TL;DR A sample table from the full results for data that look like this
Table 1: Coverage of 95% bca CIs. parameter n=5 n=10 n=20 n=40 n=80 means Control 81.4 87.6 92.2 93.0 93.6 b4GalT1-/- 81.3 90.2 90.8 93.0 93.8 difference in means diff 83.
Setup Import Models as nested using “tank” nested within “room” as two random intercepts (using lme4 to create the combinations) A safer (lme4) way to create the combinations of “room” and “tank”: as two random intercepts using “tank2” Don’t do this This is a skeletal post to show the equivalency of different ways of thinking about “nested” factors in a mixed model. The data are measures of life history traits in lice that infect salmon.
If I’m going to evaluate the widespread use of t-tests/ANOVAs on count data in bench biology then I’d like to know what these data look like, specifically the shape (“overdispersion”) parameter.
Set up library(ggplot2) library(readxl) library(ggpubr) library(cowplot) library(plyr) #mapvalues library(data.table) # glm packages library(MASS) library(pscl) #zeroinfl library(DHARMa) library(mvabund) data_path <- "../data" # notebook, console source("../../../R/clean_labels.R") # notebook, console Data from The enteric nervous system promotes intestinal health by constraining microbiota composition Import read_enteric <- function(sheet_i, range_i, file_path, wide_2_long=TRUE){ dt_wide <- data.
Update to the earlier post, which was written in response to my own thinking about how to teach stastics to experimental biologists working in fields that are dominated by hypothesis testing instead of estimation. That is, should these researchers learn GLMs or is a t-test on raw or log-transformed data on something like count data good enough – or even superior? My post was written without the benefit of either [Ives](Ives, Anthony R.
This post has been updated.
A skeleton simulation of different strategies for NHST for count data if all we care about is a p-value, as in bench biology where p-values are used to simply give one confidence that something didn’t go terribly wrong (similar to doing experiments in triplicate – it’s not the effect size that matters only “we have experimental evidence of a replicable effect”).
tl;dr - At least for Type I error at small \(n\), log(response) and Wilcoxan have the best performance over the simulation space.
[updated to include additional output from MuMIn, BMA, and BAS]
This post is a follow up to my inital post, which was written as as a way for me to pen my mental thoughts on the recent review of “Model averaging in ecology: a review of Bayesian, information‐theoretic and tactical approaches for predictive inference”. It was also written without contacting and discussing the issue with the authors. This post benefits from a series of e-mails with the lead author Carsten Dormann and the last author Florian Hartig.
