Plant growth in the absence of insects
Matthew A. Barbour
2020-06-23
Last updated: 2020-06-23
Checks: 7 0
Knit directory: genes-to-foodweb-stability/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 86116c8 | mabarbour | 2020-06-23 | bioRxiv version of code and data. |
| html | 86116c8 | mabarbour | 2020-06-23 | bioRxiv version of code and data. |
Setup
# load data
ChamberNoInsectsDF <- read_csv("data/PreExperimentNoInsectsPlantBiomass.csv") %>%
mutate(Cage = as.character(Cage),
Pot = as.character(Pot))
# conduct analyses at cage level
CageLevelBiomass <- ChamberNoInsectsDF %>%
# sum biomass across both pots
group_by(Cage, Temperature, Richness, Composition, Col, gsm1, AOP2, AOP2.gsoh) %>%
summarise_at(vars(Biomass_g), list(sum)) %>%
# tidy data
ungroup() %>%
select(cage = Cage, temp = Temperature, rich = Richness, com = Composition, Col, gsm1, AOP2, AOP2.gsoh, Biomass_g) %>%
# adjust temp and rich so effect of +1 C is comparable to +1 genotype
mutate(temp = ifelse(temp == "20 C", 0, 3),
rich = rich - 1)
# source in ANOVA GLM for adjusted F-tests
source('code/glm-ftest.R')Show equivalence of Analysis of deviance and ANOVA
An Analysis of deviance on a GLM with a gaussian error distribution is equivalent to ANOVA. However, unadjusted F-tests are inappropriate because all terms are tested against residual variation rather than the intended error level (e.g. com for rich). The analysis below is just to prove this equivalence. I’m doing this so I can use the same function glm.ftest.v2 for the ANOVA in the following section.
glm.ftest.v2(
model = glm(data = CageLevelBiomass,
family = gaussian(link = "identity"),
# logging improves residual distribution
formula = log(Biomass_g) ~ temp + rich + com + temp:rich + temp:com),
test.formula = list(c("temp","temp:com"),
c("rich","com"),
c("temp:rich","temp:com")))[[1]] term df dev mean_dev F P
1 temp 1 6.002 6.002 75.51 0.0000
2 rich 1 0.038 0.038 0.48 0.4948
3 com 9 3.396 0.377 4.75 0.0003
4 temp:rich 1 0.027 0.027 0.34 0.5656
5 temp:com 9 0.826 0.092 1.15 0.3511
6 Residuals 38 3.021 0.079 NA NAanova(aov(log(Biomass_g) ~ temp + rich + com + temp:rich + temp:com, CageLevelBiomass))Analysis of Variance Table
Response: log(Biomass_g)
Df Sum Sq Mean Sq F value Pr(>F)
temp 1 6.0019 6.0019 75.5075 1.455e-10 ***
rich 1 0.0378 0.0378 0.4753 0.4947633
com 9 3.3961 0.3773 4.7473 0.0002907 ***
temp:rich 1 0.0267 0.0267 0.3360 0.5655796
temp:com 9 0.8256 0.0917 1.1540 0.3511397
Residuals 38 3.0205 0.0795
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Reproduce Table S4
# fit ANOVA
biomass_noinsects_glmf <- glm.ftest.v2(
model = glm(data = CageLevelBiomass,
family = gaussian(link = "identity"),
# logging improves residual distribution
formula = log(Biomass_g) ~ temp + rich + com + temp:rich + temp:com),
test.formula = list(c("temp","temp:com"),
c("rich","com"),
c("temp:rich","temp:com")))[[3]] %>%
# tidy table
select(Source = treatment,
`df (Source)` = num_df,
`df (Error)` = den_df,
Deviance = deviance,
`Mean Deviance` = mean_deviance,
F = F, P = P, Error = error)
# reproduce table S4 in Supplementary Materials
biomass_noinsects_glmf %>%
kable(., caption = "Analysis of variance for plant biomass (log transformed) in the absence of insects.", booktabs = T) %>%
kable_styling(latex_options = c("striped", "hold_position"))| Source | df (Source) | df (Error) | Deviance | Mean Deviance | F | P | Error |
|---|---|---|---|---|---|---|---|
| temp | 1 | 9 | 6.00 | 6.00 | 65.429 | <0.001 | temp:com |
| rich | 1 | 9 | 0.04 | 0.04 | 0.100 | 0.759 | com |
| temp:rich | 1 | 9 | 0.03 | 0.03 | 0.291 | 0.603 | temp:com |
Reproduce Fig. S8
# calculate 95% confidence intervals with `com` as the cluster level
geno_biomass_CI_raw <- conf_int(
glm(data = CageLevelBiomass,
family = gaussian(link = "identity"),
formula = log(Biomass_g) ~ -1 + temp + Col + gsm1 + AOP2 + AOP2.gsoh),
vcov = "CR2",
test = "naive-t",
coefs = c("Col","gsm1","AOP2","AOP2.gsoh"),
cluster = CageLevelBiomass$com) %>%
data.frame() %>%
rownames_to_column(var = "term")
# note that I back transform to original scale for plotting
# plot on original scale
geno_biomass_CI_raw %>%
mutate(term = factor(term,
levels = c("Col","gsm1","AOP2","AOP2.gsoh"))) %>%
ggplot(aes(x = term, y = exp(beta), color = term)) +
geom_point(size = 3) +
geom_linerange(aes(ymax = exp(CI_U), ymin = exp(CI_L))) +
geom_linerange(aes(ymin = exp(beta - SE), ymax = exp(beta + SE)), size = 1.25) +
scale_y_continuous(name = "Plant biomass (g)") +
scale_x_discrete(labels = c("Col-0","gsm1","AOP2","AOP2/gsoh")) +
scale_color_manual(values = c("darkgreen","steelblue","darkorange","firebrick1"), guide = F) +
xlab("") +
theme_cowplot(font_size = 18, line_size = 1)
| Version | Author | Date |
|---|---|---|
| 86116c8 | mabarbour | 2020-06-23 |
Save analysis
Write out an .RData file to use for creating the Supplementary Material Results.
save.image(file = "output/plant-growth-no-insects.RData")
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.0.0 clubSandwich_0.3.5 kableExtra_1.1.0 forcats_0.4.0
[5] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3 readr_1.3.1
[9] tidyr_1.0.2 tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 lubridate_1.7.4 lattice_0.20-38 zoo_1.8-6
[5] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.20 R6_2.4.0
[9] cellranger_1.1.0 backports_1.1.4 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 highr_0.8 pillar_1.4.2 rlang_0.4.4
[17] lazyeval_0.2.2 readxl_1.3.1 rstudioapi_0.10 whisker_0.3-2
[21] rmarkdown_2.0 labeling_0.3 webshot_0.5.1 munsell_0.5.0
[25] broom_0.5.2 compiler_3.6.3 httpuv_1.5.1 modelr_0.1.5
[29] xfun_0.9 pkgconfig_2.0.2 htmltools_0.3.6 tidyselect_0.2.5
[33] workflowr_1.6.0 viridisLite_0.3.0 crayon_1.3.4 dbplyr_1.4.2
[37] withr_2.1.2 later_1.0.0 grid_3.6.3 nlme_3.1-140
[41] jsonlite_1.6 gtable_0.3.0 lifecycle_0.1.0 DBI_1.0.0
[45] git2r_0.26.1 magrittr_1.5 scales_1.0.0 cli_1.1.0
[49] stringi_1.4.3 fs_1.3.1 promises_1.0.1 xml2_1.2.2
[53] generics_0.0.2 vctrs_0.2.2 sandwich_2.5-1 tools_3.6.3
[57] glue_1.3.1 hms_0.5.3 yaml_2.2.0 colorspace_1.4-1
[61] rvest_0.3.5 knitr_1.26 haven_2.2.0