Package {CoTiMA}

A128 example matrix

Description

A128 example matrix

Usage

A128

Format

An object of class matrix (inherits from array) with 2 rows and 2 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

A313 example matrix

Description

A313 example matrix

Usage

A313

Format

An object of class matrix (inherits from array) with 2 rows and 2 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaBiG-object reproducing results of Guthier et al. (2020)

Description

ctmaBiG-object reproducing results of Guthier et al. (2020)

Usage

CoTiMABiG_D_BO

Format

An object of class CoTiMAFit of length 10.

Author(s)

C. Guthier, C. Dormann & J. Cortina CoTiMA@uni-mainz.org

ctmaFit-object with a 'full' CoTiMA of 3 studies

Description

ctmaFit-object with a 'full' CoTiMA of 3 studies

Usage

CoTiMAFullFit_3

Format

An object of class CoTiMAFit of length 13.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaFit-object with a 'full' CoTiMA of 6 studies

Description

ctmaFit-object with a 'full' CoTiMA of 6 studies

Usage

CoTiMAFullFit_6

Format

An object of class CoTiMAFit of length 10.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaFit-object with a 'full' CoTiMA of 6 studies

Description

ctmaFit-object with a 'full' CoTiMA of 6 studies

Usage

CoTiMAFullFit_6_new

Format

An object of class CoTiMAFit of length 11.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

1st fitted ctmaFit-object in a series of 2 to test equality of 2 cross effects

Description

1st fitted ctmaFit-object in a series of 2 to test equality of 2 cross effects

Usage

CoTiMAFullInv23Fit_6

Format

An object of class CoTiMAFit of length 12.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

2nd fitted ctmaFit-object in a series of 2 to test equality of 2 cross effects

Description

2nd fitted ctmaFit-object in a series of 2 to test equality of 2 cross effects

Usage

CoTiMAFullInvEq23Fit_6

Format

An object of class CoTiMAFit of length 11.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaInit-object with of 3 primary studies

Description

ctmaInit-object with of 3 primary studies

Usage

CoTiMAInitFit_3

Format

An object of class CoTiMAFit of length 17.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaInit-object with 6 primary studies

Description

ctmaInit-object with 6 primary studies

Usage

CoTiMAInitFit_6

Format

An object of class CoTiMAFit of length 18.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaInit-object with a 'full' CoTiMA of 6 studies using NUTS sampler

Description

ctmaInit-object with a 'full' CoTiMA of 6 studies using NUTS sampler

Usage

CoTiMAInitFit_6_NUTS

Format

An object of class CoTiMAFit of length 17.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaInit-object with 6 primary studies

Description

ctmaInit-object with 6 primary studies

Usage

CoTiMAInitFit_6_new

Format

An object of class CoTiMAFit of length 18.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaInit-object created by Guthier et al. (2020) with 48 primary studies

Description

ctmaInit-object created by Guthier et al. (2020) with 48 primary studies

Usage

CoTiMAInitFit_D_BO

Format

An object of class CoTiMAFit of length 12.

Author(s)

C. Guthier, C. Dormann & J. Cortina CoTiMA@uni-mainz.org

ctmaFit-object with a categorical moderator of the full drift matrix

Description

ctmaFit-object with a categorical moderator of the full drift matrix

Usage

CoTiMAMod1onFullFit_6

Format

An object of class CoTiMAFit of length 13.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaFit-object with a categorical moderator of the full drift matrix

Description

ctmaFit-object with a categorical moderator of the full drift matrix

Usage

CoTiMAMod1onFullFit_6_cats12

Format

An object of class CoTiMAFit of length 11.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaFit-object with a continuous moderator of 2 cross effects

Description

ctmaFit-object with a continuous moderator of 2 cross effects

Usage

CoTiMAMod2on23Fit_6

Format

An object of class CoTiMAFit of length 13.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaFit-object with with only one cross effect and this one set equal across primary studies

Description

ctmaFit-object with with only one cross effect and this one set equal across primary studies

Usage

CoTiMAPart134Inv3Fit_6

Format

An object of class CoTiMAFit of length 13.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaPower-object reproducing results of Guthier et al. (2020)

Description

ctmaPower-object reproducing results of Guthier et al. (2020)

Usage

CoTiMAPower_D_BO

Format

An object of class CoTiMAFit of length 10.

Author(s)

C. Guthier, C. Dormann & J. Cortina CoTiMA@uni-mainz.org

This are preset arguments

Description

This are preset arguments

object created to store standard parameters passed forward to ctStanFit

Usage

CoTiMAStanctArgs

CoTiMAStanctArgs

Format

An object of class list of length 37.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaPrep-object created with 3 primary studies

Description

ctmaPrep-object created with 3 primary studies

Usage

CoTiMAstudyList_3

Format

An object of class CoTiMAFit of length 28.

Author(s)

C. Guthier, C. Dormann & J. Cortina CoTiMA@uni-mainz.org

ctmaPrep-object created with 6 primary studies

Description

ctmaPrep-object created with 6 primary studies

Usage

CoTiMAstudyList_6

Format

An object of class CoTiMAFit of length 30.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaPrep-object created with 6 primary studies

Description

ctmaPrep-object created with 6 primary studies

Usage

CoTiMAstudyList_6_new

Format

An object of class CoTiMAFit of length 30.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageM1 example vector

Description

ageM1 example vector

Usage

ageM1

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageM128 example vector

Description

ageM128 example vector

Usage

ageM128

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageM18 example vector

Description

ageM18 example vector

Usage

ageM18

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageM201 example vector

Description

ageM201 example vector

Usage

ageM201

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageM313 example vector

Description

ageM313 example vector

Usage

ageM313

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageM32 example vector

Description

ageM32 example vector

Usage

ageM32

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageM4 example vector

Description

ageM4 example vector

Usage

ageM4

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageSD1 example vector

Description

ageSD1 example vector

Usage

ageSD1

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageSD128 example vector

Description

ageSD128 example vector

Usage

ageSD128

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageSD18 example vector

Description

ageSD18 example vector

Usage

ageSD18

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageSD201 example vector

Description

ageSD201 example vector

Usage

ageSD201

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageSD313 example vector

Description

ageSD313 example vector

Usage

ageSD313

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageSD32 example vector

Description

ageSD32 example vector

Usage

ageSD32

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ageSD4 example vector

Description

ageSD4 example vector

Usage

ageSD4

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

alphas128 example vector

Description

alphas128 example vector

Usage

alphas128

Format

An object of class numeric of length 9.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

alphas313 example vector

Description

alphas313 example vector

Usage

alphas313

Format

An object of class numeric of length 6.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

burnout1 example vector

Description

burnout1 example vector

Usage

burnout1

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

burnout128 example vector

Description

burnout128 example vector

Usage

burnout128

Format

An object of class character of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

burnout18 example vector

Description

burnout18 example vector

Usage

burnout18

Format

An object of class character of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

burnout201 example vector

Description

burnout201 example vector

Usage

burnout201

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

burnout313 example vector

Description

burnout313 example vector

Usage

burnout313

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

burnout32 example vector

Description

burnout32 example vector

Usage

burnout32

Format

An object of class character of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

burnout4 example vector

Description

burnout4 example vector

Usage

burnout4

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

combineVariables128 example vector

Description

combineVariables128 example vector

Usage

combineVariables128

Format

An object of class list of length 3.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

combineVariablesNames128 example vector

Description

combineVariablesNames128 example vector

Usage

combineVariablesNames128

Format

An object of class character of length 3.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

country1 example vector

Description

country1 example vector

Usage

country1

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

country128 example vector

Description

country128 example vector

Usage

country128

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

country18 example vector

Description

country18 example vector

Usage

country18

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

country201 example vector

Description

country201 example vector

Usage

country201

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

country313 example vector

Description

country313 example vector

Usage

country313

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

country32 example vector

Description

country32 example vector

Usage

country32

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

country4 example vector

Description

country4 example vector

Usage

country4

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

ctmaAllInvFit

Description

Fit a CoTiMA model with all params (drift, T0var, diffusion) invariant across primary studies

Usage

ctmaAllInvFit(
  ctmaInitFit = NULL,
  activeDirectory = NULL,
  activateRPB = FALSE,
  digits = 4,
  drift = drift,
  coresToUse = c(1),
  n.manifest = 0,
  indVarying = FALSE,
  scaleTime = NULL,
  optimize = TRUE,
  priors = FALSE,
  finishsamples = NULL,
  iter = NULL,
  chains = NULL,
  verbose = NULL,
  loadAllInvFit = c(),
  saveAllInvFit = c(),
  silentOverwrite = FALSE,
  customPar = FALSE,
  T0means = 0,
  manifestMeans = 0,
  CoTiMAStanctArgs = NULL,
  lambda = NULL,
  manifestVars = NULL,
  indVaryingT0 = NULL
)

Arguments

Value

returns a fitted CoTiMA object, in which all drift parameters, Time 0 variances and covariances, and diffusion parameters were set invariant across primary studies

ctmaBiG

Description

Analysis of publication bias and generalizability. The function takes a CoTiMA fit object (created with ctmaInit) and estimates fixed and random effects of single drift coefficients, heterogeneity (Q, I square, H square, tau square), PET-PEESE corrections, Egger's tests, and z-curve analysis yielding expected replication and detection rates (ERR, EDR).

Usage

ctmaBiG(
  ctmaInitFit = NULL,
  activeDirectory = NULL,
  PETPEESEalpha = 0.1,
  activateRPB = FALSE,
  digits = 4,
  finishsamples = 1000,
  zcurve = FALSE,
  undoTimeScaling = TRUE,
  dt = NULL,
  excludeStudies = c()
)

Arguments

Value

ctmaBiG returns a list containing some arguments supplied, the results of analyses of publication bias and generalizability, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, and coresToUse. Further arguments, which are just copied from the init-fit object supplied, are, n.studies, n.latent, studyList, statisticsList, modelResults (all parameter estimates and their standard error), and parameter names. All new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises a title (model='Analysis of Publication Bias & Generalizability') and "estimates", which is another list comprising "Fixed Effects of Drift Coefficients", "Heterogeneity", "Random Effects of Drift Coefficients", "PET-PEESE corrections", "Egger's tests" (constant of the WLS regression of drift coefficients on their standard errors (SE) with 1/SE^2 as weights), "Egger's tests Alt. Version" (constant of the OLS regression of the standard normal deviates of the drift coefficients on their precision), and "Z-Curve 2.0 Results". Plot type is plot.type=c("funnel", "forest") and model.type="BiG".

Examples

## Not run: 
# perform analyses of publication bias and generalizability
CoTiMAInitFit_D_BO$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMABiG_D_BO <- ctmaBiG(ctmaInitFit=CoTiMAInitFit_D_BO, zcurve=FALSE)

## End(Not run)

# display results
summary(CoTiMABiG_D_BO)

## Not run: 
# get funnel & forest plots
CoTiMABiG_D_BO$activeDirectory <- "/Users/tmp/" # adapt!
plot(CoTiMABiG_D_BO)

## End(Not run)

ctmaBiGOMX

Description

Analysis of publication bias and fixed and ranom effects analysis of single drift coefficients if OLD OpenMx fit files are supplied

Usage

ctmaBiGOMX(
  ctmaInitFit = NULL,
  activeDirectory = NULL,
  PETPEESEalpha = 0.1,
  activateRPB = FALSE,
  digits = 4
)

Arguments

Value

returns a CoTiMA fit object with results of publication bias analysis, fixed and random effect analysis, Egger's tests, PET-PEESE corrections.

ctmaCombPRaw

Description

Combine Pseudo Raw Data (extract them from 'CoTiMAFit object'$studyFitList)

Usage

ctmaCombPRaw(listOfStudyFits = NULL, moderatorValues = NULL)

Arguments

Value

returns a pseudo raw data set that combines pseudo raw data and moderators of primary studies

ctmaCompFit

Description

Performs log-liklihood ratio tests to compare the fit of 2 models (CoTiMAFit objects created with ctmaFit or ctmaEqual), i.e., the difference between the two -2 times LLs between the first model and the more constrained second model. The nested structure of the two models is assumed to be given and not checked.

Usage

ctmaCompFit(model1 = NULL, model2 = NULL)

Arguments

Value

Returns the the difference between the two -2 times LLs (Diff_Minus2LL), the associated difference in degrees of freedom (Diff_df (= Diff_n.params)), and the probability (prob).

Examples

minus2llDiffTest <- ctmaCompFit(CoTiMAFullInv23Fit_6,
                                CoTiMAFullInvEq23Fit_6)
summary(minus2llDiffTest)

ctmaCorRel

Description

Disattenuates the entries in a correlation matrix using a vector of reliabilities.

Usage

ctmaCorRel(empcov = NULL, alphas = NULL)

Arguments

Value

A corrected correlation matrix (corEmpcov). Corrections leading to r > 1.0 are set to 1.0.

Examples

empcov313new <- ctmaCorRel(empcov=empcov313, alphas=alphas313)

ctmaEmpCov

Description

changes a full covariance matrix by selecting target variables, recoding them, combining them (compute the mean of two or more variables), and by adding rows/columns with NA if focal variables are not available.

Usage

ctmaEmpCov(
  targetVariables = NULL,
  recodeVariables = c(),
  combineVariables = c(),
  combineVariablesNames = c(),
  missingVariables = c(),
  n.latent = NULL,
  Tpoints = NULL,
  sampleSize = NULL,
  pairwiseN = NULL,
  empcov = NULL
)

Arguments

Value

returns a list with two elements. The first element (results$r) contains the adapted correlation matrix, and the second element (results$pairwiseNNew) an adapted version of a matrix of pairwise N if pariwiseN was provided for the original correlation matrix supplied.

Examples

source17 <- c()
delta_t17 <- c(12)
sampleSize17 <- 440
empcov17 <- matrix(
  c( 1.00, -0.60, -0.36,  0.20,  0.62, -0.47, -0.18,  0.20,
    -0.60,  1.00,  0.55, -0.38, -0.43,  0.52,  0.27, -0.21,
    -0.36,  0.55,  1.00, -0.47, -0.26,  0.37,  0.51, -0.28,
     0.20, -0.38, -0.47,  1.00,  0.15, -0.28, -0.35,  0.56,
     0.62, -0.43, -0.26,  0.15,  1.00, -0.63, -0.30,  0.27,
    -0.47,  0.52,  0.37, -0.28, -0.63,  1.00,  0.55, -0.37,
    -0.18,  0.27,  0.51, -0.35, -0.30,  0.55,  1.00, -0.51,
     0.20, -0.21, -0.28,  0.56,  0.27, -0.37, -0.51,  1.00),
 nrow=8, ncol=8)
moderator17 <- c(3, 2)
rownames(empcov17) <- colnames(empcov17) <-
  c("Workload_1", "Exhaustion_1", "Cynicism_1", "Values_1",
    "Workload_2", "Exhaustion_2", "Cynicism_2", "Values_2")
targetVariables17 <-
  c("Workload_1", "Exhaustion_1", "Cynicism_1",
    "Workload_2", "Exhaustion_2", "Cynicism_2")
recodeVariables17 <- c("Workload_1", "Workload_2")
combineVariables17 <- list("Workload_1", c("Exhaustion_1", "Cynicism_1"),
                           "Workload_2", c("Exhaustion_2", "Cynicism_2"))
combineVariablesNames17 <- c("Demands_1",  "Burnout_1",
                             "Demands_2",  "Burnout_2")
missingVariables17 <- c();
results17 <- ctmaEmpCov(targetVariables = targetVariables17,
                        recodeVariables = recodeVariables17,
                        combineVariables = combineVariables17,
                        combineVariablesNames = combineVariablesNames17,
                        missingVariables = missingVariables17,
                        n.latent = 2, sampleSize = sampleSize17,
                        Tpoints = 2, empcov = empcov17)
empcov17 <- results17$r

ctmaEqual

Description

test if the two or more invariant drift parameters in the CoTiMAFit object supplied are equal. The supplied CoTiMA fit-object (ctmaInvariantFit) has to be a model fitted with ctmaFit where at least two parameters were set invariant across primary studies (e.g., 2 cross effects). All parameters that are set invariant in the supplied model are then constrained to be equal by ctmaEqual (no user action required), the model is fitted, and a log-liklihood ratio test is performed informing about the probability that equality applies.

Usage

ctmaEqual(
  ctmaInvariantFit = NULL,
  activeDirectory = NULL,
  activateRPB = FALSE,
  digits = 4,
  coresToUse = 2
)

Arguments

Value

returns a model where two or more parameters were set equal across primary studies and a log-likelihood difference test informing about the probability that the equality assumption is correct.

Examples

# Fit a CoTiMA with a set of parameters set equal that were set
# invariant in a previous model (of which the fit object is
# supplied in argument ctmaInvariantFit)
## Not run: 
CoTiMAFullInv23Fit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInvEq23Fit_6 <- ctmaEqual(ctmaInvariantFit=CoTiMAFullInv23Fit_6)

## End(Not run)

ctmaExtract

Description

Extracts objects required for CoTiMA from list of results returned by ctmaGenData to be used afterwards with ctmaPrep

Usage

ctmaExtract(
  activeDirectory = NULL,
  ctmaGenDataList = NULL,
  envir = NULL,
  useRawData = FALSE
)

Arguments

ctmaFit

Description

Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).

Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).

Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).

Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).

Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).

Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).

Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).

Usage

ctmaFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  allInvModel = FALSE,
  binaries = NULL,
  catsToCompare = NULL,
  chains = NULL,
  cint = 0,
  cluster = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  digits = 4,
  drift = NULL,
  driftsToCompare = NULL,
  equalDrift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  ind.mod.names = NULL,
  ind.mod.number = NULL,
  ind.mod.type = "cont",
  indVarying = FALSE,
  indVaryingT0 = NULL,
  inits = NULL,
  invariantDrift = NULL,
  iter = NULL,
  lambda = NULL,
  manifestMeans = 0,
  manifestVars = 0,
  mod.names = NULL,
  mod.number = NULL,
  mod.type = "cont",
  moderatedDrift = NULL,
  modsToCompare = NULL,
  optimize = TRUE,
  primaryStudyList = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  scaleClus = TRUE,
  scaleMod = TRUE,
  scaleTI = FALSE,
  scaleTime = NULL,
  T0means = 0,
  T0var = "auto",
  transfMod = NULL,
  useSampleFraction = NULL,
  verbose = 0,
  WEC = FALSE,
  WECnt = TRUE
)

ctmaFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  allInvModel = FALSE,
  binaries = NULL,
  catsToCompare = NULL,
  chains = NULL,
  cint = 0,
  cluster = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  digits = 4,
  drift = NULL,
  driftsToCompare = NULL,
  equalDrift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  ind.mod.names = NULL,
  ind.mod.number = NULL,
  ind.mod.type = "cont",
  indVarying = FALSE,
  indVaryingT0 = NULL,
  inits = NULL,
  invariantDrift = NULL,
  iter = NULL,
  lambda = NULL,
  manifestMeans = 0,
  manifestVars = 0,
  mod.names = NULL,
  mod.number = NULL,
  mod.type = "cont",
  moderatedDrift = NULL,
  modsToCompare = NULL,
  optimize = TRUE,
  primaryStudyList = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  scaleClus = TRUE,
  scaleMod = TRUE,
  scaleTI = FALSE,
  scaleTime = NULL,
  T0means = 0,
  T0var = "auto",
  transfMod = NULL,
  useSampleFraction = NULL,
  verbose = 0,
  WEC = FALSE,
  WECnt = TRUE
)

ctmaFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  allInvModel = FALSE,
  binaries = NULL,
  catsToCompare = NULL,
  chains = NULL,
  cint = 0,
  cluster = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  digits = 4,
  drift = NULL,
  driftsToCompare = NULL,
  equalDrift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  ind.mod.names = NULL,
  ind.mod.number = NULL,
  ind.mod.type = "cont",
  indVarying = FALSE,
  indVaryingT0 = NULL,
  inits = NULL,
  invariantDrift = NULL,
  iter = NULL,
  lambda = NULL,
  manifestMeans = 0,
  manifestVars = 0,
  mod.names = NULL,
  mod.number = NULL,
  mod.type = "cont",
  moderatedDrift = NULL,
  modsToCompare = NULL,
  optimize = TRUE,
  primaryStudyList = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  scaleClus = TRUE,
  scaleMod = TRUE,
  scaleTI = FALSE,
  scaleTime = NULL,
  T0means = 0,
  T0var = "auto",
  transfMod = NULL,
  useSampleFraction = NULL,
  verbose = 0,
  WEC = FALSE,
  WECnt = TRUE
)

ctmaFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  allInvModel = FALSE,
  binaries = NULL,
  catsToCompare = NULL,
  chains = NULL,
  cint = 0,
  cluster = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  digits = 4,
  drift = NULL,
  driftsToCompare = NULL,
  equalDrift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  ind.mod.names = NULL,
  ind.mod.number = NULL,
  ind.mod.type = "cont",
  indVarying = FALSE,
  indVaryingT0 = NULL,
  inits = NULL,
  invariantDrift = NULL,
  iter = NULL,
  lambda = NULL,
  manifestMeans = 0,
  manifestVars = 0,
  mod.names = NULL,
  mod.number = NULL,
  mod.type = "cont",
  moderatedDrift = NULL,
  modsToCompare = NULL,
  optimize = TRUE,
  primaryStudyList = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  scaleClus = TRUE,
  scaleMod = TRUE,
  scaleTI = FALSE,
  scaleTime = NULL,
  T0means = 0,
  T0var = "auto",
  transfMod = NULL,
  useSampleFraction = NULL,
  verbose = 0,
  WEC = FALSE,
  WECnt = TRUE
)

ctmaFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  allInvModel = FALSE,
  binaries = NULL,
  catsToCompare = NULL,
  chains = NULL,
  cint = 0,
  cluster = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  digits = 4,
  drift = NULL,
  driftsToCompare = NULL,
  equalDrift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  ind.mod.names = NULL,
  ind.mod.number = NULL,
  ind.mod.type = "cont",
  indVarying = FALSE,
  indVaryingT0 = NULL,
  inits = NULL,
  invariantDrift = NULL,
  iter = NULL,
  lambda = NULL,
  manifestMeans = 0,
  manifestVars = 0,
  mod.names = NULL,
  mod.number = NULL,
  mod.type = "cont",
  moderatedDrift = NULL,
  modsToCompare = NULL,
  optimize = TRUE,
  primaryStudyList = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  scaleClus = TRUE,
  scaleMod = TRUE,
  scaleTI = FALSE,
  scaleTime = NULL,
  T0means = 0,
  T0var = "auto",
  transfMod = NULL,
  useSampleFraction = NULL,
  verbose = 0,
  WEC = FALSE,
  WECnt = TRUE
)

ctmaFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  allInvModel = FALSE,
  binaries = NULL,
  catsToCompare = NULL,
  chains = NULL,
  cint = 0,
  cluster = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  digits = 4,
  drift = NULL,
  driftsToCompare = NULL,
  equalDrift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  ind.mod.names = NULL,
  ind.mod.number = NULL,
  ind.mod.type = "cont",
  indVarying = FALSE,
  indVaryingT0 = NULL,
  inits = NULL,
  invariantDrift = NULL,
  iter = NULL,
  lambda = NULL,
  manifestMeans = 0,
  manifestVars = 0,
  mod.names = NULL,
  mod.number = NULL,
  mod.type = "cont",
  moderatedDrift = NULL,
  modsToCompare = NULL,
  optimize = TRUE,
  primaryStudyList = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  scaleClus = TRUE,
  scaleMod = TRUE,
  scaleTI = FALSE,
  scaleTime = NULL,
  T0means = 0,
  T0var = "auto",
  transfMod = NULL,
  useSampleFraction = NULL,
  verbose = 0,
  WEC = FALSE,
  WECnt = TRUE
)

ctmaFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  allInvModel = FALSE,
  binaries = NULL,
  catsToCompare = NULL,
  chains = NULL,
  cint = 0,
  cluster = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  digits = 4,
  drift = NULL,
  driftsToCompare = NULL,
  equalDrift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  ind.mod.names = NULL,
  ind.mod.number = NULL,
  ind.mod.type = "cont",
  indVarying = FALSE,
  indVaryingT0 = NULL,
  inits = NULL,
  invariantDrift = NULL,
  iter = NULL,
  lambda = NULL,
  manifestMeans = 0,
  manifestVars = 0,
  mod.names = NULL,
  mod.number = NULL,
  mod.type = "cont",
  moderatedDrift = NULL,
  modsToCompare = NULL,
  optimize = TRUE,
  primaryStudyList = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  scaleClus = TRUE,
  scaleMod = TRUE,
  scaleTI = FALSE,
  scaleTime = NULL,
  T0means = 0,
  T0var = "auto",
  transfMod = NULL,
  useSampleFraction = NULL,
  verbose = 0,
  WEC = FALSE,
  WECnt = TRUE
)

Arguments

Value

ctmaFit returns a list containing somearguments supplied, the fitted model, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are n.studies = 1 (required for proper plotting), data (synthesised pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="ct" ("omx" was deprecated, "stanct" was used until ctsem version 3.10.2).

ctmaFit returns a list containing somearguments supplied, the fitted model, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are n.studies = 1 (required for proper plotting), data (synthesised pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="ct" ("omx" was deprecated, "stanct" was used until ctsem version 3.10.2).

ctmaFit returns a list containing somearguments supplied, the fitted model, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are n.studies = 1 (required for proper plotting), data (synthesised pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="ct" ("omx" was deprecated, "stanct" was used until ctsem version 3.10.2).

ctmaFit returns a list containing somearguments supplied, the fitted model, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are n.studies = 1 (required for proper plotting), data (synthesised pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="ct" ("omx" was deprecated, "stanct" was used until ctsem version 3.10.2).

ctmaFit returns a list containing somearguments supplied, the fitted model, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are n.studies = 1 (required for proper plotting), data (synthesised pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="ct" ("omx" was deprecated, "stanct" was used until ctsem version 3.10.2).

ctmaFit returns a list containing somearguments supplied, the fitted model, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are n.studies = 1 (required for proper plotting), data (synthesised pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="ct" ("omx" was deprecated, "stanct" was used until ctsem version 3.10.2).

ctmaFit returns a list containing somearguments supplied, the fitted model, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are n.studies = 1 (required for proper plotting), data (synthesised pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="ct" ("omx" was deprecated, "stanct" was used until ctsem version 3.10.2).

Examples

## Not run: 
# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)
summary(CoTiMAFullFit_6)

## End(Not run)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))
summary(CoTiMAFullInv23Fit_6)

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",
                                 mod.names=c("Control"))
summary(CoTiMAMod1onFullFit_6)

## End(Not run)

## Not run: 
# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)
summary(CoTiMAFullFit_6)

## End(Not run)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))
summary(CoTiMAFullInv23Fit_6)

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",
                                 mod.names=c("Control"))
summary(CoTiMAMod1onFullFit_6)

## End(Not run)

## Not run: 
# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)
summary(CoTiMAFullFit_6)

## End(Not run)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))
summary(CoTiMAFullInv23Fit_6)

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",
                                 mod.names=c("Control"))
summary(CoTiMAMod1onFullFit_6)

## End(Not run)

## Not run: 
# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)
summary(CoTiMAFullFit_6)

## End(Not run)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))
summary(CoTiMAFullInv23Fit_6)

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",
                                 mod.names=c("Control"))
summary(CoTiMAMod1onFullFit_6)

## End(Not run)

## Not run: 
# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)
summary(CoTiMAFullFit_6)

## End(Not run)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))
summary(CoTiMAFullInv23Fit_6)

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",
                                 mod.names=c("Control"))
summary(CoTiMAMod1onFullFit_6)

## End(Not run)

## Not run: 
# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)
summary(CoTiMAFullFit_6)

## End(Not run)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))
summary(CoTiMAFullInv23Fit_6)

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",
                                 mod.names=c("Control"))
summary(CoTiMAMod1onFullFit_6)

## End(Not run)

## Not run: 
# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)
summary(CoTiMAFullFit_6)

## End(Not run)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))
summary(CoTiMAFullInv23Fit_6)

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",
                                 mod.names=c("Control"))
summary(CoTiMAMod1onFullFit_6)

## End(Not run)

ctmaFitList

Description

Combines CoTiMAFit objects into a list with class CoTiMAFit to inform generic functions what to do

Usage

ctmaFitList(...)

Arguments

Value

a list that combines all objects supplied and is assigned the class 'CoTiMAFit'

Examples

## Not run: 
CoTiMAInitFit_3$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullFit_3$activeDirectory <- "/Users/tmp/" # adapt!
plot(ctmaFitList(CoTiMAInitFit_3, CoTiMAFullFit_3),
     timeUnit="Months",
     timeRange=c(1, 144, 1) )
     
## End(Not run)

ctmaFitToPrep

Description

Extracts information from fitted CoTiMA objects to (re-)crearte list of primary studies originally created with ctmaPrep

Usage

ctmaFitToPrep(ctmaFitObject = NULL, reUseEmprawData = FALSE)

Arguments

Value

list that could be used for fitting new CoTiMA models with ctmaInit or ctmaFit.

Examples

newStudyList <- ctmaFitToPrep(CoTiMAInitFit_3)

ctmaGenData

Description

Generates data from lists of parameters (drift, diffusion etc). Experimental!!

Generates data from lists of parameters (drift, diffusion etc). Experimental!!

Generates data from lists of parameters (drift, diffusion etc). Experimental!!

Usage

ctmaGenData(
  activeDirectory = NULL,
  burnin = 0,
  cint = NULL,
  coresToUse = 2,
  ctmaExtract = FALSE,
  diff = NULL,
  digits = 4,
  doPar = FALSE,
  drift = NULL,
  empirical = TRUE,
  envir = NULL,
  sampleSizes = 100,
  lambda = NULL,
  latentNames = NULL,
  manifestMeans = NULL,
  manifestVars = NULL,
  manifestNames = NULL,
  missings = NULL,
  modValues = 0,
  n.latent = NULL,
  n.manifest = NULL,
  randomIntercepts = NULL,
  T0means = 0,
  T0var = NULL,
  TIpreds = NULL,
  tpoints = 10,
  tpointTargets = NULL,
  useRawData = NULL
)

ctmaGenData(
  activeDirectory = NULL,
  burnin = 0,
  cint = NULL,
  coresToUse = 2,
  ctmaExtract = FALSE,
  diff = NULL,
  digits = 4,
  doPar = FALSE,
  drift = NULL,
  empirical = TRUE,
  envir = NULL,
  sampleSizes = 100,
  lambda = NULL,
  latentNames = NULL,
  manifestMeans = NULL,
  manifestVars = NULL,
  manifestNames = NULL,
  missings = NULL,
  modValues = 0,
  n.latent = NULL,
  n.manifest = NULL,
  randomIntercepts = NULL,
  T0means = 0,
  T0var = NULL,
  TIpreds = NULL,
  tpoints = 10,
  tpointTargets = NULL,
  useRawData = NULL
)

ctmaGenData(
  activeDirectory = NULL,
  burnin = 0,
  cint = NULL,
  coresToUse = 2,
  ctmaExtract = FALSE,
  diff = NULL,
  digits = 4,
  doPar = FALSE,
  drift = NULL,
  empirical = TRUE,
  envir = NULL,
  sampleSizes = 100,
  lambda = NULL,
  latentNames = NULL,
  manifestMeans = NULL,
  manifestVars = NULL,
  manifestNames = NULL,
  missings = NULL,
  modValues = 0,
  n.latent = NULL,
  n.manifest = NULL,
  randomIntercepts = NULL,
  T0means = 0,
  T0var = NULL,
  TIpreds = NULL,
  tpoints = 10,
  tpointTargets = NULL,
  useRawData = NULL
)

Arguments

Value

ctmaGenData returns a list containing ...

ctmaGenData returns a list containing ...

ctmaGenData returns a list containing ...

Examples

# Fit a ctsem model to all three primary studies summarized in
# CoTiMAstudyList_3 and save the three fitted models
## Not run: 
CoTiMAInitFit_3 <- ctmaInit(primaryStudies=CoTiMAstudyList_3,
                            n.latent=2,
                            checkSingleStudyResults=FALSE,
                            activeDirectory="/Users/tmp/") # adapt!
summary(CoTiMAInitFit_3)

## End(Not run)

# Fit a ctsem model to all three primary studies summarized in
# CoTiMAstudyList_3 and save the three fitted models
## Not run: 
CoTiMAInitFit_3 <- ctmaInit(primaryStudies=CoTiMAstudyList_3,
                            n.latent=2,
                            checkSingleStudyResults=FALSE,
                            activeDirectory="/Users/tmp/") # adapt!
summary(CoTiMAInitFit_3)

## End(Not run)

# Fit a ctsem model to all three primary studies summarized in
# CoTiMAstudyList_3 and save the three fitted models
## Not run: 
CoTiMAInitFit_3 <- ctmaInit(primaryStudies=CoTiMAstudyList_3,
                            n.latent=2,
                            checkSingleStudyResults=FALSE,
                            activeDirectory="/Users/tmp/") # adapt!
summary(CoTiMAInitFit_3)

## End(Not run)

ctmaGetPub

Description

Retrieves publication and citation information from google scholar based on the supplied author names and their google ID (user)

Usage

ctmaGetPub(authorList = NULL, flush = FALSE, yearsToExclude = NULL)

Arguments

Value

list with (cumulative) frequencies and (cumulative) citations in google scholar

Note

Set flush=TRUE only if retrieving is necessary (e.g., first retrieval on a day)

Examples

## Not run: 
pubList_8 <- ctmaGetPub(authorList = list( c("J; de Jonge",
              "https://scholar.google.de/citations?hl=de&user=0q27IckAAAAJ"),
              c("Arnold B.; Bakker", "user=FTl3bwUAAAAJ"),
              c("Evangelia; Demerouti", "user=9mj5LvMAAAAJ"),
              c("Joachim; Stoeber", "user=T9xdVusAAAAJ"),
              c("Claude; Fernet", "user=KwzjP4sAAAAJ"),
              c("Frederic; Guay", "user=99vnhX4AAAAJ"),
              c("Caroline; Senecal", "user=64ArFWQAAAAJ"),
              c("Stéphanie; Austin", "user=PPyTI7EAAAAJ")),
              flush=FALSE)
summary(pubList_8)

## End(Not run)

ctmaInit

Description

Fits ctsem models to each primary study in the supplied list of primary studies prepared by ctmaPrep.

Fits ctsem models to each primary study in the supplied list of primary studies prepared by ctmaPrep.

Usage

ctmaInit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  binaries = NULL,
  chains = NULL,
  checkSingleStudyResults = FALSE,
  cint = 0,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  customPar = FALSE,
  diff = NULL,
  digits = 4,
  doPar = 1,
  drift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  indVarying = FALSE,
  indVaryingT0 = NULL,
  iter = NULL,
  lambda = NULL,
  loadSingleStudyModelFit = c(),
  manifestMeans = 0,
  manifestVars = NULL,
  n.latent = NULL,
  n.manifest = 0,
  optimize = TRUE,
  primaryStudies = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  saveRawData = list(),
  saveSingleStudyModelFit = c(),
  scaleTI = NULL,
  scaleTime = NULL,
  silentOverwrite = FALSE,
  T0means = 0,
  T0var = "auto",
  useSV = FALSE,
  verbose = 0
)

ctmaInit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  binaries = NULL,
  chains = NULL,
  checkSingleStudyResults = FALSE,
  cint = 0,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  customPar = FALSE,
  diff = NULL,
  digits = 4,
  doPar = 1,
  drift = NULL,
  experimental = FALSE,
  finishsamples = NULL,
  fit = TRUE,
  indVarying = FALSE,
  indVaryingT0 = NULL,
  iter = NULL,
  lambda = NULL,
  loadSingleStudyModelFit = c(),
  manifestMeans = 0,
  manifestVars = NULL,
  n.latent = NULL,
  n.manifest = 0,
  optimize = TRUE,
  primaryStudies = NULL,
  priors = FALSE,
  randomIntercepts = FALSE,
  sameInitialTimes = FALSE,
  saveRawData = list(),
  saveSingleStudyModelFit = c(),
  scaleTI = NULL,
  scaleTime = NULL,
  silentOverwrite = FALSE,
  T0means = 0,
  T0var = "auto",
  useSV = FALSE,
  verbose = 0
)

Arguments

Value

ctmaFit returns a list containing some arguments supplied, the fitted models, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, n.latent, n.manifest, and primaryStudyList. The study count is returned as n.studies, the created matrix of loadings of manifest on latent factors is returned as lambda, and a re-organized list of primary studies with some information ommited is returned as studyList. The fitted models for each primary study are found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are emprawList (containing the synthesised raw data created), statisticsList (comprising baisc stats such as average sample size, no. of measurement points, etc.), a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef), and the paramter names internally used. The summary list, which is printed if the summary function is applied to the returned object, comprises "estimates" (the aggregated effects), possible randomIntercepts,confidenceIntervals, the minus2ll value and its n.parameters, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="stanct" ("omx" was deprecated).

ctmaFit returns a list containing some arguments supplied, the fitted models, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, n.latent, n.manifest, and primaryStudyList. The study count is returned as n.studies, the created matrix of loadings of manifest on latent factors is returned as lambda, and a re-organized list of primary studies with some information ommited is returned as studyList. The fitted models for each primary study are found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are emprawList (containing the synthesised raw data created), statisticsList (comprising baisc stats such as average sample size, no. of measurement points, etc.), a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef), and the paramter names internally used. The summary list, which is printed if the summary function is applied to the returned object, comprises "estimates" (the aggregated effects), possible randomIntercepts,confidenceIntervals, the minus2ll value and its n.parameters, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="stanct" ("omx" was deprecated).

Examples

# Fit a ctsem model to all three primary studies summarized in
# CoTiMAstudyList_3 and save the three fitted models
## Not run: 
CoTiMAInitFit_3 <- ctmaInit(primaryStudies=CoTiMAstudyList_3,
                            n.latent=2,
                            checkSingleStudyResults=FALSE,
                            activeDirectory="/Users/tmp/") # adapt!
summary(CoTiMAInitFit_3)

## End(Not run)

# Fit a ctsem model to all three primary studies summarized in
# CoTiMAstudyList_3 and save the three fitted models
## Not run: 
CoTiMAInitFit_3 <- ctmaInit(primaryStudies=CoTiMAstudyList_3,
                            n.latent=2,
                            checkSingleStudyResults=FALSE,
                            activeDirectory="/Users/tmp/") # adapt!
summary(CoTiMAInitFit_3)

## End(Not run)

ctmaLCS

Description

Transforms estimates obtained with ctmaFit into LCS (latent change score) terminology. LCS models can be estimated with CT CLPM, but results have to be transformed. When time intervals vary much between and within persons, LCS models are virtually impossible to fit. However, CT CLPM models can be fitted, and the results - after transformation - show what LCS estimates would have been (cf Voelke & Oud, 2015; their terminology to label LCS effects is used in the output created by ctmaLCS)

Usage

ctmaLCS(
  CoTiMAFit = NULL,
  undoTimeScaling = TRUE,
  digits = 4,
  activateRPB = FALSE
)

Arguments

Value

Returns LCS effects derived from CT CoTiMA CLPM estimates.

Examples

## Not run: 
LCSresults <- ctmaLCS(CoTiMAFullFit_6)

## End(Not run)

ctmaLabels

Description

used for consistent labeling of names and parameters

Usage

ctmaLabels(
  n.latent = NULL,
  n.manifest = 0,
  lambda = NULL,
  manifestVars = NULL,
  drift = NULL,
  diff = NULL,
  invariantDrift = NULL,
  moderatedDrift = NULL,
  equalDrift = NULL,
  T0means = 0,
  manifestMeans = 0
)

Arguments

Value

returns consistently named parameters (e.g., "V1toV2") as well es their symbolic values, which are used to fix or free parameters when fitting a 'CoTiMA' model

ctmaMMtoCINT

Description

Compute covariance of CINT-based random intercepts obtained with a MANIFESTMEANS model specification and vice versa.

Usage

ctmaMMtoCINT(
  ctmaFitObject = NULL,
  undoTimeScaling = FALSE,
  digits = 4,
  cintCov = NULL,
  mmCov = NULL,
  drift = NULL
)

Arguments

Value

returns covariance of CINT-based random intercepts.

Examples

## Not run: 
RI_cov <- ctmaMMtoCINT(ctmaFitObject=CoTiMAFullFit_3)
print(RI_cov)

## End(Not run)

Numerically determine optimal time lags

Description

Numerically searches a grid of time lags for the largest absolute cross-effect implied by a continuous-time drift matrix. The OTL in a standard CoTiMA summary uses the mean estimated drift matrix only. With CI = TRUE, the search is repeated for transformed drift draws.

Usage

ctmaOTL(
  ctmaFitFit = NULL,
  timeRange = NULL,
  driftMat = NULL,
  undoTimeScaling = NULL,
  digits = 4,
  CI = FALSE,
  confidence = 0.95
)

Arguments

Details

For ML/MAP fits, intervals use the approximate transformed draws stored by ctsem (normally Hessian-based); for Bayesian fits they are posterior credible intervals. Precision is limited by timeRange. An optimum on its boundary suggests using a wider grid.

Value

A list with the grid, mean drift matrix, maximum absolute cross-effects, and OTLs. With CI = TRUE, it also contains optimalCrossLagCI, maxCrossEffectCI, confidence, nDriftDraws, and uncertaintySource.

Examples

## Not run: 
OTL <- ctmaOTL(ctmaFitFit = CoTiMA::CoTiMAFullFit_6_new)
OTL_CI <- ctmaOTL(ctmaFitFit = CoTiMA::CoTiMAFullFit_6_new,
                  CI = TRUE, confidence = .95)

## End(Not run)

ctmaOptimizeFit

Description

Replaces deprecated ctmaOptimizeInit, which was limited to initial fitting (i.e., applies ctmaInit) of a primary study reFits times to capitalize on chance for obtaining a hard-to-find optimal fit. Now, optimizing a CoTiMA model generated with ctmaFit can also be done. Using ctmaOptimizeFit could be helpful if a model yields out-of-range estimates, which could happen if the fitting algorithm unfortunately used random start values that resulted in a locally but not globally optimal fit. Essentially, using ctmaOptimizeFit is like gambling, hoping that at least one set of starting values (the number it tries is specified in the reFits argument) enables finding the global optimal fit.

Usage

ctmaOptimizeFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaFitFit = NULL,
  ctmaInitFit = NULL,
  ctStanFit = NULL,
  ctStanData = NULL,
  customPar = FALSE,
  finishsamples = NULL,
  iter = 5000,
  primaryStudies = NULL,
  problemStudy = NULL,
  randomIV = FALSE,
  randomPar = FALSE,
  randomRI = FALSE,
  randomScaleTI = FALSE,
  randomScaleTime = c(1, 1),
  saveModelFits = FALSE,
  shuffleStudyList = FALSE,
  reFits = NULL,
  scaleTime = NULL,
  scaleTI = NULL,
  verbose = 1,
  ctStanFitArgs = list(stanmodeltext = NA, iter = 1000, intoverstates = TRUE, binomial =
    FALSE, fit = TRUE, intoverpop = "auto", sameInitialTimes = FALSE, stationary = FALSE,
    plot = FALSE, derrind = NA, optimize = TRUE, optimcontrol = list(), nlcontrol =
    list(), nopriors = NA, priors = FALSE, chains = 2, inits = NULL, compileArgs =
    list(), forcerecompile = FALSE, saveCompile = TRUE, savescores = FALSE,
    savesubjectmatrices = FALSE, saveComplexPars = FALSE, gendata = FALSE, control =
    list())
)

Arguments

Value

returns a list with bestFit (= the best fit achieved), all_minus2ll (= all -2ll values for all fitted models), and summary, which is printed if the summary function is applied to the returned object, and which shows the summary information of the ctsem model with the best fit.

Examples

## Not run: 
optimFit313 <- ctmaOptimizeFit(primaryStudies=CoTiMAstudyList_3,
                                activeDirectory="/Users/tmp/",  # adapt!
                                problemStudy=which(CoTiMAstudyList_3$studyNumbers == 313),
                                reFits=10,
                                n.latent=2)
summary(optimFit313)

## End(Not run)

ctmaOptimizeInit

Description

Initial fitting (i.e., applies ctmaInit) to a primary study reFit times to capitalize on chance for obtaining a hard-to-find optimal fit. This could be very helpful if a primary yields out-of-range estimates, which could happen if the fitting algorithm unfortunately used random start values that resulted in a locally but not globally optimal fit. Essentially, using ctmaOptimizeInit is like gambling, hoping that at leas one set of starting values (the number is tries is specified in the reFits argument) eneables finding the global optimal fit. On unix-like machines (e.g. MacOS), this could be done in parallel mode if coresToUse > 1.

Usage

ctmaOptimizeInit(
  primaryStudies = NULL,
  activeDirectory = NULL,
  problemStudy = NULL,
  reFits = NULL,
  finishsamples = NULL,
  n.latent = NULL,
  coresToUse = c(1),
  indVarying = FALSE,
  randomScaleTime = c(1, 1),
  activateRPB = FALSE,
  checkSingleStudyResults = FALSE,
  customPar = FALSE,
  T0means = 0,
  manifestMeans = 0,
  manifestVars = NULL,
  CoTiMAStanctArgs = NULL,
  scaleTime = NULL
)

Arguments

Value

returns a list with bestFit (= the best fit achieved), all_minus2ll (= all -2ll values for all fitted models), and summary, which is printed if the summary function is applied to the returned object, and which shows the summary information of the ctsem model with the best fit.

Note

All but one of multiple cores are used on unix-type machines for parallel fitting

During fitting, not output is generated. Be patient.

Examples

## Not run: 
optimFit313 <- ctmaOptimizeInit(primaryStudies=CoTiMAstudyList_3,
                                activeDirectory="/Users/tmp/",  # adapt!
                                problemStudy=which(CoTiMAstudyList_3$studyNumbers == 313),
                                reFits=10,
                                n.latent=2)
summary(optimFit313)

## End(Not run)

ctmaPRaw

Description

Uses empirical correlation matrices to synthesise pseudo raw data (i.e. random data, that perfectly reproduce the correlations)

Usage

ctmaPRaw(
  empCovMat = NULL,
  empNMat = matrix(0, 0, 0),
  empN = NULL,
  studyNumber = NULL,
  empMeanVector = NULL,
  empVarVector = NULL,
  activateRPB = FALSE,
  experimental = FALSE
)

Arguments

Map ctsem parameters to posterior columns

Description

Creates labels and column mappings for ctsem population means, time-independent predictor effects, individually varying parameters, and their covariance parameters. The input may be either a fitted ctsem object or a CoTiMA fit containing one pooled ctsem fit in studyFitList.

Usage

ctmaParMap(fit, est = NULL)

Arguments

Value

A list with data frames named rawpopmeans, tipredeffectparams, individually_varying, and random_effect_covariance. The mapping includes the corresponding columns of fit$stanfit$rawposterior where applicable.

Examples

## Not run: 
# A CoTiMA full-fit object:
parameterMap <- ctmaParMap(CoTiMAFullFit)

# The pooled ctsem fit extracted from the same object:
ctsemFit <- CoTiMAFullFit$studyFitList
parameterMap <- ctmaParMap(ctsemFit)

# Optional consistency checks against ctExtract():
estimates <- ctsem::ctExtract(ctsemFit)
parameterMap <- ctmaParMap(ctsemFit, est = estimates)

## End(Not run)

ctmaPlot

Description

Forest plot, funnel plots, plots of discrete time cross-lagged and autoregressive effect, and plots of required sample sizes

Usage

ctmaPlot(
  ctmaFitObject = NULL,
  activeDirectory = NULL,
  saveFilePrefix = "ctmaPlot",
  activateRPB = FALSE,
  plotCrossEffects = TRUE,
  plotAutoEffects = TRUE,
  timeUnit = "timeUnit (not specified)",
  timeRange = c(),
  yLimitsForEffects = c(),
  mod.number = 1,
  mod.values = -2:2,
  aggregateLabel = "",
  xLabels = NULL,
  undoTimeScaling = TRUE,
  ...
)

Arguments

Value

depending on the CoTiMA fit object supplied, generates funnel plots, forest plots, discrete time plots of autoregressive and cross-lagged effects, plots of required samples sizes across a range of discrete time intervals to achieve desired levels of statistical power, and post hoc power of primary studies. Plots are saved to disk.

Examples

## Not run: 
# cannot run without proper activeDirectory specified. Adapt!
CoTiMAFullFit_3$activeDirectory <- "/Users/tmp/" # adapt!
plot(ctmaFitList(CoTiMAInitFit_3, CoTiMAFullFit_3),
     timeUnit="Months", timeRange=c(1, 144, 1),
     plotAutoEffects=FALSE)

## End(Not run)

## Not run: 
# cannot run without proper activeDirectory specified. Adapt!
CoTiMABiG_D_BO$activeDirectory <- "/Users/tmp/" # adapt!
plot(CoTiMABiG_D_BO)

## End(Not run)

ctmaPlotCtsemMod

Description

Plots moderator models using ctsem-fit objects

Usage

ctmaPlotCtsemMod(
  ctStanFitObject = NULL,
  fitSummary = NULL,
  activeDirectory = NULL,
  TIpred.pos = 1,
  saveFilePrefix = "Moderator Plot ",
  scaleTime = 1,
  mod.sd.to.plot = -1:1,
  mod.raw.to.plot = NULL,
  digits = 4,
  timeUnit = "not specified",
  timeRange = NULL,
  mod.type = "cont",
  no.mod.cats = NULL,
  n.x.labels = NULL,
  x.Values.For.Mod.Values = NULL,
  plot = TRUE,
  plot.xMin = 0,
  plot.xMax = NULL,
  plot.yMin = NULL,
  plot.yMax = NULL,
  plot..type = "l",
  plot.lty = 1,
  plot.col = "grey",
  plot.lwd = 1.5,
  dot.plot = TRUE,
  dot.plot.type = "b",
  dot.plot.col = "black",
  dot.plot.lwd = 0.5,
  dot.plot.lty = 3,
  dot.plot.pch = 16,
  dot.plot.cex = 3
)

Arguments

Value

writes png figures to disc using the path specified in the activeDirectory arguments.

Examples

#Plot a categorical moderator
## Not run: 
ctmaPlotCtsemMod(ctStanFitObject = ctsemFit,
                 activeDirectory=NULL,
                 mod.sd.to.plot = NULL,
                 timeUnit = "Months",
                 timeRange = c(0, 12, .1),
                 mod.type = "cat",
                 no.mod.cats = NULL

## End(Not run)

ctmaPower

Description

Fits or loads an invariant continuous-time model, then computes plug-in Gaussian discrete-time regression power and required sample sizes across positive time lags. Verified unrestricted regressions use covariance calculations; supported restrictions use lavaan regression fits.

Usage

ctmaPower(
  ctmaInitFit = NULL,
  activeDirectory = NULL,
  statisticalPower = c(),
  failSafeN = NULL,
  failSafeP = NULL,
  timeRange = NULL,
  useMBESS = FALSE,
  coresToUse = 1,
  digits = 4,
  indVarying = FALSE,
  activateRPB = FALSE,
  silentOverwrite = FALSE,
  loadAllInvFit = c(),
  saveAllInvFit = c(),
  loadAllInvWOSingFit = c(),
  saveAllInvWOSingFit = c(),
  skipScaling = TRUE,
  useSampleFraction = NULL,
  optimize = TRUE,
  priors = FALSE,
  finishsamples = NULL,
  iter = NULL,
  chains = NULL,
  verbose = NULL,
  customPar = FALSE,
  scaleTime = NULL,
  powerMethod = c("auto", "covariance", "lavaan"),
  regressionModel = NULL
)

Arguments

Details

The fast default includes both tails of the noncentral-t test and finds the minimum integer N by bisection. useMBESS=TRUE retains MBESS's legacy upper-tail probability. Calculations concern plug-in discrete-time regression effects, not the power of the pooled CT drift test. Missing post-hoc powers remain NA. The legacy Opt. Lag row selects the first actual minimizing lag; all ties are returned separately. Console and summary notes identify whether covariance calculations or lavaan fits were used. The state projection requires directly observed variables, identity loadings and zero measurement error; recorded latent measurement/augmented-state models require simulation-based power analysis.

Value

A "CoTiMAFit" object containing the invariant model, matrix estimates, regression eligibility/details, calculation/interpretation notes, required sample sizes and post-hoc power tables. power_method records the route. The legacy Opt. Lag row selects the first tied minimum; summary$estimates[["All optimal lags"]] retains every tied lag. Infinite N denotes an unattainable target or a requirement beyond the exact-integer search range; unavailable powers remain NA.

Examples

## Not run: 
CoTiMAInitFit_D_BO$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAPower_D_BO <- ctmaPower(ctmaInitFit=CoTiMAInitFit_D_BO,
                              statisticalPower = c(.50, .80, .95),
                              finishsamples = 10000)
summary(CoTiMAPower_D_BO)

## End(Not run)

ctmaPrep

Description

Combines information of primary studies into a list object and returns this list. This list is then used as input to fit 'ctsem' models. Primary study information is expected to be assigned to 'numbered' objects. Some of these objects are pre-defined (e.g., 'empcov', 'ageM'). Most of the pre-defined objects could be empty, or they could be dropped by entering their names in the excludedElements-object (e.g., excludedElements = c('ageM')), but dropping them is not really necessary. Additional elements could also be added, which could be useful to put together all information about primary studies at the convenience of the researcher.

Usage

ctmaPrep(
  selectedStudies = NULL,
  excludedElements = NULL,
  addElements = NULL,
  digits = 4,
  moderatorLabels = NULL,
  moderatorValues = NULL,
  newRawDataDirectory = NULL,
  n.latent = NULL,
  summary = TRUE,
  activeDirectory = NULL,
  ctmaPrepObject = NULL,
  excludedStudies = NULL
)

Arguments

Value

List of primary studies and parameters for the following CoTiMA (plus StudyInformation which could be saved to Excel)

Note

The following example shows information a researcher has about three studies, which have the numbers '2', '4' and '17'. All information about these studies are stored in objects ending with '2', '4', and '17', respectively. In most instances, one relevant piece of information is the empirical correlation (or covariance) matrix reported in this study, which is stored in the objects 'empcov2', 'empcov4', and 'empcov17'. Note that full and symmetric matrices are required for ctmaPrep. Usually, sample sizes ('sampleSize2', 'sampleSize4', & 'sampleSize17') and time lags ('delta_t2', 'delta_t4', & 'delta_t17'), are required, too.

Examples

# First Study
empcov2 <- matrix(c(1.00, 0.45, 0.57, 0.18,
                    0.45, 1.00, 0.31, 0.66,
                    0.57, 0.31, 1.00, 0.40,
                    0.18, 0.66, 0.40, 1.00), nrow=4, ncol=4)
delta_t2 <- 12
sampleSize2 <- 148
moderator2 <- c(1, 0.72)
source2 <- c("Houkes, I,", "Janssen, P, P, M,", "de Jonge, J",
              "& Bakker, A, B", "Study1", "2003")
addedByResearcher2 <- "something you want to add"

# Second Study
empcov3 <- matrix(c(1.00, 0.43, 0.71, 0.37,
                    0.43, 1.00, 0.34, 0.69,
                    0.71, 0.34, 1.00, 0.50,
                    0.37, 0.69, 0.50, 1.00), nrow=4, ncol=4)
delta_t3 <- 12
sampleSize3 <- 88
moderator3 <- c(1, 0.72)
source3 <- c("Houkes, I,", "Janssen, P, P, M,", "de Jonge, J",
              "& Bakker, A, B", "Study2", "2003")
addedByResearcher3 <- ""

# Third Study
empcov313 <- matrix(c(1.00, 0.38, 0.54, 0.34, 0.60, 0.28,
                      0.38, 1.00, 0.34, 0.68, 0.28, 0.68,
                      0.54, 0.34, 1.00, 0.47, 0.66, 0.39,
                      0.34, 0.68, 0.47, 1.00, 0.38, 0.72,
                      0.60, 0.28, 0.66, 0.38, 1.00, 0.38,
                      0.28, 0.68, 0.39, 0.72, 0.38, 1.00), nrow=6, ncol=6)
delta_t313 <- c(1.5, 1.5)
sampleSize313 <- 335
moderator313 <- c(0.8,	2.47)
source313 <- c("Demerouti", "Bakker", "& Bulters", "2004")
addedByResearcher313 <- "check correlation matrix"

# Add Labels and Values for Moderators (just for optional excel tables)
moderatorLabels <- c("Control", "Social Support")
moderatorValues <- list("continuous", c("1 = very low", "2 = low",
                       "3 = medium", "4 = high", "5 = very high"))

CoTiMAstudyList_3 <- ctmaPrep(selectedStudies = c(2, 3, 313),
                              activeDirectory="/user/",
                              excludedElements = "ageM",
                              addElements = "addedByResearcher",
                              moderatorLabels=moderatorLabels,
                              moderatorValues=moderatorValues)

ctmaPub

Description

Compute publication and citation scores for studies based on the (team of) authors' publication scores .

Usage

ctmaPub(
  getPubObj = NULL,
  primaryStudyList = NULL,
  yearsToExclude = 0,
  recency = 5,
  targetYear = NULL,
  indFUN = "sum",
  colFUN = "mean",
  addAsMod = FALSE
)

Arguments

Value

returns NEPP (= the \*number\* of studies published by the authors of the primary studies supplied UNTIL the year when the primary study was published), NEPPRecency (like NEPP, but limited to the number of years before the publication as specified with the recency argument), "Meaning of NEPP" and "Meaning of NEPPRecency" which explain what \*number\* exactly means (e.g., could be the mean of the sum of each author's publication, or the sum of the maximum publications per year of the authors), and "primaryStudyList(full)", which just returns the primaryStudyList supplied).

Examples

pubResults_6 <- ctmaPub(getPubObj=pubList_8,
                        primaryStudyList=CoTiMAstudyList_6)
summary(pubResults_6)

Fit a hierarchical study-level random-effects CoTiMA model

Description

Fits an object returned by ctmaRePrep using Bayesian sampling, maximum a posteriori optimization, or joint/conditional maximum likelihood. MAP and conditional ML use the exported rstan::optimizing() interface.

Usage

ctmaReFit(
  prepared,
  estimation = c("bayes", "map", "ml"),
  method = NULL,
  cores = 2,
  chains = 4,
  iter = 2000,
  seed = 1234,
  init = NULL,
  init_jitter = NULL,
  verbose = TRUE,
  refresh = 100,
  optimizer_uncertainty = c("none", "hessian", "surrogate", "is", "bootstrap",
    "fullbootstrap", "sandwich", "opg"),
  compile_args = list(),
  fit_args = list()
)

Arguments

Value

A "CoTiMAFit" object with subtype "CoTiMAStudyREFit". Apply summary() for population, heterogeneity, correlation, and study-specific estimates.

See Also

ctmaRePrep, ctmaReMLFit

Examples

## Not run: 
bayesFit <- ctmaReFit(prepared, estimation = "bayes", chains = 4,
  iter = 2000, cores = 4)
mapFit <- ctmaReFit(prepared, estimation = "map", cores = 1)

## End(Not run)

Parallel multistart conditional ML for CoTiMA study random effects

Description

Compiles the prepared Stan model once and runs independent rstan::optimizing() starts on forked workers. The best finite objective is retained. Study effects are optimized rather than integrated out, so this is joint/conditional and not marginal maximum likelihood.

Usage

ctmaReMLFit(
  prepared,
  cores = max(1L, parallel::detectCores(logical = FALSE) - 1L),
  n_starts = max(4L, cores),
  parameterization = c("auto", "centered", "noncentered"),
  seed = 1234L,
  init = NULL,
  init_jitter = NULL,
  algorithm = "LBFGS",
  iter = 2000L,
  refresh = 100L,
  poll_seconds = 2,
  compile_args = list(),
  optim_args = list(),
  verbose = TRUE
)

Arguments

Value

A "CoTiMAFit" object with subtype "CoTiMAStudyREFit", plus all optimization attempts, the selected start, and its objective value.

See Also

ctmaRePrep, ctmaReFit

Examples

## Not run: 
mlFit <- ctmaReMLFit(prepared, cores = 4, n_starts = 8,
  parameterization = "centered", iter = 2000)

## End(Not run)

Estimate study-level random-effects moments from a CoTiMA-FE fit

Description

Reconstructs transformed study-specific parameters from the raw posterior draws of a CoTiMA-FE model with study indicators and summarizes their empirical between-study distribution. This is a post-hoc method-of-moments analysis: it does not refit a hierarchical model and does not shrink study estimates toward the population mean.

Usage

ctmaReMoments(
  fit,
  study_design = NULL,
  probs = c(0.025, 0.975),
  weighting = c("equal", "inverse_variance"),
  SEweighting = NULL,
  SEsource = c("auto", "posterior", "hessian"),
  cores = 2,
  include_matrices = TRUE,
  include_individual = TRUE
)

Arguments

Details

For posterior draw d, study s, and parameter p, the function first calculates the raw study value

\eta_{sp}^{(d)}=\mu_p^{(d)}+\sum_j x_{sj}\beta_{pj}^{(d)}

and then applies the parameter-specific ctsem transformation \theta_{sp}^{(d)}=g_p(\eta_{sp}^{(d)}). With equal weighting, every study receives the same weight and the between-study variance uses the ordinary sample-variance denominator K-1. With inverse-variance weighting, parameter-specific fixed weights are estimated from the across-draw SD of each transformed study parameter. The weighted sample-variance denominator is \sum w-(\sum w^2)/(\sum w). Precision weighting changes the estimand and is best treated as a sensitivity analysis.

Value

An object inheriting from "CoTiMAFit" and "CoTiMAReMoments". Its main elements are studyParameters, distributionParameters, studySE, studyWeights, effectiveNumberOfStudies, covarianceDraws, correlationDraws, studyMatrices, and matrixSummary.

See Also

ctmaParMap, ctmaRePrep, ctmaReFit

Examples

## Not run: 
moments <- ctmaReMoments(CoTiMAFullFit, cores = 4)
summary(moments)
moments$studyParameters
moments$distributionParameters

## End(Not run)

Prepare a hierarchical study-level random-effects CoTiMA model

Description

Adds study-level random effects to selected ctsem DRIFT, DIFFUSION, and T0VAR parameters. Effects are inserted on ctsem's unconstrained parameter scale before parameter-specific transformations are applied.

Usage

ctmaRePrep(
  model,
  study_id = NULL,
  study_id_ti = NULL,
  study_dummy_ti = NULL,
  reDRIFT = NULL,
  reDIFFUSION = NULL,
  reT0VAR = NULL,
  covariance = c("independent", "equicorrelated", "full", "block"),
  covarianceBlocks = NULL,
  reBlocks = NULL,
  blockStructures = NULL,
  parameterization = c("auto", "centered", "noncentered"),
  init = c("auto", "random"),
  init_jitter = 0.02,
  remove_study_fixed_effects = TRUE,
  sd_prior = 0.25,
  lkj_eta = 2,
  verbose = TRUE
)

Arguments

Value

An object of class "CoTiMARePrep" and "ctmaStudyREModel", containing patched Stan code, Stan data, parameter mappings, study labels, covariance blocks, and initialization.

See Also

ctmaReFit, ctmaReMLFit

Examples

## Not run: 
prepared <- ctmaRePrep(
  pooledFit,
  study_dummy_ti = paste0("TI", 1:9),
  reDRIFT = matrix(TRUE, 2, 2),
  reDIFFUSION = matrix(FALSE, 2, 2),
  reT0VAR = matrix(FALSE, 2, 2),
  covariance = "block",
  covarianceBlocks = list(DRIFT = "equicorrelated")
)

## End(Not run)

ctmaRedHet

Description

Computes the Reduction in Heterogeneity in drift effects after introducing study-level moderators

Usage

ctmaRedHet(
  activateRPB = FALSE,
  activeDirectory = NULL,
  ctmaFitObject = NULL,
  ctmaFitObjectMod = NULL,
  digits = 4,
  dt = NULL,
  undoTimeScaling = TRUE
)

Arguments

ctmaSV

Description

derives start values by average discrete time SEM effects, converting them to continuous time, and inversely apply transformations used by 'ctsem'

Usage

ctmaSV(
  ctmaInitFit = NULL,
  activeDirectory = NULL,
  primaryStudies = NULL,
  coresToUse = 1,
  replaceSV = TRUE
)

Arguments

Value

returns a modified list of primary studies with starting values added or replaced

Examples

## Not run: 
newPrimaryStudyList <- ctmaSV(ctmaInitFit=CoTiMAInitFit_6)

## End(Not run)

ctmaSaveFile

Description

Internal fcuntion to save files

Usage

ctmaSaveFile(
  activateRPB,
  activeDirectory = activeDirectory,
  SaveObject,
  FileName,
  Directory,
  silentOverwrite = FALSE
)

Arguments

Value

No return value. Just saves files

ctmaScaleInits

Description

This function re-scales inits for drifts and sets all other inits to 0 (because it is too complicated to re-scale inits for diffusions). It uses the internal transformations of ctStanFit (i.e., tforms) to transform the raw estimates, then re-scale them, and finally use the inverse of tform to supply raw estimates as inits.

Usage

ctmaScaleInits(
  CoTiMAFit = NULL,
  ctsemFit = NULL,
  newTimeScale = NULL,
  autoRefit = FALSE
)

Arguments

ctmaShapeRawData

Description

Raw data objects are re-shaped (dealing with missing time points, wrong time intervals etc)

Raw data objects are re-shaped (dealing with missing time points, wrong time intervals etc)

Usage

ctmaShapeRawData(
  dataFrame = NULL,
  id = NULL,
  inputDataFrameFormat = NULL,
  inputTimeFormat = "time",
  missingValues = NA,
  n.manifest = NULL,
  manifest.per.latent = NULL,
  Tpoints = NULL,
  allInputVariablesNames = NULL,
  orderInputVariablesNames = NULL,
  targetInputVariablesNames = NULL,
  targetInputTDpredNames = NULL,
  targetInputTIpredNames = NULL,
  targetTimeVariablesNames = NULL,
  outputDataFrameFormat = "long",
  outputVariablesNames = "Y",
  outputTDpredNames = NULL,
  outputTIpredNames = NULL,
  outputTimeVariablesNames = "time",
  outputTimeFormat = "time",
  scaleTime = 1,
  minInterval = 1e-04,
  minTolDelta = NULL,
  maxTolDelta = NULL,
  negTolDelta = FALSE,
  min.val.n.Vars = 1,
  min.val.Tpoints = 1,
  standardization = "none"
)

ctmaShapeRawData(
  dataFrame = NULL,
  id = NULL,
  inputDataFrameFormat = NULL,
  inputTimeFormat = "time",
  missingValues = NA,
  n.manifest = NULL,
  manifest.per.latent = NULL,
  Tpoints = NULL,
  allInputVariablesNames = NULL,
  orderInputVariablesNames = NULL,
  targetInputVariablesNames = NULL,
  targetInputTDpredNames = NULL,
  targetInputTIpredNames = NULL,
  targetTimeVariablesNames = NULL,
  outputDataFrameFormat = "long",
  outputVariablesNames = "Y",
  outputTDpredNames = NULL,
  outputTIpredNames = NULL,
  outputTimeVariablesNames = "time",
  outputTimeFormat = "time",
  scaleTime = 1,
  minInterval = 1e-04,
  minTolDelta = NULL,
  maxTolDelta = NULL,
  negTolDelta = FALSE,
  min.val.n.Vars = 1,
  min.val.Tpoints = 1,
  standardization = "none"
)

Arguments

Value

A reshaped raw data file

A reshaped raw data file

Examples

## Not run: 
tmpData <- data.frame(matrix(c(1,  2,  1, 2,  1, 2,  11, 26, 1,
                               NA, NA, 3, NA, 3, NA, 12, 27, 1,
                               1,  2,  1, 2,  1, 2,  NA, 24, 0 ),
                          nrow=3, byrow=TRUE))
colnames(tmpData) <- c("first_T0", "second_T0", "first_T1", "second_T1",
                         "TD1_0", "TD1_1",
                        "time1", "time2", "sex")
shapedData <- ctmaShapeRawData(dataFrame=tmpData,
                               inputDataFrameFormat="wide",
                               inputTimeFormat="time",
                               n.manifest=2,
                               Tpoints=2,
                               orderInputVariablesNames="time",
                               targetInputVariablesNames=c("first_T0", "second_T0",
                                                           "first_T1", "second_T1"),
                               targetInputTDpredNames=c("TD1_0", "TD1_1"),
                               targetInputTIpredNames="sex",
                               targetTimeVariablesNames=c("time1", "time2"),
                               scaleTime=1/12,
                               maxTolDelta=1.2)
head(shapedData)

## End(Not run)

## Not run: 
tmpData <- data.frame(matrix(c(1,  2,  1, 2,  1, 2,  11, 26, 1,
                               NA, NA, 3, NA, 3, NA, 12, 27, 1,
                               1,  2,  1, 2,  1, 2,  NA, 24, 0 ),
                          nrow=3, byrow=TRUE))
colnames(tmpData) <- c("first_T0", "second_T0", "first_T1", "second_T1",
                         "TD1_0", "TD1_1",
                        "time1", "time2", "sex")
shapedData <- ctmaShapeRawData(dataFrame=tmpData,
                               inputDataFrameFormat="wide",
                               inputTimeFormat="time",
                               n.manifest=2,
                               Tpoints=2,
                               orderInputVariablesNames="time",
                               targetInputVariablesNames=c("first_T0", "second_T0",
                                                           "first_T1", "second_T1"),
                               targetInputTDpredNames=c("TD1_0", "TD1_1"),
                               targetInputTIpredNames="sex",
                               targetTimeVariablesNames=c("time1", "time2"),
                               scaleTime=1/12,
                               maxTolDelta=1.2)
head(shapedData)

## End(Not run)

ctmaStanResample

Description

re-sample from a fitted stanct model to achieve desired number of finishsamples (could be useful to prevent exhausted memory)

Usage

ctmaStanResample(ctmaFittedModel = NULL, nsamples = 25, overallSamples = 500)

Arguments

Value

returns a CoTiMA fit object with an increased number of finish samples

ctmaStdParams

Description

Computes standardized drift effects from a CoTiMA or ctsem fit object. Can only handle CLPM or RI-CLPM fit objects.

Usage

ctmaStdParams(
  fit = NULL,
  times = 1,
  digits = 4,
  standardize = TRUE,
  oneTailed = FALSE
)

Arguments

Value

ctmaStdParams returns a list of standardized discrete time drift matrices for different time intervals.

Examples

## Not run: 
ctmaStdParams(CoTiMAFullFit_3_orig, times=c(.1, 1, 2), digits=6, standardize=TRUE)

## End(Not run)

Create APA-style parameter tables

Description

Creates editable Word, legacy Word XML, PDF or LaTeX tables from conventional CoTiMA and native ctsem matrix-parameter summaries. It never fits a new model. Unsupported summary structures fail explicitly.

Usage

ctmaTables(
  fit,
  summary = NULL,
  parameters = NULL,
  statistics = NULL,
  activeDirectory = NULL,
  defaultOrder = TRUE,
  format = "docx",
  file = "ctma_table",
  title = "Model parameter estimates",
  digits = 4,
  labels = NULL,
  missing = c("error", "note"),
  overwrite = FALSE,
  timeUnit = NULL,
  tableNote = NULL,
  interactive = base::interactive(),
  timeIntervals = NULL,
  driftDraws = NULL,
  dtPointOnly = FALSE,
  modelNames = NULL,
  recomputeSummary = c("ask", "yes", "no"),
  layout = c("long", "wide"),
  sortBy = c("study", "parameter"),
  rowLayout = c("parameter", "drift", "all"),
  fontSize = 10
)

Arguments

Details

Table notes use explicit likelihood and parameter-count metadata; log-posterior values are never substituted for log-likelihood. MAP interval interpretation remains that of the stored approximation. Model comparisons align matrix coordinates and require matching substantive variable identities and time units. DT conversion assumes constant drift. It summarizes the chosen drift representation, not a mixture of individual trajectories. Arbitrary hierarchical, empirical-moment, or raw random-effect summaries are not silently converted into matrix-parameter estimates. Obtain a supported transformed summary or select the relevant native fit first.

Value

A ctmaTables list containing numeric data, formatted display, notes, output paths, validated summary, metadata, available matrix entries/statistics, and per-model tables for comparisons. The layout component records presentation options; panels contains the formatted column panels used for export.

Examples

## Not run: 
ctmaTables(initFit, parameters = c(drift, diffusion),
  statistics = c("estimate", "sd"), format = "none")
tables <- ctmaTables(fit, parameters = c("drift", "asymCINT"),
  statistics = c("estimate", "2.5", "median", "97.5"), format = "none")
ctmaTables(fit, parameters = "drift", statistics = c("estimate", "sd"),
  activeDirectory = tempdir(), format = c("docx", "tex"))

## End(Not run)

delta_t1 example vector

Description

delta_t1 example vector

Usage

delta_t1

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

delta_t128 example vector

Description

delta_t128 example vector

Usage

delta_t128

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

delta_t18 example vector

Description

delta_t18 example vector

Usage

delta_t18

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

delta_t201 example vector

Description

delta_t201 example vector

Usage

delta_t201

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

delta_t228 example vector

Description

delta_t228 example vector

Usage

delta_t228

Format

An object of class logical of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

delta_t313 example vector

Description

delta_t313 example vector

Usage

delta_t313

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

delta_t32 example vector

Description

delta_t32 example vector

Usage

delta_t32

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

delta_t4 example vector

Description

delta_t4 example vector

Usage

delta_t4

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

demands1 example vector

Description

demands1 example vector

Usage

demands1

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

demands128 example vector

Description

demands128 example vector

Usage

demands128

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

demands18 example vector

Description

demands18 example vector

Usage

demands18

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

demands201 example vector

Description

demands201 example vector

Usage

demands201

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

demands313 example vector

Description

demands313 example vector

Usage

demands313

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

demands32 example vector

Description

demands32 example vector

Usage

demands32

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

demands4 example vector

Description

demands4 example vector

Usage

demands4

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

dl_link example path

Description

dl_link example path

Usage

dl_link

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

empcov1 example matrix

Description

empcov1 example matrix

Usage

empcov1

Format

An object of class matrix (inherits from array) with 4 rows and 4 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

empcov128 example matrix

Description

empcov128 example matrix

Usage

empcov128

Format

An object of class matrix (inherits from array) with 4 rows and 4 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

empcov18 example matrix

Description

empcov18 example matrix

Usage

empcov18

Format

An object of class matrix (inherits from array) with 4 rows and 4 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

empcov201 example matrix

Description

empcov201 example matrix

Usage

empcov201

Format

An object of class matrix (inherits from array) with 6 rows and 6 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

empcov313 example matrix

Description

empcov313 example matrix

Usage

empcov313

Format

An object of class matrix (inherits from array) with 6 rows and 6 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

empcov32 example matrix

Description

empcov32 example matrix

Usage

empcov32

Format

An object of class matrix (inherits from array) with 4 rows and 4 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

empcov4 example matrix

Description

empcov4 example matrix

Usage

empcov4

Format

An object of class matrix (inherits from array) with 4 rows and 4 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

malePercent1 example vector

Description

malePercent1 example vector

Usage

malePercent1

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

malePercent128 example vector

Description

malePercent128 example vector

Usage

malePercent128

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

malePercent18 example vector

Description

malePercent18 example vector

Usage

malePercent18

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

malePercent201 example vector

Description

malePercent201 example vector

Usage

malePercent201

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

malePercent313 example vector

Description

malePercent313 example vector

Usage

malePercent313

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

malePercent32 example vector

Description

malePercent32 example vector

Usage

malePercent32

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

malePercent4 example vector

Description

malePercent4 example vector

Usage

malePercent4

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderator1 example vector

Description

moderator1 example vector

Usage

moderator1

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderator128 example vector

Description

moderator128 example vector

Usage

moderator128

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderator18 example vector

Description

moderator18 example vector

Usage

moderator18

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderator201 example vector

Description

moderator201 example vector

Usage

moderator201

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderator313 example vector

Description

moderator313 example vector

Usage

moderator313

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderator32 example vector

Description

moderator32 example vector

Usage

moderator32

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderator4 example vector

Description

moderator4 example vector

Usage

moderator4

Format

An object of class numeric of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderatorLabels example vector

Description

moderatorLabels example vector

Usage

moderatorLabels

Format

An object of class character of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

moderatorValues example vector

Description

moderatorValues example vector

Usage

moderatorValues

Format

An object of class list of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

occupation1 example vector

Description

occupation1 example vector

Usage

occupation1

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

occupation128 example vector

Description

occupation128 example vector

Usage

occupation128

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

occupation18 example vector

Description

occupation18 example vector

Usage

occupation18

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

occupation201 example vector

Description

occupation201 example vector

Usage

occupation201

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

occupation313 example vector

Description

occupation313 example vector

Usage

occupation313

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

occupation32 example vector

Description

occupation32 example vector

Usage

occupation32

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

occupation4 example vector

Description

occupation4 example vector

Usage

occupation4

Format

An object of class character of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

pairwiseN128 example vector

Description

pairwiseN128 example vector

Usage

pairwiseN128

Format

An object of class matrix (inherits from array) with 9 rows and 9 columns.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

plot.CoTiMAFit

Description

call ctmaPlot if a CoTiMAFit object is supplied to plot()

Usage

## S3 method for class 'CoTiMAFit'
plot(x, ...)

Arguments

Value

returns a call to 'ctmaPlot', which is used to plot CoTiMA fit objects

pubList_8 example list

Description

pubList_8 example list

Usage

pubList_8

Format

An object of class CoTiMAFit of length 9.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

rawData228 example list

Description

rawData228 example list

Usage

rawData228

Format

An object of class list of length 7.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

recodeVariables128 example vector

Description

recodeVariables128 example vector

Usage

recodeVariables128

Format

An object of class character of length 2.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

results128 example list

Description

results128 example list

Usage

results128

Format

An object of class list of length 3.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

sampleSize1 example vector

Description

sampleSize1 example vector

Usage

sampleSize1

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

sampleSize128 example vector

Description

sampleSize128 example vector

Usage

sampleSize128

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

sampleSize18 example vector

Description

sampleSize18 example vector

Usage

sampleSize18

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

sampleSize201 example vector

Description

sampleSize201 example vector

Usage

sampleSize201

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

sampleSize313 example vector

Description

sampleSize313 example vector

Usage

sampleSize313

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

sampleSize32 example vector

Description

sampleSize32 example vector

Usage

sampleSize32

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

sampleSize4 example vector

Description

sampleSize4 example vector

Usage

sampleSize4

Format

An object of class numeric of length 1.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

source1 example vector

Description

source1 example vector

Usage

source1

Format

An object of class character of length 6.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

source128 example vector

Description

source128 example vector

Usage

source128

Format

An object of class character of length 4.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

source18 example vector

Description

source18 example vector

Usage

source18

Format

An object of class character of length 4.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

source201 example vector

Description

source201 example vector

Usage

source201

Format

An object of class character of length 6.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

source313 example vector

Description

source313 example vector

Usage

source313

Format

An object of class character of length 4.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

source4 example vector

Description

source4 example vector

Usage

source4

Format

An object of class character of length 6.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

Summary method for CoTiMA fit objects

Description

Provides the standard summary for conventional CoTiMA fit objects and redirects hierarchical study-level random-effects fits created by ctmaReFit() or ctmaReMLFit() to the corresponding random-effects summary method.

defines summary for 'CoTiMA' fit objects

Provides the standard summary for conventional CoTiMA fit objects and redirects moment-based and hierarchical study-level random-effects fits to their corresponding summary methods.

Usage

## S3 method for class 'CoTiMAFit'
summary(object, ..., probs = c(0.025, 0.5, 0.975))

## S3 method for class 'CoTiMAFit'
summary(object, ..., probs = c(0.025, 0.5, 0.975))

## S3 method for class 'CoTiMAFit'
summary(object, ..., probs = c(0.025, 0.5, 0.975))

Arguments

Value

For a conventional CoTiMA fit, the stored object$summary is printed and returned. For a hierarchical study-level random-effects fit, a list containing population, study-specific, heterogeneity, correlation, and Time-0 parameter summaries is returned.

returns a printed summary of a 'CoTiMA' fit object

For a conventional CoTiMA fit, the stored object$summary is printed and returned. For a hierarchical study-level random-effects fit, a list containing population, study-specific, raw-scale and transformed-scale heterogeneity, correlation, and Time-0 parameter summaries is returned.

targetVariables1 example vector

Description

targetVariables1 example vector

Usage

targetVariables1

Format

An object of class character of length 4.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

targetVariables128 example vector

Description

targetVariables128 example vector

Usage

targetVariables128

Format

An object of class character of length 7.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

targetVariables313 example vector

Description

targetVariables313 example vector

Usage

targetVariables313

Format

An object of class character of length 6.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

targetVariables4 example vector

Description

targetVariables4 example vector

Usage

targetVariables4

Format

An object of class character of length 4.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

variableNames128 example vector

Description

variableNames128 example vector

Usage

variableNames128

Format

An object of class character of length 9.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

variableNames18 example vector

Description

variableNames18 example vector

Usage

variableNames18

Format

An object of class character of length 4.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

variableNames201 example vector

Description

variableNames201 example vector

Usage

variableNames201

Format

An object of class character of length 6.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org

variableNames32 example vector

Description

variableNames32 example vector

Usage

variableNames32

Format

An object of class character of length 4.

Author(s)

C. Dormann & M. Homburg CoTiMA@uni-mainz.org