enumRsimulation: enumR simulation function

In smasongarrison/enumR: Factor Enumeration Functions

Description

This function simulates factor analyses based on numerous parameters, listed below.

Usage

enumRsimulation(seed = 12345, ndatasets = 200, patternmatrix = NULL,
  effectmatrix = NULL, nfactors = 5, loading = 0.5, items = NULL,
  items_p_f = 5, itemsR_p_f = 2, itemsR = NULL, loading_norm = FALSE,
  loading_norm_sd = 0.05, rfactors = 0, r_norm = FALSE,
  r_norm_SD = 0.015, samplesize = 300, method = "ml",
  rotation = "oblimin", custom_item = FALSE, f1_items = NULL,
  f2_items = NULL, f3_items = NULL, f4_items = NULL, f5_items = NULL,
  f6_items = NULL, f7_items = NULL, f8_items = NULL, f9_items = NULL,
  f10_items = NULL, f1_itemsR = NULL, f2_itemsR = NULL,
  f3_itemsR = NULL, f4_itemsR = NULL, f5_itemsR = NULL,
  f6_itemsR = NULL, f7_itemsR = NULL, f8_itemsR = NULL,
  f9_itemsR = NULL, f10_itemsR = NULL)

Arguments

seed	numeric value for setting a seed. Allows results to be reproduced.
ndatasets	Number of datasets generated
patternmatrix	OPTIONAL matrix of factor loadings. If not provided, one is generated from rfactors and nfactors.
effectmatrix	OPTIONAL matrix of item loadings by factor. If not provided, one is generated from items and nfactors.
nfactors	Number of factors to extract.
loading	Factor loading magnitude. Default is .5
items_p_f	number of items per factor
itemsR_p_f	number of reverse-scored items per factor
itemsR	Number of items per factor that are reverse scored
loading_norm	If TRUE, generate factor loadings that average to loading
loading_norm_sd	If loading_norm is TRUE, standard deviation of loadings.
rfactors	Intercorrelations between factors. Default is 0
r_norm	If TRUE, generate normally distributed factor loadings with mean loading and sd r_norm_sd. Does not check if correlation matrix is positive definite.
samplesize	Number of observations
method	factoring method – fm="pa" Principal Axis Factor Analysis, fm = "minres" minimum residual (OLS) factoring fm="ml" Maximum Likelihood FA, fm="pc" Principal Components"
rotation	what rotation to use c("none", "varimax", "oblimin","promax")
custom_item	If TRUE, overrides items_p_f and itemsR_p_f. Primary used for importing from shiny interface. @param items OPTIONAL vector of total number of items per factor
f1_items	Total number of items on factor 1
f2_items	Total number of items on factor 2
f3_items	Total number of items on factor 3
f4_items	Total number of items on factor 4
f5_items	Total number of items on factor 5
f6_items	Total number of items on factor 6
f7_items	Total number of items on factor 7
f8_items	Total number of items on factor 8
f9_items	Total number of items on factor 9
f10_items	Total number of items on factor 10
f1_itemsR	Number of reverse-scored items on factor 1
f2_itemsR	Number of reverse-scored items on factor 2
f3_itemsR	Number of reverse-scored items on factor 3
f4_itemsR	Number of reverse-scored items on factor 4
f5_itemsR	Number of reverse-scored items on factor 5
f6_itemsR	Number of reverse-scored items on factor 6
f7_itemsR	Number of reverse-scored items on factor 7
f8_itemsR	Number of reverse-scored items on factor 8
f9_itemsR	Number of reverse-scored items on factor 9
f10_itemsR	Number of reverse-scored items on factor 10 ... parameters to pass to the factor analysis program The most important of these is if using a correlation matrix is covmat= xx
cor	What kind of correlation to find, defaults to covariance matrix, but see fa for the choices
r_norm_sd	If r_norm is TRUE, standard deviation for normally distributed intercorrelations.
true_model	If TRUE, Estimates model using nfactors only. Is used in enumRsimulation function. Default is FALSE.
use	If doing covariances or Pearson R, should we use "pairwise" or "complete cases"
items_p_f	number of items per factor
itemsR_p_f	number of reverse-scored items perfactor
custom_item	If TRUE, overrides items_p_f and itemsR_p_f. Primary used for importing from shiny interface.

Value

dataframe

smasongarrison/enumR documentation built on Feb. 3, 2021, 10:03 p.m.