Publications by Joe Brown
Criterion Sensitivity Testing in Matilda
library(tidyverse) library(matilda) gcp_data <- read.csv("data/gcp_data.csv", stringsAsFactors = F) ocean_uptake_df <- gcp_data %>% select(Year, ocean_sink) %>% rename(ocean_uptake = ocean_sink, year = Year) %>% filter(year > 1799) %>% na.omit() criterion_ocean_uptake <- new_criterion(var = OCEAN_UPTAKE(), ...
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Matilda Visualization pt. 3: Probability Distribution Plot
Background Once you have produced a result from matilda there are a number of useful ways to visualize the data. We will cover some of those in a series of tutorials to help you plot Matilda results, including: Spaghetti plot - these are plots aimed to visualize the trajectories of all model iterations and the weights associated with each. Pr...
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Matilda Visualization pt. 2: Probability Bar Plot
Background Once you have produced a result from matilda there are a number of useful ways to visualize the data. We will cover some of those in a series of tutorials to help you plot Matilda results, including: Spaghetti plot - these are plots aimed to visualize the trajectories of all model iterations and the weights associated with each. Pr...
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Matilda Visualization pt. 1: Spaghetti Plot
Background Once you have produced a result from matilda there are a number of useful ways to visualize the data. We will cover some of those in a series of tutorials to help you plot Matilda results, including: Spaghetti plot - these are plots aimed to visualize the trajectories of all model iterations and the weights associated with each. Pr...
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Single-node Parallel Computing with Matilda
Background To this point you have worked through cases for which matilda can be used to compute probabilistic climate projections. A high number of runs (10k+) is often needed to get a stabilized solution that is robust enough to be used for inference. In these cases you may have noticed that using matilda functions (specifically iterate_model...
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Using apply() with Matilda Functions
Background There are cases in which we may want to run matilda functions on multiple objects. This could be, for example, across different parameter sets or across different scenarios. In this tutorial we will briefly address how to conduct this type of analysis easily using lapply() and Map() functions. We will go through these processes: Run...
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