R Markdown Metabolic Thermal Responses of CoTS
Movement PC
25/01/2021
installing packages
r = getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
options(repos = r)
library(car) #for regression diagnostics## Loading required package: carData## Registered S3 methods overwritten by 'tibble':
## method from
## format.tbl pillar
## print.tbl pillarlibrary(broom) #for tidy output
library(broom.mixed)## Registered S3 methods overwritten by 'broom.mixed':
## method from
## augment.lme broom
## augment.merMod broom
## glance.lme broom
## glance.merMod broom
## glance.stanreg broom
## tidy.brmsfit broom
## tidy.gamlss broom
## tidy.lme broom
## tidy.merMod broom
## tidy.rjags broom
## tidy.stanfit broom
## tidy.stanreg broom##
## Attaching package: 'broom.mixed'## The following object is masked from 'package:broom':
##
## tidyMCMClibrary(ggfortify) #for model diagnostics## Loading required package: ggplot2library(knitr) #for kable
library(effects) #for partial effects plots## lattice theme set by effectsTheme()
## See ?effectsTheme for details.library(emmeans) #for estimating marginal means
library(MASS) #for glm.nb
library(MuMIn) #for AICc
library(tidyverse) #for data wrangling## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.1 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x dplyr::recode() masks car::recode()
## x dplyr::select() masks MASS::select()
## x purrr::some() masks car::some()
## x broom.mixed::tidyMCMC() masks broom::tidyMCMC()library(nlme)##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapselibrary(lme4) #for lmer## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack##
## Attaching package: 'lme4'## The following object is masked from 'package:nlme':
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## lmListlibrary(lmerTest) #for satterthwaite p-values with lmer##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(glmmTMB) #for glmmTMB
library(DHARMa) #for residuals and diagnostics## This is DHARMa 0.3.3.0. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa') Note: Syntax of plotResiduals has changed in 0.3.0, see ?plotResiduals for detailslibrary(performance) #for diagnostic plots
library(see)
library(ggpubr)
install.packages("r2glmm")## Installing package into '/Users/jc483592/Library/R/3.6/library'
## (as 'lib' is unspecified)##
## The downloaded binary packages are in
## /var/folders/07/_tql0phs7tl47hkcnxsw7dx40xb_yc/T//Rtmp76Crm0/downloaded_packageslibrary(r2glmm)Citrate synthase analysis
installing data
rm(list = ls())
setwd("~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff")
CS <- read.csv('CSdata0201.csv')
str(CS)## 'data.frame': 172 obs. of 8 variables:
## $ Starfish.ID : int 1 1 1 1 1 1 2 2 2 2 ...
## $ Life.stage : Factor w/ 2 levels "Adult","Sub-adult": 1 1 1 1 1 1 1 1 1 1 ...
## $ Weight : int 1575 1575 1575 1575 1575 1575 1376 1376 1376 1376 ...
## $ Weight2 : int 1001 1001 1001 1001 1001 1001 802 802 802 802 ...
## $ Reef : Factor w/ 4 levels "Big Broadhurst",..: 3 3 3 3 3 3 2 2 2 2 ...
## $ Enzyme : Factor w/ 1 level "CS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Temperature : int 15 20 25 30 35 40 15 20 25 30 ...
## $ Enzyme.Activity: num 0.3173 0.3412 0.2393 0.1004 0.0641 ...reformatting data
CS = CS %>%
mutate(Starfish.ID=factor(Starfish.ID))
glimpse(CS)## Rows: 172
## Columns: 8
## $ Starfish.ID <fct> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, …
## $ Life.stage <fct> Adult, Adult, Adult, Adult, Adult, Adult, Adult, Adult…
## $ Weight <int> 1575, 1575, 1575, 1575, 1575, 1575, 1376, 1376, 1376, …
## $ Weight2 <int> 1001, 1001, 1001, 1001, 1001, 1001, 802, 802, 802, 802…
## $ Reef <fct> Kelso, Kelso, Kelso, Kelso, Kelso, Kelso, Keeper, Keep…
## $ Enzyme <fct> CS, CS, CS, CS, CS, CS, CS, CS, CS, CS, CS, CS, CS, CS…
## $ Temperature <int> 15, 20, 25, 30, 35, 40, 15, 20, 25, 30, 35, 40, 15, 20…
## $ Enzyme.Activity <dbl> 0.31731618, 0.34125000, 0.23933824, 0.10036765, 0.0640…model
CSmod <- lmer(Enzyme.Activity ~ poly(Temperature,3) * Life.stage + (1|Starfish.ID), REML=FALSE, data=CS)
anova(CSmod)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(Temperature, 3) 3.7282 1.24273 3 143.294 205.4237 < 2e-16
## Life.stage 0.0227 0.02269 1 29.057 3.7515 0.06254
## poly(Temperature, 3):Life.stage 0.0583 0.01942 3 143.294 3.2106 0.02493
##
## poly(Temperature, 3) ***
## Life.stage .
## poly(Temperature, 3):Life.stage *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(CSmod)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## Enzyme.Activity ~ poly(Temperature, 3) * Life.stage + (1 | Starfish.ID)
## Data: CS
##
## AIC BIC logLik deviance df.resid
## -329.1 -297.6 174.5 -349.1 162
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3717 -0.5740 0.0193 0.4894 2.3897
##
## Random effects:
## Groups Name Variance Std.Dev.
## Starfish.ID (Intercept) 0.00323 0.05683
## Residual 0.00605 0.07778
## Number of obs: 172, groups: Starfish.ID, 29
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.21123 0.01497 29.23132
## poly(Temperature, 3)1 -1.77911 0.09702 143.73611
## poly(Temperature, 3)2 -0.01630 0.09685 143.58001
## poly(Temperature, 3)3 0.30255 0.09647 143.38556
## Life.stageSub-adult 0.04930 0.02546 29.05732
## poly(Temperature, 3)1:Life.stageSub-adult -0.45551 0.16288 143.35448
## poly(Temperature, 3)2:Life.stageSub-adult 0.18495 0.16297 143.29825
## poly(Temperature, 3)3:Life.stageSub-adult -0.11537 0.16333 143.22842
## t value Pr(>|t|)
## (Intercept) 14.109 1.4e-14 ***
## poly(Temperature, 3)1 -18.338 < 2e-16 ***
## poly(Temperature, 3)2 -0.168 0.86659
## poly(Temperature, 3)3 3.136 0.00208 **
## Life.stageSub-adult 1.937 0.06254 .
## poly(Temperature, 3)1:Life.stageSub-adult -2.797 0.00587 **
## poly(Temperature, 3)2:Life.stageSub-adult 1.135 0.25832
## poly(Temperature, 3)3:Life.stageSub-adult -0.706 0.48111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pl(T,3)1 pl(T,3)2 pl(T,3)3 Lf.sS- p(T,3)1: p(T,3)2:
## ply(Tmp,3)1 0.000
## ply(Tmp,3)2 0.006 0.000
## ply(Tmp,3)3 0.000 0.012 0.000
## Lf.stgSb-dl -0.588 0.000 -0.003 0.000
## p(T,3)1:L.S 0.000 -0.596 0.000 -0.007 0.000
## p(T,3)2:L.S -0.003 0.000 -0.594 0.000 -0.003 0.000
## p(T,3)3:L.S 0.000 -0.007 0.000 -0.591 0.000 -0.006 0.000R2 value of model
r.squaredGLMM(CSmod)## Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.## R2m R2c
## [1,] 0.7154999 0.8145238Checking model diagnostics
check_model(CSmod)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 171 rows containing missing values (geom_text_repel).## `geom_smooth()` using formula 'y ~ x'
Rightingresid2 = simulateResiduals(CSmod, plot=TRUE)## Warning in checkModel(fittedModel): DHARMa: fittedModel not in class of
## supported models. Absolutely no guarantee that this will work!
predicted estimated marginal means
acute.grid = with(CS, list(Life.stage=levels(Life.stage), Temperature=seq(min(Temperature), max(Temperature), len=100)))newdata= emmeans(CSmod, ~Temperature|Life.stage, at=acute.grid) %>%
as.data.frame
newdata## Temperature Life.stage emmean SE df lower.CL
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## 6 16.26263 Adult 0.38936199 0.01964477 75.64769 0.3502330886
## 7 16.51515 Adult 0.38890572 0.01932432 71.65624 0.3503802316
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## 11 17.52525 Adult 0.38475100 0.01878863 65.15633 0.3472292184
## 12 17.77778 Adult 0.38315702 0.01878719 65.15610 0.3456381065
## 13 18.03030 Adult 0.38135176 0.01881884 65.55563 0.3437739739
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## 161 30.15152 Sub-adult 0.18291328 0.02540122 60.95625 0.1321196628
## 162 30.40404 Sub-adult 0.17723427 0.02555355 62.27193 0.1261578871
## 163 30.65657 Sub-adult 0.17162025 0.02570870 63.62262 0.1202553569
## 164 30.90909 Sub-adult 0.16607459 0.02586431 64.98771 0.1144198002
## 165 31.16162 Sub-adult 0.16060063 0.02601803 66.34579 0.1086590004
## 166 31.41414 Sub-adult 0.15520172 0.02616751 67.67505 0.1029807397
## 167 31.66667 Sub-adult 0.14988123 0.02631042 68.95360 0.0973927436
## 168 31.91919 Sub-adult 0.14464251 0.02644455 70.15985 0.0919026251
## 169 32.17172 Sub-adult 0.13948890 0.02656775 71.27289 0.0865178296
## 170 32.42424 Sub-adult 0.13442377 0.02667800 72.27286 0.0812455786
## 171 32.67677 Sub-adult 0.12945047 0.02677344 73.14131 0.0760928134
## 172 32.92929 Sub-adult 0.12457235 0.02685239 73.86151 0.0710661355
## 173 33.18182 Sub-adult 0.11979277 0.02691336 74.41885 0.0661717444
## 174 33.43434 Sub-adult 0.11511508 0.02695513 74.80118 0.0614153717
## 175 33.68687 Sub-adult 0.11054263 0.02697674 74.99914 0.0568022077
## 176 33.93939 Sub-adult 0.10607879 0.02697755 75.00662 0.0523368227
## 177 34.19192 Sub-adult 0.10172689 0.02695731 74.82113 0.0480230779
## 178 34.44444 Sub-adult 0.09749030 0.02691615 74.44434 0.0438640269
## 179 34.69697 Sub-adult 0.09337238 0.02685470 73.88262 0.0398618055
## 180 34.94949 Sub-adult 0.08937646 0.02677413 73.14759 0.0360175075
## 181 35.20202 Sub-adult 0.08550592 0.02667623 72.25680 0.0323310481
## 182 35.45455 Sub-adult 0.08176410 0.02656350 71.23438 0.0288010124
## 183 35.70707 Sub-adult 0.07815436 0.02643921 70.11170 0.0254244911
## 184 35.95960 Sub-adult 0.07468005 0.02630757 68.92798 0.0221969052
## 185 36.21212 Sub-adult 0.07134452 0.02617377 67.73091 0.0191118244
## 186 36.46465 Sub-adult 0.06815113 0.02604412 66.57717 0.0161607844
## 187 36.71717 Sub-adult 0.06510324 0.02592615 65.53294 0.0133331150
## 188 36.96970 Sub-adult 0.06220419 0.02582870 64.67444 0.0106157889
## 189 37.22222 Sub-adult 0.05945735 0.02576195 64.08860 0.0079933115
## 190 37.47475 Sub-adult 0.05686606 0.02573744 63.87394 0.0054476696
## 191 37.72727 Sub-adult 0.05443368 0.02576800 64.14166 0.0029583675
## 192 37.97980 Sub-adult 0.05216357 0.02586764 65.01699 0.0005025787
## 193 38.23232 Sub-adult 0.05005907 0.02605122 66.64024 -0.0019445518
## 194 38.48485 Sub-adult 0.04812355 0.02633417 69.16668 -0.0044094207
## 195 38.73737 Sub-adult 0.04636035 0.02673195 72.76344 -0.0069192734
## 196 38.98990 Sub-adult 0.04477283 0.02725962 77.60033 -0.0095013041
## 197 39.24242 Sub-adult 0.04336434 0.02793122 83.83067 -0.0121815967
## 198 39.49495 Sub-adult 0.04213824 0.02875936 91.55768 -0.0149840052
## 199 39.74747 Sub-adult 0.04109789 0.02975478 100.78499 -0.0179291186
## 200 40.00000 Sub-adult 0.04024663 0.03092616 111.35687 -0.0210334653
## upper.CL
## 1 0.43317526
## 2 0.43227999
## 3 0.43137348
## 4 0.43044759
## 5 0.42949125
## 6 0.42849090
## 7 0.42743121
## 8 0.42629585
## 9 0.42506826
## 10 0.42373229
## 11 0.42227278
## 12 0.42067593
## 13 0.41892954
## 14 0.41702318
## 15 0.41494822
## 16 0.41269779
## 17 0.41026677
## 18 0.40765165
## 19 0.40485040
## 20 0.40186237
## 21 0.39868817
## 22 0.39532949
## 23 0.39178903
## 24 0.38807036
## 25 0.38417786
## 26 0.38011656
## 27 0.37589211
## 28 0.37151070
## 29 0.36697899
## 30 0.36230402
## 31 0.35749321
## 32 0.35255427
## 33 0.34749517
## 34 0.34232408
## 35 0.33704937
## 36 0.33167950
## 37 0.32622304
## 38 0.32068860
## 39 0.31508480
## 40 0.30942022
## 41 0.30370335
## 42 0.29794259
## 43 0.29214615
## 44 0.28632205
## 45 0.28047809
## 46 0.27462179
## 47 0.26876036
## 48 0.26290070
## 49 0.25704935
## 50 0.25121250
## 51 0.24539594
## 52 0.23960510
## 53 0.23384502
## 54 0.22812036
## 55 0.22243541
## 56 0.21679413
## 57 0.21120015
## 58 0.20565678
## 59 0.20016707
## 60 0.19473386
## 61 0.18935975
## 62 0.18404721
## 63 0.17879857
## 64 0.17361611
## 65 0.16850205
## 66 0.16345862
## 67 0.15848810
## 68 0.15359285
## 69 0.14877537
## 70 0.14403832
## 71 0.13938458
## 72 0.13481727
## 73 0.13033983
## 74 0.12595604
## 75 0.12167010
## 76 0.11748667
## 77 0.11341093
## 78 0.10944868
## 79 0.10560640
## 80 0.10189137
## 81 0.09831172
## 82 0.09487660
## 83 0.09159625
## 84 0.08848217
## 85 0.08554721
## 86 0.08280574
## 87 0.08027373
## 88 0.07796888
## 89 0.07591066
## 90 0.07412035
## 91 0.07262098
## 92 0.07143722
## 93 0.07059505
## 94 0.07012147
## 95 0.07004387
## 96 0.07038945
## 97 0.07118437
## 98 0.07245302
## 99 0.07421733
## 100 0.07649627
## 101 0.57702611
## 102 0.57133219
## 103 0.56585034
## 104 0.56056405
## 105 0.55545272
## 106 0.55049251
## 107 0.54565732
## 108 0.54091996
## 109 0.53625319
## 110 0.53163061
## 111 0.52702737
## 112 0.52242063
## 113 0.51778991
## 114 0.51311719
## 115 0.50838697
## 116 0.50358623
## 117 0.49870430
## 118 0.49373271
## 119 0.48866503
## 120 0.48349669
## 121 0.47822478
## 122 0.47284787
## 123 0.46736589
## 124 0.46177993
## 125 0.45609212
## 126 0.45030552
## 127 0.44442399
## 128 0.43845210
## 129 0.43239504
## 130 0.42625854
## 131 0.42004881
## 132 0.41377243
## 133 0.40743636
## 134 0.40104782
## 135 0.39461425
## 136 0.38814326
## 137 0.38164258
## 138 0.37511999
## 139 0.36858328
## 140 0.36204017
## 141 0.35549828
## 142 0.34896506
## 143 0.34244773
## 144 0.33595324
## 145 0.32948823
## 146 0.32305893
## 147 0.31667119
## 148 0.31033038
## 149 0.30404141
## 150 0.29780869
## 151 0.29163609
## 152 0.28552697
## 153 0.27948415
## 154 0.27350995
## 155 0.26760616
## 156 0.26177411
## 157 0.25601467
## 158 0.25032831
## 159 0.24471510
## 160 0.23917480
## 161 0.23370690
## 162 0.22831065
## 163 0.22298515
## 164 0.21772938
## 165 0.21254225
## 166 0.20742271
## 167 0.20236972
## 168 0.19738239
## 169 0.19245998
## 170 0.18760197
## 171 0.18280813
## 172 0.17807857
## 173 0.17341380
## 174 0.16881479
## 175 0.16428306
## 176 0.15982075
## 177 0.15543070
## 178 0.15111658
## 179 0.14688295
## 180 0.14273542
## 181 0.13868079
## 182 0.13472719
## 183 0.13088422
## 184 0.12716319
## 185 0.12357722
## 186 0.12014148
## 187 0.11687337
## 188 0.11379260
## 189 0.11092139
## 190 0.10828445
## 191 0.10590900
## 192 0.10382456
## 193 0.10206269
## 194 0.10065651
## 195 0.09963996
## 196 0.09904696
## 197 0.09891028
## 198 0.09926049
## 199 0.10012490
## 200 0.10152673exporting data
write.csv(newdata, "~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff/CSlifestage.csv")Sub-adult graph
setwd("~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff/")
SubadultCS <- read.csv('CSlifestagesubadult.csv')
CSplot <- ggplot(SubadultCS, aes(y=emmean, x=Temperature)) +
geom_line() +
theme_classic() +
geom_ribbon(aes(ymin=lower.CL, ymax=upper.CL), alpha=0.2) + ylab(expression(CS~activity~(U~mg^-1~tissue))) +
xlab(expression("Temperature " ( degree*C))) +
scale_x_continuous(breaks = seq(15, 40, by = 5)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01), breaks = seq(0, 0.6, by = 0.2), limits = c(-0.025,0.60)) +
theme(text = element_text(size=25),
axis.text.x = element_text(size=25, hjust=0.5),
axis.text.y = element_text(size=25, hjust=0.5)) + theme(panel.border = element_rect(colour = "black", fill=NA, size=1))
CSplot 
adult graph
setwd("~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff/")
AdultCS <- read.csv('CSlifestageadult.csv')
CSplot2 <- ggplot(AdultCS, aes(y=emmean, x=Temperature)) +
geom_line() +
theme_classic() +
geom_ribbon(aes(ymin=lower.CL, ymax=upper.CL), alpha=0.2) + theme(axis.text.y = element_blank(), axis.title.y = element_blank()) +
xlab(expression("Temperature " ( degree*C))) +
scale_x_continuous(breaks = seq(15, 40, by = 5)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01), breaks = seq(0, 0.6, by = 0.2), limits = c(-0.025,0.60)) +
theme(text = element_text(size=25),
axis.text.x = element_text(size=25, hjust=0.5),
axis.text.y = element_text(size=25, hjust=0.5)) + theme(panel.border = element_rect(colour = "black", fill=NA, size=1))
CSplot2
1800 x 800
putting graphs together
cowplot::plot_grid(CSplot,
CSplot2,
nrow = 1,
labels = "auto", align = "v")
Lactate dehydrogenase analysis
installing data
setwd("~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff")
LDH <- read.csv('LDHdata0201.csv')
str(LDH)## 'data.frame': 157 obs. of 7 variables:
## $ Starfish.ID : int 1 1 1 2 2 2 2 2 2 3 ...
## $ Life.stage : Factor w/ 2 levels "Adult","Sub-adult": 1 1 1 1 1 1 1 1 1 1 ...
## $ Weight : int 1575 1575 1575 1376 1376 1376 1376 1376 1376 2290 ...
## $ Weight2 : int 1001 1001 1001 802 802 802 802 802 802 1716 ...
## $ Reef : Factor w/ 4 levels "Big Broadhurst",..: 3 3 3 2 2 2 2 2 2 2 ...
## $ Temperature : int 20 25 30 20 25 30 35 40 45 20 ...
## $ Enzyme.Activity: num 0.82 1.025 1.292 0.138 0.788 ...reformatting data
LDH = LDH %>%
mutate(Starfish.ID=factor(Starfish.ID))
glimpse(LDH)## Rows: 157
## Columns: 7
## $ Starfish.ID <fct> 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, …
## $ Life.stage <fct> Adult, Adult, Adult, Adult, Adult, Adult, Adult, Adult…
## $ Weight <int> 1575, 1575, 1575, 1376, 1376, 1376, 1376, 1376, 1376, …
## $ Weight2 <int> 1001, 1001, 1001, 802, 802, 802, 802, 802, 802, 1716, …
## $ Reef <fct> Kelso, Kelso, Kelso, Keeper, Keeper, Keeper, Keeper, K…
## $ Temperature <int> 20, 25, 30, 20, 25, 30, 35, 40, 45, 20, 25, 30, 35, 40…
## $ Enzyme.Activity <dbl> 0.8203376, 1.0251608, 1.2916399, 0.1379421, 0.7884646,…the model
LDHmod <- lmer(Enzyme.Activity ~ poly(Temperature, 3) * Life.stage + (1|Starfish.ID), REML=FALSE, data=LDH)
anova(LDHmod)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(Temperature, 3) 84.394 28.1313 3 130.339 132.3701 < 2e-16
## Life.stage 0.501 0.5007 1 27.804 2.3558 0.13612
## poly(Temperature, 3):Life.stage 1.391 0.4637 3 130.339 2.1818 0.09325
##
## poly(Temperature, 3) ***
## Life.stage
## poly(Temperature, 3):Life.stage .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(LDHmod)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## Enzyme.Activity ~ poly(Temperature, 3) * Life.stage + (1 | Starfish.ID)
## Data: LDH
##
## AIC BIC logLik deviance df.resid
## 269.3 299.9 -124.7 249.3 147
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.36286 -0.55149 0.05354 0.46513 2.66453
##
## Random effects:
## Groups Name Variance Std.Dev.
## Starfish.ID (Intercept) 0.1666 0.4082
## Residual 0.2125 0.4610
## Number of obs: 157, groups: Starfish.ID, 28
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.2926 0.1038 27.4211 12.447
## poly(Temperature, 3)1 8.9477 0.5600 131.0590 15.979
## poly(Temperature, 3)2 1.4591 0.5526 128.8361 2.640
## poly(Temperature, 3)3 -0.6222 0.5587 129.1382 -1.114
## Life.stageSub-adult 0.2822 0.1839 27.8042 1.535
## poly(Temperature, 3)1:Life.stageSub-adult 2.2534 1.0267 132.5556 2.195
## poly(Temperature, 3)2:Life.stageSub-adult 1.1047 1.0092 129.2686 1.095
## poly(Temperature, 3)3:Life.stageSub-adult 0.8701 0.9975 129.2392 0.872
## Pr(>|t|)
## (Intercept) 8.51e-13 ***
## poly(Temperature, 3)1 < 2e-16 ***
## poly(Temperature, 3)2 0.00931 **
## poly(Temperature, 3)3 0.26753
## Life.stageSub-adult 0.13612
## poly(Temperature, 3)1:Life.stageSub-adult 0.02992 *
## poly(Temperature, 3)2:Life.stageSub-adult 0.27573
## poly(Temperature, 3)3:Life.stageSub-adult 0.38466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pl(T,3)1 pl(T,3)2 pl(T,3)3 Lf.sS- p(T,3)1: p(T,3)2:
## ply(Tmp,3)1 0.002
## ply(Tmp,3)2 -0.005 -0.020
## ply(Tmp,3)3 -0.009 -0.030 0.000
## Lf.stgSb-dl -0.565 -0.001 0.003 0.005
## p(T,3)1:L.S -0.001 -0.545 0.011 0.016 -0.004
## p(T,3)2:L.S 0.003 0.011 -0.548 0.000 0.009 0.031
## p(T,3)3:L.S 0.005 0.017 0.000 -0.560 0.013 0.020 0.007R2 value of model
r.squaredGLMM(LDHmod)## R2m R2c
## [1,] 0.6268133 0.7908138Checking model diagnostics
check_model(LDHmod)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 157 rows containing missing values (geom_text_repel).## `geom_smooth()` using formula 'y ~ x'
Rightingresid2 = simulateResiduals(LDHmod, plot=TRUE)## Warning in checkModel(fittedModel): DHARMa: fittedModel not in class of
## supported models. Absolutely no guarantee that this will work!
predicted estimated marginal means
acute.grid = with(LDH, list(Life.stage=levels(Life.stage), Temperature=seq(min(Temperature), max(Temperature), len=100)))newdata2= emmeans(LDHmod, ~Temperature|Life.stage, at=acute.grid) %>%
as.data.frame
newdata2## Temperature Life.stage emmean SE df lower.CL upper.CL
## 1 20.00000 Adult 0.4366204 0.1468591 90.06121 0.14486184 0.7283789
## 2 20.25253 Adult 0.4361112 0.1420752 81.78499 0.15346720 0.7187553
## 3 20.50505 Adult 0.4364083 0.1380388 74.79246 0.16140841 0.7114082
## 4 20.75758 Adult 0.4374998 0.1347043 69.07296 0.16877720 0.7062225
## 5 21.01010 Adult 0.4393741 0.1320189 64.53831 0.17567846 0.7030698
## 6 21.26263 Adult 0.4420195 0.1299238 61.06118 0.18222615 0.7018129
## 7 21.51515 Adult 0.4454242 0.1283559 58.50128 0.18853891 0.7023095
## 8 21.76768 Adult 0.4495766 0.1272499 56.72067 0.19473578 0.7044174
## 9 22.02020 Adult 0.4544649 0.1265405 55.59130 0.20093263 0.7079971
## 10 22.27273 Adult 0.4600774 0.1261637 54.99766 0.20723934 0.7129154
## 11 22.52525 Adult 0.4664024 0.1260589 54.83701 0.21375782 0.7190470
## 12 22.77778 Adult 0.4734282 0.1261695 55.01835 0.22058072 0.7262757
## 13 23.03030 Adult 0.4811431 0.1264441 55.46124 0.22779072 0.7344954
## 14 23.28283 Adult 0.4895353 0.1268367 56.09460 0.23546033 0.7436103
## 15 23.53535 Adult 0.4985933 0.1273067 56.85583 0.24365198 0.7535345
## 16 23.78788 Adult 0.5083051 0.1278190 57.69005 0.25241831 0.7641919
## 17 24.04040 Adult 0.5186592 0.1283437 58.54965 0.26180275 0.7755157
## 18 24.29293 Adult 0.5296439 0.1288556 59.39377 0.27184006 0.7874476
## 19 24.54545 Adult 0.5412473 0.1293342 60.18800 0.28255704 0.7999376
## 20 24.79798 Adult 0.5534578 0.1297626 60.90394 0.29397321 0.8129425
## 21 25.05051 Adult 0.5662638 0.1301278 61.51884 0.30610147 0.8264261
## 22 25.30303 Adult 0.5796534 0.1304200 62.01519 0.31894882 0.8403580
## 23 25.55556 Adult 0.5936150 0.1306321 62.38025 0.33251688 0.8547131
## 24 25.80808 Adult 0.6081368 0.1307595 62.60568 0.34680258 0.8694711
## 25 26.06061 Adult 0.6232072 0.1308001 62.68709 0.36179858 0.8846159
## 26 26.31313 Adult 0.6388145 0.1307534 62.62362 0.37749385 0.9001351
## 27 26.56566 Adult 0.6549468 0.1306209 62.41760 0.39387403 0.9160196
## 28 26.81818 Adult 0.6715926 0.1304055 62.07414 0.41092187 0.9322633
## 29 27.07071 Adult 0.6887401 0.1301114 61.60084 0.42861757 0.9488626
## 30 27.32323 Adult 0.7063775 0.1297440 61.00743 0.44693910 0.9658160
## 31 27.57576 Adult 0.7244933 0.1293096 60.30546 0.46586254 0.9831240
## 32 27.82828 Adult 0.7430756 0.1288155 59.50805 0.48536234 1.0007888
## 33 28.08081 Adult 0.7621128 0.1282698 58.62955 0.50541156 1.0188140
## 34 28.33333 Adult 0.7815931 0.1276809 57.68524 0.52598219 1.0372040
## 35 28.58586 Adult 0.8015048 0.1270581 56.69108 0.54704534 1.0559643
## 36 28.83838 Adult 0.8218363 0.1264107 55.66341 0.56857148 1.0751011
## 37 29.09091 Adult 0.8425758 0.1257484 54.61865 0.59053073 1.0946208
## 38 29.34343 Adult 0.8637116 0.1250812 53.57304 0.61289304 1.1145301
## 39 29.59596 Adult 0.8852320 0.1244189 52.54239 0.63562847 1.1348354
## 40 29.84848 Adult 0.9071252 0.1237713 51.54185 0.65870737 1.1555430
## 41 30.10101 Adult 0.9293796 0.1231479 50.58572 0.68210066 1.1766586
## 42 30.35354 Adult 0.9519835 0.1225581 49.68725 0.70578002 1.1981870
## 43 30.60606 Adult 0.9749251 0.1220104 48.85855 0.72971812 1.2201321
## 44 30.85859 Adult 0.9981928 0.1215131 48.11043 0.75388883 1.2424967
## 45 31.11111 Adult 1.0217748 0.1210736 47.45242 0.77826737 1.2652821
## 46 31.36364 Adult 1.0456594 0.1206985 46.89262 0.80283056 1.2884881
## 47 31.61616 Adult 1.0698348 0.1203935 46.43777 0.82755687 1.3121128
## 48 31.86869 Adult 1.0942895 0.1201632 46.09324 0.85242664 1.3361524
## 49 32.12121 Adult 1.1190117 0.1200112 45.86296 0.87742215 1.3606012
## 50 32.37374 Adult 1.1439896 0.1199398 45.74956 0.90252767 1.3854515
## 51 32.62626 Adult 1.1692116 0.1199501 45.75426 0.92772957 1.4106936
## 52 32.87879 Adult 1.1946659 0.1200420 45.87698 0.95301630 1.4363155
## 53 33.13131 Adult 1.2203408 0.1202143 46.11624 0.97837839 1.4623033
## 54 33.38384 Adult 1.2462247 0.1204644 46.46926 1.00380847 1.4886409
## 55 33.63636 Adult 1.2723058 0.1207886 46.93185 1.02930114 1.5153104
## 56 33.88889 Adult 1.2985724 0.1211821 47.49848 1.05485295 1.5422918
## 57 34.14141 Adult 1.3250127 0.1216391 48.16222 1.08046225 1.5695632
## 58 34.39394 Adult 1.3516152 0.1221528 48.91475 1.10612909 1.5971012
## 59 34.64646 Adult 1.3783680 0.1227157 49.74642 1.13185503 1.6248809
## 60 34.89899 Adult 1.4052594 0.1233196 50.64623 1.15764300 1.6528759
## 61 35.15152 Adult 1.4322778 0.1239556 51.60196 1.18349710 1.6810586
## 62 35.40404 Adult 1.4594115 0.1246144 52.60025 1.20942237 1.7094006
## 63 35.65657 Adult 1.4866486 0.1252863 53.62673 1.23542460 1.7378726
## 64 35.90909 Adult 1.5139776 0.1259617 54.66619 1.26151012 1.7664450
## 65 36.16162 Adult 1.5413866 0.1266304 55.70282 1.28768552 1.7950877
## 66 36.41414 Adult 1.5688641 0.1272828 56.72038 1.31395749 1.8237706
## 67 36.66667 Adult 1.5963982 0.1279089 57.70250 1.34033253 1.8524638
## 68 36.91919 Adult 1.6239773 0.1284995 58.63293 1.36681673 1.8811378
## 69 37.17172 Adult 1.6515896 0.1290453 59.49582 1.39341556 1.9097636
## 70 37.42424 Adult 1.6792235 0.1295378 60.27600 1.42013360 1.9383133
## 71 37.67677 Adult 1.7068672 0.1299691 60.95928 1.44697431 1.9667600
## 72 37.92929 Adult 1.7345090 0.1303319 61.53271 1.47393977 1.9950782
## 73 38.18182 Adult 1.7621372 0.1306199 61.98491 1.50103040 2.0232440
## 74 38.43434 Adult 1.7897401 0.1308278 62.30635 1.52824473 2.0512355
## 75 38.68687 Adult 1.8173060 0.1309512 62.48964 1.55557903 2.0790330
## 76 38.93939 Adult 1.8448232 0.1309875 62.52986 1.58302703 2.1066194
## 77 39.19192 Adult 1.8722800 0.1309353 62.42492 1.61057951 2.1339804
## 78 39.44444 Adult 1.8996645 0.1307949 62.17588 1.63822397 2.1611051
## 79 39.69697 Adult 1.9269653 0.1305688 61.78736 1.66594414 2.1879864
## 80 39.94949 Adult 1.9541704 0.1302615 61.26794 1.69371948 2.2146214
## 81 40.20202 Adult 1.9812683 0.1298802 60.63060 1.72152472 2.2410119
## 82 40.45455 Adult 2.0082472 0.1294350 59.89309 1.74932922 2.2671652
## 83 40.70707 Adult 2.0350954 0.1289389 59.07841 1.77709635 2.2930944
## 84 40.95960 Adult 2.0618011 0.1284090 58.21521 1.80478285 2.3188194
## 85 41.21212 Adult 2.0883527 0.1278660 57.33816 1.83233814 2.3443674
## 86 41.46465 Adult 2.1147385 0.1273355 56.48834 1.85970357 2.3697735
## 87 41.71717 Adult 2.1409468 0.1268475 55.71362 1.88681185 2.3950817
## 88 41.96970 Adult 2.1669658 0.1264376 55.06913 1.91358632 2.4203452
## 89 42.22222 Adult 2.1927838 0.1261466 54.61774 1.93994053 2.4456270
## 90 42.47475 Adult 2.2183891 0.1260211 54.43085 1.96577784 2.4710003
## 91 42.72727 Adult 2.2437700 0.1261129 54.58932 1.99099134 2.4965487
## 92 42.97980 Adult 2.2689148 0.1264791 55.18468 2.01546411 2.5223655
## 93 43.23232 Adult 2.2938118 0.1271809 56.32048 2.03906989 2.5485537
## 94 43.48485 Adult 2.3184493 0.1282826 58.11329 2.06167435 2.5752242
## 95 43.73737 Adult 2.3428155 0.1298503 60.69264 2.08313720 2.6024938
## 96 43.98990 Adult 2.3668988 0.1319495 64.19835 2.10331496 2.6304826
## 97 44.24242 Adult 2.3906874 0.1346437 68.77290 2.12206470 2.6593101
## 98 44.49495 Adult 2.4141696 0.1379919 74.54579 2.13924831 2.6890910
## 99 44.74747 Adult 2.4373338 0.1420468 81.60663 2.15473704 2.7199305
## 100 45.00000 Adult 2.4601681 0.1468540 89.96578 2.16841559 2.7519207
## 101 20.00000 Sub-adult 0.5078485 0.2181362 94.65142 0.07477289 0.9409241
## 102 20.25253 Sub-adult 0.5181513 0.2109214 86.09071 0.09885987 0.9374428
## 103 20.50505 Sub-adult 0.5286473 0.2048195 78.77739 0.12094632 0.9363483
## 104 20.75758 Sub-adult 0.5393410 0.1997652 72.73941 0.14118553 0.9374964
## 105 21.01010 Sub-adult 0.5502371 0.1956817 67.91264 0.15975116 0.9407230
## 106 21.26263 Sub-adult 0.5613403 0.1924834 64.18200 0.17683145 0.9458491
## 107 21.51515 Sub-adult 0.5726552 0.1900777 61.41147 0.19262276 0.9526876
## 108 21.76768 Sub-adult 0.5841865 0.1883686 59.46251 0.20732321 0.9610499
## 109 22.02020 Sub-adult 0.5959390 0.1872590 58.20393 0.22112724 0.9707507
## 110 22.27273 Sub-adult 0.6079172 0.1866542 57.51597 0.23422114 0.9816132
## 111 22.52525 Sub-adult 0.6201258 0.1864636 57.29131 0.24677992 0.9934716
## 112 22.77778 Sub-adult 0.6325695 0.1866027 57.43451 0.25896519 1.0061737
## 113 23.03030 Sub-adult 0.6452529 0.1869943 57.86100 0.27092398 1.0195818
## 114 23.28283 Sub-adult 0.6581808 0.1875691 58.49592 0.28278826 1.0335732
## 115 23.53535 Sub-adult 0.6713577 0.1882658 59.27330 0.29467502 1.0480404
## 116 23.78788 Sub-adult 0.6847884 0.1890314 60.13526 0.30668671 1.0628901
## 117 24.04040 Sub-adult 0.6984775 0.1898205 61.03149 0.31891195 1.0780431
## 118 24.29293 Sub-adult 0.7124297 0.1905947 61.91880 0.33142644 1.0934330
## 119 24.54545 Sub-adult 0.7266497 0.1913224 62.76065 0.34429393 1.1090055
## 120 24.79798 Sub-adult 0.7411421 0.1919778 63.52681 0.35756727 1.1247169
## 121 25.05051 Sub-adult 0.7559116 0.1925408 64.19287 0.37128941 1.1405338
## 122 25.30303 Sub-adult 0.7709628 0.1929957 64.73983 0.38549440 1.1564313
## 123 25.55556 Sub-adult 0.7863005 0.1933314 65.15363 0.40020832 1.1723927
## 124 25.80808 Sub-adult 0.8019293 0.1935403 65.42470 0.41545015 1.1884084
## 125 26.06061 Sub-adult 0.8178538 0.1936183 65.54752 0.43123256 1.2044750
## 126 26.31313 Sub-adult 0.8340787 0.1935641 65.52017 0.44756262 1.2205948
## 127 26.56566 Sub-adult 0.8506088 0.1933792 65.34398 0.46444247 1.2367751
## 128 26.81818 Sub-adult 0.8674486 0.1930669 65.02314 0.48186991 1.2530272
## 129 27.07071 Sub-adult 0.8846028 0.1926330 64.56435 0.49983895 1.2693666
## 130 27.32323 Sub-adult 0.9020761 0.1920846 63.97655 0.51834024 1.2858120
## 131 27.57576 Sub-adult 0.9198732 0.1914307 63.27056 0.53736159 1.3023848
## 132 27.82828 Sub-adult 0.9379987 0.1906811 62.45885 0.55688834 1.3191091
## 133 28.08081 Sub-adult 0.9564574 0.1898472 61.55527 0.57690375 1.3360110
## 134 28.33333 Sub-adult 0.9752538 0.1889409 60.57470 0.59738938 1.3531181
## 135 28.58586 Sub-adult 0.9943926 0.1879752 59.53285 0.61832544 1.3704597
## 136 28.83838 Sub-adult 1.0138785 0.1869635 58.44594 0.63969114 1.3880658
## 137 29.09091 Sub-adult 1.0337162 0.1859196 57.33044 0.66146500 1.4059674
## 138 29.34343 Sub-adult 1.0539103 0.1848577 56.20277 0.68362522 1.4241954
## 139 29.59596 Sub-adult 1.0744655 0.1837920 55.07907 0.70614997 1.4427811
## 140 29.84848 Sub-adult 1.0953865 0.1827366 53.97495 0.72901777 1.4617553
## 141 30.10101 Sub-adult 1.1166779 0.1817054 52.90525 0.75220778 1.4811481
## 142 30.35354 Sub-adult 1.1383445 0.1807119 51.88385 0.77570011 1.5009888
## 143 30.60606 Sub-adult 1.1603908 0.1797691 50.92352 0.79947617 1.5213054
## 144 30.85859 Sub-adult 1.1828215 0.1788889 50.03579 0.82351894 1.5421241
## 145 31.11111 Sub-adult 1.2056414 0.1780827 49.23084 0.84781326 1.5634695
## 146 31.36364 Sub-adult 1.2288550 0.1773605 48.51748 0.87234606 1.5853639
## 147 31.61616 Sub-adult 1.2524670 0.1767312 47.90307 0.89710665 1.6078274
## 148 31.86869 Sub-adult 1.2764822 0.1762022 47.39353 0.92208684 1.6308776
## 149 32.12121 Sub-adult 1.3009052 0.1757794 46.99335 0.94728120 1.6545291
## 150 32.37374 Sub-adult 1.3257405 0.1754673 46.70561 0.97268708 1.6787940
## 151 32.62626 Sub-adult 1.3509930 0.1752684 46.53198 0.99830478 1.7036813
## 152 32.87879 Sub-adult 1.3766673 0.1751837 46.47279 1.02413759 1.7291970
## 153 33.13131 Sub-adult 1.4027680 0.1752123 46.52696 1.05019176 1.7553443
## 154 33.38384 Sub-adult 1.4292998 0.1753516 46.69213 1.07647648 1.7821232
## 155 33.63636 Sub-adult 1.4562674 0.1755975 46.96457 1.10300384 1.8095310
## 156 33.88889 Sub-adult 1.4836755 0.1759440 47.33924 1.12978870 1.8375623
## 157 34.14141 Sub-adult 1.5115286 0.1763839 47.80979 1.15684854 1.8662088
## 158 34.39394 Sub-adult 1.5398316 0.1769085 48.36859 1.18420326 1.8954599
## 159 34.64646 Sub-adult 1.5685890 0.1775078 49.00673 1.21187500 1.9253030
## 160 34.89899 Sub-adult 1.5978055 0.1781708 49.71411 1.23988787 1.9557231
## 161 35.15152 Sub-adult 1.6274858 0.1788858 50.47946 1.26826767 1.9867038
## 162 35.40404 Sub-adult 1.6576345 0.1796401 51.29050 1.29704162 2.0182274
## 163 35.65657 Sub-adult 1.6882563 0.1804206 52.13401 1.32623800 2.0502747
## 164 35.90909 Sub-adult 1.7193560 0.1812141 52.99604 1.35588585 2.0828261
## 165 36.16162 Sub-adult 1.7509381 0.1820070 53.86206 1.38601461 2.1158615
## 166 36.41414 Sub-adult 1.7830073 0.1827858 54.71723 1.41665378 2.1493607
## 167 36.66667 Sub-adult 1.8155682 0.1835373 55.54657 1.44783254 2.1833040
## 168 36.91919 Sub-adult 1.8486257 0.1842488 56.33531 1.47957938 2.2176720
## 169 37.17172 Sub-adult 1.8821843 0.1849082 57.06909 1.51192173 2.2524468
## 170 37.42424 Sub-adult 1.9162486 0.1855040 57.73434 1.54488558 2.2876116
## 171 37.67677 Sub-adult 1.9508234 0.1860261 58.31850 1.57849507 2.3231517
## 172 37.92929 Sub-adult 1.9859133 0.1864652 58.81039 1.61277208 2.3590546
## 173 38.18182 Sub-adult 2.0215230 0.1868139 59.20050 1.64773578 2.3953103
## 174 38.43434 Sub-adult 2.0576572 0.1870661 59.48134 1.68340220 2.4319122
## 175 38.68687 Sub-adult 2.0943205 0.1872178 59.64777 1.71978370 2.4688573
## 176 38.93939 Sub-adult 2.1315176 0.1872672 59.69733 1.75688845 2.5061467
## 177 39.19192 Sub-adult 2.1692531 0.1872149 59.63063 1.79471984 2.5437864
## 178 39.44444 Sub-adult 2.2075318 0.1870646 59.45172 1.83327586 2.5817877
## 179 39.69697 Sub-adult 2.2463583 0.1868231 59.16848 1.87254841 2.6201681
## 180 39.94949 Sub-adult 2.2857372 0.1865008 58.79304 1.91252254 2.6589518
## 181 40.20202 Sub-adult 2.3256732 0.1861122 58.34216 1.95317568 2.6981708
## 182 40.45455 Sub-adult 2.3661711 0.1856766 57.83768 1.99447675 2.7378654
## 183 40.70707 Sub-adult 2.4072354 0.1852182 57.30689 2.03638530 2.7780855
## 184 40.95960 Sub-adult 2.4488708 0.1847670 56.78298 2.07885057 2.8188911
## 185 41.21212 Sub-adult 2.4910820 0.1843591 56.30545 2.12181063 2.8603535
## 186 41.46465 Sub-adult 2.5338737 0.1840371 55.92055 2.16519148 2.9025560
## 187 41.71717 Sub-adult 2.5772506 0.1838510 55.68177 2.20890632 2.9455948
## 188 41.96970 Sub-adult 2.6212172 0.1838577 55.65051 2.25285498 2.9895794
## 189 42.22222 Sub-adult 2.6657783 0.1841215 55.89672 2.29692357 3.0346330
## 190 42.47475 Sub-adult 2.7109385 0.1847138 56.49986 2.34098451 3.0808925
## 191 42.72727 Sub-adult 2.7567025 0.1857123 57.54971 2.38489713 3.1285079
## 192 42.97980 Sub-adult 2.8030750 0.1871997 59.14714 2.42850877 3.1776413
## 193 43.23232 Sub-adult 2.8500606 0.1892627 61.40408 2.47165685 3.2284644
## 194 43.48485 Sub-adult 2.8976641 0.1919894 64.44187 2.51417179 3.2811564
## 195 43.73737 Sub-adult 2.9458900 0.1954673 68.38649 2.55588096 3.3358990
## 196 43.98990 Sub-adult 2.9947430 0.1997807 73.35852 2.59661346 3.3928725
## 197 44.24242 Sub-adult 3.0442278 0.2050082 79.45559 2.63620560 3.4522501
## 198 44.49495 Sub-adult 3.0943491 0.2112208 86.72534 2.67450627 3.5141920
## 199 44.74747 Sub-adult 3.1451116 0.2184799 95.12989 2.71138181 3.5788414
## 200 45.00000 Sub-adult 3.1965198 0.2268368 104.50789 2.74671965 3.6463200exporting emmeans
write.csv(newdata2, "~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff/LDHlifestage.csv")graph adult
setwd("~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff/")
AdultLDH <- read.csv('LDHlifestageadult.csv')
LDHplot <- ggplot(AdultLDH, aes(y=emmean, x=Temperature)) +
geom_line() +
theme_classic() +
geom_ribbon(aes(ymin=lower.CL, ymax=upper.CL), alpha=0.2) + theme(axis.text.y = element_blank(), axis.title.y = element_blank()) +
xlab(expression("Temperature " ( degree*C))) +
scale_x_continuous(breaks = seq(20, 45, by = 5)) + scale_y_continuous(labels = scales::number_format(accuracy = 0.01), breaks = seq(0, 4, by = 1), limits = c(-0.025,4)) +
theme(text = element_text(size=25),
axis.text.x = element_text(size=25, hjust=0.5),
axis.text.y = element_text(size=25, hjust=0.5)) + theme(panel.border = element_rect(colour = "black", fill=NA, size=1))
LDHplot 
graph sub-adult
setwd("~/Public/OneDrive - James Cook University/Experiments/Enzyme analysis/Stats stuff/")
SubadultLDH <- read.csv('LDHlifestagesubadult.csv')
LDHplot2 <- ggplot(SubadultLDH, aes(y=emmean, x=Temperature)) +
geom_line() +
theme_classic() +
geom_ribbon(aes(ymin=lower.CL, ymax=upper.CL), alpha=0.2) +
ylab(expression(LDH~activity~(U~mg^-1~tissue))) +
xlab(expression("Temperature " ( degree*C))) +
scale_x_continuous(breaks = seq(20, 45, by = 5)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01), breaks = seq(0, 4, by = 1), limits = c(-0.025,4)) +
theme(text = element_text(size=25),
axis.text.x = element_text(size=25, hjust=0.5),
axis.text.y = element_text(size=25, hjust=0.5)) + theme(panel.border = element_rect(colour = "black", fill=NA, size=1))
LDHplot2
combining graphs
cowplot::plot_grid(LDHplot2,
LDHplot,
nrow = 1,
labels = "auto", align = "v")
combining all graphs
cowplot::plot_grid(CSplot,
CSplot2,
LDHplot2,
LDHplot,
nrow = 2,
labels = "auto", align = "v")
image 2100 x 1800 or pdf 20 x 23.20
Oxygen consumption rate
Set working directory
setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")Installing data
setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute <- read.csv('Acuteforrevisionnew.csv')
str(Acute)## 'data.frame': 300 obs. of 7 variables:
## $ Starfish.ID : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Trial.number : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Temperature : int 24 25 26 27 28 29 30 31 32 33 ...
## $ Life.stage : Factor w/ 2 levels "Adult","Sub-adult": 2 2 2 2 2 2 2 2 2 2 ...
## $ Oxygen.Consumption.Rate.bkrem : num 3.11 4.45 4.86 5.67 6.05 ...
## $ Weight : int 272 272 272 272 272 272 272 272 272 272 ...
## $ Oxygen.Consumption.Rate.bkrem.g: num 0.0114 0.0164 0.0179 0.0208 0.0222 ...reformatting data
Acute = Acute %>%
mutate(Starfish.ID=factor(Starfish.ID), Trial=factor(Trial.number))
glimpse(Acute)## Rows: 300
## Columns: 8
## $ Starfish.ID <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Trial.number <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Temperature <int> 24, 25, 26, 27, 28, 29, 30, 31, 32, 33…
## $ Life.stage <fct> Sub-adult, Sub-adult, Sub-adult, Sub-a…
## $ Oxygen.Consumption.Rate.bkrem <dbl> 3.112877, 4.448572, 4.861347, 5.668886…
## $ Weight <int> 272, 272, 272, 272, 272, 272, 272, 272…
## $ Oxygen.Consumption.Rate.bkrem.g <dbl> 0.01144440, 0.01635504, 0.01787260, 0.…
## $ Trial <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…the model
O2mod <- lmer(log(Oxygen.Consumption.Rate.bkrem.g) ~ poly(Temperature, 3) * Life.stage + (1|Starfish.ID), REML=FALSE, data=Acute)
anova(O2mod)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## poly(Temperature, 3) 35.321 11.7736 3 275.381 220.406 < 2.2e-16
## Life.stage 1.255 1.2550 1 25.058 23.494 5.506e-05
## poly(Temperature, 3):Life.stage 14.923 4.9744 3 275.381 93.123 < 2.2e-16
##
## poly(Temperature, 3) ***
## Life.stage ***
## poly(Temperature, 3):Life.stage ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(O2mod)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: log(Oxygen.Consumption.Rate.bkrem.g) ~ poly(Temperature, 3) *
## Life.stage + (1 | Starfish.ID)
## Data: Acute
##
## AIC BIC logLik deviance df.resid
## 81.5 118.5 -30.8 61.5 290
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6518 -0.4450 0.0044 0.5574 2.5365
##
## Random effects:
## Groups Name Variance Std.Dev.
## Starfish.ID (Intercept) 0.15435 0.3929
## Residual 0.05342 0.2311
## Number of obs: 300, groups: Starfish.ID, 25
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -4.2724 0.1201 25.0004 -35.566
## poly(Temperature, 3)1 1.9249 0.3496 275.2184 5.507
## poly(Temperature, 3)2 -2.6377 0.3487 275.1260 -7.565
## poly(Temperature, 3)3 -1.9862 0.3493 275.1813 -5.686
## Life.stageSub-adult 0.7785 0.1606 25.0578 4.847
## poly(Temperature, 3)1:Life.stageSub-adult 7.4926 0.4708 275.7425 15.914
## poly(Temperature, 3)2:Life.stageSub-adult 1.9657 0.4664 275.1442 4.215
## poly(Temperature, 3)3:Life.stageSub-adult 1.3465 0.4678 275.2599 2.879
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Temperature, 3)1 8.39e-08 ***
## poly(Temperature, 3)2 5.85e-13 ***
## poly(Temperature, 3)3 3.32e-08 ***
## Life.stageSub-adult 5.51e-05 ***
## poly(Temperature, 3)1:Life.stageSub-adult < 2e-16 ***
## poly(Temperature, 3)2:Life.stageSub-adult 3.40e-05 ***
## poly(Temperature, 3)3:Life.stageSub-adult 0.00431 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pl(T,3)1 pl(T,3)2 pl(T,3)3 Lf.sS- p(T,3)1: p(T,3)2:
## ply(Tmp,3)1 0.000
## ply(Tmp,3)2 0.004 0.016
## ply(Tmp,3)3 0.004 0.025 0.014
## Lf.stgSb-dl -0.748 0.000 -0.003 -0.003
## p(T,3)1:L.S 0.000 -0.742 -0.012 -0.018 0.003
## p(T,3)2:L.S -0.003 -0.012 -0.748 -0.010 0.000 0.001
## p(T,3)3:L.S -0.003 -0.018 -0.010 -0.747 0.000 -0.003 0.006Equation: y= -4.27 + 1.92x1 - 2.64x^2 - 1.919x1^3 + 0.78x2 + 7.49x1x2 + 1.97x12x22 + 1.35x13x23
check_model(O2mod)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 299 rows containing missing values (geom_text_repel).## `geom_smooth()` using formula 'y ~ x'
O2 = simulateResiduals(O2mod, plot=TRUE)## Warning in checkModel(fittedModel): DHARMa: fittedModel not in class of
## supported models. Absolutely no guarantee that this will work!
r squared
r.squaredGLMM(O2mod)## R2m R2c
## [1,] 0.6180154 0.9017902predicted estimated marginal means
acute.grid = with(Acute, list(Life.stage=levels(Life.stage),
Temperature=seq(min(Temperature), max(Temperature), len=100)))newdata= emmeans(O2mod, ~Temperature|Life.stage, at=acute.grid, type='response') %>%
as.data.frame
newdata## Temperature Life.stage response SE df lower.CL
## 1 24.00000 Adult 0.01076011 0.001479922 40.08204 0.008148953
## 2 24.12121 Adult 0.01068781 0.001451187 38.01631 0.008119235
## 3 24.24242 Adult 0.01062797 0.001427071 36.30445 0.008094997
## 4 24.36364 Adult 0.01058007 0.001407168 34.89589 0.008076316
## 5 24.48485 Adult 0.01054363 0.001391102 33.74685 0.008063264
## 6 24.60606 Adult 0.01051820 0.001378528 32.81948 0.008055907
## 7 24.72727 Adult 0.01050338 0.001369129 32.08098 0.008054304
## 8 24.84848 Adult 0.01049880 0.001362618 31.50291 0.008058503
## 9 24.96970 Adult 0.01050412 0.001358731 31.06054 0.008068544
## 10 25.09091 Adult 0.01051904 0.001357231 30.73224 0.008084456
## 11 25.21212 Adult 0.01054326 0.001357901 30.49911 0.008106260
## 12 25.33333 Adult 0.01057653 0.001360546 30.34452 0.008133967
## 13 25.45455 Adult 0.01061861 0.001364990 30.25388 0.008167579
## 14 25.57576 Adult 0.01066927 0.001371078 30.21431 0.008207091
## 15 25.69697 Adult 0.01072829 0.001378666 30.21452 0.008252490
## 16 25.81818 Adult 0.01079549 0.001387630 30.24456 0.008303755
## 17 25.93939 Adult 0.01087067 0.001397855 30.29576 0.008360857
## 18 26.06061 Adult 0.01095366 0.001409243 30.36059 0.008423761
## 19 26.18182 Adult 0.01104430 0.001421702 30.43256 0.008492424
## 20 26.30303 Adult 0.01114240 0.001435153 30.50613 0.008566794
## 21 26.42424 Adult 0.01124782 0.001449526 30.57668 0.008646811
## 22 26.54545 Adult 0.01136038 0.001464757 30.64040 0.008732409
## 23 26.66667 Adult 0.01147994 0.001480790 30.69424 0.008823510
## 24 26.78788 Adult 0.01160633 0.001497575 30.73586 0.008920027
## 25 26.90909 Adult 0.01173939 0.001515067 30.76358 0.009021866
## 26 27.03030 Adult 0.01187894 0.001533227 30.77628 0.009128917
## 27 27.15152 Adult 0.01202481 0.001552019 30.77340 0.009241062
## 28 27.27273 Adult 0.01217681 0.001571412 30.75484 0.009358169
## 29 27.39394 Adult 0.01233475 0.001591377 30.72093 0.009480096
## 30 27.51515 Adult 0.01249843 0.001611888 30.67235 0.009606682
## 31 27.63636 Adult 0.01266763 0.001632920 30.61012 0.009737756
## 32 27.75758 Adult 0.01284211 0.001654452 30.53551 0.009873129
## 33 27.87879 Adult 0.01302164 0.001676462 30.45000 0.010012598
## 34 28.00000 Adult 0.01320594 0.001698932 30.35526 0.010155940
## 35 28.12121 Adult 0.01339473 0.001721840 30.25305 0.010302917
## 36 28.24242 Adult 0.01358771 0.001745169 30.14526 0.010453272
## 37 28.36364 Adult 0.01378457 0.001768896 30.03377 0.010606730
## 38 28.48485 Adult 0.01398495 0.001793002 29.92051 0.010762996
## 39 28.60606 Adult 0.01418849 0.001817464 29.80737 0.010921754
## 40 28.72727 Adult 0.01439480 0.001842258 29.69618 0.011082672
## 41 28.84848 Adult 0.01460345 0.001867357 29.58872 0.011245392
## 42 28.96970 Adult 0.01481402 0.001892732 29.48663 0.011409542
## 43 29.09091 Adult 0.01502603 0.001918351 29.39145 0.011574725
## 44 29.21212 Adult 0.01523899 0.001944175 29.30459 0.011740526
## 45 29.33333 Adult 0.01545237 0.001970166 29.22729 0.011906510
## 46 29.45455 Adult 0.01566563 0.001996276 29.16062 0.012072222
## 47 29.57576 Adult 0.01587818 0.002022456 29.10548 0.012237189
## 48 29.69697 Adult 0.01608943 0.002048648 29.06260 0.012400918
## 49 29.81818 Adult 0.01629875 0.002074791 29.03249 0.012562902
## 50 29.93939 Adult 0.01650547 0.002100814 29.01550 0.012722614
## 51 30.06061 Adult 0.01670891 0.002126642 29.01175 0.012879514
## 52 30.18182 Adult 0.01690837 0.002152193 29.02119 0.013033048
## 53 30.30303 Adult 0.01710311 0.002177378 29.04355 0.013182649
## 54 30.42424 Adult 0.01729239 0.002202099 29.07839 0.013327741
## 55 30.54545 Adult 0.01747545 0.002226253 29.12506 0.013467737
## 56 30.66667 Adult 0.01765148 0.002249730 29.18274 0.013602045
## 57 30.78788 Adult 0.01781969 0.002272414 29.25042 0.013730066
## 58 30.90909 Adult 0.01797928 0.002294183 29.32694 0.013851200
## 59 31.03030 Adult 0.01812942 0.002314907 29.41099 0.013964845
## 60 31.15152 Adult 0.01826928 0.002334456 29.50110 0.014070402
## 61 31.27273 Adult 0.01839805 0.002352693 29.59570 0.014167275
## 62 31.39394 Adult 0.01851490 0.002369479 29.69310 0.014254878
## 63 31.51515 Adult 0.01861903 0.002384674 29.79155 0.014332630
## 64 31.63636 Adult 0.01870963 0.002398137 29.88923 0.014399966
## 65 31.75758 Adult 0.01878591 0.002409729 29.98431 0.014456335
## 66 31.87879 Adult 0.01884713 0.002419312 30.07496 0.014501202
## 67 32.00000 Adult 0.01889254 0.002426754 30.15938 0.014534055
## 68 32.12121 Adult 0.01892145 0.002431929 30.23587 0.014554404
## 69 32.24242 Adult 0.01893320 0.002434717 30.30283 0.014561785
## 70 32.36364 Adult 0.01892717 0.002435009 30.35884 0.014555765
## 71 32.48485 Adult 0.01890279 0.002432706 30.40268 0.014535940
## 72 32.60606 Adult 0.01885956 0.002427725 30.43338 0.014501943
## 73 32.72727 Adult 0.01879703 0.002419994 30.45029 0.014453443
## 74 32.84848 Adult 0.01871480 0.002409461 30.45311 0.014390148
## 75 32.96970 Adult 0.01861257 0.002396092 30.44196 0.014311810
## 76 33.09091 Adult 0.01849010 0.002379870 30.41741 0.014218226
## 77 33.21212 Adult 0.01834722 0.002360802 30.38058 0.014109239
## 78 33.33333 Adult 0.01818387 0.002338916 30.33313 0.013984743
## 79 33.45455 Adult 0.01800006 0.002314263 30.27740 0.013844683
## 80 33.57576 Adult 0.01779588 0.002286918 30.21638 0.013689058
## 81 33.69697 Adult 0.01757154 0.002256979 30.15386 0.013517923
## 82 33.81818 Adult 0.01732732 0.002224569 30.09439 0.013331389
## 83 33.93939 Adult 0.01706360 0.002189833 30.04347 0.013129629
## 84 34.06061 Adult 0.01678088 0.002152940 30.00750 0.012912875
## 85 34.18182 Adult 0.01647971 0.002114076 29.99397 0.012681422
## 86 34.30303 Adult 0.01616078 0.002073450 30.01147 0.012435625
## 87 34.42424 Adult 0.01582484 0.002031285 30.06987 0.012175908
## 88 34.54545 Adult 0.01547274 0.001987819 30.18040 0.011902755
## 89 34.66667 Adult 0.01510541 0.001943300 30.35586 0.011616718
## 90 34.78788 Adult 0.01472387 0.001897982 30.61082 0.011318412
## 91 34.90909 Adult 0.01432920 0.001852119 30.96183 0.011008520
## 92 35.03030 Adult 0.01392257 0.001805968 31.42779 0.010687785
## 93 35.15152 Adult 0.01350519 0.001759772 32.03028 0.010357020
## 94 35.27273 Adult 0.01307833 0.001713766 32.79406 0.010017097
## 95 35.39394 Adult 0.01264332 0.001668166 33.74756 0.009668952
## 96 35.51515 Adult 0.01220151 0.001623166 34.92360 0.009313583
## 97 35.63636 Adult 0.01175429 0.001578930 36.36006 0.008952047
## 98 35.75758 Adult 0.01130306 0.001535597 38.10072 0.008585458
## 99 35.87879 Adult 0.01084924 0.001493267 40.19616 0.008214988
## 100 36.00000 Adult 0.01039423 0.001452007 42.70460 0.007841856
## 101 24.00000 Sub-adult 0.01267424 0.001545515 40.09379 0.009905957
## 102 24.12121 Sub-adult 0.01284010 0.001545691 38.02387 0.010063162
## 103 24.24242 Sub-adult 0.01301317 0.001549159 36.31087 0.010222575
## 104 24.36364 Sub-adult 0.01319356 0.001555757 34.90364 0.010384551
## 105 24.48485 Sub-adult 0.01338137 0.001565320 33.75792 0.010549448
## 106 24.60606 Sub-adult 0.01357670 0.001577679 32.83544 0.010717626
## 107 24.72727 Sub-adult 0.01377968 0.001592671 32.10310 0.010889442
## 108 24.84848 Sub-adult 0.01399042 0.001610132 31.53218 0.011065244
## 109 24.96970 Sub-adult 0.01420903 0.001629905 31.09770 0.011245375
## 110 25.09091 Sub-adult 0.01443565 0.001651839 30.77785 0.011430165
## 111 25.21212 Sub-adult 0.01467041 0.001675790 30.55355 0.011619934
## 112 25.33333 Sub-adult 0.01491342 0.001701622 30.40803 0.011814991
## 113 25.45455 Sub-adult 0.01516483 0.001729208 30.32655 0.012015634
## 114 25.57576 Sub-adult 0.01542476 0.001758430 30.29614 0.012222147
## 115 25.69697 Sub-adult 0.01569336 0.001789181 30.30538 0.012434804
## 116 25.81818 Sub-adult 0.01597076 0.001821362 30.34427 0.012653865
## 117 25.93939 Sub-adult 0.01625711 0.001854884 30.40404 0.012879580
## 118 26.06061 Sub-adult 0.01655253 0.001889667 30.47710 0.013112188
## 119 26.18182 Sub-adult 0.01685717 0.001925640 30.55691 0.013351914
## 120 26.30303 Sub-adult 0.01717117 0.001962742 30.63792 0.013598972
## 121 26.42424 Sub-adult 0.01749466 0.002000920 30.71545 0.013853566
## 122 26.54545 Sub-adult 0.01782779 0.002040127 30.78569 0.014115885
## 123 26.66667 Sub-adult 0.01817070 0.002080327 30.84560 0.014386108
## 124 26.78788 Sub-adult 0.01852351 0.002121491 30.89285 0.014664404
## 125 26.90909 Sub-adult 0.01888636 0.002163594 30.92577 0.014950927
## 126 27.03030 Sub-adult 0.01925937 0.002206623 30.94327 0.015245821
## 127 27.15152 Sub-adult 0.01964268 0.002250566 30.94484 0.015549216
## 128 27.27273 Sub-adult 0.02003641 0.002295422 30.93041 0.015861230
## 129 27.39394 Sub-adult 0.02044066 0.002341194 30.90036 0.016181970
## 130 27.51515 Sub-adult 0.02085556 0.002387889 30.85546 0.016511528
## 131 27.63636 Sub-adult 0.02128120 0.002435522 30.79676 0.016849984
## 132 27.75758 Sub-adult 0.02171768 0.002484111 30.72562 0.017197405
## 133 27.87879 Sub-adult 0.02216509 0.002533679 30.64358 0.017553845
## 134 28.00000 Sub-adult 0.02262352 0.002584254 30.55238 0.017919343
## 135 28.12121 Sub-adult 0.02309303 0.002635868 30.45387 0.018293924
## 136 28.24242 Sub-adult 0.02357369 0.002688553 30.34998 0.018677602
## 137 28.36364 Sub-adult 0.02406555 0.002742349 30.24269 0.019070372
## 138 28.48485 Sub-adult 0.02456864 0.002797293 30.13399 0.019472220
## 139 28.60606 Sub-adult 0.02508300 0.002853429 30.02584 0.019883113
## 140 28.72727 Sub-adult 0.02560863 0.002910798 29.92014 0.020303006
## 141 28.84848 Sub-adult 0.02614553 0.002969443 29.81873 0.020731840
## 142 28.96970 Sub-adult 0.02669368 0.003029408 29.72330 0.021169541
## 143 29.09091 Sub-adult 0.02725306 0.003090734 29.63547 0.021616019
## 144 29.21212 Sub-adult 0.02782359 0.003153462 29.55666 0.022071171
## 145 29.33333 Sub-adult 0.02840523 0.003217629 29.48818 0.022534879
## 146 29.45455 Sub-adult 0.02899786 0.003283271 29.43114 0.023007013
## 147 29.57576 Sub-adult 0.02960139 0.003350417 29.38647 0.023487425
## 148 29.69697 Sub-adult 0.03021567 0.003419093 29.35492 0.023975955
## 149 29.81818 Sub-adult 0.03084055 0.003489318 29.33702 0.024472429
## 150 29.93939 Sub-adult 0.03147584 0.003561105 29.33313 0.024976660
## 151 30.06061 Sub-adult 0.03212135 0.003634458 29.34338 0.025488445
## 152 30.18182 Sub-adult 0.03277683 0.003709376 29.36770 0.026007568
## 153 30.30303 Sub-adult 0.03344203 0.003785844 29.40581 0.026533801
## 154 30.42424 Sub-adult 0.03411665 0.003863840 29.45726 0.027066901
## 155 30.54545 Sub-adult 0.03480038 0.003943331 29.52134 0.027606610
## 156 30.66667 Sub-adult 0.03549288 0.004024273 29.59719 0.028152658
## 157 30.78788 Sub-adult 0.03619375 0.004106609 29.68375 0.028704760
## 158 30.90909 Sub-adult 0.03690259 0.004190271 29.77978 0.029262617
## 159 31.03030 Sub-adult 0.03761895 0.004275179 29.88388 0.029825914
## 160 31.15152 Sub-adult 0.03834236 0.004361240 29.99448 0.030394322
## 161 31.27273 Sub-adult 0.03907229 0.004448348 30.10989 0.030967496
## 162 31.39394 Sub-adult 0.03980820 0.004536386 30.22831 0.031545072
## 163 31.51515 Sub-adult 0.04054951 0.004625224 30.34783 0.032126669
## 164 31.63636 Sub-adult 0.04129558 0.004714722 30.46647 0.032711887
## 165 31.75758 Sub-adult 0.04204576 0.004804728 30.58222 0.033300306
## 166 31.87879 Sub-adult 0.04279936 0.004895082 30.69306 0.033891481
## 167 32.00000 Sub-adult 0.04355563 0.004985615 30.79699 0.034484946
## 168 32.12121 Sub-adult 0.04431382 0.005076151 30.89208 0.035080206
## 169 32.24242 Sub-adult 0.04507310 0.005166508 30.97651 0.035676739
## 170 32.36364 Sub-adult 0.04583262 0.005256503 31.04860 0.036273991
## 171 32.48485 Sub-adult 0.04659151 0.005345951 31.10687 0.036871374
## 172 32.60606 Sub-adult 0.04734885 0.005434668 31.15008 0.037468262
## 173 32.72727 Sub-adult 0.04810366 0.005522477 31.17731 0.038063988
## 174 32.84848 Sub-adult 0.04885496 0.005609207 31.18796 0.038657841
## 175 32.96970 Sub-adult 0.04960173 0.005694699 31.18186 0.039249061
## 176 33.09091 Sub-adult 0.05034288 0.005778810 31.15929 0.039836835
## 177 33.21212 Sub-adult 0.05107734 0.005861418 31.12105 0.040420291
## 178 33.33333 Sub-adult 0.05180396 0.005942423 31.06851 0.040998495
## 179 33.45455 Sub-adult 0.05252161 0.006021758 31.00370 0.041570446
## 180 33.57576 Sub-adult 0.05322908 0.006099388 30.92930 0.042135068
## 181 33.69697 Sub-adult 0.05392517 0.006175320 30.84878 0.042691207
## 182 33.81818 Sub-adult 0.05460864 0.006249608 30.76640 0.043237624
## 183 33.93939 Sub-adult 0.05527824 0.006322359 30.68730 0.043772989
## 184 34.06061 Sub-adult 0.05593270 0.006393738 30.61759 0.044295878
## 185 34.18182 Sub-adult 0.05657071 0.006463976 30.56437 0.044804763
## 186 34.30303 Sub-adult 0.05719098 0.006533374 30.53587 0.045298009
## 187 34.42424 Sub-adult 0.05779220 0.006602311 30.54153 0.045773869
## 188 34.54545 Sub-adult 0.05837304 0.006671249 30.59212 0.046230479
## 189 34.66667 Sub-adult 0.05893217 0.006740732 30.69989 0.046665856
## 190 34.78788 Sub-adult 0.05946828 0.006811397 30.87876 0.047077892
## 191 34.90909 Sub-adult 0.05998005 0.006883970 31.14455 0.047464356
## 192 35.03030 Sub-adult 0.06046616 0.006959265 31.51522 0.047822891
## 193 35.15152 Sub-adult 0.06092531 0.007038184 32.01128 0.048151021
## 194 35.27273 Sub-adult 0.06135623 0.007121708 32.65616 0.048446154
## 195 35.39394 Sub-adult 0.06175765 0.007210892 33.47674 0.048705597
## 196 35.51515 Sub-adult 0.06212833 0.007306849 34.50397 0.048926565
## 197 35.63636 Sub-adult 0.06246709 0.007410736 35.77357 0.049106209
## 198 35.75758 Sub-adult 0.06277273 0.007523735 37.32680 0.049241646
## 199 35.87879 Sub-adult 0.06304415 0.007647032 39.21142 0.049329992
## 200 36.00000 Sub-adult 0.06328024 0.007781789 41.48252 0.049368417
## upper.CL
## 1 0.01420796
## 2 0.01406896
## 3 0.01395353
## 4 0.01386003
## 5 0.01378699
## 6 0.01373310
## 7 0.01369715
## 8 0.01367807
## 9 0.01367491
## 10 0.01368678
## 11 0.01371291
## 12 0.01375259
## 13 0.01380518
## 14 0.01387011
## 15 0.01394685
## 16 0.01403492
## 17 0.01413390
## 18 0.01424337
## 19 0.01436297
## 20 0.01449236
## 21 0.01463122
## 22 0.01477923
## 23 0.01493613
## 24 0.01510163
## 25 0.01527547
## 26 0.01545738
## 27 0.01564711
## 28 0.01584441
## 29 0.01604901
## 30 0.01626064
## 31 0.01647903
## 32 0.01670391
## 33 0.01693497
## 34 0.01717190
## 35 0.01741436
## 36 0.01766202
## 37 0.01791450
## 38 0.01817141
## 39 0.01843231
## 40 0.01869677
## 41 0.01896429
## 42 0.01923436
## 43 0.01950644
## 44 0.01977993
## 45 0.02005422
## 46 0.02032865
## 47 0.02060251
## 48 0.02087506
## 49 0.02114553
## 50 0.02141308
## 51 0.02167687
## 52 0.02193599
## 53 0.02218950
## 54 0.02243643
## 55 0.02267576
## 56 0.02290646
## 57 0.02312745
## 58 0.02333765
## 59 0.02353594
## 60 0.02372119
## 61 0.02389226
## 62 0.02404803
## 63 0.02418735
## 64 0.02430909
## 65 0.02441217
## 66 0.02449550
## 67 0.02455805
## 68 0.02459883
## 69 0.02461690
## 70 0.02461140
## 71 0.02458153
## 72 0.02452659
## 73 0.02444596
## 74 0.02433914
## 75 0.02420573
## 76 0.02404546
## 77 0.02385817
## 78 0.02364385
## 79 0.02340263
## 80 0.02313478
## 81 0.02284070
## 82 0.02252097
## 83 0.02217630
## 84 0.02180752
## 85 0.02141566
## 86 0.02100183
## 87 0.02056730
## 88 0.02011347
## 89 0.01964182
## 90 0.01915395
## 91 0.01865156
## 92 0.01813640
## 93 0.01761029
## 94 0.01707509
## 95 0.01653267
## 96 0.01598492
## 97 0.01543372
## 98 0.01488088
## 99 0.01432819
## 100 0.01377735
## 101 0.01621613
## 102 0.01638333
## 103 0.01656556
## 104 0.01676241
## 105 0.01697350
## 106 0.01719848
## 107 0.01743703
## 108 0.01768888
## 109 0.01795375
## 110 0.01823142
## 111 0.01852169
## 112 0.01882440
## 113 0.01913940
## 114 0.01946657
## 115 0.01980583
## 116 0.02015711
## 117 0.02052035
## 118 0.02089553
## 119 0.02128265
## 120 0.02168171
## 121 0.02209274
## 122 0.02251579
## 123 0.02295091
## 124 0.02339818
## 125 0.02385768
## 126 0.02432952
## 127 0.02481379
## 128 0.02531062
## 129 0.02582013
## 130 0.02634246
## 131 0.02687773
## 132 0.02742609
## 133 0.02798767
## 134 0.02856263
## 135 0.02915111
## 136 0.02975323
## 137 0.03036913
## 138 0.03099894
## 139 0.03164277
## 140 0.03230072
## 141 0.03297289
## 142 0.03365934
## 143 0.03436012
## 144 0.03507528
## 145 0.03580480
## 146 0.03654868
## 147 0.03730686
## 148 0.03807926
## 149 0.03886575
## 150 0.03966618
## 151 0.04048034
## 152 0.04130799
## 153 0.04214884
## 154 0.04300255
## 155 0.04386872
## 156 0.04474691
## 157 0.04563660
## 158 0.04653724
## 159 0.04744819
## 160 0.04836879
## 161 0.04929827
## 162 0.05023584
## 163 0.05118061
## 164 0.05213166
## 165 0.05308799
## 166 0.05404854
## 167 0.05501222
## 168 0.05597785
## 169 0.05694422
## 170 0.05791007
## 171 0.05887411
## 172 0.05983500
## 173 0.06079138
## 174 0.06174187
## 175 0.06268510
## 176 0.06361966
## 177 0.06454418
## 178 0.06545730
## 179 0.06635770
## 180 0.06724410
## 181 0.06811528
## 182 0.06897011
## 183 0.06980752
## 184 0.07062658
## 185 0.07142646
## 186 0.07220646
## 187 0.07296605
## 188 0.07370487
## 189 0.07442275
## 190 0.07511969
## 191 0.07579596
## 192 0.07645201
## 193 0.07708857
## 194 0.07770661
## 195 0.07830736
## 196 0.07889231
## 197 0.07946321
## 198 0.08002202
## 199 0.08057095
## 200 0.08111236exporting emmeans
write.csv(newdata, "~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry/modelpredictacutegnew2.csv")graph adult
setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Adult <- read.csv('modelpredictacutenew2adult.csv')
Adultplotg <- ggplot(Adult, aes(y=response, x=Temperature)) +
geom_line() +
theme_classic() +
geom_ribbon(aes(ymin=lower.CL, ymax=upper.CL), alpha=0.2) + theme(axis.text.y = element_blank(), axis.title.y = element_blank()) +
xlab(expression("Temperature " ( degree*C))) +
scale_x_continuous(breaks = seq(24, 36, by = 1)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01), limits = c(0,0.03)) +
theme(text = element_text(size=25),
axis.text.x = element_text(size=25, hjust=0.5),
axis.text.y = element_text(size=25, hjust=0.5)) + theme(panel.border = element_rect(colour = "black", fill=NA, size=1))
Adultplotg 
graph sub-adult
setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Subadult <- read.csv('modelpredictacutenew2subadult.csv')
str(Subadult)## 'data.frame': 100 obs. of 7 variables:
## $ Temperature: num 24 24.1 24.2 24.4 24.5 ...
## $ Life.stage : Factor w/ 1 level "Sub-adult": 1 1 1 1 1 1 1 1 1 1 ...
## $ response : num 0.0127 0.0128 0.013 0.0132 0.0134 ...
## $ SE : num 0.00155 0.00155 0.00155 0.00156 0.00157 ...
## $ df : num 40.1 38 36.3 34.9 33.8 ...
## $ lower.CL : num 0.00991 0.01006 0.01022 0.01038 0.01055 ...
## $ upper.CL : num 0.0162 0.0164 0.0166 0.0168 0.017 ...Subadultplotg <- ggplot(Subadult, aes(y=response, x=Temperature)) +
geom_line() + theme_classic() +
geom_ribbon(aes(ymin=lower.CL, ymax=upper.CL), alpha=0.2) +
ylab(expression(Standard~metabolic~rate~(mg~O[2]~g^-1~h^-1))) +
xlab(expression("Temperature " ( degree*C))) +
scale_x_continuous(breaks = seq(24, 36, by = 1)) + scale_y_continuous(labels = scales::number_format(accuracy = 0.01), limits = c(0,0.09), breaks= seq(0, 0.09, by = 0.03)) +
theme(text = element_text(size=25),
axis.text.x = element_text(size=25, hjust=0.5),
axis.text.y = element_text(size=25, hjust=0.5)) + theme(panel.border = element_rect(colour = "black", fill=NA, size=1))
Subadultplotg
combining plots
cowplot::plot_grid(Subadultplotg,
Adultplotg,
nrow = 1,
labels = "auto", align = "v")
ggarrange(Subadultplotg, Adultplotg, labels = c(“A”, “B”), ncol = 2, nrow = 1)
1800 x 800 20 x 23.20
26 control
Set working directory
setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")Installing data
rm(list = ls())
setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Ctl <- read.csv('26Control.csv')
str(Ctl)## 'data.frame': 75 obs. of 5 variables:
## $ Starfish : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Temperatureequivelent : int 24 25 26 27 28 29 30 31 32 33 ...
## $ OxygenconsumptionperCoT: num 2.32 2.69 2.79 2.27 2.52 ...
## $ Weight : int 311 311 311 311 311 311 311 311 311 311 ...
## $ Oxygenconsumptionperg : num 0.00747 0.00864 0.00897 0.00729 0.0081 ...Ctl = Ctl %>%
mutate(Starfish=factor(Starfish), Temperature2=factor(Temperatureequivelent))
glimpse(Ctl)## Rows: 75
## Columns: 6
## $ Starfish <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2…
## $ Temperatureequivelent <int> 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35…
## $ OxygenconsumptionperCoT <dbl> 2.3225928, 2.6861142, 2.7891780, 2.2657562, 2.…
## $ Weight <int> 311, 311, 311, 311, 311, 311, 311, 311, 311, 3…
## $ Oxygenconsumptionperg <dbl> 0.007468144, 0.008637023, 0.008968418, 0.00728…
## $ Temperature2 <fct> 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35…ModelCtl <- lmer(log(OxygenconsumptionperCoT) ~ Temperatureequivelent + (1|Starfish), REML=FALSE, data=Ctl)
anova(ModelCtl)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Temperatureequivelent 1.2649 1.2649 1 69.002 28.573 1.102e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(ModelCtl)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: log(OxygenconsumptionperCoT) ~ Temperatureequivelent + (1 | Starfish)
## Data: Ctl
##
## AIC BIC logLik deviance df.resid
## 18.8 28.1 -5.4 10.8 71
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.74641 -0.63538 0.09585 0.59262 2.36843
##
## Random effects:
## Groups Name Variance Std.Dev.
## Starfish (Intercept) 0.70701 0.8408
## Residual 0.04427 0.2104
## Number of obs: 75, groups: Starfish, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.326106 0.398259 10.654266 3.330 0.007 **
## Temperatureequivelent -0.036018 0.006738 69.002391 -5.345 1.1e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Tmprtrqvlnt -0.503ModelCtl <- lme(log(OxygenconsumptionperCoT) ~ Temperatureequivelent, random=~1|Starfish, data=Ctl)
anova(ModelCtl)## numDF denDF F-value p-value
## (Intercept) 1 68 0.455987 0.5018
## Temperatureequivelent 1 68 28.164388 <.0001summary(ModelCtl)## Linear mixed-effects model fit by REML
## Data: Ctl
## AIC BIC logLik
## 27.19535 36.35719 -9.597675
##
## Random effects:
## Formula: ~1 | Starfish
## (Intercept) Residual
## StdDev: 0.9214618 0.2119413
##
## Fixed effects: log(OxygenconsumptionperCoT) ~ Temperatureequivelent
## Value Std.Error DF t-value p-value
## (Intercept) 1.3261979 0.4276565 68 3.101082 0.0028
## Temperatureequivelent -0.0360213 0.0067875 68 -5.307013 0.0000
## Correlation:
## (Intr)
## Temperatureequivelent -0.472
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.72202865 -0.63181927 0.09954463 0.58819680 2.35564905
##
## Number of Observations: 75
## Number of Groups: 6ggscatter(Ctl, x = "Temperatureequivelent", y = "OxygenconsumptionperCoT",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Temperature equivelent", ylab = "Oxygen consumption rate")## `geom_smooth()` using formula 'y ~ x'
Ctl <- cor.test(Ctl$Temperatureequivelent, Ctl$OxygenconsumptionperCoT, conf.level = 0.95,
method = "kendall", exact=F)
Ctl##
## Kendall's rank correlation tau
##
## data: Ctl$Temperatureequivelent and Ctl$OxygenconsumptionperCoT
## z = -1.2212, p-value = 0.222
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09918195Ordinary least squares (metabolic scaling exponent)
setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute24 <- read.csv('Acute24.csv')
lmodel24 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute24)
lmodel24$coefficients## (Intercept) log(Weight)
## -4.1930085 0.9474136summary(lmodel24)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute24)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64802 -0.21278 0.00573 0.23863 0.76153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.19301 0.30690 -13.66 1.59e-12 ***
## log(Weight) 0.94741 0.05204 18.21 3.71e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3462 on 23 degrees of freedom
## Multiple R-squared: 0.9351, Adjusted R-squared: 0.9323
## F-statistic: 331.5 on 1 and 23 DF, p-value: 3.709e-15setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute25 <- read.csv('Acute25.csv')
lmodel25 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute25)
lmodel25$coefficients## (Intercept) log(Weight)
## -3.5520571 0.8630266summary(lmodel25)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute25)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89285 -0.18008 -0.02143 0.25530 0.87602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.55206 0.33375 -10.64 2.33e-10 ***
## log(Weight) 0.86303 0.05659 15.25 1.61e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3765 on 23 degrees of freedom
## Multiple R-squared: 0.91, Adjusted R-squared: 0.9061
## F-statistic: 232.6 on 1 and 23 DF, p-value: 1.615e-13setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute26 <- read.csv('Acute26.csv')
lmodel26 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute26)
lmodel26$coefficients## (Intercept) log(Weight)
## -3.0768038 0.8019523summary(lmodel26)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute26)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90575 -0.20292 0.02253 0.19780 0.77483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.07680 0.39567 -7.776 3.66e-07 ***
## log(Weight) 0.80195 0.06674 12.017 4.93e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4167 on 18 degrees of freedom
## Multiple R-squared: 0.8892, Adjusted R-squared: 0.883
## F-statistic: 144.4 on 1 and 18 DF, p-value: 4.934e-10setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute27 <- read.csv('Acute27.csv')
lmodel27 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute27)
lmodel27$coefficients## (Intercept) log(Weight)
## -3.0409050 0.8164543summary(lmodel27)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute27)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.91164 -0.17702 0.07199 0.22309 0.70894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.0409 0.3955 -7.689 4.29e-07 ***
## log(Weight) 0.8165 0.0667 12.240 3.67e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4165 on 18 degrees of freedom
## Multiple R-squared: 0.8927, Adjusted R-squared: 0.8868
## F-statistic: 149.8 on 1 and 18 DF, p-value: 3.667e-10setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute28 <- read.csv('Acute28.csv')
lmodel28 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute28)
lmodel28$coefficients## (Intercept) log(Weight)
## -2.8792988 0.8006269summary(lmodel28)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute28)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.05438 -0.21939 0.07146 0.20423 0.73412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.87930 0.36196 -7.955 4.73e-08 ***
## log(Weight) 0.80063 0.06137 13.046 4.10e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4083 on 23 degrees of freedom
## Multiple R-squared: 0.8809, Adjusted R-squared: 0.8758
## F-statistic: 170.2 on 1 and 23 DF, p-value: 4.098e-12setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute29 <- read.csv('Acute29.csv')
lmodel29 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute29)
lmodel29$coefficients## (Intercept) log(Weight)
## -2.3841611 0.7375234summary(lmodel29)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute29)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9630 -0.1482 0.0351 0.1938 0.8005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.38416 0.34386 -6.934 5.82e-07 ***
## log(Weight) 0.73752 0.05804 12.708 1.31e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3848 on 22 degrees of freedom
## Multiple R-squared: 0.8801, Adjusted R-squared: 0.8747
## F-statistic: 161.5 on 1 and 22 DF, p-value: 1.312e-11setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute30 <- read.csv('Acute30.csv')
lmodel30 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute30)
lmodel30$coefficients## (Intercept) log(Weight)
## -2.1494130 0.7199308summary(lmodel30)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute30)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.97060 -0.16280 0.00071 0.20539 0.94354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.14941 0.36577 -5.876 6.51e-06 ***
## log(Weight) 0.71993 0.06174 11.662 6.87e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4094 on 22 degrees of freedom
## Multiple R-squared: 0.8608, Adjusted R-squared: 0.8544
## F-statistic: 136 on 1 and 22 DF, p-value: 6.873e-11setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute31 <- read.csv('Acute31.csv')
lmodel31 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute31)
lmodel31$coefficients## (Intercept) log(Weight)
## -1.6982617 0.6597577summary(lmodel31)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute31)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.14951 -0.14928 -0.01946 0.20615 0.88331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.69826 0.48705 -3.487 0.00282 **
## log(Weight) 0.65976 0.08196 8.050 3.35e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4819 on 17 degrees of freedom
## Multiple R-squared: 0.7922, Adjusted R-squared: 0.78
## F-statistic: 64.8 on 1 and 17 DF, p-value: 3.353e-07setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute32 <- read.csv('Acute32.csv')
lmodel32 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute32)
lmodel32$coefficients## (Intercept) log(Weight)
## -1.334774 0.609484summary(lmodel32)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute32)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.71737 -0.21790 0.03039 0.22496 0.80221
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.33477 0.34319 -3.889 0.00079 ***
## log(Weight) 0.60948 0.05792 10.522 4.75e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3841 on 22 degrees of freedom
## Multiple R-squared: 0.8342, Adjusted R-squared: 0.8267
## F-statistic: 110.7 on 1 and 22 DF, p-value: 4.745e-10setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute33 <- read.csv('Acute33.csv')
lmodel33 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute33)
lmodel33$coefficients## (Intercept) log(Weight)
## -1.0574623 0.5858087summary(lmodel33)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute33)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.75766 -0.21191 0.03862 0.16377 0.73081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.05746 0.32135 -3.291 0.00333 **
## log(Weight) 0.58581 0.05424 10.801 2.92e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3596 on 22 degrees of freedom
## Multiple R-squared: 0.8413, Adjusted R-squared: 0.8341
## F-statistic: 116.7 on 1 and 22 DF, p-value: 2.919e-10setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute34 <- read.csv('Acute34.csv')
lmodel34 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute34)
lmodel34$coefficients## (Intercept) log(Weight)
## -0.7133963 0.5409987summary(lmodel34)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute34)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.83111 -0.17365 -0.00747 0.23559 0.66756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7134 0.3259 -2.189 0.0395 *
## log(Weight) 0.5410 0.0550 9.836 1.63e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3647 on 22 degrees of freedom
## Multiple R-squared: 0.8147, Adjusted R-squared: 0.8063
## F-statistic: 96.76 on 1 and 22 DF, p-value: 1.628e-09setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute35 <- read.csv('Acute35.csv')
lmodel35 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute35)
lmodel35$coefficients## (Intercept) log(Weight)
## -0.0009374297 0.4185110766summary(lmodel35)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute35)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.75228 -0.18332 -0.01041 0.27626 0.52401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0009374 0.3172218 -0.003 0.998
## log(Weight) 0.4185111 0.0535413 7.817 8.66e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.355 on 22 degrees of freedom
## Multiple R-squared: 0.7353, Adjusted R-squared: 0.7232
## F-statistic: 61.1 on 1 and 22 DF, p-value: 8.657e-08setwd("~/Public/OneDrive - James Cook University/Experiments/Experiments Jan-April '20/Acute respirometry")
Acute36 <- read.csv('Acute36.csv')
lmodel36 <- lm(log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight), data=Acute36)
lmodel36$coefficients## (Intercept) log(Weight)
## 0.7853414 0.2152283summary(lmodel36)##
## Call:
## lm(formula = log(Oxygen.Consumption.Rate.bkrem) ~ log(Weight),
## data = Acute36)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.59463 -0.26157 0.09987 0.42144 0.72452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.78534 0.49363 1.591 0.127
## log(Weight) 0.21523 0.08511 2.529 0.020 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5361 on 20 degrees of freedom
## Multiple R-squared: 0.2423, Adjusted R-squared: 0.2044
## F-statistic: 6.394 on 1 and 20 DF, p-value: 0.01997