80 lines
2.4 KiB
R
80 lines
2.4 KiB
R
library(sbm)
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library(ggplot2)
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library(readr)
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source("load-full.R")
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sdCol <- function(matrix) {
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sapply(seq_len(ncol(matrix)), function(col) sd(matrix[, col]))
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}
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net_table <- otu_table(cpd_phyloseq)
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rm(cpd_phyloseq)
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write.csv(net_table, "data/otu_matrix.csv")
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zip("data/otu_matrix.csv.zip", "data/otu_matrix.csv")
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unlink("data/otu_matrix.csv")
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# Ne tourne pas, probablement gros calculs matriciel
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# res <- estimateBipartiteSBM(netMat = net_table, dimLabels = c("OTU", "Sample"), model = "poisson")
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nb_inter <- sum(net_table > 0)
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all_possible_inter <- nrow(net_table) * ncol(net_table)
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(density <- nb_inter / all_possible_inter)
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t_net_table <- t(net_table)
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std_otu <- sdCol(t_net_table)
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mean_otu <- rowMeans(net_table)
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stats_df <- data.frame(otu = rownames(net_table), mean = mean_otu, std = std_otu, var = std_otu^2)
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ggplot(stats_df, aes(y = otu, x = mean)) +
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geom_point() +
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geom_errorbarh(aes(xmin = mean - std, xmax = mean + std), orientation = "y") +
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theme(
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axis.title.y = element_blank(),
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axis.text.y = element_blank(),
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axis.ticks.y = element_blank()
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)
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ggplot(stats_df, aes(x = mean, y = var)) +
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geom_line() +
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geom_point() +
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geom_abline(slope = 1, color = "red") +
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scale_y_log10() +
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scale_x_log10() +
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ggtitle("OTU for log(mean) = log(var)")
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ggplot(stats_df, aes(x = mean, y = var / mean)) +
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geom_line() +
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geom_point() +
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geom_hline(yintercept = 1, color = "red") +
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ggtitle("OTU for f(mean) = var/mean = 1")
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ggplot(stats_df, aes(x = 1 / mean, y = var / mean^2)) +
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geom_line() +
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geom_point() +
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geom_abline(slope = 1, color = "red") +
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scale_y_log10() +
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scale_x_log10() +
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ggtitle("OTU for log(1/mean) = log(var/mean^2)")
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lambdas <- seq(1, 100)
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realization_pois <- sapply(lambdas, rpois, n = 1000)
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r_pois_df <- data.frame(mean = colMeans(realization_pois), var = sdCol(realization_pois)^2)
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ggplot(r_pois_df, aes(x = mean, y = var)) +
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geom_line() +
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geom_point() +
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geom_abline(slope = 1, color = "red") +
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ggtitle("Poisson for mean = var")
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ggplot(r_pois_df, aes(x = mean, y = var / mean)) +
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geom_line() +
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geom_point() +
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geom_hline(yintercept = 1, color = "red") +
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ggtitle("Poisson for f(mean) = var/mean = 1")
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ggplot(r_pois_df, aes(x = 1 / mean, y = var / mean^2)) +
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geom_line() +
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geom_point() +
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geom_abline(slope = 1, color = "red") +
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ggtitle("Poisson for 1/lambda = var/mean^2")
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