Description Usage Format Source Examples

34 participants were asked to produce at their own pace a series of 10 symbols among "A", "B", "C", and "D" that would "look as random as possible, so that if someone else sees the sequence, she will believe it is a truly random one".

1 |

A data.frame with 34 rows and 2 variables.

Gauvrit, Singmann, Soler-Toscano & Zenil (submitted). Complexity for psychology. A user-friendly implementation of the coding theorem method.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# load data
data(exp1)
# summary statistics
nrow(exp1)
summary(exp1$age)
mean(exp1$age)
sd(exp1$age)
## Not run:
# this uses code from likelihood_d() to calculate the mean complexity K
# for all strings of length 10 with alphabet = 4:
tmp <- acss_data[nchar(rownames(acss_data)) == 10, "K.4", drop = FALSE]
tmp <- tmp[!is.na(tmp[,"K.4"]),,drop = FALSE]
tmp$count <- count_class(rownames(tmp), alphabet = 4)
(mean_K <- with(tmp, sum(K.4*count)/sum(count)))
t.test(acss(exp1$string, 4)[,"K.4"], mu = mean_K)
## End(Not run)
``` |

acss documentation built on May 29, 2017, 10:40 a.m.

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