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.
|Author||Hadley Wickham <firstname.lastname@example.org>, Niladri Roy Chowdhury <email@example.com>, Di Cook <firstname.lastname@example.org>|
|Date of publication||2014-12-17 17:57:32|
|Maintainer||Di Cook <email@example.com>|
add_true: Add true data into data frame containing null data sets.
bin_dist: Binned Distance
box_dist: Distance based on side by side Boxplots for two levels
calc_diff: Calculating the difference between true plot and the null...
calc_mean_dist: Calculating the mean distances of each plot in the lineup.
decrypt: Use decrypt to reveal the position of the real data.
distmet: Empirical distribution of the distance
distplot: Plotting the distribution of the distance measure
find_plot_data: Find plot data. If data is not specified, this function will...
lal: Los Angeles Lakers play-by-play data.
lineup: The line-up protocol.
null_dist: Generate null data with a specific distribution.
null_gen: Computing th distance for the null plots
null_lm: Generate null data with null residuals from a model.
null_permute: Generate null data by permuting a variable.
opt_bin_diff: Finds the number of bins in x and y direction which gives the...
reg_dist: Distance based on the regression parameters
resid_boot: Bootstrap residuals.
resid_pboot: Parametric bootstrap residuals.
resid_rotate: Rotation residuals.
resid_sigma: Residuals simulated by a normal model, with specified sigma
rorschach: The Rorschach protocol.
sep_dist: Distance based on separation of clusters
uni_dist: Distance for univariate data