mora_plsc: PLSC with inferences and graphs.

In LukeMoraglia/moRa: Multivariate Analysis and Graphs

Description

PLSC with inferences and graphs.

Usage

mora_plsc(
  data1,
  data2,
  design = NULL,
  make_design_nominal = TRUE,
  col4obs = "olivedrab3",
  col4group = "olivedrab3",
  center1 = TRUE,
  center2 = TRUE,
  scale1 = "SS1",
  scale2 = "SS1",
  inference = TRUE,
  important = FALSE,
  corrplot = TRUE
)

Arguments

data1	A numerical data table with observations on rows and variables on columns.
data2	A numerical data table with the same obs on row and different vars on columns.
design	(default = NULL) A design vector or matrix for the rows
make_design_nominal	(default = TRUE) If TRUE, design is a vector. If FALSE, design is a dummy-coded matrix.
col4obs	(default = "olivedrab3") A single color or vector of colors whose length is equal to nrow(data1).
col4group	(default = "olivedrab3") A single color or vector of colors whose length is the number of groups in design.
center1	(default = TRUE) Whether to center variables in data1.
center2	(default = TRUE) Whether to center variables in data2.
scale1	(default = "SS1") Whether to scale variables in data1.
scale2	(default = "SS1") Whether to scale variables in data2.
inference	(default = TRUE) When design contains 2 or more groups, computes CI and TI on observations. FALSE if no groups.
important	(default = FALSE) If TRUE, graphs have only the important saliences/bootstrap ratios.
corrplot	(default = TRUE) Whether to show a correlation matrix plot

LukeMoraglia/moRa documentation built on July 31, 2020, 5:41 a.m.