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updated R stats course material

master
chassenr 1 year ago
parent
commit
532873b33a
  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

@ -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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R_statistics_2022/R_stats_slides.pdf

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