Nothing
inst/doc/MultipleAnalyses.R
In SimDesign: Structure for Organizing Monte Carlo Simulation Designs
## ----nomessages, echo = FALSE-------------------------------------------------
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
fig.height = 5,
fig.width = 5
)
options(digits=4)
par(mar=c(3,3,1,1)+.1)
## -----------------------------------------------------------------------------
SimDesign::SimFunctions()
## -----------------------------------------------------------------------------
SimDesign::SimFunctions(nAnalyses = 2)
## -----------------------------------------------------------------------------
library(SimDesign)
# SimFunctions(nAnalyses = 2)
sample_sizes <- c(250, 500, 1000)
nitems <- c(10, 20)
Design <- createDesign(sample_size = sample_sizes,
nitems = nitems)
# create list of additional parameters which are fixed across conditions
set.seed(1)
pars_10 <- rbind(a = round(rlnorm(10, .3, .5)/1.702, 2),
d = round(rnorm(10, 0, .5)/1.702, 2))
pars_20 <- rbind(a = round(rlnorm(20, .3, .5)/1.702, 2),
d = round(rnorm(20, 0, .5)/1.702, 2))
pars <- list(ten=pars_10, twenty=pars_20)
P_logit <- function(a, d, Theta) exp(a * Theta + d) / (1 + exp(a * Theta + d))
P_ogive <- function(a, d, Theta) pnorm(a * Theta + d)
## ----eval=FALSE---------------------------------------------------------------
# Generate <- function(condition, fixed_objects = NULL) {
#
# N <- condition$sample_size
# nitems <- condition$nitems
# nitems_name <- ifelse(nitems == 10, 'ten', 'twenty')
#
# #extract objects from fixed_objects
# a <- fixed_objects[[nitems_name]]['a', ]
# d <- fixed_objects[[nitems_name]]['d', ]
#
# dat <- matrix(NA, N, nitems)
# colnames(dat) <- paste0('item_', 1:nitems)
# Theta <- rnorm(N)
# for(j in 1:nitems){
# p <- P_ogive(a[j], d[j], Theta)
# for(i in 1:N)
# dat[i,j] <- sample(c(1,0), 1, prob = c(p[i], 1 - p[i]))
# }
# as.data.frame(dat) #data.frame works nicer with lavaan
# }
#
# Analyse.FIML <- function(condition, dat, fixed_objects = NULL) {
# mod <- mirt(dat, 1L, verbose=FALSE)
# if(!extract.mirt(mod, 'converged')) stop('mirt did not converge')
# cfs <- mirt::coef(mod, simplify = TRUE, digits = Inf)
# FIML_as <- cfs$items[,1L] / 1.702
#
# ret <- c(as=unname(FIML_as))
# ret
# }
#
# Analyse.DWLS <- function(condition, dat, fixed_objects = NULL) {
# nitems <- condition$nitems
# lavmod <- paste0('F =~ ', paste0('NA*', colnames(dat)[1L], ' + '),
# paste0(colnames(dat)[-1L], collapse = ' + '),
# '\nF ~~ 1*F')
# lmod <- sem(lavmod, dat, ordered = colnames(dat))
# if(!lavInspect(lmod, 'converged')) stop('lavaan did not converge')
# cfs2 <- lavaan::coef(lmod)
# DWLS_alpha <- cfs2[1L:nitems]
# const <- sqrt(1 - DWLS_alpha^2)
# DWLS_as <- DWLS_alpha / const
#
# ret <- c(as=unname(DWLS_as))
# ret
# }
#
# Summarise <- function(condition, results, fixed_objects = NULL) {
# nitems <- condition$nitems
# nitems_name <- ifelse(nitems == 10, 'ten', 'twenty')
#
# #extract objects from fixed_objects
# a <- fixed_objects[[nitems_name]]['a', ]
# pop <- c(a, a)
#
# obt_bias <- bias(results, pop)
# obt_RMSE <- RMSE(results, pop)
# ret <- c(bias=obt_bias, RMSE=obt_RMSE)
# ret
# }
## ----eval=FALSE---------------------------------------------------------------
# res <- runSimulation(Design, replications=100, verbose=FALSE, parallel=TRUE,
# generate=Generate,
# analyse=list(FIML=Analyse.FIML, DWLS=Analyse.DWLS),
# summarise=Summarise, filename = 'mirt_lavaan',
# packages=c('mirt', 'lavaan'), fixed_objects=pars)
## ----include=FALSE------------------------------------------------------------
res <- structure(list(sample_size = c(250, 500, 1000, 250, 500, 1000
), nitems = c(10, 10, 10, 20, 20, 20), bias.FIML.as1 = c(0.00998872755798466,
-0.0211364605578725, -0.0039472918051987, 0.0606466866510326,
0.0192019747460773, 0.019976239470624), bias.FIML.as2 = c(0.0134569433967216,
0.0187201139317983, -0.00147776827122514, 0.0117871403061117,
0.0453762090796238, 0.0186079659977207), bias.FIML.as3 = c(-0.0201613259701166,
-0.0181734714318961, -0.0133933373300669, 0.0646879764208232,
0.0218366906966479, 0.00798167692557969), bias.FIML.as4 = c(0.186161341186305,
0.104237862202863, 0.0778897412095584, -0.026313815932234, -0.00635870499131507,
-0.0114981251130375), bias.FIML.as5 = c(0.00926532291093203,
0.029297553049638, 0.0035690208390262, 0.000582216674185383,
0.0202974652037632, -0.0128424807545063), bias.FIML.as6 = c(-0.00394277165975909,
-0.0154400356375581, -0.0154100791014608, -0.0115871332168039,
-0.00169280535431738, -0.02185583063946), bias.FIML.as7 = c(0.0321426765516901,
0.0023139112162729, -0.00743302248376659, -0.0563501689996753,
0.000191353333746391, -0.00062269083403889), bias.FIML.as8 = c(0.063841617844854,
0.0136240874132899, 0.0239706654002676, -0.019324789107572, -0.00713416098241873,
-0.0157261985329955), bias.FIML.as9 = c(-0.0126321813383556,
-0.00113623264989128, 0.00151647175345255, -0.00247479868708876,
-0.00124512711889233, -0.017917627196065), bias.FIML.as10 = c(-0.0137551418255338,
-0.00996289662703265, -0.015724369581452, 0.025750717572856,
0.0495214634367752, 0.012263001380898), bias.DWLS.as1 = c(0.0213300692456042,
-0.0081120110659304, 0.00849135597642985, 0.0534763484009736,
-4.70350823508348e-05, -0.00198898912373827), bias.DWLS.as2 = c(0.0304066070980667,
0.0303666456319019, 0.011398130200282, 0.0114298773633547, 0.0303730809063532,
0.0015361793423236), bias.DWLS.as3 = c(0.00246773719111794, 0.00141903952079597,
0.00540631339266324, 0.0795711029587868, 0.0330531775433939,
0.0136383667144754), bias.DWLS.as4 = c(0.132204497704931, 0.0332479254110806,
0.018813114931708, -0.00847471064945973, 0.00720439015885141,
0.00205705227622941), bias.DWLS.as5 = c(0.0191320392893439, 0.0354771384906975,
0.0102778769019432, 0.0062441336060167, 0.0107507859965388, -0.0214727665000171
), bias.DWLS.as6 = c(0.0183694257428437, 0.00597186386409428,
0.00582852666767624, 0.00640208680609534, 0.00744542125677253,
-0.0120158095590051), bias.DWLS.as7 = c(0.0410611355660595, 0.0107457642078628,
-0.00234411900870412, -0.0370124657245999, 0.0140574105187029,
0.012220363947128), bias.DWLS.as8 = c(0.0575286451947481, 0.0108730757552931,
0.0181331620498002, -0.00266809440442234, 0.00876268337603981,
0.000658260952319116), bias.DWLS.as9 = c(-0.0110572217167639,
-0.000566251052416587, 0.00213777252513437, 0.0180403647961145,
0.0175025650339115, -0.000679792738651153), bias.DWLS.as10 = c(0.00461196660602057,
0.00860520068561403, 0.00105892129699621, 0.0293744340937786,
0.0480624841941301, 0.0100024017106522), RMSE.FIML.as1 = c(0.136114724683302,
0.0820035231590711, 0.0602718630804655, 0.197687839442572, 0.165410223264985,
0.10028308108237), RMSE.FIML.as2 = c(0.135773523255822, 0.122316182433675,
0.0829661313819097, 0.200633052300666, 0.121673357983436, 0.0881231862689714
), RMSE.FIML.as3 = c(0.108991971326869, 0.0842131719350027, 0.06338600007589,
0.158497060515878, 0.105472067533837, 0.0688112173455798), RMSE.FIML.as4 = c(0.423944429084931,
0.292223091229885, 0.24006881507494, 0.0772117047077088, 0.0753675365419075,
0.0539848730775772), RMSE.FIML.as5 = c(0.169623653136512, 0.100852326267566,
0.0792406198849061, 0.149962341795796, 0.0984782215555278, 0.0953280973624482
), RMSE.FIML.as6 = c(0.124767951178948, 0.0818049168679144, 0.0594721724308959,
0.14629347996356, 0.0927538518556173, 0.0705377251884313), RMSE.FIML.as7 = c(0.181685861792146,
0.114947671878307, 0.074192073155478, 0.117084871867108, 0.0974724366252352,
0.0703488587415946), RMSE.FIML.as8 = c(0.231797645483998, 0.141319154230226,
0.104337825901184, 0.0973056056983475, 0.0812284517750514, 0.0424109426493268
), RMSE.FIML.as9 = c(0.16613193205312, 0.140760100129038, 0.0891584228213999,
0.108719996759675, 0.080308694331235, 0.0513084488030903), RMSE.FIML.as10 = c(0.153191702052709,
0.0939169728941718, 0.0607437545286223, 0.142087613218528, 0.125910660748991,
0.0839773589934654), RMSE.DWLS.as1 = c(0.135785481380796, 0.0787218803832953,
0.0605248038813955, 0.195826665179666, 0.14677612458812, 0.0906429185189553
), RMSE.DWLS.as2 = c(0.131586066027966, 0.123659417282268, 0.0798516833501493,
0.193625962651401, 0.110845768899907, 0.0757663744591717), RMSE.DWLS.as3 = c(0.109491701212598,
0.082474074479391, 0.0625244382818499, 0.163509158386427, 0.104336471143878,
0.0670832713055758), RMSE.DWLS.as4 = c(0.404291688201491, 0.251111850670021,
0.210429452484672, 0.0777287786631143, 0.0787790921225598, 0.0536674825333642
), RMSE.DWLS.as5 = c(0.166880765292909, 0.099643169312563, 0.077123029528217,
0.138727147330488, 0.0958299498529966, 0.0891417978698349), RMSE.DWLS.as6 = c(0.128222841124684,
0.0803072950196332, 0.0571965385595593, 0.143900125880395, 0.0886888466837192,
0.0658306703517972), RMSE.DWLS.as7 = c(0.175479072825633, 0.109726321996686,
0.0708072837357457, 0.108338677858974, 0.0924626464890691, 0.0698394406938808
), RMSE.DWLS.as8 = c(0.21999811367091, 0.136784063609674, 0.0987668297810263,
0.0972103249100205, 0.0839777696057619, 0.0418375203096049),
RMSE.DWLS.as9 = c(0.151628939447308, 0.130615796898171, 0.0826500106656456,
0.110758447022242, 0.0855252197988788, 0.0469238977192738
), RMSE.DWLS.as10 = c(0.148629703640329, 0.0929387781340155,
0.0587721028000359, 0.13494328596025, 0.124614758981451,
0.0834027778795365), bias.FIML.as11 = c(NA, NA, NA, 0.0830491413104031,
0.095007341187856, 0.0869018179698066), bias.FIML.as12 = c(NA,
NA, NA, 0.0100028273048038, -0.0112222333040523, -0.00938127887610686
), bias.FIML.as13 = c(NA, NA, NA, 0.0408685281973559, 0.0090841564807892,
0.00471677129591436), bias.FIML.as14 = c(NA, NA, NA, -0.00673809704034725,
-0.00662002455096607, 0.00897883015619389), bias.FIML.as15 = c(NA,
NA, NA, 0.0124036646701208, -0.00981224087538136, -0.00299896920818849
), bias.FIML.as16 = c(NA, NA, NA, 0.00900437163935713, 0.00534607839030457,
0.00330422787157367), bias.FIML.as17 = c(NA, NA, NA, 0.00475456306611124,
-0.00467176189639573, -0.00124413927456383), bias.FIML.as18 = c(NA,
NA, NA, -0.019891717055617, -0.00518918722361172, 0.00612272238230245
), bias.FIML.as19 = c(NA, NA, NA, 0.0399288789865454, 0.0131510016588795,
0.0809206926241737), bias.FIML.as20 = c(NA, NA, NA, 0.020759410539604,
0.0156651992157086, 0.00780090831049344), bias.DWLS.as11 = c(NA,
NA, NA, 0.0506455305268464, 0.0430803767927468, 0.030377189533703
), bias.DWLS.as12 = c(NA, NA, NA, 0.0261301492827064, 0.00187542053367863,
0.00219448145068458), bias.DWLS.as13 = c(NA, NA, NA, 0.0511621308126493,
0.013405419367001, 0.00353836019444587), bias.DWLS.as14 = c(NA,
NA, NA, 0.00361983463796194, 0.00169530588275873, 0.0152146719816353
), bias.DWLS.as15 = c(NA, NA, NA, 0.0280105236741299, 0.00221738217591506,
0.00825107714284046), bias.DWLS.as16 = c(NA, NA, NA, 0.0138920622611196,
0.0122639983448838, 0.00568489271733814), bias.DWLS.as17 = c(NA,
NA, NA, 0.0231409276086103, 0.0130637265083751, 0.0138470260549016
), bias.DWLS.as18 = c(NA, NA, NA, -0.00208867813813044, 0.00570286219469647,
0.012317737900196), bias.DWLS.as19 = c(NA, NA, NA, 0.0216940209036227,
-0.0148294785893468, 0.0450685641754062), bias.DWLS.as20 = c(NA,
NA, NA, 0.0203795524666364, 0.00481844765773329, -0.00613487210330998
), RMSE.FIML.as11 = c(NA, NA, NA, 0.292096980532701, 0.235534832213838,
0.184662800893681), RMSE.FIML.as12 = c(NA, NA, NA, 0.10641114349155,
0.0943348554021129, 0.060219681688861), RMSE.FIML.as13 = c(NA,
NA, NA, 0.172421435992293, 0.0938187003755333, 0.0821283359085059
), RMSE.FIML.as14 = c(NA, NA, NA, 0.145921504642245, 0.075416794830371,
0.0556287101197511), RMSE.FIML.as15 = c(NA, NA, NA, 0.118231249292452,
0.0843533835562838, 0.0465126604452353), RMSE.FIML.as16 = c(NA,
NA, NA, 0.120688925753367, 0.0842861425585318, 0.0584867605080079
), RMSE.FIML.as17 = c(NA, NA, NA, 0.114800836947261, 0.0992198033392624,
0.0650642076723423), RMSE.FIML.as18 = c(NA, NA, NA, 0.126137665827755,
0.0952061221756742, 0.0720143385500834), RMSE.FIML.as19 = c(NA,
NA, NA, 0.204538972829698, 0.161236648417108, 0.14300132761329
), RMSE.FIML.as20 = c(NA, NA, NA, 0.199244458662636, 0.104715108632983,
0.0810766087304274), RMSE.DWLS.as11 = c(NA, NA, NA, 0.279554536412178,
0.204193273694762, 0.155067053429494), RMSE.DWLS.as12 = c(NA,
NA, NA, 0.110334113732211, 0.0926437385997798, 0.0568966288404395
), RMSE.DWLS.as13 = c(NA, NA, NA, 0.170960299590031, 0.0891661235880171,
0.0763264540773726), RMSE.DWLS.as14 = c(NA, NA, NA, 0.144949491079347,
0.0728667430773017, 0.053957134217899), RMSE.DWLS.as15 = c(NA,
NA, NA, 0.121279145877991, 0.0848763047254127, 0.0478061317305351
), RMSE.DWLS.as16 = c(NA, NA, NA, 0.120349171526876, 0.0830319731318045,
0.0584462594637891), RMSE.DWLS.as17 = c(NA, NA, NA, 0.117967320778588,
0.0960678146319107, 0.064246995833448), RMSE.DWLS.as18 = c(NA,
NA, NA, 0.129007435717893, 0.0922455348160595, 0.0714939914173973
), RMSE.DWLS.as19 = c(NA, NA, NA, 0.190973406614925, 0.151258132770322,
0.121846388100839), RMSE.DWLS.as20 = c(NA, NA, NA, 0.197141715251734,
0.0979466513589997, 0.0762009270480505), REPLICATIONS = c(50L,
50L, 50L, 50L, 50L, 50L), SIM_TIME = c(9.18, 8.32, 11.42,
16.97, 18.53, 26.45), COMPLETED = c("Fri Jul 30 18:00:04 2021",
"Fri Jul 30 18:00:12 2021", "Fri Jul 30 18:00:24 2021", "Fri Jul 30 18:00:41 2021",
"Fri Jul 30 18:00:59 2021", "Fri Jul 30 18:01:26 2021"),
SEED = c(2112454368L, 2080255079L, 1094415411L, 47985954L,
982096001L, 321524667L)), row.names = c(NA, -6L), class = c("SimDesign",
"tbl_df", "tbl", "data.frame"), design_names = list(design = c("sample_size",
"nitems"), sim = c("bias.FIML.as1", "bias.FIML.as2", "bias.FIML.as3",
"bias.FIML.as4", "bias.FIML.as5", "bias.FIML.as6", "bias.FIML.as7",
"bias.FIML.as8", "bias.FIML.as9", "bias.FIML.as10", "bias.DWLS.as1",
"bias.DWLS.as2", "bias.DWLS.as3", "bias.DWLS.as4", "bias.DWLS.as5",
"bias.DWLS.as6", "bias.DWLS.as7", "bias.DWLS.as8", "bias.DWLS.as9",
"bias.DWLS.as10", "RMSE.FIML.as1", "RMSE.FIML.as2", "RMSE.FIML.as3",
"RMSE.FIML.as4", "RMSE.FIML.as5", "RMSE.FIML.as6", "RMSE.FIML.as7",
"RMSE.FIML.as8", "RMSE.FIML.as9", "RMSE.FIML.as10", "RMSE.DWLS.as1",
"RMSE.DWLS.as2", "RMSE.DWLS.as3", "RMSE.DWLS.as4", "RMSE.DWLS.as5",
"RMSE.DWLS.as6", "RMSE.DWLS.as7", "RMSE.DWLS.as8", "RMSE.DWLS.as9",
"RMSE.DWLS.as10", "bias.FIML.as11", "bias.FIML.as12", "bias.FIML.as13",
"bias.FIML.as14", "bias.FIML.as15", "bias.FIML.as16", "bias.FIML.as17",
"bias.FIML.as18", "bias.FIML.as19", "bias.FIML.as20", "bias.DWLS.as11",
"bias.DWLS.as12", "bias.DWLS.as13", "bias.DWLS.as14", "bias.DWLS.as15",
"bias.DWLS.as16", "bias.DWLS.as17", "bias.DWLS.as18", "bias.DWLS.as19",
"bias.DWLS.as20", "RMSE.FIML.as11", "RMSE.FIML.as12", "RMSE.FIML.as13",
"RMSE.FIML.as14", "RMSE.FIML.as15", "RMSE.FIML.as16", "RMSE.FIML.as17",
"RMSE.FIML.as18", "RMSE.FIML.as19", "RMSE.FIML.as20", "RMSE.DWLS.as11",
"RMSE.DWLS.as12", "RMSE.DWLS.as13", "RMSE.DWLS.as14", "RMSE.DWLS.as15",
"RMSE.DWLS.as16", "RMSE.DWLS.as17", "RMSE.DWLS.as18", "RMSE.DWLS.as19",
"RMSE.DWLS.as20"), bootCI = character(0), extra = c("REPLICATIONS",
"SIM_TIME", "COMPLETED", "SEED"), errors = "ERRORS", warnings = "WARNINGS"), ERROR_msg = structure(list(), .Names = character(0), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame")), WARNING_msg = structure(list(), .Names = character(0), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame")), extra_info = list(
sessionInfo = structure(list(platform = structure(list(version = "R version 4.0.2 (2020-06-22)",
os = "Windows 10 x64", system = "x86_64, mingw32", ui = "RStudio",
language = "(EN)", collate = "English_United States.1252",
ctype = "English_United States.1252", tz = "America/New_York",
date = "2021-07-30"), class = "platform_info"), packages = structure(list(
package = c("assertthat", "bayestestR", "cli", "cluster",
"coda", "codetools", "crayon", "curl", "datawizard",
"DBI", "dcurver", "Deriv", "digest", "dplyr", "effectsize",
"ellipsis", "emmeans", "estimability", "evaluate", "fansi",
"foreach", "generics", "glue", "GPArotation", "htmltools",
"insight", "iterators", "jsonlite", "knitr", "lattice",
"lavaan", "lifecycle", "magrittr", "MASS", "Matrix",
"mgcv", "mirt", "mnormt", "multcomp", "mvtnorm", "nlme",
"parameters", "pbapply", "pbivnorm", "permute", "pillar",
"pkgconfig", "plyr", "purrr", "R6", "Rcpp", "reshape2",
"rlang", "rmarkdown", "RPushbullet", "rstudioapi", "sandwich",
"sessioninfo", "SimDesign", "stringi", "stringr", "survival",
"TH.data", "tibble", "tidyselect", "tmvnsim", "utf8",
"vctrs", "vegan", "withr", "xfun", "xtable", "yaml",
"zoo"), ondiskversion = c("0.2.1", "0.10.5", "3.0.1",
"2.1.2", "0.19.4", "0.2.18", "1.4.1", "4.3.2", "0.1.0",
"1.1.1", "0.9.2", "4.1.3", "0.6.27", "1.0.7", "0.4.5",
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number_of_conditions = 6L, date_completed = "Fri Jul 30 18:01:26 2021",
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## -----------------------------------------------------------------------------
res
## -----------------------------------------------------------------------------
Design <- createDesign(sample_size = sample_sizes,
nitems = nitems,
indicators = c('discrete', 'continuous'))
Design
## -----------------------------------------------------------------------------
Analyse.FIML <- function(condition, dat, fixed_objects = NULL) {
AnalyseIf(condition$indicators == 'discrete')
# equivalently:
# AnalyseIf(indicators == 'discrete', condition)
# with(condition, AnalyseIf(indicators == 'discrete'))
mod <- mirt(dat, 1L, verbose=FALSE)
if(!extract.mirt(mod, 'converged')) stop('mirt did not converge')
cfs <- coef(mod, simplify = TRUE, digits = Inf)
FIML_as <- cfs$items[,1L] / 1.702
ret <- c(as=unname(FIML_as))
ret
}
## ----eval=FALSE---------------------------------------------------------------
# Generate.G1 <- function(condition, fixed_objects = NULL) {
# GenerateIf(condition$indicators == 'discrete')
# ...
# dat
# }
#
# Generate.G2 <- function(condition, fixed_objects = NULL) {
# GenerateIf(condition$indicators == 'continuous')
# ...
# dat
# }
#
# res <- runSimulation(design=Design, replications=1000,
# generate=list(G1=Generate.G1, G2=Generate.G2),
# analyse=list(DWLS=Analyse.DWLS, FIML=Analyse.FIML),
# summarise=Summarise)
## -----------------------------------------------------------------------------
Design <- createDesign(sample_size = sample_sizes,
nitems = nitems,
method = c('FIML', 'DWLS'))
Design
## ----eval=FALSE---------------------------------------------------------------
# Analyse.FIML <- function(condition, dat, fixed_objects = NULL) {
# AnalyseIf(method == 'FIML', condition)
# #...
# }
#
# Analyse.DWLS <- function(condition, dat, fixed_objects = NULL) {
# AnalyseIf(method == 'DWLS', condition)
# # ...
# }
#
# # ...
# res <- runSimulation(Design, replications=100, verbose=FALSE, parallel=TRUE,
# generate=Generate,
# analyse=list(Analyse.FIML, Analyse.DWLS),
# summarise=Summarise, filename = 'mirt_lavaan',
# packages=c('mirt', 'lavaan'), fixed_objects=pars)
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## ----nomessages, echo = FALSE-------------------------------------------------
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
fig.height = 5,
fig.width = 5
)
options(digits=4)
par(mar=c(3,3,1,1)+.1)
## -----------------------------------------------------------------------------
SimDesign::SimFunctions()
## -----------------------------------------------------------------------------
SimDesign::SimFunctions(nAnalyses = 2)
## -----------------------------------------------------------------------------
library(SimDesign)
# SimFunctions(nAnalyses = 2)
sample_sizes <- c(250, 500, 1000)
nitems <- c(10, 20)
Design <- createDesign(sample_size = sample_sizes,
nitems = nitems)
# create list of additional parameters which are fixed across conditions
set.seed(1)
pars_10 <- rbind(a = round(rlnorm(10, .3, .5)/1.702, 2),
d = round(rnorm(10, 0, .5)/1.702, 2))
pars_20 <- rbind(a = round(rlnorm(20, .3, .5)/1.702, 2),
d = round(rnorm(20, 0, .5)/1.702, 2))
pars <- list(ten=pars_10, twenty=pars_20)
P_logit <- function(a, d, Theta) exp(a * Theta + d) / (1 + exp(a * Theta + d))
P_ogive <- function(a, d, Theta) pnorm(a * Theta + d)
## ----eval=FALSE---------------------------------------------------------------
# Generate <- function(condition, fixed_objects = NULL) {
#
# N <- condition$sample_size
# nitems <- condition$nitems
# nitems_name <- ifelse(nitems == 10, 'ten', 'twenty')
#
# #extract objects from fixed_objects
# a <- fixed_objects[[nitems_name]]['a', ]
# d <- fixed_objects[[nitems_name]]['d', ]
#
# dat <- matrix(NA, N, nitems)
# colnames(dat) <- paste0('item_', 1:nitems)
# Theta <- rnorm(N)
# for(j in 1:nitems){
# p <- P_ogive(a[j], d[j], Theta)
# for(i in 1:N)
# dat[i,j] <- sample(c(1,0), 1, prob = c(p[i], 1 - p[i]))
# }
# as.data.frame(dat) #data.frame works nicer with lavaan
# }
#
# Analyse.FIML <- function(condition, dat, fixed_objects = NULL) {
# mod <- mirt(dat, 1L, verbose=FALSE)
# if(!extract.mirt(mod, 'converged')) stop('mirt did not converge')
# cfs <- mirt::coef(mod, simplify = TRUE, digits = Inf)
# FIML_as <- cfs$items[,1L] / 1.702
#
# ret <- c(as=unname(FIML_as))
# ret
# }
#
# Analyse.DWLS <- function(condition, dat, fixed_objects = NULL) {
# nitems <- condition$nitems
# lavmod <- paste0('F =~ ', paste0('NA*', colnames(dat)[1L], ' + '),
# paste0(colnames(dat)[-1L], collapse = ' + '),
# '\nF ~~ 1*F')
# lmod <- sem(lavmod, dat, ordered = colnames(dat))
# if(!lavInspect(lmod, 'converged')) stop('lavaan did not converge')
# cfs2 <- lavaan::coef(lmod)
# DWLS_alpha <- cfs2[1L:nitems]
# const <- sqrt(1 - DWLS_alpha^2)
# DWLS_as <- DWLS_alpha / const
#
# ret <- c(as=unname(DWLS_as))
# ret
# }
#
# Summarise <- function(condition, results, fixed_objects = NULL) {
# nitems <- condition$nitems
# nitems_name <- ifelse(nitems == 10, 'ten', 'twenty')
#
# #extract objects from fixed_objects
# a <- fixed_objects[[nitems_name]]['a', ]
# pop <- c(a, a)
#
# obt_bias <- bias(results, pop)
# obt_RMSE <- RMSE(results, pop)
# ret <- c(bias=obt_bias, RMSE=obt_RMSE)
# ret
# }
## ----eval=FALSE---------------------------------------------------------------
# res <- runSimulation(Design, replications=100, verbose=FALSE, parallel=TRUE,
# generate=Generate,
# analyse=list(FIML=Analyse.FIML, DWLS=Analyse.DWLS),
# summarise=Summarise, filename = 'mirt_lavaan',
# packages=c('mirt', 'lavaan'), fixed_objects=pars)
## ----include=FALSE------------------------------------------------------------
res <- structure(list(sample_size = c(250, 500, 1000, 250, 500, 1000
), nitems = c(10, 10, 10, 20, 20, 20), bias.FIML.as1 = c(0.00998872755798466,
-0.0211364605578725, -0.0039472918051987, 0.0606466866510326,
0.0192019747460773, 0.019976239470624), bias.FIML.as2 = c(0.0134569433967216,
0.0187201139317983, -0.00147776827122514, 0.0117871403061117,
0.0453762090796238, 0.0186079659977207), bias.FIML.as3 = c(-0.0201613259701166,
-0.0181734714318961, -0.0133933373300669, 0.0646879764208232,
0.0218366906966479, 0.00798167692557969), bias.FIML.as4 = c(0.186161341186305,
0.104237862202863, 0.0778897412095584, -0.026313815932234, -0.00635870499131507,
-0.0114981251130375), bias.FIML.as5 = c(0.00926532291093203,
0.029297553049638, 0.0035690208390262, 0.000582216674185383,
0.0202974652037632, -0.0128424807545063), bias.FIML.as6 = c(-0.00394277165975909,
-0.0154400356375581, -0.0154100791014608, -0.0115871332168039,
-0.00169280535431738, -0.02185583063946), bias.FIML.as7 = c(0.0321426765516901,
0.0023139112162729, -0.00743302248376659, -0.0563501689996753,
0.000191353333746391, -0.00062269083403889), bias.FIML.as8 = c(0.063841617844854,
0.0136240874132899, 0.0239706654002676, -0.019324789107572, -0.00713416098241873,
-0.0157261985329955), bias.FIML.as9 = c(-0.0126321813383556,
-0.00113623264989128, 0.00151647175345255, -0.00247479868708876,
-0.00124512711889233, -0.017917627196065), bias.FIML.as10 = c(-0.0137551418255338,
-0.00996289662703265, -0.015724369581452, 0.025750717572856,
0.0495214634367752, 0.012263001380898), bias.DWLS.as1 = c(0.0213300692456042,
-0.0081120110659304, 0.00849135597642985, 0.0534763484009736,
-4.70350823508348e-05, -0.00198898912373827), bias.DWLS.as2 = c(0.0304066070980667,
0.0303666456319019, 0.011398130200282, 0.0114298773633547, 0.0303730809063532,
0.0015361793423236), bias.DWLS.as3 = c(0.00246773719111794, 0.00141903952079597,
0.00540631339266324, 0.0795711029587868, 0.0330531775433939,
0.0136383667144754), bias.DWLS.as4 = c(0.132204497704931, 0.0332479254110806,
0.018813114931708, -0.00847471064945973, 0.00720439015885141,
0.00205705227622941), bias.DWLS.as5 = c(0.0191320392893439, 0.0354771384906975,
0.0102778769019432, 0.0062441336060167, 0.0107507859965388, -0.0214727665000171
), bias.DWLS.as6 = c(0.0183694257428437, 0.00597186386409428,
0.00582852666767624, 0.00640208680609534, 0.00744542125677253,
-0.0120158095590051), bias.DWLS.as7 = c(0.0410611355660595, 0.0107457642078628,
-0.00234411900870412, -0.0370124657245999, 0.0140574105187029,
0.012220363947128), bias.DWLS.as8 = c(0.0575286451947481, 0.0108730757552931,
0.0181331620498002, -0.00266809440442234, 0.00876268337603981,
0.000658260952319116), bias.DWLS.as9 = c(-0.0110572217167639,
-0.000566251052416587, 0.00213777252513437, 0.0180403647961145,
0.0175025650339115, -0.000679792738651153), bias.DWLS.as10 = c(0.00461196660602057,
0.00860520068561403, 0.00105892129699621, 0.0293744340937786,
0.0480624841941301, 0.0100024017106522), RMSE.FIML.as1 = c(0.136114724683302,
0.0820035231590711, 0.0602718630804655, 0.197687839442572, 0.165410223264985,
0.10028308108237), RMSE.FIML.as2 = c(0.135773523255822, 0.122316182433675,
0.0829661313819097, 0.200633052300666, 0.121673357983436, 0.0881231862689714
), RMSE.FIML.as3 = c(0.108991971326869, 0.0842131719350027, 0.06338600007589,
0.158497060515878, 0.105472067533837, 0.0688112173455798), RMSE.FIML.as4 = c(0.423944429084931,
0.292223091229885, 0.24006881507494, 0.0772117047077088, 0.0753675365419075,
0.0539848730775772), RMSE.FIML.as5 = c(0.169623653136512, 0.100852326267566,
0.0792406198849061, 0.149962341795796, 0.0984782215555278, 0.0953280973624482
), RMSE.FIML.as6 = c(0.124767951178948, 0.0818049168679144, 0.0594721724308959,
0.14629347996356, 0.0927538518556173, 0.0705377251884313), RMSE.FIML.as7 = c(0.181685861792146,
0.114947671878307, 0.074192073155478, 0.117084871867108, 0.0974724366252352,
0.0703488587415946), RMSE.FIML.as8 = c(0.231797645483998, 0.141319154230226,
0.104337825901184, 0.0973056056983475, 0.0812284517750514, 0.0424109426493268
), RMSE.FIML.as9 = c(0.16613193205312, 0.140760100129038, 0.0891584228213999,
0.108719996759675, 0.080308694331235, 0.0513084488030903), RMSE.FIML.as10 = c(0.153191702052709,
0.0939169728941718, 0.0607437545286223, 0.142087613218528, 0.125910660748991,
0.0839773589934654), RMSE.DWLS.as1 = c(0.135785481380796, 0.0787218803832953,
0.0605248038813955, 0.195826665179666, 0.14677612458812, 0.0906429185189553
), RMSE.DWLS.as2 = c(0.131586066027966, 0.123659417282268, 0.0798516833501493,
0.193625962651401, 0.110845768899907, 0.0757663744591717), RMSE.DWLS.as3 = c(0.109491701212598,
0.082474074479391, 0.0625244382818499, 0.163509158386427, 0.104336471143878,
0.0670832713055758), RMSE.DWLS.as4 = c(0.404291688201491, 0.251111850670021,
0.210429452484672, 0.0777287786631143, 0.0787790921225598, 0.0536674825333642
), RMSE.DWLS.as5 = c(0.166880765292909, 0.099643169312563, 0.077123029528217,
0.138727147330488, 0.0958299498529966, 0.0891417978698349), RMSE.DWLS.as6 = c(0.128222841124684,
0.0803072950196332, 0.0571965385595593, 0.143900125880395, 0.0886888466837192,
0.0658306703517972), RMSE.DWLS.as7 = c(0.175479072825633, 0.109726321996686,
0.0708072837357457, 0.108338677858974, 0.0924626464890691, 0.0698394406938808
), RMSE.DWLS.as8 = c(0.21999811367091, 0.136784063609674, 0.0987668297810263,
0.0972103249100205, 0.0839777696057619, 0.0418375203096049),
RMSE.DWLS.as9 = c(0.151628939447308, 0.130615796898171, 0.0826500106656456,
0.110758447022242, 0.0855252197988788, 0.0469238977192738
), RMSE.DWLS.as10 = c(0.148629703640329, 0.0929387781340155,
0.0587721028000359, 0.13494328596025, 0.124614758981451,
0.0834027778795365), bias.FIML.as11 = c(NA, NA, NA, 0.0830491413104031,
0.095007341187856, 0.0869018179698066), bias.FIML.as12 = c(NA,
NA, NA, 0.0100028273048038, -0.0112222333040523, -0.00938127887610686
), bias.FIML.as13 = c(NA, NA, NA, 0.0408685281973559, 0.0090841564807892,
0.00471677129591436), bias.FIML.as14 = c(NA, NA, NA, -0.00673809704034725,
-0.00662002455096607, 0.00897883015619389), bias.FIML.as15 = c(NA,
NA, NA, 0.0124036646701208, -0.00981224087538136, -0.00299896920818849
), bias.FIML.as16 = c(NA, NA, NA, 0.00900437163935713, 0.00534607839030457,
0.00330422787157367), bias.FIML.as17 = c(NA, NA, NA, 0.00475456306611124,
-0.00467176189639573, -0.00124413927456383), bias.FIML.as18 = c(NA,
NA, NA, -0.019891717055617, -0.00518918722361172, 0.00612272238230245
), bias.FIML.as19 = c(NA, NA, NA, 0.0399288789865454, 0.0131510016588795,
0.0809206926241737), bias.FIML.as20 = c(NA, NA, NA, 0.020759410539604,
0.0156651992157086, 0.00780090831049344), bias.DWLS.as11 = c(NA,
NA, NA, 0.0506455305268464, 0.0430803767927468, 0.030377189533703
), bias.DWLS.as12 = c(NA, NA, NA, 0.0261301492827064, 0.00187542053367863,
0.00219448145068458), bias.DWLS.as13 = c(NA, NA, NA, 0.0511621308126493,
0.013405419367001, 0.00353836019444587), bias.DWLS.as14 = c(NA,
NA, NA, 0.00361983463796194, 0.00169530588275873, 0.0152146719816353
), bias.DWLS.as15 = c(NA, NA, NA, 0.0280105236741299, 0.00221738217591506,
0.00825107714284046), bias.DWLS.as16 = c(NA, NA, NA, 0.0138920622611196,
0.0122639983448838, 0.00568489271733814), bias.DWLS.as17 = c(NA,
NA, NA, 0.0231409276086103, 0.0130637265083751, 0.0138470260549016
), bias.DWLS.as18 = c(NA, NA, NA, -0.00208867813813044, 0.00570286219469647,
0.012317737900196), bias.DWLS.as19 = c(NA, NA, NA, 0.0216940209036227,
-0.0148294785893468, 0.0450685641754062), bias.DWLS.as20 = c(NA,
NA, NA, 0.0203795524666364, 0.00481844765773329, -0.00613487210330998
), RMSE.FIML.as11 = c(NA, NA, NA, 0.292096980532701, 0.235534832213838,
0.184662800893681), RMSE.FIML.as12 = c(NA, NA, NA, 0.10641114349155,
0.0943348554021129, 0.060219681688861), RMSE.FIML.as13 = c(NA,
NA, NA, 0.172421435992293, 0.0938187003755333, 0.0821283359085059
), RMSE.FIML.as14 = c(NA, NA, NA, 0.145921504642245, 0.075416794830371,
0.0556287101197511), RMSE.FIML.as15 = c(NA, NA, NA, 0.118231249292452,
0.0843533835562838, 0.0465126604452353), RMSE.FIML.as16 = c(NA,
NA, NA, 0.120688925753367, 0.0842861425585318, 0.0584867605080079
), RMSE.FIML.as17 = c(NA, NA, NA, 0.114800836947261, 0.0992198033392624,
0.0650642076723423), RMSE.FIML.as18 = c(NA, NA, NA, 0.126137665827755,
0.0952061221756742, 0.0720143385500834), RMSE.FIML.as19 = c(NA,
NA, NA, 0.204538972829698, 0.161236648417108, 0.14300132761329
), RMSE.FIML.as20 = c(NA, NA, NA, 0.199244458662636, 0.104715108632983,
0.0810766087304274), RMSE.DWLS.as11 = c(NA, NA, NA, 0.279554536412178,
0.204193273694762, 0.155067053429494), RMSE.DWLS.as12 = c(NA,
NA, NA, 0.110334113732211, 0.0926437385997798, 0.0568966288404395
), RMSE.DWLS.as13 = c(NA, NA, NA, 0.170960299590031, 0.0891661235880171,
0.0763264540773726), RMSE.DWLS.as14 = c(NA, NA, NA, 0.144949491079347,
0.0728667430773017, 0.053957134217899), RMSE.DWLS.as15 = c(NA,
NA, NA, 0.121279145877991, 0.0848763047254127, 0.0478061317305351
), RMSE.DWLS.as16 = c(NA, NA, NA, 0.120349171526876, 0.0830319731318045,
0.0584462594637891), RMSE.DWLS.as17 = c(NA, NA, NA, 0.117967320778588,
0.0960678146319107, 0.064246995833448), RMSE.DWLS.as18 = c(NA,
NA, NA, 0.129007435717893, 0.0922455348160595, 0.0714939914173973
), RMSE.DWLS.as19 = c(NA, NA, NA, 0.190973406614925, 0.151258132770322,
0.121846388100839), RMSE.DWLS.as20 = c(NA, NA, NA, 0.197141715251734,
0.0979466513589997, 0.0762009270480505), REPLICATIONS = c(50L,
50L, 50L, 50L, 50L, 50L), SIM_TIME = c(9.18, 8.32, 11.42,
16.97, 18.53, 26.45), COMPLETED = c("Fri Jul 30 18:00:04 2021",
"Fri Jul 30 18:00:12 2021", "Fri Jul 30 18:00:24 2021", "Fri Jul 30 18:00:41 2021",
"Fri Jul 30 18:00:59 2021", "Fri Jul 30 18:01:26 2021"),
SEED = c(2112454368L, 2080255079L, 1094415411L, 47985954L,
982096001L, 321524667L)), row.names = c(NA, -6L), class = c("SimDesign",
"tbl_df", "tbl", "data.frame"), design_names = list(design = c("sample_size",
"nitems"), sim = c("bias.FIML.as1", "bias.FIML.as2", "bias.FIML.as3",
"bias.FIML.as4", "bias.FIML.as5", "bias.FIML.as6", "bias.FIML.as7",
"bias.FIML.as8", "bias.FIML.as9", "bias.FIML.as10", "bias.DWLS.as1",
"bias.DWLS.as2", "bias.DWLS.as3", "bias.DWLS.as4", "bias.DWLS.as5",
"bias.DWLS.as6", "bias.DWLS.as7", "bias.DWLS.as8", "bias.DWLS.as9",
"bias.DWLS.as10", "RMSE.FIML.as1", "RMSE.FIML.as2", "RMSE.FIML.as3",
"RMSE.FIML.as4", "RMSE.FIML.as5", "RMSE.FIML.as6", "RMSE.FIML.as7",
"RMSE.FIML.as8", "RMSE.FIML.as9", "RMSE.FIML.as10", "RMSE.DWLS.as1",
"RMSE.DWLS.as2", "RMSE.DWLS.as3", "RMSE.DWLS.as4", "RMSE.DWLS.as5",
"RMSE.DWLS.as6", "RMSE.DWLS.as7", "RMSE.DWLS.as8", "RMSE.DWLS.as9",
"RMSE.DWLS.as10", "bias.FIML.as11", "bias.FIML.as12", "bias.FIML.as13",
"bias.FIML.as14", "bias.FIML.as15", "bias.FIML.as16", "bias.FIML.as17",
"bias.FIML.as18", "bias.FIML.as19", "bias.FIML.as20", "bias.DWLS.as11",
"bias.DWLS.as12", "bias.DWLS.as13", "bias.DWLS.as14", "bias.DWLS.as15",
"bias.DWLS.as16", "bias.DWLS.as17", "bias.DWLS.as18", "bias.DWLS.as19",
"bias.DWLS.as20", "RMSE.FIML.as11", "RMSE.FIML.as12", "RMSE.FIML.as13",
"RMSE.FIML.as14", "RMSE.FIML.as15", "RMSE.FIML.as16", "RMSE.FIML.as17",
"RMSE.FIML.as18", "RMSE.FIML.as19", "RMSE.FIML.as20", "RMSE.DWLS.as11",
"RMSE.DWLS.as12", "RMSE.DWLS.as13", "RMSE.DWLS.as14", "RMSE.DWLS.as15",
"RMSE.DWLS.as16", "RMSE.DWLS.as17", "RMSE.DWLS.as18", "RMSE.DWLS.as19",
"RMSE.DWLS.as20"), bootCI = character(0), extra = c("REPLICATIONS",
"SIM_TIME", "COMPLETED", "SEED"), errors = "ERRORS", warnings = "WARNINGS"), ERROR_msg = structure(list(), .Names = character(0), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame")), WARNING_msg = structure(list(), .Names = character(0), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame")), extra_info = list(
sessionInfo = structure(list(platform = structure(list(version = "R version 4.0.2 (2020-06-22)",
os = "Windows 10 x64", system = "x86_64, mingw32", ui = "RStudio",
language = "(EN)", collate = "English_United States.1252",
ctype = "English_United States.1252", tz = "America/New_York",
date = "2021-07-30"), class = "platform_info"), packages = structure(list(
package = c("assertthat", "bayestestR", "cli", "cluster",
"coda", "codetools", "crayon", "curl", "datawizard",
"DBI", "dcurver", "Deriv", "digest", "dplyr", "effectsize",
"ellipsis", "emmeans", "estimability", "evaluate", "fansi",
"foreach", "generics", "glue", "GPArotation", "htmltools",
"insight", "iterators", "jsonlite", "knitr", "lattice",
"lavaan", "lifecycle", "magrittr", "MASS", "Matrix",
"mgcv", "mirt", "mnormt", "multcomp", "mvtnorm", "nlme",
"parameters", "pbapply", "pbivnorm", "permute", "pillar",
"pkgconfig", "plyr", "purrr", "R6", "Rcpp", "reshape2",
"rlang", "rmarkdown", "RPushbullet", "rstudioapi", "sandwich",
"sessioninfo", "SimDesign", "stringi", "stringr", "survival",
"TH.data", "tibble", "tidyselect", "tmvnsim", "utf8",
"vctrs", "vegan", "withr", "xfun", "xtable", "yaml",
"zoo"), ondiskversion = c("0.2.1", "0.10.5", "3.0.1",
"2.1.2", "0.19.4", "0.2.18", "1.4.1", "4.3.2", "0.1.0",
"1.1.1", "0.9.2", "4.1.3", "0.6.27", "1.0.7", "0.4.5",
"0.3.2", "1.6.2.1", "1.3", "0.14", "0.5.0", "1.5.1",
"0.1.0", "1.4.2", "2014.11.1", "0.5.1.1", "0.14.2", "1.0.13",
"1.7.2", "1.33", "0.20.44", "0.6.9", "1.0.0", "2.0.1",
"7.3.54", "1.3.4", "1.8.36", "1.34.1", "2.0.2", "1.4.17",
"1.1.2", "3.1.152", "0.14.0", "1.4.3", "0.6.0", "0.9.5",
"1.6.1", "2.0.3", "1.8.6", "0.3.4", "2.5.0", "1.0.7",
"1.4.4", "0.4.11", "2.9", "0.3.4", "0.13", "3.0.1", "1.1.1",
"2.6.3", "1.7.3", "1.4.0", "3.2.11", "1.0.10", "3.1.3",
"1.1.1", "1.0.2", "1.2.2", "0.3.8", "2.5.7", "2.4.2",
"0.24", "1.8.4", "2.2.1", "1.8.9"), loadedversion = c("0.2.1",
"0.10.5", "3.0.1", "2.1.2", "0.19-4", "0.2-18", "1.4.1",
"4.3.2", "0.1.0", "1.1.1", "0.9.2", "4.1.3", "0.6.27",
"1.0.7", "0.4.5", "0.3.2", "1.6.2-1", "1.3", "0.14",
"0.5.0", "1.5.1", "0.1.0", "1.4.2", "2014.11-1", "0.5.1.1",
"0.14.2", "1.0.13", "1.7.2", "1.33", "0.20-44", "0.6-9",
"1.0.0", "2.0.1", "7.3-54", "1.3-4", "1.8-36", "1.34.1",
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)), save_info = c(filename = "mirt_lavaan-1.rds"), ncores = 8L,
number_of_conditions = 6L, date_completed = "Fri Jul 30 18:01:26 2021",
total_elapsed_time = 90.87, error_seeds = structure(list(), .Names = character(0), row.names = integer(0), class = c("tbl_df",
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## -----------------------------------------------------------------------------
res
## -----------------------------------------------------------------------------
Design <- createDesign(sample_size = sample_sizes,
nitems = nitems,
indicators = c('discrete', 'continuous'))
Design
## -----------------------------------------------------------------------------
Analyse.FIML <- function(condition, dat, fixed_objects = NULL) {
AnalyseIf(condition$indicators == 'discrete')
# equivalently:
# AnalyseIf(indicators == 'discrete', condition)
# with(condition, AnalyseIf(indicators == 'discrete'))
mod <- mirt(dat, 1L, verbose=FALSE)
if(!extract.mirt(mod, 'converged')) stop('mirt did not converge')
cfs <- coef(mod, simplify = TRUE, digits = Inf)
FIML_as <- cfs$items[,1L] / 1.702
ret <- c(as=unname(FIML_as))
ret
}
## ----eval=FALSE---------------------------------------------------------------
# Generate.G1 <- function(condition, fixed_objects = NULL) {
# GenerateIf(condition$indicators == 'discrete')
# ...
# dat
# }
#
# Generate.G2 <- function(condition, fixed_objects = NULL) {
# GenerateIf(condition$indicators == 'continuous')
# ...
# dat
# }
#
# res <- runSimulation(design=Design, replications=1000,
# generate=list(G1=Generate.G1, G2=Generate.G2),
# analyse=list(DWLS=Analyse.DWLS, FIML=Analyse.FIML),
# summarise=Summarise)
## -----------------------------------------------------------------------------
Design <- createDesign(sample_size = sample_sizes,
nitems = nitems,
method = c('FIML', 'DWLS'))
Design
## ----eval=FALSE---------------------------------------------------------------
# Analyse.FIML <- function(condition, dat, fixed_objects = NULL) {
# AnalyseIf(method == 'FIML', condition)
# #...
# }
#
# Analyse.DWLS <- function(condition, dat, fixed_objects = NULL) {
# AnalyseIf(method == 'DWLS', condition)
# # ...
# }
#
# # ...
# res <- runSimulation(Design, replications=100, verbose=FALSE, parallel=TRUE,
# generate=Generate,
# analyse=list(Analyse.FIML, Analyse.DWLS),
# summarise=Summarise, filename = 'mirt_lavaan',
# packages=c('mirt', 'lavaan'), fixed_objects=pars)
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