4, 0.6)
custom_labels_x <- c("7.4", "7.6", "7.8", "8.0")
effect_temp_swap <- ggplot() +
#geom_point(data=effect_interact.both, aes(x=temp.average, y=fit), color="black") +
geom_line(data = effect_interact.both, aes(x = temp.average, y = fit, group = pH.normalized, color = pH.normalized), alpha = 0.7) +
geom_ribbon(data= effect_interact.both, aes(x=temp.average, ymin=lower, ymax=upper, group = pH.normalized, fill=pH.normalized), alpha= 0.3) +
geom_point(data=juv.arctica, mapping = aes(x=temp.average, y=mean, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Mean Shell Coloration", color = "pH", title = "Juvenile *A. islandica*")+
theme_classic()+
scale_color_viridis( option = "cividis", breaks = original_values, labels = custom_labels_x, direction = -1)+
scale_fill_viridis( option= "cividis", guide = "none", direction = -1)+
theme(
axis.text.x = element_text(size= 8, family = "Arial"),
axis.text.y = element_text(size= 8, family = "Arial"),
axis.title.x = element_text(size=10,family = "Arial"),
axis.title.y = element_text(size=10,family = "Arial", margin = margin(r = 50)),
legend.title = element_text(size = 10,family = "Arial"),
legend.text = element_text(size = 8,family = "Arial"),
plot.title = element_text(size = 12,family = "Arial", hjust = 0.5))+
theme(plot.title = ggtext::element_markdown())
effect_temp_swap
juv.arctica.model.plot<-ggarrange(effect_temp_swap,
labels = c("C"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
ncol = 1, nrow = 1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font databaseggsave("02_output/02_plots/color_juv.arctica_linear.png", juv.arctica.model.plot, width = 14, height = 4, dpi = 300, bg = 'white')
effect_temp_swap1 <- ggplot() +
#geom_point(data=effect_interact.both, aes(x=temp.average, y=fit), color="black") +
geom_line(data = effect_interact.both, aes(x = temp.average, y = fit, group = pH.normalized, color = pH.normalized), alpha = 0.7) +
geom_ribbon(data= effect_interact.both, aes(x=temp.average, ymin=lower, ymax=upper, group = pH.normalized, fill=pH.normalized), alpha= 0.3) +
geom_point(data=juv.arctica, mapping = aes(x=temp.average, y=mean, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Mean Shell Coloration", color = "pH", title = "Juvenile *A. islandica*")+
theme_classic()+
scale_color_viridis( option = "cividis", breaks = original_values, labels = custom_labels_x, direction = -1)+
scale_fill_viridis( option= "cividis", guide = "none", direction = -1)+
theme(
axis.text.x = element_text(size= 8, family = "Arial"),
axis.text.y = element_text(size= 8, family = "Arial"),
axis.title.x = element_text(size=10,family = "Arial"),
axis.title.y = element_text(size=10,family = "Arial", margin = margin(r = 50)),
legend.title = element_text(size = 10,family = "Arial"),
legend.text = element_text(size = 8,family = "Arial"),
plot.title = element_text(size = 12,family = "Arial", hjust = 0.5))+
theme(plot.title = ggtext::element_markdown())
effect_temp_swap1
juv.arctica.model.plot1<-ggarrange(effect_temp_swap1,
labels = c("C"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
ncol = 1, nrow = 1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial' not found in PostScript font databaseggsave("02_output/02_plots/color_juv.arctica_linear1.png", juv.arctica.model.plot1, width = 4, height = 4, dpi = 300, bg = 'white')all plots
#all.model.plotWIDE<-ggarrange(mercenaria.model.plot,mya.model.plot, juv.arctica.model.plot,
#ncol = 1, nrow = 3)
#ggsave("02_output/02_plots/all_color_mod_plotWIDE.png", all.model.plotWIDE, width = 5, height = 11, dpi = 500, bg = "white")
all.model.plotWIDE1<-ggarrange(mercenaria.model.plot,mya.model.plot, juv.arctica.model.plot,
ncol = 3, nrow = 1)
ggsave("02_output/02_plots/all_color_mod_plotWIDE1FINAL.tiff", all.model.plotWIDE1, width = 12.5, height = 3, dpi = 300, bg = "white")
#all.model.plotNARROW<-ggarrange(mercenaria.model.plot1,mya.model.plot1, juv.arctica.model.plot1,
# ncol = 1, nrow = 3)
#ggsave("02_output/02_plots/all_color_mod_plotNARROW.png", all.model.plotNARROW, width = 5, height = 11, dpi = 300, bg = "white")
#all.model.plotNARROW1<-ggarrange(mercenaria.model.plot1,mya.model.plot1, juv.arctica.model.plot1,
# ncol = 3, nrow =1)
#ggsave("02_output/02_plots/all_color_mod_plotNARROW1.png", all.model.plotNARROW1, width = 15, height = 4, dpi = 300, bg = "white")