Version:	0.3.15
Description:	Tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.
Title:	Tools for Graphical Inference
Maintainer:	Di Cook <dicook@monash.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/dicook/nullabor
BugReports:	https://github.com/dicook/nullabor/issues
Imports:	MASS, moments, fpc, ggplot2, dplyr, purrr, tidyr, tibble, magrittr, stats
Suggests:	forecast, viridis, knitr
Depends:	R (≥ 4.1.0)
LazyData:	true
Type:	Package
LazyLoad:	false
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2025-02-10 04:43:05 UTC; cookd
Author:	Hadley Wickham [aut, ctb], Niladri Roy Chowdhury [aut, ctb], Di Cook [aut, cre], Heike Hofmann [aut, ctb], Måns Thulin [aut, ctb]
Repository:	CRAN
Date/Publication:	2025-02-10 05:40:02 UTC

Add true data into data frame containing null data sets.

Description

Add true data into data frame containing null data sets.

Usage

add_true(samples, true, pos)

Conversion rate of 1 Australian Doller (AUD) to 1 US Dollar

Description

The dataset consists of the daily exchange rates of 1 Australian Dollar to 1 US Dollar between Jan 9 2018 and Feb 21 2018.

Binned Distance

Description

Data X is binned into X.bin bins in x-direction and Y.bins in y-direction. The number of points in each cell is then counted. Same is done for data PX. An euclidean distance is calculated between the number of points in each cell between X and PX.

Usage

bin_dist(X, PX, lineup.dat = lineup.dat, X.bin = 5, Y.bin = 5)

Arguments

Value

distance between X and PX

Examples

with(mtcars, bin_dist(data.frame(wt, mpg), data.frame(sample(wt), mpg),
lineup.dat = NULL))

Distance based on side by side Boxplots

Description

Assuming that data set X consists of a categorical group variable a numeric value, a summary of the first quartile, median and third quartile of this value is calculated for each group. The extent (as absolute difference) of the minimum and maximum value across groups is computed for first quartile, median and third quartile. Same is done for data PX. Finally an euclidean distance is calculated between the absolute differences of X and PX.

Usage

box_dist(X, PX)

Arguments

Value

distance between X and PX

Examples

if(require('dplyr')) {
  with(mtcars,
    box_dist(data.frame(as.factor(am), mpg),
    data.frame(as.factor(sample(am)), mpg))
  )
}

Calculating the difference between true plot and the null plot with the maximum distance.

Description

Distance metric is used to calculate the mean distance between the true plot and all the null plots in a lineup. The difference between the mean distance of the true plot and the maximum mean distance of the null plots is calculated.

Usage

calc_diff(lineup.dat, var, met, pos, dist.arg = NULL, m = 20)

Arguments

Value

difference between the mean distance of the true plot and the maximum mean distance of the null plots

Examples

if(require('dplyr')){
lineup.dat <- lineup(null_permute('mpg'), mtcars, pos = 1)
calc_diff(lineup.dat, var = c('mpg', 'wt'), met = 'bin_dist',
dist.arg = list(lineup.dat = lineup.dat, X.bin = 5, Y.bin = 5), pos = 1, m = 8)}

if(require('dplyr')){
calc_diff(lineup(null_permute('mpg'), mtcars, pos = 1), var = c('mpg', 'wt'), met = 'reg_dist',
dist.arg = NULL, pos = 1, m = 8)}

Calculating the mean distances of each plot in the lineup.

Description

Distance metric is used to calculate the mean distance between the true plot and all the null plots in a lineup. The mean distances of each null plot to all the other null plots are calculated. The mean distances are returned for all the plots in the lineup.

Usage

calc_mean_dist(lineup.dat, var, met, pos, dist.arg = NULL, m = 20)

Arguments

Value

the mean distances of each plot in the lineup

Examples

if(require('dplyr')){
calc_mean_dist(lineup(null_permute('mpg'), mtcars, pos = 1), var = c('mpg', 'wt'),
met = 'reg_dist', pos = 1, m = 10)}

Use decrypt to reveal the position of the real data.

Description

The real data position is encrypted by the lineup function, and writes this out as a text string. Decrypt, decrypts this text string to reveal which where the real data is.

Usage

decrypt(...)

Arguments

Examples

decrypt('0uXR2p rut L2O2')

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

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

# 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)
}

## End(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)
}

## End(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)
}

## End(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)
}

## End(Not run)

Plotting the distribution of the distance measure

Description

The permutation distribution of the distance measure is plotted with the distances for the null plots. Distance measure values for the null plots and the true plot are overlaid.

Usage

distplot(dat, m = 20)

Arguments

Examples

## 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)
  distplot(distmet(d, var = c('mpg', 'wt'), 'reg_dist', null_permute('mpg'),
    pos = 1, repl = 100, m = 8), m = 8)
}

## End(Not run)

Polls and election results from the 2012 US Election

Description

Polls and election results from the 2012 US Election

Format

A list with two data frames: polls is a data frame of 51 rows and 4 variables

State

State name

Electoral.vote

Number of electoral votes in the 2012 election

Margin

Margin between the parties with the highest number of votes and second highest number of votes. These margins are based on polls.

Democrat

logical vector True, if the democratic party is the majority party in this state.

election is a data frame of 51 rows and 5 variables

State

State name

Candidate

character string of the winner: Romney or Obama

Electoral.vote

Number of electoral votes in the 2012 election

Margin

Margin between the parties with the highest number of votes and second highest number of votes. These margins are based on the actual election outcome

Democrat

logical vector True, if the democratic party is the majority party in this state.

Find plot data. If data is not specified, this function will attempt to find the data corresponding to the last ggplot2 created or displayed. This will work in most situations where you are creating the plot and immediately displaying it, but may not work in other situations. In those cases, please specify the data explicitly.

Description

Find plot data. If data is not specified, this function will attempt to find the data corresponding to the last ggplot2 created or displayed. This will work in most situations where you are creating the plot and immediately displaying it, but may not work in other situations. In those cases, please specify the data explicitly.

Usage

find_plot_data(data)

Los Angeles Lakers play-by-play data.

Description

Play by play data from all games played by the Los Angeles lakers in the 2008/2009 season.

The line-up protocol.

Description

In this protocol the plot of the real data is embedded amongst a field of plots of data generated to be consistent with some null hypothesis. If the observe can pick the real data as different from the others, this lends weight to the statistical significance of the structure in the plot. The protocol is described in Buja, Cook, Hofmann, Lawrence, Lee, Swayne, Wickham (2009) Statistical inference for exploratory data analysis and model diagnostics, Phil. Trans. R. Soc. A, 367, 4361-4383.

Usage

lineup(method, true = NULL, n = 20, pos = sample(n, 1), samples = NULL)

Arguments

Details

Generate n - 1 null datasets and randomly position the true data. If you pick the real data as being noticeably different, then you have formally established that it is different to with p-value 1/n.

Examples

library(ggplot2)
ggplot(lineup(null_permute('mpg'), mtcars), aes(mpg, wt)) +
  geom_point() +
  facet_wrap(~ .sample)
ggplot(lineup(null_permute('cyl'), mtcars),
       aes(mpg, .sample, colour = factor(cyl))) +
       geom_point()

Check distributional assumptions using histograms and the lineup protocol.

Description

This function is used to quickly create lineup plots to check distributional assumptions using histograms with kernel density estimates. The null hypothesis is that the data follows the distribution specified by the dist argument. In the lineup protocol the plot of the real data is embedded amongst a field of plots of data generated to be consistent with some null hypothesis. If the observer can pick the real data as different from the others, this lends weight to the statistical significance of the structure in the plot. The protocol is described in Buja et al. (2009).

Usage

lineup_histograms(
  data,
  variable,
  dist = NULL,
  params = NULL,
  color_bars = "black",
  fill_bars = "grey",
  color_lines = "brown3"
)

Arguments

Details

19 null datasets are plotted together the the true data (randomly positioned) If you pick the real data as being noticeably different, then you have formally established that it is different to with p-value 0.05.

Run the decrypt message printed in the R Console to see which plot represents the true data.

Value

a ggplot

References

Buja, Cook, Hofmann, Lawrence, Lee, Swayne, Wickham. (2009). Statistical inference for exploratory data analysis and model diagnostics, Phil. Trans. R. Soc. A, 367, 4361-4383.

See Also

null_dist

Examples

data(tips)
lineup_histograms(tips, "total_bill", dist = "normal") # Normal distribution

# Some distributions require that the parameters be specified:
lineup_histograms(tips, "size", dist = "binomial", params = list(size = 6, p = 0.3))

# Style the plot using color settings and ggplot2 functions:
lineup_histograms(tips, "total_bill",
                  dist = "gamma",
                  color_bars = "steelblue",
                  color_lines = "magenta") +
    ggplot2::theme_minimal()

Check distributional assumptions using Q-Q plots and the lineup protocol.

Description

This function is used to quickly create lineup plots to check distributional assumptions using Q-Q plots. The null hypothesis is that the data follows the distribution specified by the dist argument. In the lineup protocol the plot of the real data is embedded amongst a field of plots of data generated to be consistent with some null hypothesis. If the observer can pick the real data as different from the others, this lends weight to the statistical significance of the structure in the plot. The protocol is described in Buja et al. (2009).

Usage

lineup_qq(
  data,
  variable,
  dist = NULL,
  params = NULL,
  color_points = "black",
  color_lines = "brown3",
  alpha_points = 0.5
)

Arguments

Details

19 null datasets are plotted together the the true data (randomly positioned) If you pick the real data as being noticeably different, then you have formally established that it is different to with p-value 0.05.

Run the decrypt message printed in the R Console to see which plot represents the true data.

Value

a ggplot

References

Buja, Cook, Hofmann, Lawrence, Lee, Swayne, Wickham. (2009). Statistical inference for exploratory data analysis and model diagnostics, Phil. Trans. R. Soc. A, 367, 4361-4383.

See Also

null_dist

Examples

data(tips)
lineup_qq(tips, "total_bill", dist = "normal") # Normal distribution
lineup_qq(tips, "total_bill", dist = "gamma") # Gamma distribution

# Some distributions require that the parameters be specified:
tips$proportion_tips <- tips$tip/(tips$total_bill+tips$tip)
lineup_qq(tips, "size", dist = "beta", params = list(shape1 = 0.1, shape2 = 0.2))

# Style the plot using color settings and ggplot2 functions:
lineup_qq(tips, "total_bill",
          dist = "gamma",
          color_points = "chocolate",
          color_lines = "cyan",
          alpha_points = 0.25) +
    ggplot2::theme_minimal()

Compare residual plots of a fitted model to plots of null residuals.

Description

This function is used to quickly create lineup version of the residual plots created by plot.lm and ggfortify::autoplot.lm; see Details for descriptions of these plots. In the lineup protocol the plot of the real data is embedded amongst a field of plots of data generated to be consistent with some null hypothesis. If the observer can pick the real data as different from the others, this lends weight to the statistical significance of the structure in the plot. The protocol is described in Buja et al. (2009).

Usage

lineup_residuals(
  model,
  type = 1,
  method = "rotate",
  color_points = "black",
  color_trends = "blue",
  color_lines = "brown3",
  alpha_points = 0.5,
  ...
)

Arguments

Details

Four types of plots are available:

1. Residual vs fitted. Null hypothesis: variable is linear combination of predictors.

2. Normal Q-Q plot. Null hypothesis: errors are normal. Always uses method = "pboot" to generate residuals under the null hypothesis.

3. Scale-location. Null hypothesis: errors are homoscedastic.

4. Residuals vs leverage. Used to identify points with high residuals and high leverage, which are likely to have a strong influence on the model fit.

19 null datasets are plotted together the the true data (randomly positioned). If you pick the real data as being noticeably different, then you have formally established that it is different to with p-value 0.05. Run the decrypt message printed in the R Console to see which plot represents the true data.

If the null hypothesis in the type 1 plot is violated, consider using a different model. If the null hypotheses in the type 2 or 3 plots are violated, consider using bootstrap p-values; see Section 8.1.5 of Thulin (2024) for details and recommendations.

Value

a ggplot

References

Buja, Cook, Hofmann, Lawrence, Lee, Swayne, Wickham. (2009). Statistical inference for exploratory data analysis and model diagnostics, Phil. Trans. R. Soc. A, 367, 4361-4383.

Thulin, M. (2024) Modern Statistics with R. Boca Raton: CRC Press. ISBN 9781032512440. https://www.modernstatisticswithr.com/

See Also

null_lm

Examples

data(tips)
x <- lm(tip ~ total_bill, data = tips)
lineup_residuals(x, type = 1) # Residuals vs Fitted
lineup_residuals(x, type = 2, method = "pboot") # Normal Q-Q plot
lineup_residuals(x, type = 4) # Residuals vs Leverage

# Style the plot using color settings and ggplot2 functions:
lineup_residuals(x, type = 3,
                color_points = "skyblue",
                color_trends = "darkorange") +
    ggplot2::theme_minimal()

Generate null data with a specific distribution.

Description

Null hypothesis: variable has specified distribution

Usage

null_dist(var, dist, params = NULL)

Arguments

Value

a function that given data generates a null data set. For use with lineup or rorschach

See Also

null_permute, null_lm

Examples

dframe <- data.frame(x = rnorm(150))
library(ggplot2)
# three histograms of normally distributed values
ggplot(
  data=rorschach(method=null_dist("x", "norm"), n = 3, true=dframe)
  ) +
  geom_histogram(aes(x=x, y=..density..), binwidth=0.25) +
  facet_grid(.~.sample) +
  geom_density(aes(x=x), colour="steelblue", size=1)

# uniform distributions are not as easy to recognize as such
dframe$x = runif(150)
ggplot(
  data=rorschach(method=null_dist("x", "uniform",
                 params=list(min=0, max=1)),
  n = 3, true=dframe)) +
  geom_histogram(aes(x=x, y=..density..), binwidth=0.1) +
  facet_grid(.~.sample) +
  geom_density(aes(x=x), colour="steelblue", size=1)

Computing th distance for the null plots

Description

Computing th distance for the null plots

Usage

null_gen(lineup.dat, null, met, method, m, dist.arg)

Generate null data with null residuals from a model.

Description

Null hypothesis: variable is linear combination of predictors

Usage

null_lm(f, method = "rotate", additional = FALSE, ...)

Arguments

Value

a function that given data generates a null data set. For use with lineup or rorschach

See Also

null_permute, null_dist

Examples

data(tips)
x <- lm(tip ~ total_bill, data = tips)
tips.reg <- data.frame(tips, .resid = residuals(x), .fitted = fitted(x))
library(ggplot2)
ggplot(lineup(null_lm(tip ~ total_bill, method = 'rotate'), tips.reg)) +
  geom_point(aes(x = total_bill, y = .resid)) +
  facet_wrap(~ .sample)

Generate null data by permuting a variable.

Description

Null hypothesis: variable is independent of others

Usage

null_permute(var)

Arguments

Value

a function that given data generates a null data set. For use with lineup or rorschach

See Also

null_lm, null_dist

Examples

data(mtcars)
library(ggplot2)
ggplot(data=rorschach(method=null_permute("mpg"), n = 3, true=mtcars)) +
geom_boxplot(aes(x=factor(cyl), y=mpg, fill=factor(cyl))) +facet_grid(.~.sample) +
theme(legend.position="none", aspect.ratio=1)

Generate null data by simulating from a time series model.

Description

Null hypothesis: data follows a time series model using auto.arima from the forecast package

Usage

null_ts(var, modelfn)

Arguments

Value

a function that given data generates a null data set. For use with lineup or rorschach

See Also

null_model

Examples

require(forecast)
require(ggplot2)
require(dplyr)
data(aud)
l <- lineup(null_ts("rate", auto.arima), aud)
ggplot(l, aes(x=date, y=rate)) + geom_line() +
  facet_wrap(~.sample, scales="free_y") +
  theme(axis.text = element_blank()) +
  xlab("") + ylab("")
l_dif <- l %>%
  group_by(.sample) %>%
  mutate(d=c(NA,diff(rate))) %>%
  ggplot(aes(x=d)) + geom_density() +
  facet_wrap(~.sample)

Finds the number of bins in x and y direction which gives the maximum binned distance.

Description

This function finds the optimal number of bins in both x and y direction which should be used to calculate the binned distance. The binned distance is calculated for each combination of provided choices of number of bins in x and y direction and finds the difference using calc_diff for each combination. The combination for which the difference is maximum should be used.

Usage

opt_bin_diff(
  lineup.dat,
  var,
  xlow,
  xhigh,
  ylow,
  yhigh,
  pos,
  plot = FALSE,
  m = 20
)

Arguments

Value

a dataframe with the number of bins and differences the maximum mean distance of the null plots

Examples

if(require('dplyr')){
opt_bin_diff(lineup(null_permute('mpg'), mtcars, pos = 1), var = c('mpg', 'wt'),
2, 5, 4, 8, pos = 1, plot = TRUE, m = 8)
}

P-value calculations.

Description

These set of functions allow the user to calculate a p-value from the lineup after it has been evaluated by K independent observers. The different functions accommodate different lineup construction and showing to observers. Details are in the papers Majumder et al (2012) JASA, and Hofmann et al (2015). We distinguish between three different scenarios:

• Scenario I: in each of K evaluations a different data set and a different set of (m-1) null plots is shown.

• Scenario II: in each of K evaluations the same data set but a different set of (m-1) null plots is shown.

• Scenario III: the same lineup, i.e. same data and same set of null plots, is shown to K different observers.

Usage

pvisual(
  x,
  K,
  m = 20,
  N = 10000,
  type = "scenario3",
  xp = 1,
  target = 1,
  upper.tail = TRUE
)

Arguments

Value

Vector/data frame. For comparison a p value based on a binomial distribution is provided as well.

Examples

pvisual(15, 20, m=3) # triangle test

Distance based on the regression parameters

Description

Dataset X is binned into 5 bins in x-direction. A regression line is fitted to the data in each bin and the regression coefficients are noted. Same is done for dataset PX. An euclidean distance is calculated between the two sets of regression parameters. If the relationship between X and PX looks linear, number of bins should be equal to 1.

Usage

reg_dist(X, PX, nbins = 1, intercept = TRUE, scale = TRUE)

Arguments

Value

distance between X and PX

Examples

with(mtcars, reg_dist(data.frame(wt, mpg), data.frame(sample(wt), mpg)))

Bootstrap residuals.

Description

For use with null_lm

Usage

resid_boot(model, data)

Arguments

Parametric bootstrap residuals.

Description

For use with null_lm

Usage

resid_pboot(model, data)

Arguments

Permutation residuals.

Description

For use with null_lm

Usage

resid_perm(model, data)

Arguments

Rotation residuals.

Description

For use with null_lm

Usage

resid_rotate(model, data)

Arguments

Residuals simulated by a normal model, with specified sigma

Description

For use with null_lm

Usage

resid_sigma(model, data, sigma = 1)

Arguments

The Rorschach protocol.

Description

This protocol is used to calibrate the eyes for variation due to sampling. All plots are typically null data sets, data that is consistent with a null hypothesis. The protocol is described in Buja, Cook, Hofmann, Lawrence, Lee, Swayne, Wickham (2009) Statistical inference for exploratory data analysis and model diagnostics, Phil. Trans. R. Soc. A, 367, 4361-4383.

Usage

rorschach(method, true = NULL, n = 20, p = 0)

Arguments

Sample size calculator

Description

This function calculates a table of sample sizes for with an experiment, given a lineup size, and estimates of the detection rate.

Usage

sample_size(n = 53:64, m = 20, pA = seq(1/20, 1/3, 0.01), conf = 0.95)

Arguments

Examples

pow <- sample_size()
pow
library(ggplot2)
library(viridis)
ggplot(pow, aes(x=n, y=pA, fill=prob, group=pA)) +
  geom_tile() +
  scale_fill_viridis_c("power") +
  ylab("detect rate (pA)") + xlab("sample size (n)") +
  theme_bw()

Distance based on separation of clusters

Description

The separation between clusters is defined by the minimum distances of a point in the cluster to a point in another cluster. The number of clusters are provided. If not, the hierarchical clustering method is used to obtain the clusters. The separation between the clusters for dataset X is calculated. Same is done for dataset PX. An euclidean distance is then calculated between these separation for X and PX.

Usage

sep_dist(X, PX, clustering = FALSE, nclust = 3, type = "separation")

Arguments

Value

distance between X and PX

Examples

if(require('fpc')) {
with(mtcars, sep_dist(data.frame(wt, mpg, as.numeric(as.factor(mtcars$cyl))),
              data.frame(sample(wt), mpg, as.numeric(as.factor(mtcars$cyl))),
              clustering = TRUE))
}

if (require('fpc')) {
with(mtcars, sep_dist(data.frame(wt, mpg, as.numeric(as.factor(mtcars$cyl))),
             data.frame(sample(wt), mpg, as.numeric(as.factor(mtcars$cyl))),
             nclust = 3))
}

A theme to minimally strip away the context

Description

Note this is not a complete theme hence why there are no arguments.

Usage

theme_strip()

Examples

library(ggplot2)
ggplot(cars, aes(dist, speed)) + theme_strip()

Tipping data

Description

One waiter recorded information about each tip he received over a period of a few months working in one restaurant. He collected several variables:

Usage

tips

Format

A data frame with 244 rows and 7 variables

Details

• tip in dollars,

• bill in dollars,

• sex of the bill payer,

• whether there were smokers in the party,

• day of the week,

• time of day,

• size of the party.

In all he recorded 244 tips. The data was reported in a collection of case studies for business statistics (Bryant & Smith 1995).

References

Bryant, P. G. and Smith, M (1995) Practical Data Analysis: Case Studies in Business Statistics. Homewood, IL: Richard D. Irwin Publishing:

Sample turk results

Description

Subset of data from a Turk experiment, used to show how to compute power of a lineup

Distance for univariate data

Description

The first four moments is calculated for data X and data PX. An euclidean distance is calculated between these moments for X and PX.

Usage

uni_dist(X, PX)

Arguments

Value

distance between X and PX

Examples

if(require('moments')){uni_dist(rnorm(100), rpois(100, 2))}

Power calculations.

Description

This function simply counts the proportion of people who selected the data plot, in a set of lineups. It adjusts for multiple picks by the same individual, by weighting by the total number of choices.

Usage

visual_power(data, m = 20)

Arguments

Value

vector of powers for each pic_id

Examples

data(turk_results)
visual_power(turk_results)

Wasp gene expression data.

Description

Data from Toth et al (2010) used in Niladri Roy et al (2015)