# Blocked weighted bootstrap estimation

#### Ernest Guevarra

#### 8 January 2025

The `{bbw}` package was developed primarily as a tool for analysing complex sample survey data. It was developed specifically for use with the Rapid Assessment Method (RAM) and the Simple Spatial Survey Method (S3M).

The `indicatorsHH` is a survey dataset collected from a RAM survey in Bakool, Bay, and Middle Shabelle regions of Somalia. The `villageData` contains the list of villages/clusters that were sampled in the survey that collected the `indicatorsHH` dataset. These is a good set of data to demonstrate the use of the `{bbw}` package to perform blocked weighted bootstrap estimation.

## Original bootstrapping workflow

### Bootstrap resampling with `bootBW()`

The `bootBW()` function is the original bootstrap resampling function of the package. It can be used as follows:

    boot_df <- bootBW(
      x = indicatorsHH, w = villageData, statistic = bootClassic,
      params = c("anc1", "anc2")
    )
    #> ✔ x has the appropriate/expected data structure

This call to `bootBW()` takes in the survey dataset `indicatorsHH` as its first argument (`x`). This dataset is expected to have a variable labelled as `psu` which identifies the primary sampling unit from which data was collected during the survey and then additional variables for the indicators to be estimated. The second argument (`w`) is for the dataset of the list of primary sampling units that were sampled in the survey to collect the survey data specified in `x`. This dataset, which in this case is `villageData`, should have at least a variable labelled `psu` which identified the primary sampling unit that matches the same variable in the survey dataset and a variable labelled `pop` for the population size of the primary sampling unit. The `statistic` argument specified the type of statistic to apply to the bootstrap replicates. There are two of these functions available from the `{bbw}` package - `bootClassic()` and the `bootPROBIT()`. For this example, the `bootClassic()` function is used to get the mean value of the bootstrap replicates. This is generally useful for binomial type of indicators and for continuous variables of which to get the mean of. The `params` argument takes in values of the indicator names in `x` to be estimated. In this example, two indicator names for antenatal care are specified. Finally, the argument for `replicates` specify the number of replicate bootstraps to be performed. The default of 400 replicates is used here. This results in the following (showing first 10 rows):

    head(boot_df, 10)
    #>         anc1       anc2
    #> 1  0.1864175 0.01874714
    #> 2  0.2290978 0.02035985
    #> 3  0.2343529 0.02641509
    #> 4  0.2548555 0.03084955
    #> 5  0.2698864 0.02662863
    #> 6  0.2151356 0.01819052
    #> 7  0.1937834 0.02677702
    #> 8  0.2148349 0.01678766
    #> 9  0.2593480 0.02891566
    #> 10 0.2151414 0.01922153

The result is a `data.frame()` of bootstrap replicates with number of rows equal to the number or replicates and number of columns equal to the number of `params` specified. Hence, `boot_df` has 400 rows and 2 columns.

### Bootstrap estimation

Using `boot_df` containing bootstrap replicates of the indicators `anc1` and `anc2`, estimating each indicator with a 95% confidence interval using the percentile bootstrap method. This can be simply done using the `quantile()` function from the `stats` package as follows:

    est_df <- lapply(
      X = boot_df,
      FUN = quantile,
      probs = c(0.5, 0.025, 0.975)
    ) |>
      do.call(rbind, args = _)

The `quantile()` function is used to get the 50th percentile (for the estimate) and the 2.5th and the 97.5th percentile of the bootstrap replicates to get the lower confidence limit and the upper confidence limits (respectively) of the indicator estimate. This gives the following results:

    est_df
    #>            50%       2.5%      97.5%
    #> anc1 0.2316597 0.17709920 0.28849265
    #> anc2 0.0218962 0.01347537 0.03484835

### Stratified bootstrap resampling

Note that the `indicatorsHH` dataset has geographical stratification. Specifically, the survey from which this data was collected was designed to be representative of three regions in Somalia with the regions identified through the `region` variable in `indicatorsHH`. Because of this the more appropriate bootstrap resampling approach would be to resample within each region. To do this using the original `bootBW()` function would require restructuring the survey dataset by region and then passing the region-stratified datasets individually to the `bootBW()` function. This may look something like this:

    ## Split indicators by region ----
    indicators_by_region <- split(indicatorsHH, f = indicatorsHH$region)

    ## Split psus by region ----
    psus_by_region <- split(villageData, f = villageData$region)

    ## Bootstrap
    boot_df <- Map(
      f = bootBW,
      x = indicators_by_region,
      w = psus_by_region,
      statistic = rep(list(get("bootClassic")), length(indicators_by_region)),
      params = rep(list(c("anc1", "anc2")), length(indicators_by_region))
    )
    #> ✔ x has the appropriate/expected data structure
    #> ✔ x has the appropriate/expected data structure
    #> ✔ x has the appropriate/expected data structure

The `bootBW()` function only accepts single `data.frame` inputs for `x` and `w` arguments. Hence, to resample data from within region, the datasets will have to be split into separate `data.frame` inputs per region and then `bootBW()` applied to each separately. In the example above, this is done by concatenating each of the inputs to `bootBW()` into a list and then using the `Map()` function is sent to `bootBW()` sequentially. This produces a list of the `data.frame` bootstrap resample for each region (shown below):

    class(boot_df)
    #> [1] "list"

    head(boot_df$Bay, 10)
    #>         anc1        anc2
    #> 1  0.4043419 0.013568521
    #> 2  0.3907104 0.020491803
    #> 3  0.3224044 0.023224044
    #> 4  0.2645862 0.016282225
    #> 5  0.2708618 0.008207934
    #> 6  0.3297151 0.024423338
    #> 7  0.3627717 0.004076087
    #> 8  0.3662551 0.016460905
    #> 9  0.3410641 0.016371078
    #> 10 0.2277628 0.014824798

    head(boot_df$Bakool, 10)
    #>         anc1       anc2
    #> 1  0.2916667 0.17415730
    #> 2  0.2928177 0.09497207
    #> 3  0.3260274 0.14804469
    #> 4  0.2747253 0.11864407
    #> 5  0.2900552 0.11797753
    #> 6  0.1823204 0.05849582
    #> 7  0.4065934 0.16343490
    #> 8  0.2727273 0.11731844
    #> 9  0.2821918 0.06944444
    #> 10 0.2939560 0.09749304

    head(boot_df$`Middle Shabelle`, 10)
    #>         anc1        anc2
    #> 1  0.1723447 0.011055276
    #> 2  0.2550607 0.018367347
    #> 3  0.1330724 0.010816126
    #> 4  0.2830189 0.024551464
    #> 5  0.1921569 0.014792899
    #> 6  0.2217782 0.010989011
    #> 7  0.2117647 0.007881773
    #> 8  0.2165156 0.019172553
    #> 9  0.2195122 0.015625000
    #> 10 0.2274549 0.015075377

To estimate the per region results from this bootstrap resampling, the following can be implemented:

    est_df <- lapply(
      X = boot_df,
      FUN = function(x) lapply(
        x, FUN = quantile, probs = c(0.5, 0.025, 0.975)
      ) |>
        do.call(rbind, args = _)
    )

    est_df <- data.frame(
      region = names(est_df),
      indicators = lapply(est_df, FUN = row.names) |> unlist(),
      do.call(rbind, args = est_df)
    )

    row.names(est_df) <- NULL

which results in the following output:

    est_df
    #>            region indicators       X50.       X2.5.     X97.5.
    #> 1          Bakool       anc1 0.30261405 0.188862799 0.41167127
    #> 2             Bay       anc2 0.11251780 0.050903865 0.19504237
    #> 3 Middle Shabelle       anc1 0.32391543 0.217411669 0.43781930
    #> 4          Bakool       anc2 0.01893172 0.002766156 0.03663750
    #> 5             Bay       anc1 0.20220114 0.134820317 0.27676772
    #> 6 Middle Shabelle       anc2 0.01724140 0.007237952 0.03006251

## Alternative blocked weighted bootstrap function set

From this demonstration, the `bootBW()` function proves to be straightforward to implement and can be easily incorporated into a user’s workflow based on their dataset and their analytic needs. However, as shown above, this flexibility requires a lot more extra coding from the user to get from resampling to indicator estimates.

Starting from `v0.3.0`, an alternative set of functions is available to perform blocked weighted bootstrap resampling that facilitates all the steps from resampling to estimation. Below is an example of how to use this alternative set of functions for the same tasks shown above.

This set of functions attempts to make the blocked weighted bootstrap algorithm more efficient through vectorisation and use of parallelisation techniques. The function syntax has been kept consistent with `bootBW()` for ease of transition.

### Bootstrap resampling with `boot_bw()`

The `boot_bw()` function is the alternative bootstrap resampling function of the package. It can be used as follows:

    boot_df <- boot_bw(
      x = indicatorsHH, w = villageData, statistic = bootClassic,
      params = c("anc1", "anc2")
    )

This call to `boot_bw()` takes in the survey dataset `indicatorsHH` as its first argument (`x`). This dataset is expected to have a variable labelled as `psu` which identifies the primary sampling unit from which data was collected during the survey and then additional variables for the indicators to be estimated. The second argument (`w`) is for the dataset of the list of primary sampling units that were sampled in the survey to collect the survey data specified in `x`. This dataset, which in this case is `villageData`, should have at least a variable labelled `psu` which identified the primary sampling unit that matches the same variable in the survey dataset and a variable labelled `pop` for the population size of the primary sampling unit. The `statistic` argument specified the type of statistic to apply to the bootstrap replicates. There are two of these functions available from the `{bbw}` package - `bootClassic()` and the `bootPROBIT()`. For this example, the `bootClassic()` function is used to get the mean value of the bootstrap replicates. This is generally useful for binomial type of indicators and for continuous variables of which to get the mean of. The `params` argument takes in values of the indicator names in `x` to be estimated. In this example, two indicator names for antenatal care are specified. Finally, the argument for `replicates` specify the number of replicate bootstraps to be performed. The default of 400 replicates is used here. As can be noted, the `boot_bw()` takes on the same type of arguments as `bootBW()` and the syntax is exactly the same. Hence, using this alternative function will be familiar to those who have had experience using the original function.

However, the output of the `boot_bw()` function is structured differently from the `bootBW()` function. The `boot_bw()` function produces and object of class `boot_bw`.

    class(boot_df)
    #> [1] "boot_bw"

The object `boot_bw` is a list with 4 named components: `params` for the values specified for the `params` argument, `replicates` for the number of bootstrap replicates performed, `strata` for the values specified for stratification, and `boot_data` which is the bootstrap results.

    names(boot_df)
    #> [1] "params"     "replicates" "strata"     "boot_data"

The `boot_data` component of the `boot_bw` object corresponds to the output of the `bootBW()` function.

Other than the difference in the structure of the output, this alternative function also has three additional arguments for the new features it provides.

- `strata` - the variable name in `x` that provides information on the stratification in the survey data. This is by default set to `NULL` signifying no stratification. This argument allows the user to perform stratified bootstrap resampling conveniently through the `boot_bw()` function.

- `parallel` - whether or not to use parallel computation for the bootstrap resampling. This is by default set to FALSE in which case bootstrap resampling is done sequentially as is with the `bootBW()` function. If set to TRUE, the function sets up parallel computing and utilises the machines available cores (see `cores` argument below).

- `cores` - the number of cores to use for parallel computation. This is only evaluated if `parallel = TRUE`. By default, this is set to 1 less the total available number of cores of the current machine.

To use these new features and functionality, the call to `boot_bw()` would look something like this:

    boot_df <- boot_bw(
      x = indicatorsHH, w = villageData, statistic = bootClassic,
      params = c("anc1", "anc2"), strata = "region", parallel = TRUE
    )

This produces a `boot_bw` class `list` object with the same components as above. The only different is that the `boot_data` component is a `list` (instead of a `data.frame`) with each component being the `data.frame` bootstrap resampling output for each of the strata in the dataset.

    class(boot_df)
    #> [1] "boot_bw"

    class(boot_df$boot_data)
    #> [1] "list"

    names(boot_df$boot_data)
    #> [1] "Bakool"          "Bay"             "Middle Shabelle"

### Bootstrap estimation

The `boot_bw_estimate()` function can then be applied to the output of the `boot_bw()` function to get the indicator estimates with 95% confidence interval.

    boot_bw_estimate(boot_df)
    #>            region indicator        est        lcl        ucl
    #> 1          Bakool      anc1 0.43888889 0.38881944 0.48888889
    #> 2          Bakool      anc2 0.38055556 0.32497749 0.43062500
    #> 3             Bay      anc1 0.71619066 0.63887512 0.77849135
    #> 4             Bay      anc2 0.00254615 0.00000000 0.01294677
    #> 5 Middle Shabelle      anc1 0.20757542 0.14514451 0.28293531
    #> 6 Middle Shabelle      anc2 0.05065259 0.03133757 0.07453108
    #>            se
    #> 1 0.027718319
    #> 2 0.027983726
    #> 3 0.036466569
    #> 4 0.003743969
    #> 5 0.036375151
    #> 6 0.011463590

These two functions can be piped to each other for a single workflow from bootstrap resampling to estimation.

    boot_bw(
      x = indicatorsHH, w = villageData, statistic = bootClassic,
      params = c("anc1", "anc2"), strata = "region", parallel = TRUE
    ) |>
      boot_bw_estimate()
    #>            region indicator         est       lcl        ucl
    #> 1          Bakool      anc1 0.438888889 0.3805556 0.49444444
    #> 2          Bakool      anc2 0.376731302 0.3138889 0.43888889
    #> 3             Bay      anc1 0.719130072 0.6487833 0.78255787
    #> 4             Bay      anc2 0.002534854 0.0000000 0.01262706
    #> 5 Middle Shabelle      anc1 0.203423968 0.1428536 0.27819673
    #> 6 Middle Shabelle      anc2 0.051256281 0.0339071 0.07622767
    #>            se
    #> 1 0.030425611
    #> 2 0.030033802
    #> 3 0.034273078
    #> 4 0.003372086
    #> 5 0.033966913
    #> 6 0.010573679