nullabor (version 0.3.5)

distmet: Empirical distribution of the distance

Description

The empirical distribution of the distance measures is calculated based on the mean distance of each of the null plots from the other null plots in a lineup. At this moment this method works only for null_permute method. This function helps get some assessment of whether the actual data plot is very different from the null plots.

Usage

distmet(lineup.dat, var, met, method, pos, repl = 1000, dist.arg = NULL,
  m = 20)

Arguments

lineup.dat

lineup data

var

a vector of names of the variables to be used

met

distance metric needed to calculate the distance as a character

method

method for generating null data sets

pos

position of the observed data in the lineup

repl

number of sets of null plots selected to obtain the distribution; 1000 by default

dist.arg

a list or vector of inputs for the distance metric met; NULL by default

m

the number of plots in the lineup; m = 20 by default

Value

lineup has the data used for the calculations

null_values contains new null samples from which to compare nulls in lineup

diff difference in distance between nulls and actual data and that of the null that is most different from other nulls. A negative value means that the actual data plot is similar to the null plots.

closest list of the five closest nulls to the actual data plot

pos position of the actual data plot in the lineup

Examples

# NOT RUN {
# Each of these examples uses a small number of nulls (m=8), and a small number of
# repeated sampling from the null distribution (repl=100), to make it faster to run.
# In your own examples you should think about increasing each of these, at least to the defaults.
# }
# NOT RUN {
if (require('dplyr')) {
  d <- lineup(null_permute('mpg'), mtcars, pos = 1)
  dd <- distmet(d, var = c('mpg', 'wt'),
    'reg_dist', null_permute('mpg'), pos = 1, repl = 100, m = 8)
  distplot(dd, m=8)
}
# }
# NOT RUN {
# }
# NOT RUN {
d <- lineup(null_permute('mpg'), mtcars, pos=4, n=8)
library(ggplot2)
ggplot(d, aes(mpg, wt)) + geom_point() + facet_wrap(~ .sample, ncol=4)
if (require('dplyr')) {
  dd <- distmet(d, var = c('mpg', 'wt'), 'bin_dist', null_permute('mpg'),
    pos = 4, repl = 100, dist.arg = list(lineup.dat = d, X.bin = 5,
    Y.bin = 5), m = 8)
  distplot(dd, m=8)
}
# }
# NOT RUN {
# Example using bin_dist
# }
# NOT RUN {
if (require('dplyr')) {
  d <- lineup(null_permute('mpg'), mtcars, pos = 1)
  library(ggplot2)
  ggplot(d, aes(mpg, wt)) + geom_point() + facet_wrap(~ .sample, ncol=5)
  dd <- distmet(d, var = c('mpg', 'wt'),
    'bin_dist', null_permute('mpg'), pos = 1, repl = 500,
    dist.arg = list(lineup.dat = d, X.bin = 5, Y.bin = 5))
  distplot(dd)
}
# }
# NOT RUN {
# Example using uni_dist
# }
# NOT RUN {
mod <- lm(wt ~ mpg, data = mtcars)
resid.dat <- data.frame(residual = mod$resid)
d <- lineup(null_dist('residual', dist = 'normal'), resid.dat, pos=19)
ggplot(d, aes(residual)) + geom_histogram(binwidth = 0.25) + facet_wrap(~ .sample, ncol=5)
if (require('dplyr')) {
  dd <- distmet(d, var = 'residual', 'uni_dist', null_dist('residual',
    dist = 'normal'), pos = 19, repl = 500)
  distplot(dd)
}
# }

Run the code above in your browser using DataCamp Workspace