| xmu_describe_data_WLS {umx} | R Documentation |
Determine if a dataset will need statistics for the means if used in a WLS model.
Description
Given either a data.frame or raw mxData, 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 |
allContinuousMethod |
the method used to process data when all columns are continuous (default = "cumulants") |
verbose |
Whether or not to report diagnostics. |
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, means are modeled and allContinuousMethod is ignored.
Value
list describing the data.
See Also
Other xmu internal not for end user:
umxModel(),
umxRenameMatrix(),
umx_APA_pval(),
umx_fun_mean_sd(),
umx_get_bracket_addresses(),
umx_make(),
umx_standardize(),
umx_string_to_algebra(),
xmuHasSquareBrackets(),
xmuLabel_MATRIX_Model(),
xmuLabel_Matrix(),
xmuLabel_RAM_Model(),
xmuMI(),
xmuMakeDeviationThresholdsMatrices(),
xmuMakeOneHeadedPathsFromPathList(),
xmuMakeTwoHeadedPathsFromPathList(),
xmuMaxLevels(),
xmuMinLevels(),
xmuPropagateLabels(),
xmuRAM2Ordinal(),
xmuTwinSuper_Continuous(),
xmuTwinSuper_NoBinary(),
xmuTwinUpgradeMeansToCovariateModel(),
xmu_CI_merge(),
xmu_CI_stash(),
xmu_DF_to_mxData_TypeCov(),
xmu_PadAndPruneForDefVars(),
xmu_bracket_address2rclabel(),
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_print_algebras(),
xmu_rclabel_2_bracket_address(),
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_summary_RAM_group_parameters(),
xmu_twin_add_WeightMatrices(),
xmu_twin_check(),
xmu_twin_get_var_names(),
xmu_twin_make_def_means_mats_and_alg(),
xmu_twin_upgrade_selDvs2SelVars()
Examples
# ====================================
# = 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