metamerise {metamer} | R Documentation |
Create metamers
Description
Produces very dissimilar datasets with the same statistical properties.
Usage
metamerise(
data,
preserve,
minimize = NULL,
change = colnames(data),
round = truncate_to(2),
stop_if = n_tries(100),
keep = NULL,
annealing = TRUE,
K = 0.02,
start_probability = 0.5,
perturbation = 0.08,
name = "",
verbose = interactive()
)
metamerize(
data,
preserve,
minimize = NULL,
change = colnames(data),
round = truncate_to(2),
stop_if = n_tries(100),
keep = NULL,
annealing = TRUE,
K = 0.02,
start_probability = 0.5,
perturbation = 0.08,
name = "",
verbose = interactive()
)
new_metamer(data, preserve, round = truncate_to(2))
Arguments
data |
A |
preserve |
A function whose result must be kept exactly the same. Must take the data as argument and return a numeric vector. |
minimize |
An optional function to minimize in the process. Must take the data as argument and return a single numeric. |
change |
A character vector with the names of the columns that need to be changed. |
round |
A function to apply to the result of |
stop_if |
A stopping criterium. See n_tries. |
keep |
Max number of metamers to return. |
annealing |
Logical indicating whether to perform annealing. |
K |
speed/quality tradeoff parameter. |
start_probability |
initial probability of rejecting bad solutions. |
perturbation |
Numeric with the magnitude of the random perturbations.
Can be of length 1 or |
name |
Character for naming the metamers. |
verbose |
Logical indicating whether to show a progress bar. |
Details
It follows Matejka & Fitzmaurice (2017) method of constructing metamers.
Beginning from a starting dataset, it iteratively adds a small perturbation,
checks if preserve
returns the same value (up to signif
significant digits)
and if minimize
has been lowered, and accepts the solution for the next
round. If annealing
is TRUE
, it also accepts solutions with bigger
minimize
with an ever decreasing probability to help the algorithm avoid
local minimums.
The annealing scheme is adapted from de Vicente et al. (2003).
If data
is a metamer_list
, the function will start the algorithm from the
last metamer of the list. Furthermore, if preserve
and/or minimize
are missing, the previous functions will be carried over from the previous call.
minimize
can be also a vector of functions. In that case, the process minimizes
the product of the functions applied to the data.
Value
A metamer_list
object (a list of data.frames).
References
Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17, 1290–1294. https://doi.org/10.1145/3025453.3025912 de Vicente, Juan, Juan Lanchares, and Román Hermida. (2003). ‘Placement by Thermodynamic Simulated Annealing’. Physics Letters A 317(5): 415–23.
See Also
delayed_with()
for a convenient way of making functions suitable for
preserve
, mean_dist_to()
for a convenient way of minimizing the distance
to a known target in minimize
, mean_self_proximity()
for maximizing the
"self distance" to prevent data clumping.
Examples
data(cars)
# Metamers of `cars` with the same mean speed and dist, and correlation
# between the two.
means_and_cor <- delayed_with(mean_speed = mean(speed),
mean_dist = mean(dist),
cor = cor(speed, dist))
set.seed(42) # for reproducibility.
metamers <- metamerize(cars,
preserve = means_and_cor,
round = truncate_to(2),
stop_if = n_tries(1000))
print(metamers)
last <- tail(metamers)
# Confirm that the statistics are the same
cbind(original = means_and_cor(cars),
metamer = means_and_cor(last))
# Visualize
plot(tail(metamers))
points(cars, col = "red")