NRW {SemNeT}

Naive Random Walk Network Estimation

Description

Estimates a semantic network using the Naive Random Walk method described in Lerner, Ogrocki, and Thomas (2009)

Usage

NRW(data, type = c("num", "prop"), threshold = 0)

Arguments

data

Matrix or data frame. A preprocessed verbal fluency matrix where rows are participants and columns are verbal fluency responses

type

Character. Type of threshold to apply.

• "num" Minimum number of co-occurrences

• "prop" Minimum proportion of co-occurrences

Defaults to "num"

threshold

Numeric. Value of the minimum number or proportion of co-occurrences. Defaults to 0 for both "num" and "prop"

Value

Returns a undirected semantic network

Author(s)

Alexander Christensen <[e-mail]>

References

Lerner, A. J., Ogrocki, P. K., & Thomas, P. J. (2009). Network graph analysis of category fluency testing. Cognitive and Behavioral Neurology, 22, 45-52.

Examples

# Get data
data <- open.clean

# Organize group data
## Get group data
group <- open.group

## Low and high openness to experience groups
low <- data[which(group == "Low"),]
high <- data[which(group == "High"),]

# Compute networks
low.net <- NRW(low)
high.net <- NRW(high)