Helper providing boilerplate start values for means and variance in twin models
xmu_starts can handle several common/boilerplate situations in which means and variance start values
are used in twin models.
xmu_starts(
mzData,
dzData,
selVars = selVars,
sep = NULL,
equateMeans = NULL,
nSib,
varForm = c("Cholesky"),
SD = TRUE,
divideBy = 3
)mzData |
Data for MZ pairs. |
dzData |
Data for DZ pairs. |
selVars |
Variable names: If sep = NULL, then treated as full names for both sibs. |
sep |
All the variables full names. |
equateMeans |
(NULL) |
nSib |
How many subjects in a family. |
varForm |
currently just "Cholesky" style. |
SD |
= TRUE (FALSE = variance, not SD). |
divideBy |
= 3 (A,C,E) 1/3rd each. Use 1 to do this yourself post-hoc. |
varStarts and meanStarts
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(),
umx,
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_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_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()
data(twinData)
selDVs = c("wt", "ht")
mzData = twinData[twinData$zygosity %in% "MZFF", ]
dzData = twinData[twinData$zygosity %in% "DZFF", ]
round(sqrt(var(dzData[,tvars(selDVs, "")], na.rm=TRUE)/3),3)
xmu_starts(mzData, dzData, selVars=selDVs, nSib= 2, sep="", equateMeans=TRUE, varForm="Cholesky")
# Variance instead of SD
round(var(dzData[,tvars(selDVs, "")], na.rm=TRUE)/3,3)
xmu_starts(mzData, dzData, selVars = selDVs, nSib = 2, sep= "",
equateMeans= TRUE, varForm= "Cholesky", SD= FALSE)
# one variable
xmu_starts(mzData, dzData, selVars= "wt", nSib = 2, sep="", equateMeans = TRUE)
# Ordinal/continuous mix
data(twinData)
twinData= umx_scale_wide_twin_data(data=twinData,varsToScale="wt",sep= "")
# Cut BMI column to form ordinal obesity variables
cuts = quantile(twinData[, "bmi1"], probs = c(.5, .8), na.rm = TRUE)
obLevels = c('normal', 'overweight', 'obese')
twinData$obese1= cut(twinData$bmi1,breaks=c(-Inf,cuts,Inf),labels=obLevels)
twinData$obese2= cut(twinData$bmi2,breaks=c(-Inf,cuts,Inf),labels=obLevels)
# Make the ordinal variables into mxFactors
ordDVs = c("obese1", "obese2")
twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
mzData = twinData[twinData$zygosity %in% "MZFF",]
dzData = twinData[twinData$zygosity %in% "DZFF",]
xmu_starts(mzData, dzData, selVars = c("wt","obese"), sep= "",
nSib= 2, equateMeans = TRUE, SD= FALSE)
xmu_starts(mxData(mzData, type="raw"), mxData(mzData, type="raw"),
selVars = c("wt","obese"), sep= "", nSib= 2, equateMeans = TRUE, SD= FALSE)
# ==============
# = Three sibs =
# ==============
data(twinData)
twinData$wt3 = twinData$wt2
twinData$ht3 = twinData$ht2
selDVs = c("wt", "ht")
mzData = twinData[twinData$zygosity %in% "MZFF", ]
dzData = twinData[twinData$zygosity %in% "DZFF", ]
xmu_starts(mzData, dzData, selVars=selDVs, sep="", nSib=3, equateMeans=TRUE)
xmu_starts(mzData, dzData, selVars=selDVs, sep="", nSib=3, equateMeans=FALSE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.