xmuTwinUpgradeMeansToCovariateModel {umx}

Not for end-users: Add a means model with covariates to a twin model

Description

Does the following to model (i.e., a umx top/MZ/DZ supermodel):

1. Change top.expMeans to top.intercept.

2. Create top.meansBetas for beta weights in rows (of covariates) and columns for each variable.

3. Add matrices for each twin's data.cov vars (matrixes are called T1DefVars).

4. Switch mxExpectationNormal in each data group to point to the local expMean.

5. Add "expMean" algebra to each data group.

• grp.expMean sums top.intercept and grp.DefVars %*% top.meansBetas for each twin.

Usage

xmuTwinUpgradeMeansToCovariateModel(model, fullVars, fullCovs, nSib, sep)

Arguments

model

The umxSuperModel() we are modifying (must have MZ DZ and top submodels)

fullVars

the FULL names of manifest variables

fullCovs

the FULL names of definition variables

nSib

How many siblings

sep

How twin variable names have been expanded, e.g. "_T".

Details

In umx models with no covariates, means live in top$expMean

Value

• model, now with means model extended to covariates.

See Also

• called by xmuTwinSuper_Continuous()

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(), 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_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_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

## Not run: 
data(twinData) # ?twinData from Australian twins.
twinData[, c("ht1", "ht2")] = twinData[, c("ht1", "ht2")] * 10
mzData = twinData[twinData$zygosity %in% "MZFF", ]
dzData = twinData[twinData$zygosity %in% "DZFF", ]
# m1 = umxACE(selDVs= "ht", sep= "", dzData= dzData, mzData= mzData, autoRun= FALSE)
# m2 = xmuTwinUpgradeMeansToCovariateModel(m1, fullVars = c("ht1", "ht2"),
# 	fullCovs = c("age1", "sex1", "age2", "sex2"), sep = "")


## End(Not run)