A-Maine-Tank-2022_Bivalve-Height_V8
McMahon
2024-05-14
this script will be the same as 7 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 dfs Diana sent me and read them in here.
remove dead and lost
We only had 66 removed. This seems a bit high to me and I wonder if the scallops that were removed weren’t marked in coloration? or if something else strange happened. But rolling with this for now. From Diana there should be 66 deaths across these groups.
## [1] 477 15## [1] 406 15## [1] 35look at data
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
strange specimens
based on our conversation with Brittany, it seems safe to assume that measurements where growth is below 0 are mostly a reflection of sampling error and not actual loss in shell max height over the experiment. There I would like to make the assumption that any - values here are really a reflection of 0 growth. In some sense we are flooring percent change in height to 0. At this point all specimens marked as dead or lost have been removed so this shouldn’t effect those and all other specimens with missing data are NA for change in height so they wont be included in this either.
Another approach to this would be to add the value of the most negative number to the whole column so that the lowest % change is 0 % change, but this makes less sense to me in the context of this data set.
height.all.w.negatives<-height.all
height.all$`percentage change height` <- replace(height.all$`percentage change height`, which(height.all$`percentage change height` < 0), 0)
height.all$prop.change.height<-height.all$`percentage change height`/100
height.all %>%
ggplot(aes(prop.change.height, group = species, fill=species)) +
geom_boxplot()## Warning: Removed 35 rows containing non-finite values (`stat_boxplot()`).
height.all %>%
ggplot(aes(x=temp.average, y=prop.change.height,color = species, fill=species)) +
geom_point()+
geom_smooth(method = loess) #sweet no longer any below 0 ## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 35 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 35 rows containing missing values (`geom_point()`).
look for trends
Scallop doesn’t have all temp pH groups covered, will not be able to model I don’t think… Also mercenaria 12 temp seems to almost be showing two trends, I wonder if this is explained by tank?
In this version it is updated so each species shows geom_smooth with each temp/pH grouping its own line. pH scale is switched so light color = more acidic. Remove SE coloring as plot was too busy.Noting here that it could be nice to have this plot in Altair in the future, so you could hover over points, but thats a rabbit hole for another day.
## Loading required package: ggpp##
## Attaching package: 'ggpp'## The following object is masked from 'package:ggplot2':
##
## annotate## Loading required package: viridisLite## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
summary table
summary_by_species <- describeBy(height.all, group = "species")## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Infsummary_by_species##
## Descriptive statistics by group
## species: ad.arctica
## vars n mean sd median trimmed mad min max
## tank* 1 10 8.10 5.47 6.50 8.00 7.41 1.00 16.00
## Sample_ID_20220415* 2 10 5.50 3.03 5.50 5.50 3.71 1.00 10.00
## Max_heigh_mm 3 10 53.34 3.94 55.02 53.74 2.49 46.61 56.81
## Max_height_mm 4 10 53.81 3.87 55.31 54.17 2.62 47.46 57.21
## percentage change height 5 10 0.93 0.59 0.75 0.93 0.51 0.00 1.82
## ph* 6 10 2.60 1.26 3.00 2.62 1.48 1.00 4.00
## temp* 7 10 2.30 0.67 2.00 2.38 0.74 1.00 3.00
## died* 8 0 NaN NA NA NaN NA Inf -Inf
## species* 9 10 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## temp.average 10 10 9.88 1.94 9.18 10.08 2.22 6.14 12.06
## temp.stdev 11 10 0.49 0.13 0.44 0.47 0.14 0.33 0.75
## pH.average.YSI 12 10 7.72 0.26 7.82 7.73 0.26 7.36 8.00
## pH.stdev.YSI 13 10 0.07 0.01 0.07 0.07 0.01 0.04 0.08
## pH.average 14 10 7.72 0.25 7.81 7.72 0.28 7.39 8.01
## pH.stdev 15 10 0.06 0.01 0.06 0.06 0.01 0.05 0.08
## prop.change.height 16 10 0.01 0.01 0.01 0.01 0.01 0.00 0.02
## range skew kurtosis se
## tank* 15.00 0.19 -1.74 1.73
## Sample_ID_20220415* 9.00 0.00 -1.56 0.96
## Max_heigh_mm 10.20 -0.65 -1.40 1.25
## Max_height_mm 9.75 -0.58 -1.53 1.22
## percentage change height 1.82 0.17 -1.41 0.19
## ph* 3.00 -0.20 -1.77 0.40
## temp* 2.00 -0.31 -1.14 0.21
## died* -Inf NA NA NA
## species* 0.00 NaN NaN 0.00
## temp.average 5.92 -0.32 -1.12 0.61
## temp.stdev 0.42 0.59 -1.05 0.04
## pH.average.YSI 0.64 -0.28 -1.79 0.08
## pH.stdev.YSI 0.04 -0.77 -0.16 0.00
## pH.average 0.62 -0.27 -1.79 0.08
## pH.stdev 0.03 0.44 -1.08 0.00
## prop.change.height 0.02 0.17 -1.41 0.00
## ------------------------------------------------------------
## species: juv.arctica
## vars n mean sd median trimmed mad min max
## tank* 1 66 8.35 4.65 8.00 8.31 5.93 1.00 16.00
## Sample_ID_20220415* 2 66 33.50 19.20 33.50 33.50 24.46 1.00 66.00
## Max_heigh_mm 3 63 12.94 2.12 12.66 12.97 2.15 6.64 17.35
## Max_height_mm 4 66 21.60 3.04 21.59 21.68 3.22 14.87 28.67
## percentage change height 5 63 68.27 20.16 65.30 67.28 21.07 30.89 127.86
## ph* 6 66 2.59 1.14 3.00 2.61 1.48 1.00 4.00
## temp* 7 66 1.98 0.69 2.00 1.98 0.00 1.00 3.00
## died* 8 0 NaN NA NA NaN NA Inf -Inf
## species* 9 66 2.00 0.00 2.00 2.00 0.00 2.00 2.00
## temp.average 10 66 9.01 1.94 9.03 8.99 0.58 6.14 12.06
## temp.stdev 11 66 0.51 0.16 0.44 0.49 0.16 0.33 0.82
## pH.average.YSI 12 66 7.72 0.23 7.82 7.73 0.28 7.36 8.02
## pH.stdev.YSI 13 66 0.07 0.02 0.07 0.07 0.01 0.04 0.14
## pH.average 14 66 7.74 0.23 7.81 7.74 0.28 7.39 8.05
## pH.stdev 15 66 0.07 0.02 0.06 0.06 0.01 0.05 0.14
## prop.change.height 16 63 0.68 0.20 0.65 0.67 0.21 0.31 1.28
## range skew kurtosis se
## tank* 15.00 0.06 -1.29 0.57
## Sample_ID_20220415* 65.00 0.00 -1.25 2.36
## Max_heigh_mm 10.71 -0.18 -0.20 0.27
## Max_height_mm 13.80 -0.16 -0.28 0.37
## percentage change height 96.97 0.52 -0.25 2.54
## ph* 3.00 -0.10 -1.43 0.14
## temp* 2.00 0.02 -0.93 0.08
## died* -Inf NA NA NA
## species* 0.00 NaN NaN 0.00
## temp.average 5.92 0.08 -0.93 0.24
## temp.stdev 0.49 0.68 -0.99 0.02
## pH.average.YSI 0.66 -0.23 -1.41 0.03
## pH.stdev.YSI 0.10 2.07 5.18 0.00
## pH.average 0.66 -0.25 -1.35 0.03
## pH.stdev 0.09 2.22 5.12 0.00
## prop.change.height 0.97 0.52 -0.25 0.03
## ------------------------------------------------------------
## species: mercenaria
## vars n mean sd median trimmed mad min max
## tank* 1 115 8.07 4.57 8.00 8.01 5.93 1.00 16.00
## Sample_ID_20220415* 2 115 58.00 33.34 58.00 58.00 43.00 1.00 115.00
## Max_heigh_mm 3 115 12.02 1.20 11.86 11.97 1.10 9.60 15.21
## Max_height_mm 4 115 14.13 2.78 13.59 13.93 2.67 9.69 20.75
## percentage change height 5 115 18.43 19.76 13.02 15.79 19.31 0.00 71.26
## ph* 6 115 2.50 1.13 2.00 2.51 1.48 1.00 4.00
## temp* 7 115 1.93 0.73 2.00 1.91 1.48 1.00 3.00
## died* 8 0 NaN NA NA NaN NA Inf -Inf
## species* 9 115 3.00 0.00 3.00 3.00 0.00 3.00 3.00
## temp.average 10 115 8.88 2.07 9.03 8.83 3.75 6.14 12.06
## temp.stdev 11 115 0.53 0.16 0.44 0.52 0.16 0.33 0.82
## pH.average.YSI 12 115 7.71 0.23 7.63 7.71 0.33 7.36 8.02
## pH.stdev.YSI 13 115 0.07 0.02 0.07 0.07 0.01 0.04 0.14
## pH.average 14 115 7.72 0.23 7.67 7.72 0.39 7.39 8.05
## pH.stdev 15 115 0.07 0.02 0.06 0.07 0.01 0.05 0.14
## prop.change.height 16 115 0.18 0.20 0.13 0.16 0.19 0.00 0.71
## range skew kurtosis se
## tank* 15.00 0.05 -1.19 0.43
## Sample_ID_20220415* 114.00 0.00 -1.23 3.11
## Max_heigh_mm 5.61 0.43 -0.56 0.11
## Max_height_mm 11.06 0.58 -0.45 0.26
## percentage change height 71.26 0.97 -0.08 1.84
## ph* 3.00 0.04 -1.41 0.11
## temp* 2.00 0.11 -1.16 0.07
## died* -Inf NA NA NA
## species* 0.00 NaN NaN 0.00
## temp.average 5.92 0.13 -1.14 0.19
## temp.stdev 0.49 0.47 -1.22 0.02
## pH.average.YSI 0.66 -0.09 -1.43 0.02
## pH.stdev.YSI 0.10 1.95 4.38 0.00
## pH.average 0.66 -0.12 -1.38 0.02
## pH.stdev 0.09 2.05 4.19 0.00
## prop.change.height 0.71 0.97 -0.08 0.02
## ------------------------------------------------------------
## species: mya
## vars n mean sd median trimmed mad min max
## tank* 1 150 8.40 4.56 8.00 8.39 5.93 1.00 16.00
## Sample_ID_20220415* 2 150 75.50 43.45 75.50 75.50 55.60 1.00 150.00
## Max_heigh_mm 3 150 15.32 1.22 15.29 15.28 1.14 12.07 20.94
## Max_height_mm 4 150 19.17 2.16 19.27 19.25 1.91 12.13 23.93
## percentage change height 5 150 25.43 12.29 25.87 25.42 14.31 0.00 54.09
## ph* 6 150 2.50 1.12 2.50 2.50 0.74 1.00 4.00
## temp* 7 150 1.95 0.69 2.00 1.93 0.00 1.00 3.00
## died* 8 0 NaN NA NA NaN NA Inf -Inf
## species* 9 150 4.00 0.00 4.00 4.00 0.00 4.00 4.00
## temp.average 10 150 8.92 1.95 9.03 8.88 0.58 6.14 12.06
## temp.stdev 11 150 0.52 0.16 0.44 0.51 0.16 0.33 0.82
## pH.average.YSI 12 150 7.71 0.23 7.72 7.71 0.23 7.36 8.02
## pH.stdev.YSI 13 150 0.07 0.02 0.07 0.07 0.01 0.04 0.14
## pH.average 14 150 7.72 0.23 7.74 7.72 0.22 7.39 8.05
## pH.stdev 15 150 0.07 0.02 0.06 0.06 0.01 0.05 0.14
## prop.change.height 16 150 0.25 0.12 0.26 0.25 0.14 0.00 0.54
## range skew kurtosis se
## tank* 15.00 0.00 -1.23 0.37
## Sample_ID_20220415* 149.00 0.00 -1.22 3.55
## Max_heigh_mm 8.87 0.73 2.51 0.10
## Max_height_mm 11.80 -0.36 0.09 0.18
## percentage change height 54.09 0.01 -0.66 1.00
## ph* 3.00 0.00 -1.37 0.09
## temp* 2.00 0.07 -0.93 0.06
## died* -Inf NA NA NA
## species* 0.00 NaN NaN 0.00
## temp.average 5.92 0.09 -0.93 0.16
## temp.stdev 0.49 0.59 -1.12 0.01
## pH.average.YSI 0.66 -0.14 -1.39 0.02
## pH.stdev.YSI 0.10 2.01 4.75 0.00
## pH.average 0.66 -0.15 -1.33 0.02
## pH.stdev 0.09 2.17 4.73 0.00
## prop.change.height 0.54 0.01 -0.66 0.01
## ------------------------------------------------------------
## species: scallop
## vars n mean sd median trimmed mad min max
## tank* 1 65 8.51 4.33 9.00 8.49 5.93 1.00 16.00
## Sample_ID_20220415* 2 65 33.00 18.91 33.00 33.00 23.72 1.00 65.00
## Max_heigh_mm 3 65 32.42 3.33 32.81 32.36 3.68 25.01 40.44
## Max_height_mm 4 33 54.40 7.46 55.57 54.61 7.16 34.81 69.96
## percentage change height 5 33 67.52 18.09 68.72 68.34 17.47 28.26 103.84
## ph* 6 65 2.46 1.12 2.00 2.45 1.48 1.00 4.00
## temp* 7 65 1.88 0.72 2.00 1.85 1.48 1.00 3.00
## died* 8 0 NaN NA NA NaN NA Inf -Inf
## species* 9 65 5.00 0.00 5.00 5.00 0.00 5.00 5.00
## temp.average 10 65 8.71 1.99 9.00 8.63 3.71 6.14 12.06
## temp.stdev 11 65 0.54 0.17 0.55 0.54 0.25 0.33 0.82
## pH.average.YSI 12 65 7.70 0.23 7.63 7.71 0.30 7.36 8.02
## pH.stdev.YSI 13 65 0.07 0.02 0.07 0.07 0.01 0.04 0.14
## pH.average 14 65 7.72 0.23 7.67 7.72 0.33 7.39 8.05
## pH.stdev 15 65 0.07 0.02 0.06 0.06 0.01 0.05 0.14
## prop.change.height 16 33 0.68 0.18 0.69 0.68 0.17 0.28 1.04
## range skew kurtosis se
## tank* 15.00 0.01 -1.24 0.54
## Sample_ID_20220415* 64.00 0.00 -1.26 2.35
## Max_heigh_mm 15.43 0.13 -0.35 0.41
## Max_height_mm 35.15 -0.34 0.03 1.30
## percentage change height 75.58 -0.36 -0.35 3.15
## ph* 3.00 0.03 -1.39 0.14
## temp* 2.00 0.18 -1.09 0.09
## died* -Inf NA NA NA
## species* 0.00 NaN NaN 0.00
## temp.average 5.92 0.19 -1.08 0.25
## temp.stdev 0.49 0.31 -1.51 0.02
## pH.average.YSI 0.66 -0.10 -1.41 0.03
## pH.stdev.YSI 0.10 2.20 5.61 0.00
## pH.average 0.66 -0.13 -1.36 0.03
## pH.stdev 0.09 2.18 4.96 0.00
## prop.change.height 0.76 -0.36 -0.35 0.03#summary_by_species <- height.all %>%
# group_by(species) %>%
# summarise(
# mean_height = mean(prop.change.height),
# median_height = median(prop.change.height),
# min_height = min(prop.change.height),
# max_height = max(prop.change.height),
# N=n ()
##)
#write.csv(summary_by_species,'02_output/01_modified_data/height_summary_by_species.csv', row.names=F)add a pH where minimum pH is 0 (to avoid weird intercepts later on)
Not totally sure if this is necessary for temperature too - did not do it here.
## [1] 7.39
Individual Species Groups
plot scallop and adult arctica growth
#species.labs <- c( `ad.arctica` = "Adult Arctica islandica",
#`scallop` = "Placopecten magellanicus",
# )
#species.labs <- c("Arctica islandica","Mercenaria mercenaria", "Mya arenaria")
height.scal.plot.1 <- ggplot(scallop,aes(x=ph, y=prop.change.height, color=temp)) +
geom_smooth(aes(group=interaction(species,temp)),method="lm", se=FALSE)+
geom_point(aes(x=ph, y=prop.change.height,color=temp), size=3, position = position_dodge(width = 0.5)) +
xlab("pH Treatment")+
ylab("% ∆ in Shell Height")+
labs(x="pH Treatment", y="% ∆ in Shell Height", color = "Temp (C)")+
scale_color_viridis(discrete = TRUE, option="plasma")+
#stat_poly_eq(formula = x ~ y) +
facet_wrap(~species, ncol = 1,
labeller = as_labeller(species.labs))+
theme_classic()+
theme(
strip.text.x = element_text(
size = 12, color = "black", face = "italic"),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
#strip.background = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(size= 10, family = "Arial"),
axis.text.y = element_text(size= 10, family = "Arial"),
axis.title.x = element_text(size=12,family = "Arial",hjust = 0.5),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
)
height.scal.plot.1## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 32 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 32 rows containing missing values (`geom_point()`).
height.scal.plot.2 <- ggplot(scallop,aes(x=temp, y=prop.change.height, color=ph)) +
geom_smooth(aes(group=interaction(species,ph)),method="lm", se=FALSE)+
geom_point(aes(x=temp, y=prop.change.height,color=ph), size=3, position = position_dodge(width = 0.5)) +
labs(x="Temp Treatment (C)", y="% ∆ in Shell Height", color = "pH")+
scale_color_viridis(discrete = TRUE, direction = -1, option="cividis")+
#stat_poly_eq(formula = x ~ y) +
facet_wrap(~species, ncol = 1,
labeller = as_labeller(species.labs))+
theme_classic()+
theme(
strip.text.x = element_text(
size = 12, color = "black", face = "italic"),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
#strip.background = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(size= 10, family = "Arial"),
axis.text.y = element_text(size= 10, family = "Arial"),
axis.title.x = element_text(size=12,family = "Arial",hjust = 0.5),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
)
height.scal.plot.2## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 32 rows containing non-finite values (`stat_smooth()`).
## Removed 32 rows containing missing values (`geom_point()`).
height.ad.plot.1 <- ggplot(ad.arctica,aes(x=ph, y=prop.change.height, color=temp)) +
geom_smooth(aes(group=interaction(species,temp)),method="lm", se=FALSE)+
geom_point(aes(x=ph, y=prop.change.height,color=temp), size=3, position = position_dodge(width = 0.5)) +
xlab("pH Treatment")+
ylab("% ∆ in Shell Height")+
labs(x="pH Treatment", y="% ∆ in Shell Height", color = "Temp (C)")+
scale_color_viridis(discrete = TRUE, option="plasma")+
#stat_poly_eq(formula = x ~ y) +
facet_wrap(~species, ncol = 1,
labeller = as_labeller(species.labs))+
theme_classic()+
theme(
strip.text.x = element_text(
size = 12, color = "black", face = "italic"),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
#strip.background = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(size= 10, family = "Arial"),
axis.text.y = element_text(size= 10, family = "Arial"),
axis.title.x = element_text(size=12,family = "Arial",hjust = 0.5),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
)
height.ad.plot.1## `geom_smooth()` using formula = 'y ~ x'
height.ad.plot.2 <- ggplot(ad.arctica,aes(x=temp, y=prop.change.height, color=ph)) +
geom_smooth(aes(group=interaction(species,ph)),method="lm", se=FALSE)+
geom_point(aes(x=temp, y=prop.change.height,color=ph), size=3, position = position_dodge(width = 0.5)) +
labs(x="Temp Treatment (C)", y="% ∆ in Shell Height", color = "pH")+
scale_color_viridis(discrete = TRUE, direction = -1, option="cividis")+
#stat_poly_eq(formula = x ~ y) +
facet_wrap(~species, ncol = 1,
labeller = as_labeller(species.labs))+
theme_classic()+
theme(
strip.text.x = element_text(
size = 12, color = "black", face = "italic"),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
#strip.background = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(size= 10, family = "Arial"),
axis.text.y = element_text(size= 10, family = "Arial"),
axis.title.x = element_text(size=12,family = "Arial",hjust = 0.5),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
)
height.ad.plot.2## `geom_smooth()` using formula = 'y ~ x'
height_plot_combined<-ggarrange(height.scal.plot.1, height.scal.plot.2, height.ad.plot.1, height.ad.plot.2, ncol = 2, nrow = 2)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 32 rows containing non-finite values (`stat_smooth()`).
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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
## 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## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 32 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 32 rows containing missing values (`geom_point()`).## 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## `geom_smooth()` using formula = 'y ~ x'## 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$y, : font
## family 'Arial' not found in PostScript font database## `geom_smooth()` using formula = 'y ~ x'## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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 databaseheight_plot_combined
ggsave("02_output/02_plots/height.scal.ad.plot.png", height_plot_combined, width = 6, height = 8, dpi = 300, bg="white")scallop and ad arctica for figure 4
original_values <- c(0, 0.2, 0.4, 0.6)
custom_labels_x <- c("7.4", "7.6", "7.8", "8.0")
#################################################
effect_temp_swap_scallop <- 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=scallop, mapping = aes(x=temp.average, y=prop.change.height, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Proportional Change in Height", color = "pH", title="Juvenile *P. magellanicus*")+
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"),
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_scallop## Warning: Removed 32 rows containing missing values (`geom_point()`).
scallop.plot<-ggarrange(effect_temp_swap_scallop,
labels = c("E"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
ncol = 1, nrow = 1)## Warning: Removed 32 rows containing missing values (`geom_point()`).## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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$y, : font
## family 'Arial' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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$y, : font
## family 'Arial' not found in PostScript font database## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): 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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effect_temp_swap_ad.arctica <- 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=ad.arctica, mapping = aes(x=temp.average, y=prop.change.height, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Proportional Change in Height", color = "pH", title="Adult *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"),
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_ad.arctica
ad.arctica_plot<-ggarrange(effect_temp_swap_ad.arctica,
labels = c("D"),
font.label = list(size = 12, color = "black", face = "bold", family = "Arial"),
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check structure
Non normal, majorly skewed many at 0
## 'data.frame': 115 obs. of 17 variables:
## $ tank : Factor w/ 16 levels "H1","H10","H11",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ Sample_ID_20220415 : chr "b47" "b39" "b38" "b43" ...
## $ Max_heigh_mm : num 11.4 11.4 12.4 11.9 10.2 ...
## $ Max_height_mm : num 17.4 17.2 18.3 17.2 15 ...
## $ percentage change height: num 53.6 49.9 47.5 44.7 46 ...
## $ 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 ...
## $ died : chr NA NA NA NA ...
## $ species : Factor w/ 5 levels "ad.arctica","juv.arctica",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ 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 ...
## $ prop.change.height : num 0.536 0.499 0.475 0.447 0.46 ...
## $ pH.normalized : num 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.61 ...
##
## Shapiro-Wilk normality test
##
## data: mercenaria$prop.change.height
## W = 0.85068, p-value = 2.09e-09## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 115 0.18 0.2 0.13 0.16 0.19 0 0.71 0.71 0.97 -0.08 0.02## [1] 0## [1] 115 17
linear model
‘normal’ linear mixed effect model
first we will try this and see how it looks as per Brittany’s suggestion. This will not specify a family, most similar to what I did way back when with CCA.
best fit includes temp, observed vs expected is off QQ line, residual vs predicted is wacky. We can see if other models look better or worse.
check the normality assumptions of full model
looks a bit funky/kurtose, see difference in variance in temperature groups. Also some strange patterning in residuals.
height.1 <- lmer(prop.change.height~temp.average*pH.normalized+(1|tank), data = mercenaria, na.action = na.fail)
simulationOutput1<-simulateResiduals(height.1)
plot(simulationOutput1)
shapiro.test(resid(height.1)) # ! non normal##
## Shapiro-Wilk normality test
##
## data: resid(height.1)
## W = 0.8921, p-value = 1.298e-07plot(height.1)
#plot(top_model1)
testZeroInflation(height.1)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = Inf, p-value < 2.2e-16
## alternative hypothesis: two.sidedplotResiduals(height.1, mercenaria$temp)
plotResiduals(height.1, mercenaria$ph)
#plotResiduals(top_model1, mercenaria$temp)make models and likelihood ratio test
when talking with Diana we decided that 1) there is evidence that temperature effects growth (see long lived arctica records etc.) but that we were not really testing that here. We were more interested in whether there was a relationship with pH. I will therefore test against a null model of … 1) temperature + tank 2) tank alone are we asking vs full model? or main effects?
full.m<-lmer(prop.change.height~temp.average*pH.normalized+(1|tank), data = mercenaria, na.action = na.fail)
main.m<-lmer(prop.change.height~temp.average+pH.normalized+(1|tank), data = mercenaria, na.action = na.fail)
full.v.main<-anova(full.m, main.m, test="Chisq")## refitting model(s) with ML (instead of REML)full.v.main # no dif - drops AIC, drop interaction ## Data: mercenaria
## Models:
## main.m: prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## full.m: prop.change.height ~ temp.average * pH.normalized + (1 | tank)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## main.m 5 -249.72 -235.99 129.86 -259.72
## full.m 6 -248.26 -231.79 130.13 -260.26 0.5481 1 0.4591summary(main.m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## Data: mercenaria
##
## REML criterion at convergence: -243.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7026 -0.2263 0.0282 0.5222 2.7092
##
## Random effects:
## Groups Name Variance Std.Dev.
## tank (Intercept) 0.006423 0.08014
## Residual 0.004525 0.06727
## Number of obs: 115, groups: tank, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.60480 0.10563 11.76761 -5.725 0.000103 ***
## temp.average 0.08646 0.01061 11.84902 8.147 3.39e-06 ***
## pH.normalized 0.04371 0.09307 11.98456 0.470 0.646995
## ---
## 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 -0.375 0.094confint(main.m)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 0.04756438 0.10969799
## .sigma 0.05891418 0.07801634
## (Intercept) -0.80252479 -0.40725191
## temp.average 0.06661002 0.10631516
## pH.normalized -0.13048709 0.21771482drop1(main.m)## Single term deletions using Satterthwaite's method:
##
## Model:
## prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## temp.average 0.300366 0.300366 1 11.849 66.3752 3.389e-06 ***
## pH.normalized 0.000998 0.000998 1 11.985 0.2206 0.647
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(main.m)| prop.change.height | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.60 | -0.81 – -0.40 | <0.001 |
| temp average | 0.09 | 0.07 – 0.11 | <0.001 |
| pH normalized | 0.04 | -0.14 – 0.23 | 0.639 |
| Random Effects | |||
| σ2 | 0.00 | ||
| τ00 tank | 0.01 | ||
| ICC | 0.59 | ||
| N tank | 16 | ||
| Observations | 115 | ||
| Marginal R2 / Conditional R2 | 0.744 / 0.894 | ||
formal plot
i dont think we want three lines here because we only have one predictor? might need to think through this a bit more. cant make a plot with both since pH isnt in model.
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))
Another way to plot
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 -0.05806880 0.06882930 -0.19445865 0.078321057
## 2 9 0.0 0.17201470 0.03815258 0.09641282 0.247616585
## 3 12 0.0 0.40209820 0.06818960 0.26697595 0.537220440
## 4 6 0.2 -0.06592969 0.04614203 -0.15736321 0.025503824
## 5 9 0.2 0.18227502 0.02488040 0.13297286 0.231577189
## 6 12 0.2 0.43047974 0.04325440 0.34476826 0.516191225
## 7 6 0.4 -0.07379059 0.04047314 -0.15399082 0.006409638
## 8 9 0.4 0.19253535 0.02284410 0.14726824 0.237802454
## 9 12 0.4 0.45886129 0.04215161 0.37533505 0.542387524
## 10 6 0.6 -0.08165149 0.05713525 -0.19486879 0.031565815
## 11 9 0.6 0.20279567 0.03411855 0.13518748 0.270403861
## 12 12 0.6 0.48724283 0.06608531 0.35629038 0.618195276original_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=prop.change.height, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Proportional Change in Height", 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"),
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/height_mercenaria_linear.png", mercenaria.model.plot, width = 6, height = 5, dpi = 300, bg = 'white')as expected, temperature was a significant predictor in the full model and a full model of all was not significantly different than a model which only included temperature. pH had no influence.
another plot, ph
based on Tredennick which bases it off of https://biologyforfun.wordpress.com/2015/06/17/confidence-intervals-for-prediction-in-glmms/, should keep in mind that confidence or prediction intervals might underestimate uncertainty around our estimates.
mercenaria_vec <- seq(min(mercenaria$temp.average),max(mercenaria$temp.average))
upper_ph <- quantile(mercenaria$pH.normalized, 0.9)
lower_ph <- quantile(mercenaria$pH.normalized, 0.1)
newdat <- tibble(temp.average = mercenaria_vec,
Tank = mercenaria$tank[1],
pH.normalized = lower_ph,
ph_level = "low") %>%
bind_rows(
tibble(temp.average = mercenaria_vec,
Tank = mercenaria$tank[1],
pH.normalized = upper_ph,
ph_level = "high")
) %>%
as.data.frame()
m <- full.m
allout <- tibble() # empty object
for(do_level in unique(newdat$ph_level)) {
tmpdat <- newdat %>%
filter(ph_level == do_level)
mm <- model.matrix(~pH.normalized*temp.average, tmpdat)
tmpdat$y <- mm%*%fixef(m)
# predict(m, newdat, re.form=NA) would give the same results
pvar1 <- diag(mm %*% tcrossprod(vcov(m),mm))
outdat <- data.frame(tmpdat,
lower_ci = tmpdat$y-1.96*sqrt(pvar1),
upper_ci = tmpdat$y+1.96*sqrt(pvar1))
allout <- bind_rows(allout, outdat)
}
mercenaria.int.ci.plot<-ggplot(allout, aes(x = temp.average, y = y,
color = ph_level, fill = ph_level)) +
geom_hline(aes(yintercept = 0), linetype = 3) +
geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci), color = NA, alpha = 0.25) +
geom_line() +
scale_color_manual(values = rev(c("coral","dodgerblue")),
labels = c("High pH", "Low pH"),
name = NULL) +
scale_fill_manual(values = rev(c("coral","dodgerblue")),
labels = c("High pH", "Low pH"),
name = NULL) +
#scale_y_continuous(breaks = c(0,1)) +
#scale_x_continuous(breaks = c(6,9,12)) +
labs(x = "Mean Temperature", y = "Proportional Change in Height") +
theme_classic() +
theme(legend.position = c(0.8, 0.85))
mercenaria.int.ci.plot
ggsave("02_output/02_plots/height_mercenaria_int.ci.plot.png", height=3.2, width=4, units = "in")okay so this plot shows there is no interaction, but is otherwise pretty unhelpful, I am also a bit concerned that the axis scales don’t make sense. I have a feeling all our plots would look like this/the same bc it was only siginificant in one case, therfore I am not sure how helpful it is.
mya
check structure
This distribution looks pretty normal.
## 'data.frame': 150 obs. of 17 variables:
## $ tank : Factor w/ 16 levels "H1","H10","H11",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ Sample_ID_20220415 : chr "r39" "r40" "r37" "r38" ...
## $ Max_heigh_mm : num 13.8 16.5 17.1 13.8 14.3 ...
## $ Max_height_mm : num 19.9 21.6 22.2 15.6 15 ...
## $ percentage change height: num 44.62 31.04 30.25 12.68 4.76 ...
## $ 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 ...
## $ died : chr NA NA NA NA ...
## $ species : Factor w/ 5 levels "ad.arctica","juv.arctica",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ 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 ...
## $ prop.change.height : num 0.4462 0.3104 0.3025 0.1268 0.0476 ...
## $ pH.normalized : num 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.61 ...
##
## Shapiro-Wilk normality test
##
## data: mya$prop.change.height
## W = 0.98778, p-value = 0.2124## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 150 0.25 0.12 0.26 0.25 0.14 0 0.54 0.54 0.01 -0.66 0.01linear model
‘normal’ linear mixed effect model
first we will try this and see how it looks as per Brittany’s suggestion. This will not specify a family, most similar to what I did way back when with CCA.
best fit includes temp, observed vs expected is off QQ line, residual vs predicted is wacky. We can see if other models look better or worse.
check the normality assumptions of full model
looks a bit funky/kurtose, see difference in variance in temperature groups. Also some strange patterning in residuals.
height.1 <- lmer(prop.change.height~temp.average*pH.normalized+(1|tank), data = mya, na.action = na.fail)
simulationOutput1<-simulateResiduals(height.1)
plot(simulationOutput1)
shapiro.test(resid(height.1)) # ! non normal##
## Shapiro-Wilk normality test
##
## data: resid(height.1)
## W = 0.98946, p-value = 0.3221plot(height.1)
#plot(top_model1)
testZeroInflation(height.1)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = Inf, p-value < 2.2e-16
## alternative hypothesis: two.sidedplotResiduals(height.1, mya$temp)
plotResiduals(height.1, mya$ph)
#plotResiduals(top_model1, mya$temp)make models and likelihood ratio test
when talking with Diana we decided that 1) there is evidence that temperature effects growth (see long lived arctica records etc.) but that we were not really testing that here. We were more interested in whether there was a relationship with pH. I will therefore test against a null model of … 1) temperature + tank 2) tank alone are we asking vs full model? or main effects?
full.m<-lmer(prop.change.height~temp.average*pH.normalized+(1|tank), data = mya, na.action = na.fail)
main.m<-lmer(prop.change.height~temp.average+pH.normalized+(1|tank), data = mya, na.action = na.fail)
full.v.main<-anova(full.m, main.m, test="Chisq")## refitting model(s) with ML (instead of REML)full.v.main # no dif - drops AIC, drop interaction ## Data: mya
## Models:
## main.m: prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## full.m: prop.change.height ~ temp.average * pH.normalized + (1 | tank)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## main.m 5 -215.91 -200.86 112.96 -225.91
## full.m 6 -213.91 -195.85 112.96 -225.91 0.001 1 0.975summary(main.m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## Data: mya
##
## REML criterion at convergence: -206.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29429 -0.58316 0.00947 0.67847 2.23781
##
## Random effects:
## Groups Name Variance Std.Dev.
## tank (Intercept) 0.001121 0.03348
## Residual 0.012438 0.11152
## Number of obs: 150, groups: tank, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.072040 0.062757 13.237098 1.148 0.27132
## temp.average 0.021370 0.006351 13.690657 3.365 0.00475 **
## pH.normalized -0.026586 0.054867 13.028571 -0.485 0.63604
## ---
## 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 -0.377 0.097confint(main.m)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 0.000000000 0.05515095
## .sigma 0.099386885 0.12629520
## (Intercept) -0.045817179 0.19027507
## temp.average 0.009407677 0.03329598
## pH.normalized -0.129821572 0.07651674drop1(main.m)## Single term deletions using Satterthwaite's method:
##
## Model:
## prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## temp.average 0.14083 0.14083 1 13.691 11.3226 0.004754 **
## pH.normalized 0.00292 0.00292 1 13.029 0.2348 0.636040
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(main.m)| prop.change.height | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.07 | -0.05 – 0.20 | 0.253 |
| temp average | 0.02 | 0.01 – 0.03 | 0.001 |
| pH normalized | -0.03 | -0.14 – 0.08 | 0.629 |
| Random Effects | |||
| σ2 | 0.01 | ||
| τ00 tank | 0.00 | ||
| ICC | 0.08 | ||
| N tank | 16 | ||
| Observations | 150 | ||
| Marginal R2 / Conditional R2 | 0.118 / 0.191 | ||
formal plot
i dont think we want three lines here because we only have one predictor? might need to think through this a bit more. cant make a plot with both since pH isnt in model.
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))
Another way to plot
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 0.1993400 0.04092660 0.1184549 0.2802251
## 2 9 0.0 0.2644614 0.02287098 0.2192605 0.3096624
## 3 12 0.0 0.3295828 0.04203060 0.2465158 0.4126498
## 4 6 0.2 0.1946269 0.02746439 0.1403478 0.2489060
## 5 9 0.2 0.2590335 0.01484378 0.2296970 0.2883699
## 6 12 0.2 0.3234401 0.02652063 0.2710262 0.3758540
## 7 6 0.4 0.1899138 0.02404763 0.1423874 0.2374402
## 8 9 0.4 0.2536056 0.01362957 0.2266688 0.2805423
## 9 12 0.4 0.3172973 0.02582734 0.2662536 0.3683411
## 10 6 0.6 0.1852007 0.03386701 0.1182678 0.2521336
## 11 9 0.6 0.2481776 0.02047836 0.2077053 0.2886499
## 12 12 0.6 0.3111545 0.04071480 0.2306880 0.3916211original_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=prop.change.height, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Proportional Change in Height", 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"),
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"),
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## family 'Arial' not found in PostScript font databaseggsave("02_output/02_plots/height_mya_linear.png", mya.model.plot, width = 6, height = 5, dpi = 300, bg = 'white')as expected, temperature was a significant predictor in the full model and a full model of all was not significantly different than a model which only included temperature. pH had no influence.
another plot, ph
based on Tredennick which bases it off of https://biologyforfun.wordpress.com/2015/06/17/confidence-intervals-for-prediction-in-glmms/, should keep in mind that confidence or prediction intervals might underestimate uncertainty around our estimates.
mya_vec <- seq(min(mya$temp.average),max(mya$temp.average))
upper_ph <- quantile(mya$pH.normalized, 0.9)
lower_ph <- quantile(mya$pH.normalized, 0.1)
newdat <- tibble(temp.average = mya_vec,
Tank = mya$tank[1],
pH.normalized = lower_ph,
ph_level = "low") %>%
bind_rows(
tibble(temp.average = mya_vec,
Tank = mya$tank[1],
pH.normalized = upper_ph,
ph_level = "high")
) %>%
as.data.frame()
m <- full.m
allout <- tibble() # empty object
for(do_level in unique(newdat$ph_level)) {
tmpdat <- newdat %>%
filter(ph_level == do_level)
mm <- model.matrix(~pH.normalized*temp.average, tmpdat)
tmpdat$y <- mm%*%fixef(m)
# predict(m, newdat, re.form=NA) would give the same results
pvar1 <- diag(mm %*% tcrossprod(vcov(m),mm))
outdat <- data.frame(tmpdat,
lower_ci = tmpdat$y-1.96*sqrt(pvar1),
upper_ci = tmpdat$y+1.96*sqrt(pvar1))
allout <- bind_rows(allout, outdat)
}
mya.int.ci.plot<-ggplot(allout, aes(x = temp.average, y = y,
color = ph_level, fill = ph_level)) +
geom_hline(aes(yintercept = 0), linetype = 3) +
geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci), color = NA, alpha = 0.25) +
geom_line() +
scale_color_manual(values = rev(c("coral","dodgerblue")),
labels = c("High pH", "Low pH"),
name = NULL) +
scale_fill_manual(values = rev(c("coral","dodgerblue")),
labels = c("High pH", "Low pH"),
name = NULL) +
#scale_y_continuous(breaks = c(0,1)) +
#scale_x_continuous(breaks = c(6,9,12)) +
labs(x = "Mean Temperature", y = "Proportional Change in Height") +
theme_classic() +
theme(legend.position = c(0.8, 0.85))
mya.int.ci.plot
ggsave("02_output/02_plots/height_mya_int.ci.plot.png", height=3.2, width=4, units = "in")okay so this plot shows there is no interaction, but is otherwise pretty unhelpful, I am also a bit concerned that the axis scales don’t make sense. I have a feeling all our plots would look like this/the same bc it was only significant in one case, therefore I am not sure how helpful it is.
juvenile arctica
check structure
This distribution looks pretty normal, although it looks like there is one outlier above 1 (g69). Could possibly make max growth = 1 but I think I will leave for now.
## 'data.frame': 66 obs. of 17 variables:
## $ tank : Factor w/ 16 levels "H1","H10","H11",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ Sample_ID_20220415 : chr "g32" "g29" "g30" "g31" ...
## $ Max_heigh_mm : num 15 13.3 14.2 16.2 14.4 ...
## $ Max_height_mm : num 27.7 25.5 23.9 28.7 23.3 ...
## $ percentage change height: num 84.4 92.3 67.6 77.3 61.5 ...
## $ 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 ...
## $ died : chr NA NA NA NA ...
## $ species : Factor w/ 5 levels "ad.arctica","juv.arctica",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ 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 ...
## $ prop.change.height : num 0.844 0.923 0.676 0.773 0.615 ...
## $ pH.normalized : num 0.2 0.2 0.2 0.2 0.61 ...
##
## Shapiro-Wilk normality test
##
## data: juv.arctica$prop.change.height
## W = 0.96809, p-value = 0.1012## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 63 0.68 0.2 0.65 0.67 0.21 0.31 1.28 0.97 0.52 -0.25 0.03## [1] 0## [1] 4linear model
‘normal’ linear mixed effect model
first we will try this and see how it looks as per Brittany’s suggestion. This will not specify a family, most similar to what I did way back when with CCA.
best fit includes temp, observed vs expected is off QQ line, residual vs predicted is wacky. We can see if other models look better or worse.
check the normality assumptions of full model
looks a bit funky/kurtose, see difference in variance in temperature groups. Also some strange patterning in residuals.
height.1 <- lmer(prop.change.height~temp.average*pH.normalized+(1|tank), data = juv.arctica, na.action = na.fail)
simulationOutput1<-simulateResiduals(height.1)
plot(simulationOutput1)
shapiro.test(resid(height.1)) # ! non normal##
## Shapiro-Wilk normality test
##
## data: resid(height.1)
## W = 0.94464, p-value = 0.006836plot(height.1)
#plot(top_model1)
testZeroInflation(height.1)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedplotResiduals(height.1, juv.arctica$temp)
plotResiduals(height.1, juv.arctica$ph)
#plotResiduals(top_model1, juv.arctica$temp)make models and likelihood ratio test
when talking with Diana we decided that 1) there is evidence that temperature effects growth (see long lived arctica records etc.) but that we were not really testing that here. We were more interested in whether there was a relationship with pH. I will therefore test against a null model of … 1) temperature + tank 2) tank alone are we asking vs full model? or main effects?
full.m<-lmer(prop.change.height~temp.average*pH.normalized+(1|tank), data = juv.arctica, na.action = na.fail)
main.m<-lmer(prop.change.height~temp.average+pH.normalized+(1|tank), data = juv.arctica, na.action = na.fail)
full.v.main<-anova(full.m, main.m, test="Chisq")## refitting model(s) with ML (instead of REML)full.v.main # no dif - drops AIC, drop interaction ## Data: juv.arctica
## Models:
## main.m: prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## full.m: prop.change.height ~ temp.average * pH.normalized + (1 | tank)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## main.m 5 -44.763 -34.047 27.381 -54.763
## full.m 6 -44.256 -31.397 28.128 -56.256 1.4933 1 0.2217summary(main.m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## Data: juv.arctica
##
## REML criterion at convergence: -38.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8291 -0.6244 0.0138 0.6179 3.7785
##
## Random effects:
## Groups Name Variance Std.Dev.
## tank (Intercept) 0.001295 0.03598
## Residual 0.024664 0.15705
## Number of obs: 63, groups: tank, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.116780 0.109861 14.584785 1.063 0.305
## temp.average 0.063107 0.011044 14.788089 5.714 4.33e-05 ***
## pH.normalized -0.008728 0.097179 13.835307 -0.090 0.930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmp.vr
## temp.averag -0.930
## pH.normalzd -0.380 0.077confint(main.m)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 0.00000000 0.08530094
## .sigma 0.12999150 0.18858436
## (Intercept) -0.09030652 0.32445215
## temp.average 0.04228462 0.08395225
## pH.normalized -0.19677250 0.17751909drop1(main.m)## Single term deletions using Satterthwaite's method:
##
## Model:
## prop.change.height ~ temp.average + pH.normalized + (1 | tank)
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## temp.average 0.80529 0.80529 1 14.788 32.6497 4.332e-05 ***
## pH.normalized 0.00020 0.00020 1 13.835 0.0081 0.9297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(main.m)| prop.change.height | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.12 | -0.10 – 0.34 | 0.292 |
| temp average | 0.06 | 0.04 – 0.09 | <0.001 |
| pH normalized | -0.01 | -0.20 – 0.19 | 0.929 |
| Random Effects | |||
| σ2 | 0.02 | ||
| τ00 tank | 0.00 | ||
| ICC | 0.05 | ||
| N tank | 16 | ||
| Observations | 63 | ||
| Marginal R2 / Conditional R2 | 0.378 / 0.409 | ||
formal plot
i dont think we want three lines here because we only have one predictor? might need to think through this a bit more. cant make a plot with both since pH isnt in model.
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))
Another way to plot
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 0.5438041 0.06922427 0.4052866 0.6823215
## 2 9 0.0 0.6797093 0.04002847 0.5996125 0.7598061
## 3 12 0.0 0.8156146 0.07224533 0.6710520 0.9601772
## 4 6 0.2 0.5137870 0.04631160 0.4211177 0.6064563
## 5 9 0.2 0.6819577 0.02587944 0.6301730 0.7337423
## 6 12 0.2 0.8501283 0.04523869 0.7596059 0.9406507
## 7 6 0.4 0.4837700 0.03978918 0.4041520 0.5633879
## 8 9 0.4 0.6842060 0.02204619 0.6400917 0.7283203
## 9 12 0.4 0.8846420 0.04139455 0.8018117 0.9674723
## 10 6 0.6 0.4537529 0.05574265 0.3422121 0.5652937
## 11 9 0.6 0.6864543 0.03242137 0.6215793 0.7513294
## 12 12 0.6 0.9191557 0.06496380 0.7891635 1.0491480original_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=prop.change.height, color=pH.normalized), size=3) +
labs(x="Temperature (C)", y="Proportional Change in Height", 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"),
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$y, : font
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## family 'Arial' not found in PostScript font databaseggsave("02_output/02_plots/height_juv.arctica_linear.png", juv.arctica.model.plot, width = 6, height = 5, dpi = 300, bg = 'white')as expected, temperature was a significant predictor in the full model and a full model of all was not significantly different than a model which only included temperature. pH had no influence.
another plot, ph
based on Tredennick which bases it off of https://biologyforfun.wordpress.com/2015/06/17/confidence-intervals-for-prediction-in-glmms/, should keep in mind that confidence or prediction intervals might underestimate uncertainty around our estimates.
juv.arctica_vec <- seq(min(juv.arctica$temp.average),max(juv.arctica$temp.average))
upper_ph <- quantile(juv.arctica$pH.normalized, 0.9)
lower_ph <- quantile(juv.arctica$pH.normalized, 0.1)
newdat <- tibble(temp.average = juv.arctica_vec,
Tank = juv.arctica$tank[1],
pH.normalized = lower_ph,
ph_level = "low") %>%
bind_rows(
tibble(temp.average = juv.arctica_vec,
Tank = juv.arctica$tank[1],
pH.normalized = upper_ph,
ph_level = "high")
) %>%
as.data.frame()
m <- full.m
allout <- tibble() # empty object
for(do_level in unique(newdat$ph_level)) {
tmpdat <- newdat %>%
filter(ph_level == do_level)
mm <- model.matrix(~pH.normalized*temp.average, tmpdat)
tmpdat$y <- mm%*%fixef(m)
# predict(m, newdat, re.form=NA) would give the same results
pvar1 <- diag(mm %*% tcrossprod(vcov(m),mm))
outdat <- data.frame(tmpdat,
lower_ci = tmpdat$y-1.96*sqrt(pvar1),
upper_ci = tmpdat$y+1.96*sqrt(pvar1))
allout <- bind_rows(allout, outdat)
}
juv.arctica.int.ci.plot<-ggplot(allout, aes(x = temp.average, y = y,
color = ph_level, fill = ph_level)) +
geom_hline(aes(yintercept = 0), linetype = 3) +
geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci), color = NA, alpha = 0.25) +
geom_line() +
scale_color_manual(values = rev(c("coral","dodgerblue")),
labels = c("High pH", "Low pH"),
name = NULL) +
scale_fill_manual(values = rev(c("coral","dodgerblue")),
labels = c("High pH", "Low pH"),
name = NULL) +
#scale_y_continuous(breaks = c(0,1)) +
#scale_x_continuous(breaks = c(6,9,12)) +
labs(x = "Mean Temperature", y = "Proportional Change in Height") +
theme_classic() +
theme(legend.position = c(0.8, 0.85))
juv.arctica.int.ci.plot
ggsave("02_output/02_plots/height_juv.arctica_int.ci.plot.png", height=3.2, width=4, units = "in")okay so this plot shows there is no interaction, but is otherwise pretty unhelpful, I am also a bit concerned that the axis scales don’t make sense. I have a feeling all our plots would look like this/the same bc it was only significant in one case, therefore I am not sure how helpful it is.
all combined model plots
#all.model.plot<-ggarrange(mercenaria.model.plot,mya.model.plot, juv.arctica.model.plot, ad.arctica_plot, scallop.plot,
# ncol = 1, nrow = 5)
#ggsave("02_output/02_plots/all_height_mod_plot.png", all.model.plot, width = 5, height = 20, dpi = 300, bg = "white")
#all.model.plot<-ggarrange(mercenaria.model.plot,mya.model.plot, juv.arctica.model.plot, ad.arctica_plot, scallop.plot,
# ncol = 2, nrow = 3)
#ggsave("02_output/02_plots/all_height_mod_plot1.png", all.model.plot, width = 10, height = 13, dpi = 300, bg = "white")
all.model.plot<-ggarrange(mercenaria.model.plot,mya.model.plot, juv.arctica.model.plot, ad.arctica_plot, scallop.plot,
ncol = 3, nrow = 2)
ggsave("02_output/02_plots/all_height_mod_plot2FINAL.tiff", all.model.plot, width = 10, height = 5, dpi = 300, bg = "white")