Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width=6,
fig.height=4
)
# Legge denne i YAML på toppen for å skrive ut til tex
#output:
# pdf_document:
# keep_tex: true
# Original:
# rmarkdown::html_vignette:
# toc: true
## ----setup--------------------------------------------------------------------
# Start the multiblock R package
library(multiblock)
## -----------------------------------------------------------------------------
# Find directory extdata from the multiblock package
mbdir <- system.file('extdata/', package = "multiblock")
# Comma separated values, row names in first column
meta_data <- read.csv(paste0(mbdir, "/meta_data.csv"), row.names = 1)
# If working directory matches file location:
# meta_data <- read.csv('meta_data.csv', row.names = 1)
meta_data
# Semi-colon separated values (locales where the decimal point is comma),
# no row names
proteins <- read.csv2(paste0(mbdir, "/proteins.csv"))
proteins
# Blank space separated data without labels
genes <- read.table(paste0(mbdir, "/genes.dat"))
genes
## -----------------------------------------------------------------------------
# Column-centring
genes_centred <- scale(genes, scale=FALSE)
colMeans(genes_centred) # Check mean values
# Autoscaling
genes_scaled <- scale(genes)
apply(genes_scaled, 2, sd) # Check standard deviations
# SNV (transpose, autoscale, re-transpose)
genes_snv <- t(scale(t(genes)))
apply(genes_snv, 1, sd) # Check standard deviations
## -----------------------------------------------------------------------------
# Default is sum coding
dummycode(meta_data$colour)
# Treatment coding
dummycode(meta_data$colour, "contr.treatment")
# Full dummy-coding (rank deficient)
dummycode(meta_data$colour, drop = FALSE)
# Replace categorical with dummy-coded, use I() to index by common name
meta_data2 <- meta_data
meta_data2$colour <- I(dummycode(meta_data$colour, drop = FALSE))
meta_data2
meta_data2$colour
## -----------------------------------------------------------------------------
# Direct approach
blocks1 <- list(meta = meta_data2, proteins = proteins, genes = genes)
# Two-step approach
blocks2 <- list(meta_data2, proteins, genes)
names(blocks2) <- c('meta', 'proteins', 'genes')
# Same result
identical(blocks1, blocks2)
# Access by name or number
blocks1[['meta']]
blocks2[[1]]
## -----------------------------------------------------------------------------
# Construct block data.frame from list
df1 <- block.data.frame(blocks1)
# Construct block data.frame from data.frame:
# First merge blocks into data.frame
my_data <- cbind(meta_data2, proteins, genes)
# Then construct block data.frame using named
# list of indexes
df2 <- block.data.frame(my_data, block_inds =
list(meta = 1:2, proteins = 3:5, genes = 6:8))
# Same result
identical(df1,df2)
# Access by name or number
df1[[2]]
df2[['proteins']]
df1[c(1,3)]
df1[-2]
df2[c('proteins','genes')]
# Use with formula interface (see other vignettes)
# sopls(meta ~ proteins + genes, data = df1)
# Use with single list interface (see other vignettes)
# mfa(df1[c(1,3)], ncomp = 3)
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