test.bootSemNeT: Statistical tests for 'bootSemNeT'

In SemNeT: Methods and Measures for Semantic Network Analysis

Statistical tests for bootSemNeT

Description

Computes statistical tests for bootstrapped networks from bootSemNeT

Usage

test.bootSemNeT(
  ...,
  test = c("ANCOVA", "ANOVA", "t-test"),
  measures = c("ASPL", "CC", "Q"),
  formula = NULL,
  groups = NULL
)

Arguments

...	Object(s) from bootSemNeT
test	Character. Type of statistical test to be used. "t-test" Computes t-tests for all measures "ANOVA" Computes ANOVAs and includes Tukey's HSD for pairwise comparisons (TukeyHSD) "ANCOVA" Computes ANCOVAs that control for the number of nodes and edges in the networks and includes adjusted means and Tukey's HSD for pairwise comparisons (TukeyHSD)
measures	Character. Network measures to be tested. ASPL Average shortest path length CC Clustering coefficient Q Modularity statistic
formula	Character. A formula for specifying an ANOVA structure. The formula should have the predictor variable as "y" and include the names the variables are grouped by (e.g., formula = "y ~ group_var1 * group_var2"). See Two-way ANOVA example in examples
groups	Data frame. A data frame specifying the groups to be input into the formula. The column names should be the variable names of interest. The groups should be in the same order as the groups input into bootSemNeT

Value

Returns a list containing the objects:

ASPL	Test statistics for each proportion of nodes remaining for ASPL
CC	Test statistics for each proportion of nodes remaining for CC
Q	Test statistics for each proportion of nodes remaining for Q

If two groups:

A matrix in each object has the following columns:

t-statistic	Statistic from the t.test
df	Degrees of freedom
p-value	p-value with values equal to 0 being p < .001
d	Cohen's d
CI95.lower	Lower bound of the 95 percent confidence interval
CI95.upper	Upper bound of the 95 percent confidence interval
Direction	Direction of the effect. The argument groups will specify specifically which group is higher or lower on the measure. If no groups are input, then "d" and "p" are used to represent data and paired samples from bootSemNeT, respectively

Row names refer to the proportion of nodes remaining in bootstrapped networks

If three or more groups:

A list containing two objects:

ANOVA	A matrix containing the F-statistic, group degrees of freedom, residual degrees of freedom, p-value, and partial eta squared p.eta.sq
HSD	A matrix containing the differences between each group (diff), lower (lwr) and upper (upr) bounds of the 95% confidence interval, and the adjusted p-value (p.adj)

Author(s)

Alexander Christensen <[e-mail]>

Examples

# Simulate Dataset
one <- sim.fluency(20)
two <- sim.fluency(20)

# Run partial bootstrap networks
two.result <- bootSemNeT(one, two, prop = .50, iter = 100,
sim = "cosine", cores = 2, type = "node", method = "TMFG")

# Compute tests
test.bootSemNeT(two.result)


# Two-way ANOVA example
## Simulated data
hihi <- sim.fluency(50, 500)
hilo <- sim.fluency(50, 500)
lohi <- sim.fluency(50, 500)
lolo <- sim.fluency(50, 500)

## Create groups
groups <- matrix(
c("high", "high",
"high", "low",
"low", "high",
"low", "low"
), ncol = 2, byrow = TRUE)

## Change column names (variable names)
colnames(groups) <- c("gf","caq")

## Run partial bootstrap networks
boot.fifty <- bootSemNeT(hihi, hilo, lohi, lolo, prop = .50,
type = "node", method = "TMFG", cores = 2, iter = 100)
boot.sixty <- bootSemNeT(hihi, hilo, lohi, lolo, prop = .60,
type = "node", method = "TMFG", cores = 2, iter = 100)

## Compute tests
test.bootSemNeT(boot.fifty, boot.sixty,
test = "ANOVA", formula = "y ~ gf*caq", groups = groups)

SemNeT documentation built on Aug. 12, 2023, 5:06 p.m.