umx (version 4.0.0)

xmu_standardize_ACEv: Standardize an ACE variance components model (ACEv)

Description

xmu_standardize_ACE allows umx_standardize to standardize an ACE variance components model.

Usage

xmu_standardize_ACEv(model, ...)

Arguments

model

An umxACEv() model to standardize.

...

Other parameters.

Value

• A standardized umxACEv() model.

References

• https://tbates.github.io, https://github.com/tbates/umx

See Also

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_describe_data_WLS(), 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_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 {
require(umx)
data(twinData)
selDVs = c("bmi")
mzData <- twinData[twinData$zygosity %in% "MZFF",][1:80,] # 80 pairs for speed
dzData <- twinData[twinData$zygosity %in% "DZFF",][1:80,]
m1  = umxACEv(selDVs = selDVs, sep="", dzData = dzData, mzData = mzData)
std = umx_standardize(m1)
# }