coda4microbiome (version 0.2.4)

explore_lr_longitudinal: explore_lr_longitudinal

Description

Explores the association of summary (integral) of each log-ratio trajectory with the outcome. Summarizes the importance of each variable (taxa) as the aggregation of the association measures of those log-ratios involving the variable. The output includes a plot of the association of the log-ratio with the outcome where the variables (taxa) are ranked by importance

Usage

explore_lr_longitudinal(
  x,
  y,
  x_time,
  subject_id,
  ini_time,
  end_time,
  showPlots = FALSE,
  decreasing = TRUE,
  covar = NULL,
  shownames = FALSE,
  maxrow = 15,
  maxcol = 15,
  showtitle = TRUE,
  mar = c(0, 0, 1, 0)
)

Value

list with "max log-ratio","names max log-ratio","order of importance","name of most important variables","association log-ratio with y","top log-ratios plot"

Arguments

x

abundance matrix or data frame in long format (several rows per individual)

y

outcome (binary); data type: numeric, character or factor vector

x_time

observation times

subject_id

subject id

ini_time

initial time to be analyzed

end_time

end time to be analyzed

showPlots

if TRUE, shows the plot (default = FALSE)

decreasing

order of importance (default = TRUE)

covar

data frame with covariates (default = NULL)

shownames

if TRUE, shows the names of the variables in the rows of the plot (default = FALSE)

maxrow

maximum number of rows to display in the plot (default = 15)

maxcol

maximum number of columns to display in the plot (default = 15)

showtitle

logical, if TRUE, shows the title of the plot (default = TRUE)

mar

mar numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot (default mar=c(0,0,1,0))

Author

M. Calle - T. Susin

Examples

set.seed(123) # to reproduce the results

data(ecam_filtered, package = "coda4microbiome")   # load the data

x=x_ecam # microbiome abundance
x_time = metadata$day_of_life    # observation times
subject_id = metadata$studyid   # subject id
y= metadata$diet           # diet ("bd"= breast diet, "fd"=formula diet)
ini_time = 0
end_time = 90

ecam_logratios<-explore_lr_longitudinal(x,y,x_time,subject_id,ini_time,end_time)