SEMsensitivity (version 0.1.0)

Test10: Use depth method to drop fit measure below threshold

Description

This function uses the depth method to iteratively remove the most influential data points based on influence scores to reduce the fit measure below a specified threshold. The method is based on approximating the influence of each data point and is similar to TEST 8 but focused on fit metrics like CFI, RMSEA, etc.

Usage

Test10(
  df,
  model,
  fit,
  max_final,
  N,
  measureTest = "cfi",
  fitThreshold = 0.9,
  highGood = TRUE,
  ...
)

Value

A list of class TestResult10 containing:

methodname

The name of the method used.

testindex

The index of the test performed.

original_fit_value

The original value of the fit measurement before data points were removed.

final_fit_value

The fit value after the influential data points were removed.

num_drops

The number of data points dropped to achieve the desired fit value reduction.

depthdiffFit

The difference between the original fit value and the threshold.

depthDropScore

The cumulative drop in the fit value after removing points.

final_drops

The indices of the most influential data points dropped.

Arguments

df

A data frame containing the dataset.

model

A specified SEM model.

fit

The SEM object after fitting the model.

max_final

The maximum number of influential data points to consider for removal.

N

The total number of data points.

measureTest

The fit measurement name to be tested (e.g., "cfi", "tli", "rmsea"). Default is "cfi".

fitThreshold

The threshold of the fit measurement. For example, for CFI (measureTest = "cfi"), the threshold could be 0.9. Default is 0.9.

highGood

A boolean indicating whether higher values of the fit measure are better. For instance, for CFI, this should be set to TRUE. Default is TRUE.

...

Other arguments.

Examples

# \donttest{
library(lavaan)
library(dplyr)
library(semfindr)

# Import data
df <- PoliticalDemocracy

# Build SEM model
model <- '
  ind60 =~ x1 + x2 + x3
  dem60 =~ y1 + y2 + y3 + y4
  dem65 =~ y5 + y6 + y7 + y8
  dem60 ~ ind60
  dem65 ~ ind60 + dem60
'

fit <- lavaan::sem(model, data = df)
max_final <- ceiling(10 * nrow(df) / 100)  # dropping 10% of the data points
N <- nrow(df)

Test10_result <- Test10(df, model, fit, max_final, N,
measureTest = "cfi", fitThreshold = 0.9, highGood = TRUE)
summary(Test10_result)
# }