acss (version 0.2-5)

matthews2013: Data from Experiment 1 in Matthews (2013)

Description

Mean responses on a 6-point scale ("definitely random" to "definitely not random") of participants to 216 strings of length 21.

Usage

matthews2013

Format

A data.frame with 216 rows and 3 variables.

Source

Matthews, W. (2013). Relatively random: Context effects on perceived randomness and predicted outcomes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(5), 1642-1648.

Examples

## Not run: 
# data(matthews2013)
# 
# spans <- 3:11
# # note, the next loop takes more than 5 minutes.
# for (i in spans) {
#   matthews2013[,paste0("K2_span", i)] <- 
#     sapply(local_complexity(matthews2013$string, alphabet=2, span = i), mean)
# }
# 
# lm_list <- vector("list", 8)
# for (i in seq_along(spans)) {
#   lm_list[[i]] <- lm(as.formula(paste0("mean ~ K2_span", spans[i])), matthews2013)
# }
# 
# plot(spans, sapply(lm_list, function(x) summary(x)$r.squared), type = "o")
# 
# # do more predictors increase fit?
# require(MASS)
# m_initial <- lm(mean ~ 1, matthews2013)
# m_step <- stepAIC(m_initial, 
#                   scope = as.formula(paste("~", paste(paste0("K2_span", spans), 
#                   collapse = "+"))))
# summary(m_step)
# 
# m_initial2 <- lm(as.formula(paste("mean ~", paste(paste0("K2_span", spans), 
#                   collapse = "+"))), matthews2013)
# m_step2 <- stepAIC(m_initial2)
# summary(m_step2)
# 
# ## End(Not run)