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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     NA
anova(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 ' ' 1

Reproduce 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"))
Analysis of variance for plant biomass (log transformed) in the absence of insects.
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