### updated R stats course material

master
chassenr 1 year ago
parent
commit
532873b33a
3 changed files with 4 additions and 4 deletions
1. 8
R_statistics_2022/R_stats_script.R
2. BIN
R_statistics_2022/R_stats_slides.pdf
3. BIN
R_statistics_2022/machine_learning_slides.pdf

#### 8 R_statistics_2022/R_stats_script.R Unescape Escape View File

 `@ -868,7 +868,7 @@ plot(` `# To use spiec-easi for network inference, we will need to prepare the data into a specific R object type.` `# Also, since it uses clr-transformation to deal with compositionality, we need to use the filtered data set` `# that was already used in the RDA` `physeqo_pro <-phyloseq(` `physeqo_pro <- phyloseq(` ` otu_table(otu_pro_filt, taxa_are_rows = T),` ` tax_table(as.matrix(tax_pro[rownames(otu_pro_filt), ]))` `)` `@ -1001,7 +1001,7 @@ cluster.pick.rep <- lapply(` `names(cluster.pick.rep) <- paste0("M", 1:length(cluster.pick.rep))` ``` ``` `# Look e.g. at taxonomic composition` `i = 2` `i = 1` `View(tax_pro[cluster.pick.rep[[i]], ])` ``` ``` `# Correlation of eigengenes with environmental parameters` `@ -1038,9 +1038,9 @@ plot(` ` to = c(1, 10)` ` ),` ` vertex.label = NA, ` ` vertex.color = ifelse(node_stats\$degree > 15, "red", "blue"),` ` # vertex.color = ifelse(node_stats\$degree > 15, "red", "blue"),` ` # vertex.color = ifelse(node_stats\$closeness > 0.045, "red", "blue"),` ` # vertex.color = ifelse(node_stats\$betweenness > 5000, "red", "blue"),` ` vertex.color = ifelse(node_stats\$betweenness > 5000, "red", "blue"),` ` edge.color = "grey", ` ` edge.width = abs(E(adj.g.pos)\$weight) * 5,` ` main = ""`

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