This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer.
Since I’ve been working so much with GAMs for this project, I decided to read sections of Simon Wood’s book, Generalized additive models: an introduction with R more thoroughly.
This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer.
A major goal of my postdoc project is to determine whether drought has an effect on plant vital rates (growth, survival, reproduction, recruitment).
This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. DLNMs themselves may not be that computationally expensive, but when combined with random effects and other smoothers, and a large-ish dataset, I’ve noticed gam() being painfully slow.
Welcome / Disclaimer This is the beginning of a series of blog posts where I publicly stumble my way through figuring out some confusing, complicated, and, frankly, cutting-edge modeling and statistics.
This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. The dlnm package The dlnm package offers two ways of fitting crossbasis functions: an “internal” and an “external” method.
What’s a matrix-column? The tibble package in R allows for the construction of “tibbles”—a sort of “enhanced” data frame. Most of these enhancements are fairly mundane, such as better printing in the console and not modifying column names.
Taking a potentially continuous treatment, binning it into categories, and doing ANOVA results in reduced statistical power and complicated interpretation. Yet, as a graduate student, I was advised to bin continuous treatment variables into categories multiple times by different people.
If your data is just 1’s and 0’s, it can be difficult to visualize alongside a best-fit line from a logistic regression.
Even with transparency, the overplotted data points just turn into a smear on the top and bottom of your plot, adding little information.
If you’ve ever tried to look “under the hood” of an R function, you know that sometimes it can be tricky to figure out what’s going on, especially if you use R more as a statistical tool than as a programming language.
A wide range of chemical information is freely available online, including identifiers, experimental and predicted chemical properties. However, these data are scattered over various data sources and not easily accessible to researchers. Manual …