The RAM-OP Workflow is summarised in the diagram below.
The oldr package provides functions to use for all steps after data collection. These functions were developed specifically for the data structure created by the EpiData or the Open Data Kit collection tools. The data structure produced by these collection tools is shown by the dataset testSVY included in the oldr package.
testSVY
#> # A tibble: 192 × 90
#> ad2 psu hh id d1 d2 d3 d4 d5 f1 f2a f2b f2c
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 201 1 1 1 67 2 5 2 3 2 1 1
#> 2 1 201 2 1 1 74 1 2 2 3 2 1 1
#> 3 1 201 3 1 1 60 1 2 2 2 2 2 2
#> 4 1 201 3 2 1 60 2 2 2 3 2 2 1
#> 5 1 201 4 1 1 85 2 5 2 3 2 1 1
#> 6 1 201 5 1 2 86 1 5 1 4 2 1 1
#> 7 1 201 6 1 1 80 1 5 2 3 2 1 1
#> 8 1 201 6 2 1 60 2 5 2 3 2 2 1
#> 9 1 201 7 1 1 62 1 2 2 2 2 1 1
#> 10 1 201 8 1 1 72 2 5 2 2 2 1 1
#> # ℹ 182 more rows
#> # ℹ 77 more variables: f2d <int>, f2e <int>, f2f <int>, f2g <int>, f2h <int>,
#> # f2i <int>, f2j <int>, f2k <int>, f2l <int>, f2m <int>, f2n <int>,
#> # f2o <int>, f2p <int>, f2q <int>, f2r <int>, f2s <int>, f3 <int>, f4 <int>,
#> # f5 <int>, f6 <int>, f7 <int>, a1 <int>, a2 <int>, a3 <int>, a4 <int>,
#> # a5 <int>, a6 <int>, a7 <int>, a8 <int>, k6a <int>, k6b <int>, k6c <int>,
#> # k6d <int>, k6e <int>, k6f <int>, ds1 <int>, ds2 <int>, ds3 <int>, …Once RAM-OP data is collected, it will need to be processed and recoded based on the definitions of the various indicators included in RAM-OP. The oldr package provides a suite functions to perform this processing and recoding. These functions and their syntax can be easily remembered as the create_op_ functions as their function names start with the create_ verb followed by the op_ label and then followed by an indicator or indicator set specific identifier or short name. Finally, an additional tag for male or female can be added to the main function to provide gender-specific outputs.
Currently, a standard RAM-OP can provide results for the 13 indicators or indicator sets for older people. The following table shows these indicators/indicator sets alongside the functions related to them:
| Indicator / Indicator Set | Related Functions |
|---|---|
| Demography and situation | create_op_demo; create_op_demo_males; create_op_demo_females |
| Food intake | create_op_food; create_op_food_males; create_op_food_females |
| Severe food insecurity | create_op_hunger; create_op_hunger_males; create_op_hunger_females |
| Disability | create_op_disability; create_op_disability_males; create_op_disability_females |
| Activities of daily living | create_op_adl; create_op_adl_males; create_op_adl_females |
| Mental health and well-being | create_op_mental; create_op_mental_males; create_op_mental_females |
| Dementia | create_op_dementia; create_op_dementia_males; create_op_dementia_females |
| Health and health-seeking behaviour | create_op_health; create_op_health_males; create_op_health_females |
| Sources of income | create_op_income; create_op_income_males; create_op_income_females |
| Water, sanitation, and hygiene | create_op_wash; create_op_wash_males; create_op_wash_females |
| Anthropometry and anthropometric screening coverage | create_op_anthro; create_op_anthro_males; create_op_anthro_females |
| Visual impairment | create_op_visual; create_op_visual_males; create_op_visual_females |
| Miscellaneous | create_op_misc; create_op_misc_males; create_op_misc_females |
A final function in the processing and recoding set - create_op - is provided to perform the processing and recoding of all indicators or indicator sets. This function allows for the specification of which indicators or indicator sets to process and recode which is useful for cases where not all the indicators or indicator sets have been collected or if only specific indicators or indicator sets need to be analysed or reported. This function also specifies whether a specific gender subset of the data is needed.
For a standard RAM-OP implementation, this step is performed in R as follows:
## Process and recode all standard RAM-OP indicators in the testSVY dataset
create_op(svy = testSVY)which results in the following output:
#> # A tibble: 192 × 138
#> psu sex1 sex2 resp1 resp2 resp3 resp4 age ageGrp1 ageGrp2 ageGrp3
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 201 0 1 1 0 0 0 67 0 1 0
#> 2 201 1 0 1 0 0 0 74 0 0 1
#> 3 201 1 0 1 0 0 0 60 0 1 0
#> 4 201 0 1 1 0 0 0 60 0 1 0
#> 5 201 0 1 1 0 0 0 85 0 0 0
#> 6 201 1 0 0 1 0 0 86 0 0 0
#> 7 201 1 0 1 0 0 0 80 0 0 0
#> 8 201 0 1 1 0 0 0 60 0 1 0
#> 9 201 1 0 1 0 0 0 62 0 1 0
#> 10 201 0 1 1 0 0 0 72 0 0 1
#> # ℹ 182 more rows
#> # ℹ 127 more variables: ageGrp4 <dbl>, ageGrp5 <dbl>, marital1 <dbl>,
#> # marital2 <dbl>, marital3 <dbl>, marital4 <dbl>, marital5 <dbl>,
#> # marital6 <dbl>, alone <dbl>, MF <dbl>, DDS <dbl>, FG01 <dbl>, FG02 <dbl>,
#> # FG03 <dbl>, FG04 <dbl>, FG05 <dbl>, FG06 <dbl>, FG07 <dbl>, FG08 <dbl>,
#> # FG09 <dbl>, FG10 <dbl>, FG11 <dbl>, proteinRich <dbl>, pProtein <dbl>,
#> # aProtein <dbl>, pVitA <dbl>, aVitA <dbl>, xVitA <dbl>, ironRich <dbl>, …Once data has been processed and appropriate recoding for indicators has been performed, indicator estimates can now be calculated.
It is important to note that estimation procedures need to account for the sample design. All major statistical analysis software can do this (details vary). There are two things to note:
The RAM-OP sample is a two-stage sample. Subjects are sampled from a small number of primary sampling units (PSUs).
The RAM-OP sample is not prior weighted. This means that per-PSU sampling weights are needed. These are usually the populations of the PSU.
This sample design will need to be specified to statistical analysis software being used. If no weights are provided, then the analysis may produce estimates that place undue weight to observations from smaller communities with confidence intervals with lower than nominal coverage (i.e. they will be too narrow).
The oldr package uses blocked weighted bootstrap estimation approach:
Blocked : The block corresponds to the PSU or cluster.
Weighted : The RAM-OP sampling procedure does not use population proportional sampling to weight the sample prior to data collection as is done with SMART type surveys. This means that a posterior weighting procedure is required. The standard RAM-OP software uses a “roulette wheel” algorithm to weight (i.e. by population) the selection probability of PSUs in bootstrap replicates.
A total of m PSUs are sampled with-replacement from the survey dataset where m is the number of PSUs in the survey sample. Individual records within each PSU are then sampled with-replacement. A total of n records are sampled with-replacement from each of the selected PSUs where n is the number of individual records in a selected PSU. The resulting collection of records replicates the original survey in terms of both sample design and sample size. A large number of replicate surveys are taken (the standard RAM-OP software uses \(r = 399\) replicate surveys but this can be changed). The required statistic (e.g. the mean of an indicator value) is applied to each replicate survey. The reported estimate consists of the 50th (point estimate), 2.5th (lower 95% confidence limit), and the 97.5th (upper 95% confidence limit) percentiles of the distribution of the statistic observed across all replicate surveys. The blocked weighted bootstrap procedure is outlined in the figure below.
The principal advantages of using a bootstrap estimator are:
Bootstrap estimators work well with small sample sizes.
The method is non-parametric and uses empirical rather than theoretical distributions. There are no assumptions of things like normality to worry about.
The method allows estimation of the sampling distribution of almost any statistic using only simple computational methods.
The prevalence of GAM, MAM, and SAM are estimated using a PROBIT estimator. This type of estimator provides better precision than a classic estimator at small sample sizes as discussed in the following literature:
World Health Organisation, Physical Status: The use and interpretation of anthropometry. Report of a WHO expert committee, WHO Technical Report Series 854, WHO, Geneva, 1995
Dale NM, Myatt M, Prudhon C, Briend, A, “Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys”, Public Health Nutrition, 1–6. https://doi.org/10.1017/s1368980012003345, 2012
Blanton CJ, Bilukha, OO, “The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision”, Emerging Themes in Epidemiology, 10(1), 2013, p. 8
An estimate of GAM prevalence can be made using a classic estimator:
\[ \text{prevalence} ~ = ~ \frac{\text{Number of respondents with MUAC < 210}}{\text{Total number of respondents}} \]
On the other hand, the estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the inverse cumulative distribution function. This function converts parameters of the distribution of an indicator (e.g. the mean and standard deviation of a normally distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02% of the population are predicted (or estimated) to fall below the 210 mm threshold.
Both the classic and the PROBIT methods can be thought of as estimating area:
The principal advantage of the PROBIT approach is that the required sample size is usually smaller than that required to estimate prevalence with a given precision using the classic method.
The PROBIT method assumes that MUAC is a normally distributed variable. If this is not the case then the distribution of MUAC is transformed towards normality.
The prevalence of SAM is estimated in a similar way to GAM. The prevalence of MAM is estimated as the difference between the GAM and SAM prevalence estimates:
\[ \widehat{\text{GAM prevalence}} ~ = ~ \widehat{\text{GAM prevalence}} - \widehat{\text{SAM prevalence}} \]
The function estimateClassic in oldr implements the blocked weighted bootstrap classic estimator of RAM-OP. This function uses the bootClassic statistic to estimate indicator values.
The estimateClassic function is used for all the standard RAM-OP indicators except for anthropometry. The function is used as follows:
## Process and recode RAM-OP data (testSVY)
df <- create_op(svy = testSVY)
## Perform classic estimation on recoded data using appropriate weights provided by testPSU
classicDF <- estimate_classic(x = df, w = testPSU)This results in (using limited replicates to reduce computing time):
#> # A tibble: 136 × 10
#> INDICATOR EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES UCL.MALES EST.FEMALES
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 resp1 0.865 0.823 0.891 0.827 0.728 0.917 0.875
#> 2 resp2 0.0938 0.075 0.131 0.0933 0.0353 0.162 0.0982
#> 3 resp3 0.0365 0.0229 0.0562 0.0533 0.00519 0.177 0.0175
#> 4 resp4 0.00521 0 0.0313 0.0123 0 0.0339 0
#> 5 age 71.1 70.2 72.5 71.9 68.8 75.2 70.8
#> 6 ageGrp1 0 0 0 0 0 0 0
#> 7 ageGrp2 0.505 0.422 0.595 0.452 0.257 0.626 0.557
#> 8 ageGrp3 0.240 0.186 0.3 0.286 0.136 0.360 0.188
#> 9 ageGrp4 0.208 0.145 0.264 0.238 0.111 0.328 0.243
#> 10 ageGrp5 0.0417 0.0115 0.0677 0.0714 0.0175 0.109 0.0182
#> # ℹ 126 more rows
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>The function estimateProbit in oldr implements the blocked weighted bootstrap PROBIT estimator of RAM-OP. This function uses the probit_GAM and the probit_SAM statistic to estimate indicator values.
The estimateProbit function is used for only the anthropometric indicators. The function is used as follows:
## Process and recode RAM-OP data (testSVY)
df <- create_op(svy = testSVY)
## Perform probit estimation on recoded data using appropriate weights provided by testPSU
probitDF <- estimate_probit(x = df, w = testPSU)This results in (using limited replicates to reduce computing time):
#> # A tibble: 3 × 10
#> INDICATOR EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES UCL.MALES EST.FEMALES
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 GAM 0.0273 0.00632 0.0419 7.18e-3 2.34e- 3 0.0261 0.0471
#> 2 MAM 0.0234 0.00624 0.0415 6.02e-3 2.34e- 3 0.0261 0.0467
#> 3 SAM 0.000502 0.0000413 0.00330 9.48e-8 1.67e-32 0.00160 0.000465
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>The two sets of estimates are then merged using the merge_op function as follows:
## Merge classicDF and probitDF
resultsDF <- merge_op(x = classicDF, y = probitDF)
resultsDFwhich results in:
#> # A tibble: 139 × 13
#> INDICATOR GROUP LABEL TYPE EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES
#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 resp1 Survey Resp… Prop… 8.65e-1 0.823 0.891 0.827 0.728
#> 2 resp2 Survey Resp… Prop… 9.38e-2 0.075 0.131 0.0933 0.0353
#> 3 resp3 Survey Resp… Prop… 3.65e-2 0.0229 0.0562 0.0533 0.00519
#> 4 resp4 Survey Resp… Prop… 5.21e-3 0 0.0313 0.0123 0
#> 5 age Demography… Mean… Mean 7.11e+1 70.2 72.5 71.9 68.8
#> 6 ageGrp1 Demography… Self… Prop… 0 0 0 0 0
#> 7 ageGrp2 Demography… Self… Prop… 5.05e-1 0.422 0.595 0.452 0.257
#> 8 ageGrp3 Demography… Self… Prop… 2.40e-1 0.186 0.3 0.286 0.136
#> 9 ageGrp4 Demography… Self… Prop… 2.08e-1 0.145 0.264 0.238 0.111
#> 10 ageGrp5 Demography… Self… Prop… 4.17e-2 0.0115 0.0677 0.0714 0.0175
#> # ℹ 129 more rows
#> # ℹ 4 more variables: UCL.MALES <dbl>, EST.FEMALES <dbl>, LCL.FEMALES <dbl>,
#> # UCL.FEMALES <dbl>Once indicators has been estimated, the outputs can then be used to create relevant charts to visualise the results. A set of functions that start with the verb chart_op_ is provided followed by the indicator identifier to specify the type of indicator to visualise. The output of the function is a PNG file saved in the specified filename appended to the indicator identifier within the current working directory or saved in the specified filename appended to the indicator identifier in the specified directory path.
The following shows how to produce the chart for ADLs saved with filename test appended at the start inside a temporary directory:
chart_op_adl(x = create_op(testSVY), filename = file.path(tempdir(), "test"))
#> png
#> 2The resulting PNG file can be found in the temporary directory
file.exists(path = file.path(tempdir(), "test.png"))
#> [1] FALSEand will look something like this:
Finally, estimates can be reported through report tables. The report_op_table function facilitates this through the following syntax:
report_op_table(estimates = resultsDF, filename = file.path(tempdir(), "TEST"))The resulting CSV file is found in the temporary directory
file.exists(path = file.path(tempdir(), "TEST.csv"))
#> [1] FALSEand will look something like this:
#> X X.1 X.2 X.3 X.4 X.5 X.6
#> 1 Survey
#> 2 ALL MALES
#> 3 INDICATOR TYPE EST LCL UCL EST LCL
#> 4 99 2 0.8646 0.8229 0.8906 0.8267 0.7278
#> 5 96 2 0.0938 0.0750 0.1313 0.0933 0.0353
#> 6 98 2 0.0365 0.0229 0.0563 0.0533 0.0052
#> 7 97 2 0.0052 0.0000 0.0313 0.0123 0.0000
#> 8
#> 9 Demography and situation
#> 10 ALL MALES
#> 11 INDICATOR TYPE EST LCL UCL EST LCL
#> 12 54 1 71.0781 70.2240 72.5094 71.8690 68.7771
#> 13 106 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 107 2 0.5052 0.4219 0.5948 0.4524 0.2568
#> 15 108 2 0.2396 0.1865 0.3000 0.2857 0.1360
#> 16 109 2 0.2083 0.1448 0.2635 0.2375 0.1114
#> 17 105 2 0.0417 0.0115 0.0677 0.0714 0.0175
#> 18 115 2 0.4167 0.3125 0.4573 1.0000 1.0000
#> 19 114 2 0.5833 0.5427 0.6875 0.0000 0.0000
#> 20 51 2 0.0208 0.0156 0.0583 0.0263 0.0000
#> 21 49 2 0.2760 0.2615 0.3688 0.5000 0.4226
#> 22 48 2 0.1250 0.0948 0.1552 0.1818 0.0790
#> 23 47 2 0.0833 0.0406 0.1250 0.0750 0.0496
#> 24 52 2 0.4583 0.3708 0.5667 0.1852 0.1583
#> 25 50 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 127 2 0.1406 0.1000 0.1615 0.2208 0.1055
#> 27
#> 28 Diet
#> 29 ALL MALES
#> 30 INDICATOR TYPE EST LCL UCL EST LCL
#> 31 53 1 2.5677 2.4906 2.6833 2.4286 2.2449
#> 32 25 1 4.4948 4.3615 4.7021 4.5200 4.2355
#> 33 14 2 0.8958 0.8708 0.9469 0.8929 0.8413
#> 34 23 2 0.5417 0.4750 0.5760 0.4878 0.4078
#> 35 18 2 0.5833 0.5635 0.6604 0.5679 0.3863
#> 36 20 2 0.0573 0.0396 0.0979 0.0247 0.0000
#> 37 15 2 0.0521 0.0115 0.0615 0.0519 0.0000
#> 38 17 2 0.2865 0.2510 0.3510 0.4405 0.3702
#> 39 19 2 0.3958 0.3458 0.4740 0.4634 0.3394
#> 40 21 2 0.0208 0.0010 0.0406 0.0000 0.0000
#> 41 16 2 0.2031 0.1583 0.2750 0.2464 0.1436
#> 42 24 2 0.5104 0.4604 0.5531 0.3951 0.3166
#> 43 22 2 0.9635 0.9312 0.9917 0.9870 0.9540
#> 44
#> 45 Nutrients
#> 46 ALL MALES
#> 47 INDICATOR TYPE EST LCL UCL EST LCL
#> 48 88 2 0.4583 0.4083 0.5573 0.4933 0.3647
#> 49 89 2 0.3958 0.3458 0.4740 0.4634 0.3394
#> 50 87 2 0.1354 0.0760 0.1542 0.0625 0.0303
#> 51 83 2 0.6042 0.5885 0.6979 0.5238 0.4275
#> 52 2 2 0.0625 0.0229 0.0854 0.0519 0.0024
#> 53 3 2 0.6302 0.6167 0.7240 0.5802 0.4454
#> 54 42 2 0.6667 0.6104 0.6813 0.6220 0.5587
#> 55 9 2 0.0208 0.0010 0.0406 0.0000 0.0000
#> 56 140 2 0.5729 0.5094 0.7021 0.7000 0.6584
#> 57 135 2 0.6302 0.5615 0.7292 0.7143 0.6608
#> 58 137 2 0.8125 0.7833 0.8531 0.8158 0.7575
#> 59 138 2 0.5729 0.5094 0.7021 0.7000 0.6584
#> 60 139 2 0.8802 0.8292 0.9010 0.8961 0.8296
#> 61 136 2 0.3646 0.2969 0.4396 0.4667 0.3947
#> 62 134 2 0.3490 0.2948 0.4365 0.4667 0.3947
#> 63
#> 64 Food Security
#> 65 ALL MALES
#> 66 INDICATOR TYPE EST LCL UCL EST LCL
#> 67 45 2 0.7708 0.7260 0.8375 0.8052 0.6179
#> 68 60 2 0.1719 0.1198 0.2177 0.1375 0.0691
#> 69 113 2 0.0260 0.0062 0.0469 0.0260 0.0121
#> 70
#> 71 Disability (WG)
#> 72 ALL MALES
#> 73 INDICATOR TYPE EST LCL UCL EST LCL
#> 74 129 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 75 130 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 131 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 132 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 28 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 79 29 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 30 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 31 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 55 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 83 56 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 57 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 58 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 92 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 87 93 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 94 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 95 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 101 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 91 102 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 103 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 104 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 10 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 95 11 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 12 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 13 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 63 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 99 5 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 6 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 7 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 62 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104 Activities of daily living
#> 105 ALL MALES
#> 106 INDICATOR TYPE EST LCL UCL EST LCL
#> 107 35 2 0.9740 0.9385 0.9896 0.9565 0.8536
#> 108 37 2 0.9896 0.9667 1.0000 0.9750 0.8894
#> 109 39 2 0.9896 0.9667 1.0000 0.9750 0.8894
#> 110 40 2 0.9635 0.9354 0.9844 0.9740 0.8680
#> 111 36 2 0.7448 0.6948 0.7771 0.7733 0.7533
#> 112 38 2 1.0000 0.9865 1.0000 1.0000 0.9393
#> 113 44 1 5.6562 5.5562 5.7250 5.6184 5.2207
#> 114 41 2 0.9740 0.9490 0.9990 0.9750 0.8894
#> 115 82 2 0.0104 0.0000 0.0344 0.0000 0.0000
#> 116 112 2 0.0104 0.0000 0.0333 0.0250 0.0000
#> 117 126 2 0.6250 0.5260 0.6698 0.5658 0.4569
#> 118 125 2 0.1042 0.0729 0.1385 0.1299 0.0664
#> 119
#> 120 Mental health
#> 121 ALL MALES
#> 122 INDICATOR TYPE EST LCL UCL EST LCL
#> 123 43 1 12.2552 11.5052 12.9740 11.8312 9.8316
#> 124 110 2 0.5156 0.4167 0.5406 0.5065 0.3637
#> 125 85 2 0.2031 0.1521 0.2479 0.1558 0.0803
#> 126
#> 127 Health
#> 128 ALL MALES
#> 129 INDICATOR TYPE EST LCL UCL EST LCL
#> 130 46 2 0.4844 0.3958 0.5365 0.3580 0.2745
#> 131 128 2 0.7500 0.6731 0.8788 0.6207 0.4713
#> 132 74 2 0.1071 0.0182 0.3771 0.2500 0.0333
#> 133 79 2 0.3636 0.1643 0.4785 0.3333 0.0848
#> 134 80 2 0.1000 0.0143 0.2661 0.0000 0.0000
#> 135 81 2 0.1000 0.0069 0.1983 0.2143 0.0250
#> 136 73 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 77 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 75 2 0.0455 0.0000 0.1629 0.0000 0.0000
#> 139 78 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 76 2 0.1000 0.0333 0.4494 0.2083 0.0143
#> 141 91 2 0.8750 0.8219 0.9146 0.8442 0.7874
#> 142 1 2 0.8095 0.7610 0.8716 0.7500 0.6377
#> 143 65 2 0.0333 0.0000 0.1941 0.2105 0.0167
#> 144 70 2 0.8750 0.6299 0.9575 0.7143 0.4942
#> 145 71 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 72 2 0.0345 0.0000 0.0920 0.1053 0.0105
#> 147 64 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 68 2 0.0400 0.0000 0.1018 0.0000 0.0000
#> 149 66 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 69 2 0.0000 0.0000 0.0276 0.0000 0.0000
#> 151 67 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 8 2 0.0260 0.0052 0.0312 0.0130 0.0000
#> 153 133 2 0.3958 0.3510 0.4781 0.5195 0.3973
#> 154 86 2 0.3333 0.2781 0.3542 0.2208 0.1869
#> 155
#> 156 Income
#> 157 ALL MALES
#> 158 INDICATOR TYPE EST LCL UCL EST LCL
#> 159 27 2 0.5573 0.5260 0.6052 0.6667 0.5989
#> 160 116 2 0.3698 0.3021 0.4302 0.5185 0.3709
#> 161 124 2 0.1302 0.0750 0.1688 0.1829 0.1131
#> 162 121 2 0.0208 0.0115 0.0406 0.0390 0.0029
#> 163 123 2 0.0625 0.0260 0.1010 0.0132 0.0000
#> 164 119 2 0.0052 0.0000 0.0146 0.0000 0.0000
#> 165 122 2 0.0156 0.0000 0.0333 0.0250 0.0000
#> 166 118 2 0.0208 0.0062 0.0344 0.0145 0.0026
#> 167 117 2 0.3333 0.2771 0.3896 0.3537 0.2595
#> 168 120 2 0.0052 0.0000 0.0188 0.0000 0.0000
#> 169
#> 170 WASH
#> 171 ALL MALES
#> 172 INDICATOR TYPE EST LCL UCL EST LCL
#> 173 34 2 0.6198 0.5802 0.7010 0.5584 0.4049
#> 174 100 2 0.7083 0.6542 0.7854 0.6310 0.4522
#> 175 33 2 0.2604 0.2094 0.2958 0.2099 0.1462
#> 176 32 2 0.2500 0.2000 0.2917 0.2099 0.1462
#> 177
#> 178 Relief
#> 179 ALL MALES
#> 180 INDICATOR TYPE EST LCL UCL EST LCL
#> 181 84 2 0.0260 0.0156 0.0604 0.0290 0.0000
#> 182 4 2 0.0417 0.0323 0.0615 0.0395 0.0000
#> 183 90 2 0.0312 0.0083 0.0458 0.0238 0.0000
#> 184
#> 185 Anthropometry
#> 186 ALL MALES
#> 187 INDICATOR TYPE EST LCL UCL EST LCL
#> 188 26 2 0.0273 0.0063 0.0419 0.0072 0.0023
#> 189 59 2 0.0234 0.0062 0.0415 0.0060 0.0023
#> 190 111 2 0.0005 0.0000 0.0033 0.0000 0.0000
#> X.7 X.8 X.9 X.10
#> 1
#> 2 FEMALES
#> 3 UCL EST LCL UCL
#> 4 0.9168 0.8750 0.8457 0.9146
#> 5 0.1619 0.0982 0.0745 0.1308
#> 6 0.1767 0.0175 0.0000 0.0576
#> 7 0.0339 0.0000 0.0000 0.0131
#> 8
#> 9
#> 10 FEMALES
#> 11 UCL EST LCL UCL
#> 12 75.1989 70.7583 69.1446 72.9162
#> 13 0.0000 0.0000 0.0000 0.0000
#> 14 0.6260 0.5565 0.4248 0.6203
#> 15 0.3600 0.1875 0.1579 0.2791
#> 16 0.3283 0.2432 0.1366 0.3260
#> 17 0.1093 0.0182 0.0000 0.0591
#> 18 1.0000 0.0000 0.0000 0.0000
#> 19 0.0000 1.0000 1.0000 1.0000
#> 20 0.0813 0.0333 0.0125 0.0646
#> 21 0.6374 0.1565 0.0947 0.2198
#> 22 0.2600 0.0642 0.0349 0.1095
#> 23 0.1421 0.0351 0.0049 0.0698
#> 24 0.2644 0.6875 0.6095 0.7941
#> 25 0.0000 0.0000 0.0000 0.0000
#> 26 0.3096 0.1148 0.0620 0.2045
#> 27
#> 28
#> 29 FEMALES
#> 30 UCL EST LCL UCL
#> 31 2.6544 2.6396 2.4155 2.9008
#> 32 4.7217 4.7615 4.4840 4.8453
#> 33 0.9570 0.9273 0.8673 0.9463
#> 34 0.5648 0.5586 0.4875 0.6227
#> 35 0.6473 0.6036 0.4737 0.6720
#> 36 0.0501 0.0642 0.0248 0.1295
#> 37 0.1009 0.0367 0.0187 0.0445
#> 38 0.5071 0.2589 0.2214 0.3185
#> 39 0.5608 0.4732 0.3313 0.5218
#> 40 0.0300 0.0360 0.0035 0.0680
#> 41 0.2958 0.2589 0.2165 0.3072
#> 42 0.5182 0.5676 0.4666 0.6631
#> 43 1.0000 0.9652 0.9422 0.9817
#> 44
#> 45
#> 46 FEMALES
#> 47 UCL EST LCL UCL
#> 48 0.5841 0.5500 0.3802 0.5769
#> 49 0.5608 0.4732 0.3313 0.5218
#> 50 0.1191 0.1583 0.0867 0.2044
#> 51 0.6766 0.6696 0.5867 0.7444
#> 52 0.1009 0.0631 0.0442 0.1109
#> 53 0.6882 0.6875 0.5957 0.7724
#> 54 0.6791 0.6875 0.5629 0.7434
#> 55 0.0300 0.0360 0.0035 0.0680
#> 56 0.7505 0.5856 0.4704 0.6879
#> 57 0.7505 0.6757 0.5608 0.7284
#> 58 0.8665 0.8333 0.7528 0.8661
#> 59 0.7505 0.5856 0.4704 0.6879
#> 60 0.9336 0.8525 0.7843 0.8739
#> 61 0.5213 0.3304 0.2971 0.4239
#> 62 0.5213 0.3214 0.2940 0.3952
#> 63
#> 64
#> 65 FEMALES
#> 66 UCL EST LCL UCL
#> 67 0.8765 0.7857 0.7478 0.8424
#> 68 0.3586 0.1607 0.0929 0.1875
#> 69 0.0655 0.0250 0.0018 0.0431
#> 70
#> 71
#> 72 FEMALES
#> 73 UCL EST LCL UCL
#> 74 1.0000 1.0000 1.0000 1.0000
#> 75 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000
#> 78 1.0000 1.0000 1.0000 1.0000
#> 79 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000
#> 82 1.0000 1.0000 1.0000 1.0000
#> 83 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000
#> 86 1.0000 1.0000 1.0000 1.0000
#> 87 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000
#> 90 1.0000 1.0000 1.0000 1.0000
#> 91 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000
#> 94 1.0000 1.0000 1.0000 1.0000
#> 95 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000
#> 98 1.0000 1.0000 1.0000 1.0000
#> 99 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 FEMALES
#> 106 UCL EST LCL UCL
#> 107 0.9831 0.9910 0.9637 1.0000
#> 108 1.0000 1.0000 0.9913 1.0000
#> 109 1.0000 1.0000 0.9913 1.0000
#> 110 0.9976 0.9554 0.9433 0.9967
#> 111 0.8489 0.7391 0.6420 0.7634
#> 112 1.0000 1.0000 1.0000 1.0000
#> 113 5.7790 5.6757 5.5930 5.7380
#> 114 1.0000 0.9910 0.9634 1.0000
#> 115 0.0000 0.0090 0.0000 0.0366
#> 116 0.1106 0.0000 0.0000 0.0000
#> 117 0.6580 0.6161 0.5126 0.6883
#> 118 0.1944 0.0833 0.0787 0.1287
#> 119
#> 120
#> 121 FEMALES
#> 122 UCL EST LCL UCL
#> 123 14.9531 12.8739 11.6603 13.8111
#> 124 0.7060 0.5135 0.4841 0.6132
#> 125 0.3179 0.2162 0.1665 0.2784
#> 126
#> 127
#> 128 FEMALES
#> 129 UCL EST LCL UCL
#> 130 0.5133 0.5045 0.4481 0.5396
#> 131 0.8229 0.8214 0.7387 0.9537
#> 132 0.5364 0.1000 0.0000 0.4667
#> 133 0.4262 0.3636 0.0600 0.6258
#> 134 0.0000 0.1818 0.0167 0.3067
#> 135 0.4667 0.0000 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000
#> 138 0.0000 0.0000 0.0000 0.3600
#> 139 0.0000 0.0000 0.0000 0.0000
#> 140 0.3238 0.1667 0.0000 0.5709
#> 141 0.9222 0.8624 0.8040 0.9238
#> 142 0.9077 0.8830 0.7730 0.9273
#> 143 0.4219 0.1176 0.0000 0.1715
#> 144 0.8250 0.8000 0.4701 1.0000
#> 145 0.0000 0.0000 0.0000 0.0000
#> 146 0.2536 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.1000 0.0000 0.4935
#> 149 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0000 0.0400
#> 151 0.0000 0.0000 0.0000 0.0000
#> 152 0.0479 0.0348 0.0195 0.1095
#> 153 0.6142 0.3514 0.2378 0.4278
#> 154 0.3367 0.3167 0.2623 0.4098
#> 155
#> 156
#> 157 FEMALES
#> 158 UCL EST LCL UCL
#> 159 0.7400 0.5351 0.4752 0.6011
#> 160 0.5448 0.3028 0.2376 0.3968
#> 161 0.3080 0.0357 0.0271 0.0602
#> 162 0.0755 0.0091 0.0000 0.0505
#> 163 0.0386 0.0721 0.0473 0.0986
#> 164 0.0000 0.0182 0.0103 0.0423
#> 165 0.0500 0.0000 0.0000 0.0000
#> 166 0.0576 0.0000 0.0000 0.0482
#> 167 0.4150 0.3391 0.2793 0.4338
#> 168 0.0249 0.0000 0.0000 0.0471
#> 169
#> 170
#> 171 FEMALES
#> 172 UCL EST LCL UCL
#> 173 0.6760 0.6435 0.5796 0.7328
#> 174 0.7147 0.7391 0.6979 0.8275
#> 175 0.3122 0.2523 0.1492 0.2788
#> 176 0.3122 0.2377 0.1422 0.2667
#> 177
#> 178
#> 179 FEMALES
#> 180 UCL EST LCL UCL
#> 181 0.0665 0.0351 0.0037 0.0739
#> 182 0.0870 0.0446 0.0055 0.0794
#> 183 0.0738 0.0182 0.0018 0.0432
#> 184
#> 185
#> 186 FEMALES
#> 187 UCL EST LCL UCL
#> 188 0.0261 0.0471 0.0210 0.0586
#> 189 0.0261 0.0467 0.0135 0.0554
#> 190 0.0016 0.0005 0.0000 0.0091The oldr package functions were designed in such a way that they can be piped to each other to provide the desired output. Below we use the base R pipe operator |>.
testSVY |>
create_op() |>
estimate_op(w = testPSU, replicates = 9) |>
report_op_table(filename = file.path(tempdir(), "TEST"))This results in a CSV file TEST.report.csv in the temporary directory
file.exists(file.path(tempdir(), "TEST.report.csv"))
#> [1] TRUEwith the following structure:
#> X X.1 X.2 X.3 X.4 X.5
#> 1 Survey
#> 2 ALL MALES
#> 3 INDICATOR TYPE EST LCL UCL EST
#> 4 99 2 84.3750 81.8750 86.3542 84.2105
#> 5 96 2 9.3750 8.0208 12.8125 6.5789
#> 6 98 2 3.6458 2.7083 6.1458 4.4944
#> 7 97 2 1.0417 0.1042 3.1250 3.0303
#> 8
#> 9 Demography and situation
#> 10 ALL MALES
#> 11 INDICATOR TYPE EST LCL UCL EST
#> 12 54 1 71.0104 70.1094 72.0312 71.0886
#> 13 106 2 0.0000 0.0000 0.0000 0.0000
#> 14 107 2 54.1667 47.1875 58.7500 51.5152
#> 15 108 2 23.4375 16.1458 30.0000 28.1690
#> 16 109 2 17.7083 13.3333 23.7500 17.1053
#> 17 105 2 5.7292 4.6875 8.7500 3.7975
#> 18 115 2 39.0625 31.5625 48.9583 100.0000
#> 19 114 2 60.9375 51.0417 68.4375 0.0000
#> 20 51 2 3.6458 1.6667 4.5833 1.3158
#> 21 49 2 32.8125 21.1458 37.6042 51.8987
#> 22 48 2 10.4167 8.3333 12.9167 19.7368
#> 23 47 2 7.8125 4.8958 8.3333 9.0909
#> 24 52 2 47.3958 41.6667 55.4167 21.2121
#> 25 50 2 0.0000 0.0000 0.0000 0.0000
#> 26 127 2 13.0208 8.4375 20.1042 13.1579
#> 27
#> 28 Diet
#> 29 ALL MALES
#> 30 INDICATOR TYPE EST LCL UCL EST
#> 31 53 1 2.5573 2.4792 2.8042 2.5493
#> 32 25 1 4.5781 4.3948 4.9073 4.5263
#> 33 14 2 92.1875 88.6458 93.6458 90.1408
#> 34 23 2 53.6458 47.2917 63.6458 41.7722
#> 35 18 2 59.8958 51.6667 68.3333 58.2278
#> 36 20 2 6.7708 3.8542 10.4167 4.5455
#> 37 15 2 3.1250 1.7708 8.2292 5.1948
#> 38 17 2 36.9792 24.8958 41.3542 42.1053
#> 39 19 2 42.1875 38.1250 47.3958 39.2405
#> 40 21 2 3.1250 0.3125 5.0000 0.0000
#> 41 16 2 22.3958 15.7292 27.2917 22.7848
#> 42 24 2 50.0000 37.1875 56.0417 45.4545
#> 43 22 2 96.3542 94.4792 99.8958 97.3684
#> 44
#> 45 Nutrients
#> 46 ALL MALES
#> 47 INDICATOR TYPE EST LCL UCL EST
#> 48 88 2 48.9583 44.5833 54.4792 46.4789
#> 49 89 2 42.1875 38.1250 47.3958 39.2405
#> 50 87 2 12.5000 9.4792 21.5625 9.2105
#> 51 83 2 63.0208 54.1667 73.1250 54.4304
#> 52 2 2 6.2500 4.2708 11.4583 6.7416
#> 53 3 2 65.6250 56.6667 76.5625 60.5263
#> 54 42 2 67.1875 61.3542 76.8750 60.7595
#> 55 9 2 3.1250 0.3125 5.0000 0.0000
#> 56 140 2 64.0625 51.0417 70.4167 63.6364
#> 57 135 2 69.7917 56.5625 73.4375 67.1053
#> 58 137 2 83.3333 78.4375 87.2917 75.2809
#> 59 138 2 64.0625 51.0417 70.4167 63.6364
#> 60 139 2 87.5000 83.1250 91.0417 86.8421
#> 61 136 2 42.7083 29.7917 47.0833 48.1013
#> 62 134 2 41.6667 28.9583 45.1042 44.7368
#> 63
#> 64 Food Security
#> 65 ALL MALES
#> 66 INDICATOR TYPE EST LCL UCL EST
#> 67 45 2 76.5625 73.1250 80.0000 78.4810
#> 68 60 2 19.2708 14.8958 22.2917 20.2532
#> 69 113 2 1.5625 1.0417 4.0625 1.3158
#> 70
#> 71 Disability (WG)
#> 72 ALL MALES
#> 73 INDICATOR TYPE EST LCL UCL EST
#> 74 129 2 100.0000 100.0000 100.0000 100.0000
#> 75 130 2 0.0000 0.0000 0.0000 0.0000
#> 76 131 2 0.0000 0.0000 0.0000 0.0000
#> 77 132 2 0.0000 0.0000 0.0000 0.0000
#> 78 28 2 100.0000 100.0000 100.0000 100.0000
#> 79 29 2 0.0000 0.0000 0.0000 0.0000
#> 80 30 2 0.0000 0.0000 0.0000 0.0000
#> 81 31 2 0.0000 0.0000 0.0000 0.0000
#> 82 55 2 100.0000 100.0000 100.0000 100.0000
#> 83 56 2 0.0000 0.0000 0.0000 0.0000
#> 84 57 2 0.0000 0.0000 0.0000 0.0000
#> 85 58 2 0.0000 0.0000 0.0000 0.0000
#> 86 92 2 100.0000 100.0000 100.0000 100.0000
#> 87 93 2 0.0000 0.0000 0.0000 0.0000
#> 88 94 2 0.0000 0.0000 0.0000 0.0000
#> 89 95 2 0.0000 0.0000 0.0000 0.0000
#> 90 101 2 100.0000 100.0000 100.0000 100.0000
#> 91 102 2 0.0000 0.0000 0.0000 0.0000
#> 92 103 2 0.0000 0.0000 0.0000 0.0000
#> 93 104 2 0.0000 0.0000 0.0000 0.0000
#> 94 10 2 100.0000 100.0000 100.0000 100.0000
#> 95 11 2 0.0000 0.0000 0.0000 0.0000
#> 96 12 2 0.0000 0.0000 0.0000 0.0000
#> 97 13 2 0.0000 0.0000 0.0000 0.0000
#> 98 63 2 100.0000 100.0000 100.0000 100.0000
#> 99 5 2 0.0000 0.0000 0.0000 0.0000
#> 100 6 2 0.0000 0.0000 0.0000 0.0000
#> 101 7 2 0.0000 0.0000 0.0000 0.0000
#> 102 62 2 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104 Activities of daily living
#> 105 ALL MALES
#> 106 INDICATOR TYPE EST LCL UCL EST
#> 107 35 2 96.3542 94.0625 97.9167 97.3684
#> 108 37 2 98.4375 96.9792 99.4792 97.4684
#> 109 39 2 98.4375 96.9792 99.4792 97.4684
#> 110 40 2 95.3125 92.8125 98.7500 97.3684
#> 111 36 2 73.9583 66.6667 76.9792 78.4810
#> 112 38 2 100.0000 98.2292 100.0000 98.6842
#> 113 44 1 5.6250 5.5552 5.6865 5.6579
#> 114 41 2 97.3958 95.5208 98.4375 97.4684
#> 115 82 2 1.5625 0.0000 4.2708 0.0000
#> 116 112 2 1.0417 0.1042 2.8125 2.5316
#> 117 126 2 57.8125 51.8750 72.1875 57.1429
#> 118 125 2 10.4167 5.6250 15.9375 13.9241
#> 119
#> 120 Mental health
#> 121 ALL MALES
#> 122 INDICATOR TYPE EST LCL UCL EST
#> 123 43 1 11.8906 10.6073 12.9615 11.0423
#> 124 110 2 45.8333 37.2917 56.0417 45.0704
#> 125 85 2 22.3958 14.4792 28.5417 24.7191
#> 126
#> 127 Health
#> 128 ALL MALES
#> 129 INDICATOR TYPE EST LCL UCL EST
#> 130 46 2 43.7500 39.4792 52.0833 36.8421
#> 131 128 2 75.0000 63.9572 90.4000 73.5294
#> 132 74 2 12.5000 0.0000 50.0621 10.0000
#> 133 79 2 36.3636 23.1111 69.4639 33.3333
#> 134 80 2 6.6667 0.0000 23.6364 0.0000
#> 135 81 2 14.2857 0.5128 24.9697 22.2222
#> 136 73 2 0.0000 0.0000 0.0000 0.0000
#> 137 77 2 0.0000 0.0000 0.0000 0.0000
#> 138 75 2 0.0000 0.0000 15.1515 0.0000
#> 139 78 2 0.0000 0.0000 0.0000 0.0000
#> 140 76 2 15.3846 0.0000 42.7826 20.0000
#> 141 91 2 85.4167 80.8333 89.1667 80.3030
#> 142 1 2 82.6923 75.1163 85.6149 75.4386
#> 143 65 2 7.1429 0.0000 17.7455 7.1429
#> 144 70 2 84.0000 61.6364 93.7402 83.3333
#> 145 71 2 0.0000 0.0000 0.0000 0.0000
#> 146 72 2 4.0000 0.0000 11.8222 5.8824
#> 147 64 2 0.0000 0.0000 0.0000 0.0000
#> 148 68 2 2.7027 0.0000 18.7273 0.0000
#> 149 66 2 0.0000 0.0000 0.0000 0.0000
#> 150 69 2 0.0000 0.0000 3.9407 0.0000
#> 151 67 2 0.0000 0.0000 0.0000 0.0000
#> 152 8 2 2.6042 0.6250 4.6875 1.1236
#> 153 133 2 44.7917 34.2708 49.7917 51.8987
#> 154 86 2 30.7292 24.5833 33.6458 22.7848
#> 155
#> 156 Income
#> 157 ALL MALES
#> 158 INDICATOR TYPE EST LCL UCL EST
#> 159 27 2 57.2917 51.3542 69.6875 59.2105
#> 160 116 2 39.5833 30.9375 48.2292 43.4211
#> 161 124 2 11.9792 6.0417 16.5625 21.3483
#> 162 121 2 3.1250 1.1458 8.0208 6.3291
#> 163 123 2 5.7292 3.2292 10.0000 2.6316
#> 164 119 2 0.0000 0.0000 3.2292 0.0000
#> 165 122 2 1.0417 0.0000 3.0208 1.2987
#> 166 118 2 2.0833 1.0417 5.6250 3.7975
#> 167 117 2 32.8125 30.4167 35.8333 25.0000
#> 168 120 2 0.5208 0.1042 1.5625 0.0000
#> 169
#> 170 WASH
#> 171 ALL MALES
#> 172 INDICATOR TYPE EST LCL UCL EST
#> 173 34 2 60.9375 57.6042 65.5208 56.3380
#> 174 100 2 72.9167 64.2708 77.2917 60.5263
#> 175 33 2 24.4792 19.3750 29.2708 31.1688
#> 176 32 2 23.9583 17.5000 29.1667 29.5775
#> 177
#> 178 Relief
#> 179 ALL MALES
#> 180 INDICATOR TYPE EST LCL UCL EST
#> 181 84 2 3.6458 2.6042 7.7083 2.6316
#> 182 4 2 4.1667 1.9792 7.7083 5.2632
#> 183 90 2 2.6042 1.5625 3.6458 2.5974
#> 184
#> 185 Anthropometry
#> 186 ALL MALES
#> 187 INDICATOR TYPE EST LCL UCL EST
#> 188 26 2 2.6577 1.0365 3.9335 0.8760
#> 189 59 2 2.5939 0.9551 3.7951 0.8760
#> 190 111 2 0.0892 0.0000 0.2192 0.0000
#> X.6 X.7 X.8 X.9 X.10
#> 1
#> 2 FEMALES
#> 3 LCL UCL EST LCL UCL
#> 4 78.3971 87.5807 85.2174 82.7586 90.8084
#> 5 4.6082 10.9560 11.6071 8.8638 15.8385
#> 6 2.7848 9.8006 1.7857 0.0000 5.5232
#> 7 1.6331 5.2632 0.0000 0.0000 0.8929
#> 8
#> 9
#> 10 FEMALES
#> 11 LCL UCL EST LCL UCL
#> 12 68.7156 72.4660 71.0179 68.8629 72.3244
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 42.2235 62.0483 54.7009 43.4313 61.1394
#> 15 19.9453 38.5659 20.6897 11.8501 27.9310
#> 16 4.7927 25.7729 20.6897 18.2434 30.6897
#> 17 1.4884 11.2759 2.6786 0.0000 8.0678
#> 18 100.0000 100.0000 0.0000 0.0000 0.0000
#> 19 0.0000 0.0000 100.0000 100.0000 100.0000
#> 20 0.0000 4.3747 4.3103 1.7365 6.9445
#> 21 42.8415 59.3071 15.6522 6.9704 20.9360
#> 22 5.2153 27.6673 5.1724 2.1735 8.3893
#> 23 1.5760 24.3207 4.3103 1.2500 11.9520
#> 24 11.3264 24.5570 69.8276 62.6987 74.9180
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 6.2792 21.2714 12.9310 6.5385 13.7131
#> 27
#> 28
#> 29 FEMALES
#> 30 LCL UCL EST LCL UCL
#> 31 2.3682 2.7291 2.6518 2.4628 2.7316
#> 32 4.2855 4.9194 4.6552 4.5433 4.9095
#> 33 86.5593 96.0526 92.8571 85.8128 95.5467
#> 34 38.0172 63.3333 55.3571 48.0165 61.1946
#> 35 46.9805 71.2310 58.2609 51.9150 71.3737
#> 36 0.2532 7.6316 6.8376 2.1429 11.4432
#> 37 2.6316 12.2621 1.7094 0.0000 4.0932
#> 38 35.1572 59.1707 30.1724 26.0345 36.3458
#> 39 28.6691 44.7368 42.8571 36.8919 46.5271
#> 40 0.0000 6.1722 2.6786 1.6593 4.9954
#> 41 20.2051 28.1532 20.5357 12.7031 27.7722
#> 42 31.6722 53.1540 54.0984 48.2143 68.9655
#> 43 93.7246 100.0000 98.2143 93.5961 100.0000
#> 44
#> 45
#> 46 FEMALES
#> 47 LCL UCL EST LCL UCL
#> 48 33.6624 51.3158 49.1379 41.6901 52.4707
#> 49 28.6691 44.7368 42.8571 36.8919 46.5271
#> 50 5.6091 19.7140 10.3448 6.5517 16.1505
#> 51 49.8137 71.9846 60.0000 54.5055 72.6710
#> 52 2.8648 14.9381 4.4643 3.4483 7.0818
#> 53 54.2853 74.9076 62.6087 58.0357 75.5111
#> 54 53.2998 71.0542 69.2308 64.4643 81.4019
#> 55 0.0000 6.1722 2.6786 1.6593 4.9954
#> 56 55.2595 84.5136 58.9286 56.5967 62.5608
#> 57 61.2392 84.7334 62.5000 58.8966 68.0130
#> 58 69.9067 87.4861 86.3248 79.6429 89.9883
#> 59 55.2595 84.5136 58.9286 56.5967 62.5608
#> 60 78.7342 93.9778 88.7931 80.0000 90.8899
#> 61 38.8939 68.5616 37.3913 29.5443 43.4456
#> 62 38.2989 68.5616 36.5217 29.3658 42.1325
#> 63
#> 64
#> 65 FEMALES
#> 66 LCL UCL EST LCL UCL
#> 67 60.2632 83.8338 80.3279 74.6176 84.7537
#> 68 13.1711 34.2105 13.3929 8.1034 21.8476
#> 69 0.2247 6.9496 2.5862 0.0000 5.8208
#> 70
#> 71
#> 72 FEMALES
#> 73 LCL UCL EST LCL UCL
#> 74 100.0000 100.0000 100.0000 100.0000 100.0000
#> 75 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 100.0000 100.0000 100.0000 100.0000 100.0000
#> 79 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 100.0000 100.0000 100.0000 100.0000 100.0000
#> 83 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 100.0000 100.0000 100.0000 100.0000 100.0000
#> 87 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 100.0000 100.0000 100.0000 100.0000 100.0000
#> 91 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 100.0000 100.0000 100.0000 100.0000 100.0000
#> 95 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 100.0000 100.0000 100.0000 100.0000 100.0000
#> 99 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 FEMALES
#> 106 LCL UCL EST LCL UCL
#> 107 88.7081 98.7981 97.3913 94.5170 100.0000
#> 108 93.4381 99.7753 100.0000 97.6756 100.0000
#> 109 93.4381 99.7753 100.0000 97.6756 100.0000
#> 110 92.1258 98.7981 95.6897 88.7500 99.6581
#> 111 68.5149 89.9518 68.7500 59.8276 74.1549
#> 112 94.7505 100.0000 100.0000 100.0000 100.0000
#> 113 5.4375 5.8497 5.6087 5.4768 5.6832
#> 114 93.4381 99.7753 95.6897 91.4286 100.0000
#> 115 0.0000 0.0000 4.3103 0.0000 8.5714
#> 116 0.2247 6.5619 0.0000 0.0000 0.0000
#> 117 47.8054 70.2871 63.3929 54.8606 71.9906
#> 118 6.0528 22.4532 8.0357 5.9932 15.0165
#> 119
#> 120
#> 121 FEMALES
#> 122 LCL UCL EST LCL UCL
#> 123 9.8605 12.7229 12.5086 11.9506 13.4978
#> 124 30.8214 55.1899 49.5726 43.5326 58.1773
#> 125 4.6769 31.3700 20.4918 16.5817 30.9829
#> 126
#> 127
#> 128 FEMALES
#> 129 LCL UCL EST LCL UCL
#> 130 27.8628 51.0965 44.4444 38.0211 55.1724
#> 131 56.4667 80.0952 80.3571 76.2542 89.3939
#> 132 0.0000 52.9870 8.3333 0.0000 26.2626
#> 133 0.0000 58.9091 58.3333 35.7576 100.0000
#> 134 0.0000 0.0000 11.1111 0.0000 33.3333
#> 135 2.2222 79.0000 0.0000 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 0.0000 0.0000 0.0000 0.0000 7.2727
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 1.3333 41.5556 0.0000 0.0000 31.6667
#> 141 73.4450 90.6329 88.3929 83.6094 95.9463
#> 142 71.1671 82.5357 86.3636 80.3962 93.5294
#> 143 0.0000 24.2105 5.2632 0.0000 30.4167
#> 144 49.5614 100.0000 76.9231 55.4386 100.0000
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0000 28.5965 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0000 5.0000 0.0000 21.7949
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0000 0.0000 22.3026
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 0.0000 2.6116 1.7857 0.8562 6.2894
#> 153 36.4593 59.8431 37.3913 30.2918 43.6241
#> 154 15.6254 33.0993 30.3571 25.2174 44.4091
#> 155
#> 156
#> 157 FEMALES
#> 158 LCL UCL EST LCL UCL
#> 159 47.3475 66.3603 52.1368 42.3645 59.8966
#> 160 33.6730 51.5190 31.2500 23.9729 39.3770
#> 161 17.5571 34.9137 6.8966 1.5825 11.9805
#> 162 0.3030 6.7090 0.8197 0.0000 3.8054
#> 163 0.0000 6.6054 11.3043 6.3660 15.2709
#> 164 0.0000 0.0000 0.0000 0.0000 1.7705
#> 165 0.0000 6.0759 0.0000 0.0000 0.0000
#> 166 0.0000 8.0764 1.6393 0.0000 3.5468
#> 167 22.5345 34.0760 38.5246 30.6650 42.8588
#> 168 0.0000 3.3011 0.0000 0.0000 2.3991
#> 169
#> 170
#> 171 FEMALES
#> 172 LCL UCL EST LCL UCL
#> 173 52.6671 67.4778 60.0000 56.0776 72.6786
#> 174 56.7748 76.3477 72.3214 66.7365 82.3064
#> 175 20.3682 33.6576 20.8696 14.8998 26.1622
#> 176 20.0000 33.6576 20.8696 13.5323 25.9836
#> 177
#> 178
#> 179 FEMALES
#> 180 LCL UCL EST LCL UCL
#> 181 0.0000 7.6145 2.6786 1.0468 7.4243
#> 182 0.2532 11.9505 4.3478 1.9414 7.9310
#> 183 0.2532 4.3550 1.7094 0.0000 4.8276
#> 184
#> 185
#> 186 FEMALES
#> 187 LCL UCL EST LCL UCL
#> 188 0.1101 1.4847 3.4169 1.4613 8.9322
#> 189 0.1101 1.4754 3.0045 1.3700 8.1392
#> 190 0.0000 0.0093 0.1417 0.0059 0.9586If the preferred output is a report with combined charts and tables of results, the following piped operations can be performed:
testSVY |>
create_op() |>
estimate_op(w = testPSU, replicates = 9) |>
report_op_html(
svy = testSVY, filename = file.path(tempdir(), "ramOPreport")
)which results in an HTML file saved in the specified output directory that looks something like this:
