SEMsensitivity (version 0.1.0)

Test13: Use Brute Search with Cut Method to Try to Switch Sign of Parameter

Description

This function uses a brute-force search method with pruning (cutting) to iteratively search for combinations of data points that switch the sign of a specific path in a Structural Equation Modeling (SEM) model.

Usage

Test13(
  df,
  model,
  var_one,
  var_two,
  PAR,
  threshold,
  fit,
  estimates,
  conc,
  int,
  par_value,
  max_final,
  N,
  signFactor,
  ...
)

Value

A list of class TestResult13 containing:

bruteSearchDrops

The indices of the most influential data points selected by the brute search method.

initialValue

The original value of the parameter.

finalValue

The final value of the parameter after applying the brute search method.

methodname

The name of the method used.

testindex

The index of the test performed.

PAR

The path of interest that was evaluated.

threshold

The threshold used for the percentage of dropped points.

N

The total number of data points.

max_final

The maximum number of points allowed to be dropped.

par_value

The original value of the parameter.

Arguments

df

A data frame containing the dataset.

model

A specified SEM model.

var_one

The first variable of interest.

var_two

The second variable of interest.

PAR

The path of interest.

threshold

The threshold for the percentage of data dropped.

fit

The SEM object.

estimates

The estimates from the SEM model.

conc

A data frame containing the parameter of interest.

int

The value of the path of interest.

par_value

The original value of the parameter of interest.

max_final

The maximum number of influential data points to consider.

N

The total number of data points.

signFactor

A factor indicating the direction of parameter change (positive or negative).

...

Other arguments.

Examples

# \donttest{
library(lavaan)
library(dplyr)
library(semfindr)
library(R.utils)


# Import data
df <- PoliticalDemocracy

# Build Model
model <- '
  # measurement model
  ind60 =~ x1 + x2 + x3
  dem60 =~ y1 + y2 + y3 + y4
  dem65 =~ y5 + y6 + y7 + y8
  # regressions
  dem60 ~ ind60
  dem65 ~ ind60 + dem60
  # residual correlations
  y1 ~~ y5
  y2 ~~ y4 + y6
  y3 ~~ y7
  y4 ~~ y8
  y6 ~~ y8
'

var_one <- 'dem65' # first term
var_two <- 'ind60' # second term
PAR <- c("dem65~ind60") # full relation
threshold <- 10

# Fit SEM model
fit <- lavaan::sem(model, data = df)
summary(fit)

# Get Estimates of Parameters from SEM
estimates <- parameterEstimates(fit)

# Determine the value of the parameter of interest
conc <- data.frame(lhs = estimates$lhs, rhs = estimates$rhs, est = estimates$est)
int <- conc %>% filter(lhs == var_one & rhs == var_two)
par_value <- int$est # this is the value of the parameter of interest

# Compute the max number of points to be dropped
max_final <- ceiling(threshold * nrow(df) / 100) # perform rounding if necessary
N <- nrow(df) # store the number of observations in df for convenience

# Determine whether the parameter is negative or positive in order
# to assess which direction to perturb it
signFactor <- ifelse(par_value >= 0L, TRUE, FALSE)

Test13_result = Test13(df, model, var_one, var_two, PAR, threshold, fit, estimates,
conc, int, par_value, max_final, N, signFactor)
summary(Test13_result)
# }