SEMsensitivity (version 0.1.0)

Test3: Specified Approximation Method

Description

This function determines when a specific path in a Structural Equation Modeling (SEM) model changes sign by iteratively removing most influential data points (determined by naive method) and outputs relevant results. Use method 3 - Specified Approximation Method.

Usage

Test3(
  df,
  model,
  var_one,
  var_two,
  PAR,
  threshold,
  fit,
  estimates,
  conc,
  int,
  par_value,
  max_final,
  N,
  signFactor,
  equalCons = 0L,
  calcMeth = "Hessian",
  ...
)

Value

A list of class TestResult3 containing:

methodname

The name of the method used.

testindex

The index of the test performed.

max_drops

The maximum number of data points dropped.

est_diff

The expected change in parameter value.

act_diff

The actual change in parameter value.

final_par_value

The parameter value after dropping the influential points.

initial_par_value

The original parameter value.

sign_switch_possible

Logical indicating whether it was possible to change the sign of the parameter.

dropped_points

The indices of the most influential data points.

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 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).

equalCons

The equality constraint used in the SEM model (default is 0).

calcMeth

The method used for approximation (default is 'Hessian').

...

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

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

Test3_result = Test3(df, model, var_one, var_two, PAR, threshold, fit, estimates,
conc, int, par_value, max_final, N, signFactor,equalCons = 0L)
summary(Test3_result)
# }