README.md
In statsccpr/MultCal2Sim:
MultCal2Sim
The MultCal2Sim package for R has tools to perform calibration with pre-computed estimates. MultCal2Sim will calibrate initial estimates from the first data source onto estimated reference control-totals from the second data source. There are two choices of computation tools available, post-stratification and raking. See presentation slides here https://ucla.box.com/v/multcal2sim-slides
Installation
You can install MultCal2Sim from github with:
# install.packages('devtools')
# library(devtools)
devtools::install_github("statsccpr/MultCal2Sim")
Examples
We use the survey::api dataset that contains infomation about student performance in California Schools.
library(survey)
data(api,package = 'survey')
form_outcome = ~enroll
form_poststrat = ~sch.wide+both
We want an improved estimate of the total for the outcome enroll where sch.wide and both are post-strata variables.
The to-be-calibrated outcome is found in Data Source 1, apiclus1, which is a cluster sample of school districts. Acting as the target data source is Data Source 2, apistrat, which is a sample design (pre) stratified by type of school (Elementary/Middle/High School),
# stratified sample via ?apistrat
des_targ = svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
# # one-stage cluster sample via ?apiclus1
des_2be_cal = svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
Poststratification
Step 0: Computing estimates for strata cell totals
If the user already has estimates for the post-strata cells, the user can skip to step 1. Otherwise, we have helper functions for the user to compute the required post-strata cell estimates.
est_tot_from_des_joint(form_additive,design_refer): estimate joint totals from a survey design object est_tot_from_des_1marg(form_1_term_only,design_refer): estimate marginal totals for a single variable from a survey design object
We'll first focus on the MCS poststratification method, thus requiring joint control totals. Later we'll demonstrate how marginal totals are used for the MCS raking method.
# helper function to compute estimated totals within joint strata
library(MultCal2Sim)
df_targ_tot_joint = est_tot_from_des_joint(form_additive=form_poststrat,
design_refer=des_targ)
Going forward, we need the following objects
df_targ_tot_joint # data.frame with estimates of the post-strata cell totals
#> sch.wide both Freq SE
#> 1 No No 1065.69 150.7916
#> 2 Yes No 1170.74 183.4941
#> 3 No Yes 0.00 0.0000
#> 4 Yes Yes 3957.57 213.1103
form_poststrat # formula specifying poststrata
#> ~sch.wide + both
form_outcome # formula specifying the outcome
#> ~enroll
des_2be_cal # survey.design object containing the outcome to be calibrated
#> 1 - level Cluster Sampling design
#> With (15) clusters.
#> svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
Step 1: Simulating draws using the estimated strata control totals from the target data source
Simulate m = 1, 2, 3, ..., M draws for each strata cell s = 1, 2, 3, ..., S in the first step. The ?sim_tot_from_est() function will simulate totals from pre-existing estimates. It will return one simulation of either joint totals or marginal totals in a data frame.
# simulate one draw
sim_tot_from_est(df_or_list_est_tot=df_targ_tot_joint,type_strata='joint',lgl_rej_neg_sim=TRUE)
#> sch.wide both Freq
#> 1 No No 1170.571
#> 2 Yes No 1130.715
#> 3 No Yes 0.000
#> 4 Yes Yes 3921.192
# simulate 10 draws for joint totals
list_sim_out_joint = lapply(1:10,FUN=sim_tot_from_est,df_or_list_est_tot=df_targ_tot_joint,type_strata='joint')
# list of 10 draws
str(list_sim_out_joint,1)
#> List of 10
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
# first draw
list_sim_out_joint[[1]]
#> sch.wide both Freq
#> 1 No No 995.4881
#> 2 Yes No 1205.8742
#> 3 No Yes 0.0000
#> 4 Yes Yes 4157.2966
Step 2: Calibrating the estimates from the first data source onto the simulated control totals
The user can pick either post-stratification or raking to calibrate the initial estimate to each draw (simulated in step 1). The function ?cal_2_sim() will calibrate (poststratify or rake) an outcome onto simulted poststrata totals. It will return a list of multiple intermediate estimates. Here we use post-stratification.
# using mcsp to calibrate the estimate to the first draw
cal_2_sim(des_2be_cal=des_2be_cal,
df_or_list_sim=list_sim_out_joint[[1]],
form_outcome=form_outcome,form_poststrat=form_poststrat,
type_cal='mcsp')
#> total SE
#> enroll 3521337 310697
# repeat the task 10 times via ?lapply()
list_cal_out_joint = lapply(list_sim_out_joint,FUN=cal_2_sim,
des_2be_cal=des_2be_cal,
form_outcome=form_outcome,
form_poststrat=form_poststrat,
type_cal='mcsp')
str(list_cal_out_joint)
#> List of 10
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3521337
#> ..$ SE : num 310697
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3508682
#> ..$ SE : num 306333
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3308235
#> ..$ SE : num 267692
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3607524
#> ..$ SE : num 312647
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3382115
#> ..$ SE : num 296683
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3351404
#> ..$ SE : num 277309
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3329892
#> ..$ SE : num 278551
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3606328
#> ..$ SE : num 354130
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3533218
#> ..$ SE : num 309933
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3665366
#> ..$ SE : num 329621
Step 3: Computing the final point estimate and the standard error
The ?combine_est() function will combine the list of intermediate estimates (from step 2). The final point estimate is the average of intermediate calibrations, and its standard error will be returned as well.
# combine estimates
combine_est(list_cal_out_joint)
#> theta_mcs se_root_T T_var B U_bar
#> 1 3481410 334041.6 111583815649 16675465094 93240804045
Raking onto marginal totals (MCSR)
Alternative to poststratification onto joint totals, we demonstrate raking onto marginal totals. For raking, the control variable specification requires a list where each element is a one term formula (unlike poststratification requiring one formula of terms).
# marginal targets
args(est_tot_from_des_1marg)
#> function (form_1_term_only, design_refer)
#> NULL
form_poststrat
#> ~sch.wide + both
# expect error if more than 1 RHS term in formula
form_poststrat
#> ~sch.wide + both
# est_tot_from_des_1marg(form_1_term_only=~sch.wide + both,design_refer=des_targ)
is.list(form_poststrat)
#> [1] FALSE
# correct, list of one term formulae
list_form_marg = list(~sch.wide,~both)
is.list(list_form_marg)
#> [1] TRUE
list_targ_tot_marg = lapply(list_form_marg,FUN=est_tot_from_des_1marg, design_refer=des_targ)
args(sim_tot_from_est)
#> function (df_or_list_est_tot, type_strata, lgl_rej_neg_sim = TRUE)
#> NULL
# step 1, simulate
# simulate one draw of margin(s)
sim_tot_from_est(df_or_list_est_tot=list_targ_tot_marg,type_strata='marginal',lgl_rej_neg_sim=TRUE)
#> [[1]]
#> sch.wide Freq
#> 1 No 959.3283
#> 2 Yes 5334.6884
#>
#> [[2]]
#> both Freq
#> 1 No 2143.122
#> 2 Yes 4179.254
# simulate 10 draws
list_sim_out_marg = lapply(1:10,FUN=sim_tot_from_est,df_or_list_est_tot=list_targ_tot_marg,type_strata='marginal')
# list of draws
str(list_sim_out_marg,2)
#> List of 10
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
# 5th draw
list_sim_out_marg[[5]]
#> [[1]]
#> sch.wide Freq
#> 1 No 991.4722
#> 2 Yes 5167.2936
#>
#> [[2]]
#> both Freq
#> 1 No 2052.733
#> 2 Yes 3720.079
# step 2, calibrate via mcsr to compute intermediate estimates
list_cal_out_marg = lapply(list_sim_out_marg,FUN=cal_2_sim,
des_2be_cal=des_2be_cal,
form_outcome=form_outcome,
form_poststrat=list_form_marg,
type_cal='mcsr')
# step 3: combine the estimates and compute final uncertainty
combine_est(list_cal_out_marg)
#> theta_mcs se_root_T T_var B U_bar
#> 1 3447281 344266.9 118519673026 20066237623 96446811641
Overall MCS helper that wraps together steps 1-3
For the data arguments, the ?mcs() wrapper needs a survey object with the outcome to be calibrated, the precomputed estimates of the control totals, a formula of the outcome, and a formula of the poststrata controls.
mcs(des_2be_cal=des_2be_cal,
df_or_list_est_tot=list_targ_tot_marg,
form_outcome=form_outcome,
form_poststrat=list_form_marg,
type_cal='mcsr',
num_sim=50,
lgl_rej_neg_sim=TRUE)
#> theta_mcs se_root_T T_var B U_bar
#> 1 3465124 345557.8 119410177285 23501860118 95438279965
The ?mcs() wrapper has a parallel option
# parallel
mcs(des_2be_cal=des_2be_cal,
df_or_list_est_tot=list_targ_tot_marg,
form_outcome=form_outcome,
form_poststrat=list_form_marg,
type_cal='mcsr',
num_sim=50,
parallel = TRUE,
num_core = 4,
lgl_rej_neg_sim=TRUE)
# mcs(des_2be_cal=des_2be_cal,
# df_or_list_est_tot=df_targ_tot_joint,
# form_outcome=form_outcome,
# form_poststrat=form_poststrat,
# type_cal='mcsp',
# num_sim=200,
# parallel = TRUE,
# num_core = 4,
# lgl_rej_neg_sim=TRUE)
sessionInfo()
#> R version 3.3.3 (2017-03-06)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server 2012 R2 x64 (build 9600)
#>
#> locale:
#> [1] LC_COLLATE=English_United States.1252
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] MultCal2Sim_0.1.0 survey_3.33-2 survival_2.41-3 Matrix_1.2-10
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_0.12.16 lattice_0.20-35 digest_0.6.14 rprojroot_1.3-2
#> [5] backports_1.1.2 magrittr_1.5 evaluate_0.10.1 stringi_1.1.7
#> [9] rmarkdown_1.10 splines_3.3.3 tools_3.3.3 stringr_1.3.1
#> [13] yaml_2.1.18 htmltools_0.3.6 knitr_1.20
statsccpr/MultCal2Sim documentation built on Sept. 3, 2022, 12:39 p.m.
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MultCal2Sim
The MultCal2Sim package for R has tools to perform calibration with pre-computed estimates. MultCal2Sim will calibrate initial estimates from the first data source onto estimated reference control-totals from the second data source. There are two choices of computation tools available, post-stratification and raking. See presentation slides here https://ucla.box.com/v/multcal2sim-slides
Installation
You can install MultCal2Sim from github with:
# install.packages('devtools')
# library(devtools)
devtools::install_github("statsccpr/MultCal2Sim")
Examples
We use the survey::api dataset that contains infomation about student performance in California Schools.
library(survey)
data(api,package = 'survey')
form_outcome = ~enroll
form_poststrat = ~sch.wide+both
We want an improved estimate of the total for the outcome enroll where sch.wide and both are post-strata variables.
The to-be-calibrated outcome is found in Data Source 1, apiclus1, which is a cluster sample of school districts. Acting as the target data source is Data Source 2, apistrat, which is a sample design (pre) stratified by type of school (Elementary/Middle/High School),
# stratified sample via ?apistrat
des_targ = svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
# # one-stage cluster sample via ?apiclus1
des_2be_cal = svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
Poststratification
Step 0: Computing estimates for strata cell totals
If the user already has estimates for the post-strata cells, the user can skip to step 1. Otherwise, we have helper functions for the user to compute the required post-strata cell estimates.
est_tot_from_des_joint(form_additive,design_refer): estimate joint totals from a survey design object est_tot_from_des_1marg(form_1_term_only,design_refer): estimate marginal totals for a single variable from a survey design object
We'll first focus on the MCS poststratification method, thus requiring joint control totals. Later we'll demonstrate how marginal totals are used for the MCS raking method.
# helper function to compute estimated totals within joint strata
library(MultCal2Sim)
df_targ_tot_joint = est_tot_from_des_joint(form_additive=form_poststrat,
design_refer=des_targ)
Going forward, we need the following objects
df_targ_tot_joint # data.frame with estimates of the post-strata cell totals
#> sch.wide both Freq SE
#> 1 No No 1065.69 150.7916
#> 2 Yes No 1170.74 183.4941
#> 3 No Yes 0.00 0.0000
#> 4 Yes Yes 3957.57 213.1103
form_poststrat # formula specifying poststrata
#> ~sch.wide + both
form_outcome # formula specifying the outcome
#> ~enroll
des_2be_cal # survey.design object containing the outcome to be calibrated
#> 1 - level Cluster Sampling design
#> With (15) clusters.
#> svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
Step 1: Simulating draws using the estimated strata control totals from the target data source
Simulate m = 1, 2, 3, ..., M draws for each strata cell s = 1, 2, 3, ..., S in the first step. The ?sim_tot_from_est() function will simulate totals from pre-existing estimates. It will return one simulation of either joint totals or marginal totals in a data frame.
# simulate one draw
sim_tot_from_est(df_or_list_est_tot=df_targ_tot_joint,type_strata='joint',lgl_rej_neg_sim=TRUE)
#> sch.wide both Freq
#> 1 No No 1170.571
#> 2 Yes No 1130.715
#> 3 No Yes 0.000
#> 4 Yes Yes 3921.192
# simulate 10 draws for joint totals
list_sim_out_joint = lapply(1:10,FUN=sim_tot_from_est,df_or_list_est_tot=df_targ_tot_joint,type_strata='joint')
# list of 10 draws
str(list_sim_out_joint,1)
#> List of 10
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
#> $ :'data.frame': 4 obs. of 3 variables:
# first draw
list_sim_out_joint[[1]]
#> sch.wide both Freq
#> 1 No No 995.4881
#> 2 Yes No 1205.8742
#> 3 No Yes 0.0000
#> 4 Yes Yes 4157.2966
Step 2: Calibrating the estimates from the first data source onto the simulated control totals
The user can pick either post-stratification or raking to calibrate the initial estimate to each draw (simulated in step 1). The function ?cal_2_sim() will calibrate (poststratify or rake) an outcome onto simulted poststrata totals. It will return a list of multiple intermediate estimates. Here we use post-stratification.
# using mcsp to calibrate the estimate to the first draw
cal_2_sim(des_2be_cal=des_2be_cal,
df_or_list_sim=list_sim_out_joint[[1]],
form_outcome=form_outcome,form_poststrat=form_poststrat,
type_cal='mcsp')
#> total SE
#> enroll 3521337 310697
# repeat the task 10 times via ?lapply()
list_cal_out_joint = lapply(list_sim_out_joint,FUN=cal_2_sim,
des_2be_cal=des_2be_cal,
form_outcome=form_outcome,
form_poststrat=form_poststrat,
type_cal='mcsp')
str(list_cal_out_joint)
#> List of 10
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3521337
#> ..$ SE : num 310697
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3508682
#> ..$ SE : num 306333
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3308235
#> ..$ SE : num 267692
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3607524
#> ..$ SE : num 312647
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3382115
#> ..$ SE : num 296683
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3351404
#> ..$ SE : num 277309
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3329892
#> ..$ SE : num 278551
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3606328
#> ..$ SE : num 354130
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3533218
#> ..$ SE : num 309933
#> $ :'data.frame': 1 obs. of 2 variables:
#> ..$ total: num 3665366
#> ..$ SE : num 329621
Step 3: Computing the final point estimate and the standard error
The ?combine_est() function will combine the list of intermediate estimates (from step 2). The final point estimate is the average of intermediate calibrations, and its standard error will be returned as well.
# combine estimates
combine_est(list_cal_out_joint)
#> theta_mcs se_root_T T_var B U_bar
#> 1 3481410 334041.6 111583815649 16675465094 93240804045
Raking onto marginal totals (MCSR)
Alternative to poststratification onto joint totals, we demonstrate raking onto marginal totals. For raking, the control variable specification requires a list where each element is a one term formula (unlike poststratification requiring one formula of terms).
# marginal targets
args(est_tot_from_des_1marg)
#> function (form_1_term_only, design_refer)
#> NULL
form_poststrat
#> ~sch.wide + both
# expect error if more than 1 RHS term in formula
form_poststrat
#> ~sch.wide + both
# est_tot_from_des_1marg(form_1_term_only=~sch.wide + both,design_refer=des_targ)
is.list(form_poststrat)
#> [1] FALSE
# correct, list of one term formulae
list_form_marg = list(~sch.wide,~both)
is.list(list_form_marg)
#> [1] TRUE
list_targ_tot_marg = lapply(list_form_marg,FUN=est_tot_from_des_1marg, design_refer=des_targ)
args(sim_tot_from_est)
#> function (df_or_list_est_tot, type_strata, lgl_rej_neg_sim = TRUE)
#> NULL
# step 1, simulate
# simulate one draw of margin(s)
sim_tot_from_est(df_or_list_est_tot=list_targ_tot_marg,type_strata='marginal',lgl_rej_neg_sim=TRUE)
#> [[1]]
#> sch.wide Freq
#> 1 No 959.3283
#> 2 Yes 5334.6884
#>
#> [[2]]
#> both Freq
#> 1 No 2143.122
#> 2 Yes 4179.254
# simulate 10 draws
list_sim_out_marg = lapply(1:10,FUN=sim_tot_from_est,df_or_list_est_tot=list_targ_tot_marg,type_strata='marginal')
# list of draws
str(list_sim_out_marg,2)
#> List of 10
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> $ :List of 2
#> ..$ :'data.frame': 2 obs. of 2 variables:
#> ..$ :'data.frame': 2 obs. of 2 variables:
# 5th draw
list_sim_out_marg[[5]]
#> [[1]]
#> sch.wide Freq
#> 1 No 991.4722
#> 2 Yes 5167.2936
#>
#> [[2]]
#> both Freq
#> 1 No 2052.733
#> 2 Yes 3720.079
# step 2, calibrate via mcsr to compute intermediate estimates
list_cal_out_marg = lapply(list_sim_out_marg,FUN=cal_2_sim,
des_2be_cal=des_2be_cal,
form_outcome=form_outcome,
form_poststrat=list_form_marg,
type_cal='mcsr')
# step 3: combine the estimates and compute final uncertainty
combine_est(list_cal_out_marg)
#> theta_mcs se_root_T T_var B U_bar
#> 1 3447281 344266.9 118519673026 20066237623 96446811641
Overall MCS helper that wraps together steps 1-3
For the data arguments, the ?mcs() wrapper needs a survey object with the outcome to be calibrated, the precomputed estimates of the control totals, a formula of the outcome, and a formula of the poststrata controls.
mcs(des_2be_cal=des_2be_cal,
df_or_list_est_tot=list_targ_tot_marg,
form_outcome=form_outcome,
form_poststrat=list_form_marg,
type_cal='mcsr',
num_sim=50,
lgl_rej_neg_sim=TRUE)
#> theta_mcs se_root_T T_var B U_bar
#> 1 3465124 345557.8 119410177285 23501860118 95438279965
The ?mcs() wrapper has a parallel option
# parallel
mcs(des_2be_cal=des_2be_cal,
df_or_list_est_tot=list_targ_tot_marg,
form_outcome=form_outcome,
form_poststrat=list_form_marg,
type_cal='mcsr',
num_sim=50,
parallel = TRUE,
num_core = 4,
lgl_rej_neg_sim=TRUE)
# mcs(des_2be_cal=des_2be_cal,
# df_or_list_est_tot=df_targ_tot_joint,
# form_outcome=form_outcome,
# form_poststrat=form_poststrat,
# type_cal='mcsp',
# num_sim=200,
# parallel = TRUE,
# num_core = 4,
# lgl_rej_neg_sim=TRUE)
sessionInfo()
#> R version 3.3.3 (2017-03-06)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server 2012 R2 x64 (build 9600)
#>
#> locale:
#> [1] LC_COLLATE=English_United States.1252
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] MultCal2Sim_0.1.0 survey_3.33-2 survival_2.41-3 Matrix_1.2-10
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_0.12.16 lattice_0.20-35 digest_0.6.14 rprojroot_1.3-2
#> [5] backports_1.1.2 magrittr_1.5 evaluate_0.10.1 stringi_1.1.7
#> [9] rmarkdown_1.10 splines_3.3.3 tools_3.3.3 stringr_1.3.1
#> [13] yaml_2.1.18 htmltools_0.3.6 knitr_1.20
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