rrpp.data.frame: Create a data frame for lm.rrpp analysis

In RRPP: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure

Create a data frame for lm.rrpp analysis

Description

Create a data frame for lm.rrpp analysis, when covariance or distance matrices are used

Usage

rrpp.data.frame(...)

Arguments

...	Components (objects) to combine in the data frame.

Details

This function is not much different than data.frame but is more flexible to allow distance matrices and covariance matrices to be included. Essentially, this function creates a list, much like an object of class data.frame is also a list. However, rrpp.data.frame is less concerned with coercing the list into a matrix and more concerned with matching the number of observations (n). It is wise to use this function with any lm.rrpp analysis so that lm.rrpp does not have to search the global environment for needed data.

It is assumed that multiple data sets for the same subjects are in the same order.

See lm.rrpp for examples.

Author(s)

Michael Collyer

Examples

# Why use a rrpp.data.frame?
y <- matrix(rnorm(30), 10, 3)
x <- rnorm(10)
df <- data.frame(x = x, y = y)
df
rdf <- rrpp.data.frame(x = x, y = y)
rdf # looks more like a list

is.list(df)
is.list(rdf)

d <- dist(y) # distance matrix as data

# One can try this but it will result in an error
# df <- data.frame(df, d = d) 
rdf <- rrpp.data.frame(rdf, d = d) # works

fit <- lm.rrpp(d ~ x, data = rdf, iter = 99)
summary(fit)

RRPP documentation built on June 22, 2024, 6:45 p.m.