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mitchelloharawildmitchelloharawild
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Re-render 2025-3 issue
1 parent 94fa735 commit 473e330

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9 files changed

+6960
-4437
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9 files changed

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-4437
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‎_articles/RJ-2025-023/RJ-2025-023.R‎

Lines changed: 23 additions & 20 deletions
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
11
# Generated by `rjournal_pdf_article()` using `knitr::purl()`: do not edit by hand
22
# Please edit RJ-2025-023.Rmd to modify this file
33

4-
## ----setup, include=FALSE-----------------------------------------------------
4+
## ----setup, include=FALSE---------------------------------------------------------------------------------------------------------------------
55
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
66
library(dplyr)
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library(ggplot2)
@@ -14,28 +14,31 @@ library(theftdlc)
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knitr::include_graphics("figures/workflow-graphic_ecosystem-final.png")
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1616

17-
## ----echo=FALSE---------------------------------------------------------------
17+
## ----echo=FALSE-------------------------------------------------------------------------------------------------------------------------------
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temp <- tempfile()
19-
download.file("https://github.com/hendersontrent/bonn-eeg-data/raw/refs/heads/main/INP_Bonn_EEG.txt.zip",temp)
19+
download.file(
20+
"https://github.com/hendersontrent/bonn-eeg-data/raw/refs/heads/main/INP_Bonn_EEG.txt.zip",
21+
temp
22+
)
2023
bonn_eeg <- read.table(unz(temp, "INP_Bonn_EEG.txt"))
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unlink(temp)
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bonn_eeg <- as_tsibble(bonn_eeg, key = c("id", "group"), index = "timepoint")
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25-
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE----------------------
28+
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE--------------------------------------------------------------------------------------
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# install_python_pkgs(venv = "theft-eco-py", python = "/usr/local/bin/python3.10")
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# init_theft("theft-eco-py")
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## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE----------------------
33+
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE--------------------------------------------------------------------------------------
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# all_features <- calculate_features(
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# data = bonn_eeg,
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# feature_set = c("catch22", "feasts", "tsfeatures",
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# "tsfresh", "tsfel", "kats"),
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# use_compengine = FALSE, catch24 = TRUE)
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38-
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE----------------------
41+
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE--------------------------------------------------------------------------------------
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# all_features_msd <- calculate_features(
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# data = bonn_eeg,
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# feature_set = c("catch22", "feasts", "tsfeatures",
@@ -44,7 +47,7 @@ bonn_eeg <- as_tsibble(bonn_eeg, key = c("id", "group"), index = "timepoint")
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# use_compengine = FALSE)
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47-
## ----echo=TRUE----------------------------------------------------------------
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## ----echo=TRUE--------------------------------------------------------------------------------------------------------------------------------
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files <- c("all_features", "mf_results", "feature_classifiers")
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for(f in files){
@@ -63,7 +66,7 @@ plot(all_features,
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clust_method = "average")
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## ----echo = TRUE, eval = FALSE------------------------------------------------
69+
## ----echo = TRUE, eval = FALSE----------------------------------------------------------------------------------------------------------------
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# low_dim_calc <- project(all_features,
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# method = "MinMax",
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# low_dim_method = "tSNE",
@@ -82,7 +85,7 @@ plot(low_dim_calc) +
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guides(colour = guide_legend(nrow = 2, byrow = TRUE))
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85-
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE----------------------
88+
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE--------------------------------------------------------------------------------------
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# mf_results <- classify(
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# data = all_features,
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# by_set = TRUE,
@@ -91,7 +94,7 @@ plot(low_dim_calc) +
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# use_null = TRUE)
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## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE----------------------
97+
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE--------------------------------------------------------------------------------------
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# rbfClassifier <- function(formula, data){
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# mod <- e1071::svm(formula, data = data, kernel = "radial", scale = FALSE,
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# probability = TRUE)
@@ -106,7 +109,7 @@ plot(low_dim_calc) +
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# use_null = TRUE)
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## ----warning=FALSE, message=FALSE, echo=TRUE, eval=TRUE-----------------------
112+
## ----warning=FALSE, message=FALSE, echo=TRUE, eval=TRUE---------------------------------------------------------------------------------------
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set_intervals <- theftdlc::interval(
111114
mf_results,
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metric = "accuracy",
@@ -122,15 +125,15 @@ set_intervals
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plot(set_intervals)
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## ----message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE----------------------
128+
## ----message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE--------------------------------------------------------------------------------------
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# compare_features(mf_results,
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# metric = "accuracy",
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# by_set = TRUE,
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# hypothesis = "null",
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# p_adj = "none")
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133-
## ----echo=FALSE, eval=TRUE----------------------------------------------------
136+
## ----echo=FALSE, eval=TRUE--------------------------------------------------------------------------------------------------------------------
134137
knitr::kable(compare_features(mf_results,
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metric = "accuracy",
136139
by_set = TRUE,
@@ -141,15 +144,15 @@ knitr::kable(compare_features(mf_results,
141144
digits = 3)
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143146

144-
## ----message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE----------------------
147+
## ----message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE--------------------------------------------------------------------------------------
145148
# compare_features(mf_results,
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# metric = "accuracy",
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# by_set = TRUE,
148151
# hypothesis = "pairwise",
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# p_adj = "none")
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151154

152-
## ----echo=FALSE---------------------------------------------------------------
155+
## ----echo=FALSE-------------------------------------------------------------------------------------------------------------------------------
153156
knitr::kable(compare_features(mf_results,
154157
metric = "accuracy",
155158
by_set = TRUE,
@@ -160,32 +163,32 @@ knitr::kable(compare_features(mf_results,
160163
digits = 3)
161164

162165

163-
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE----------------------
166+
## ----message=FALSE, warning=FALSE, eval=FALSE, echo=TRUE--------------------------------------------------------------------------------------
164167
# feature_classifiers <- classify(data = all_features,
165168
# by_set = FALSE,
166169
# train_size = 0.8,
167170
# n_resamples = 100,
168171
# use_null = TRUE)
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171-
## ----message=FALSE, warning=FALSE, echo=TRUE----------------------------------
174+
## ----message=FALSE, warning=FALSE, echo=TRUE--------------------------------------------------------------------------------------------------
172175
feature_vs_null <- compare_features(feature_classifiers,
173176
by_set = FALSE,
174177
hypothesis = "null",
175178
n_workers = 6)
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177180

178-
## ----message=FALSE, warning=FALSE, echo=TRUE----------------------------------
181+
## ----message=FALSE, warning=FALSE, echo=TRUE--------------------------------------------------------------------------------------------------
179182
top_40 <- feature_vs_null |>
180183
dplyr::slice_max(feature_mean, n = 40)
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183-
## ----echo=TRUE, eval=FALSE----------------------------------------------------
186+
## ----echo=TRUE, eval=FALSE--------------------------------------------------------------------------------------------------------------------
184187
# top_40 |>
185188
# top_n(feature_mean, n = 20)
186189

187190

188-
## ----echo=FALSE---------------------------------------------------------------
191+
## ----echo=FALSE-------------------------------------------------------------------------------------------------------------------------------
189192
if(knitr::is_html_output()){
190193
knitr::kable(top_40 |>
191194
dplyr::select(-c(names, original_names, feature_set, metric)) |>

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