umx (version 4.0.0)

xmu_describe_data_WLS: Determine whether a dataset will need weights and summary statistics for the means if used with mxFitFunctionWLS

Description

Given either a data.frame or an mxData of type raw, this function determines whether mxFitFunctionWLS() will generate expectations for means.

Usage

xmu_describe_data_WLS(
  data,
  allContinuousMethod = c("cumulants", "marginals"),
  verbose = FALSE
)

Arguments

data

The raw data being used in a mxFitFunctionWLS() model.

allContinuousMethod

the method used to process data when all columns are continuous (default = "cumulants")

verbose

Whether or not to report diagnostics.

Value

• list describing the data.

Details

All-continuous models processed using the "cumulants" method lack means, while all continuous processed with allContinuousMethod = "marginals" will have means.

When data are not all continuous, allContinuousMethod is ignored, and means are modeled.

See Also

• mxFitFunctionWLS(), omxAugmentDataWithWLSSummary()

Other xmu internal not for end user: umxModel(), umxRenameMatrix(), umxTwinMaker(), umx_APA_pval(), umx_fun_mean_sd(), umx_get_bracket_addresses(), umx_make(), umx_standardize(), umx_string_to_algebra(), umx, xmuHasSquareBrackets(), xmuLabel_MATRIX_Model(), xmuLabel_Matrix(), xmuLabel_RAM_Model(), xmuMI(), xmuMakeDeviationThresholdsMatrices(), xmuMakeOneHeadedPathsFromPathList(), xmuMakeTwoHeadedPathsFromPathList(), xmuMaxLevels(), xmuMinLevels(), xmuPropagateLabels(), xmuRAM2Ordinal(), xmuTwinSuper_Continuous(), xmuTwinUpgradeMeansToCovariateModel(), xmu_CI_merge(), xmu_CI_stash(), xmu_DF_to_mxData_TypeCov(), xmu_PadAndPruneForDefVars(), xmu_cell_is_on(), xmu_check_levels_identical(), xmu_check_needs_means(), xmu_check_variance(), xmu_clean_label(), xmu_data_missing(), xmu_data_swap_a_block(), xmu_dot_make_paths(), xmu_dot_make_residuals(), xmu_dot_maker(), xmu_dot_move_ranks(), xmu_dot_rank_str(), xmu_extract_column(), xmu_get_CI(), xmu_lavaan_process_group(), xmu_make_TwinSuperModel(), xmu_make_bin_cont_pair_data(), xmu_make_mxData(), xmu_match.arg(), xmu_name_from_lavaan_str(), xmu_path2twin(), xmu_path_regex(), xmu_safe_run_summary(), xmu_set_sep_from_suffix(), xmu_show_fit_or_comparison(), xmu_simplex_corner(), xmu_standardize_ACEcov(), xmu_standardize_ACEv(), xmu_standardize_ACE(), xmu_standardize_CP(), xmu_standardize_IP(), xmu_standardize_RAM(), xmu_standardize_SexLim(), xmu_standardize_Simplex(), xmu_start_value_list(), xmu_starts(), xmu_twin_add_WeightMatrices(), xmu_twin_check(), xmu_twin_get_var_names(), xmu_twin_upgrade_selDvs2SelVars()

Examples

# NOT RUN {
# ====================================
# = All continuous, data.frame input =
# ====================================

tmp =xmu_describe_data_WLS(mtcars, allContinuousMethod= "cumulants", verbose = TRUE)
tmp$hasMeans # FALSE - no means with cumulants
tmp =xmu_describe_data_WLS(mtcars, allContinuousMethod= "marginals") 
tmp$hasMeans # TRUE we get means with marginals

# ==========================
# = mxData object as input =
# ==========================
tmp = mxData(mtcars, type="raw")
xmu_describe_data_WLS(tmp, allContinuousMethod= "cumulants", verbose = TRUE)$hasMeans # FALSE
xmu_describe_data_WLS(tmp, allContinuousMethod= "marginals")$hasMeans  # TRUE

# =======================================
# = One var is a factor: Means modeled =
# =======================================
tmp = mtcars
tmp$cyl = factor(tmp$cyl)
xmu_describe_data_WLS(tmp, allContinuousMethod= "cumulants")$hasMeans # TRUE - always has means
xmu_describe_data_WLS(tmp, allContinuousMethod= "marginals")$hasMeans # TRUE

# }