The RAM-OP Workflow
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>, …Processing and recoding data
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>, …Estimating indicators
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).
Blocked weighted bootstrap
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.
PROBIT estimator
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:
\[ \widehat{\text{GAM prevalence}} ~ = ~ \widehat{\text{GAM prevalence}} - \widehat{\text{SAM prevalence}} \]
Classic estimator
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 0.849 0.818 0.872 0.854 0.800 0.892 0.887
#> 2 resp2 0.115 0.0573 0.124 0.0976 0.0600 0.118 0.102
#> 3 resp3 0.0469 0.0177 0.0833 0.0441 0.0377 0.0775 0.00926
#> 4 resp4 0.0104 0.00104 0.0146 0 0 0.0376 0
#> 5 age 70.5 68.9 72.6 71.8 69.8 72.6 71.3
#> 6 ageGrp1 0 0 0 0 0 0 0
#> 7 ageGrp2 0.552 0.427 0.608 0.5 0.434 0.525 0.539
#> 8 ageGrp3 0.224 0.188 0.285 0.293 0.191 0.387 0.190
#> 9 ageGrp4 0.172 0.147 0.272 0.15 0.0442 0.228 0.212
#> 10 ageGrp5 0.0365 0.00937 0.0698 0.0658 0.0398 0.115 0.0345
#> # ℹ 126 more rows
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>PROBIT estimator
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 0.0203 9.83e-3 0.0529 6.55e- 3 6.61e- 4 0.0191 0.0390
#> 2 MAM 0.0189 9.73e-3 0.0529 6.55e- 3 5.37e- 4 0.0191 0.0379
#> 3 SAM 0.00000911 3.40e-8 0.00128 8.84e-14 3.43e-23 0.000494 0.000684
#> # 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:
which 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… 0.849 8.18e-1 0.872 0.854 0.800
#> 2 resp2 Survey Resp… Prop… 0.115 5.73e-2 0.124 0.0976 0.0600
#> 3 resp3 Survey Resp… Prop… 0.0469 1.77e-2 0.0833 0.0441 0.0377
#> 4 resp4 Survey Resp… Prop… 0.0104 1.04e-3 0.0146 0 0
#> 5 age Demography… Mean… Mean 70.5 6.89e+1 72.6 71.8 69.8
#> 6 ageGrp1 Demography… Self… Prop… 0 0 0 0 0
#> 7 ageGrp2 Demography… Self… Prop… 0.552 4.27e-1 0.608 0.5 0.434
#> 8 ageGrp3 Demography… Self… Prop… 0.224 1.88e-1 0.285 0.293 0.191
#> 9 ageGrp4 Demography… Self… Prop… 0.172 1.47e-1 0.272 0.15 0.0442
#> 10 ageGrp5 Demography… Self… Prop… 0.0365 9.37e-3 0.0698 0.0658 0.0398
#> # ℹ 129 more rows
#> # ℹ 4 more variables: UCL.MALES <dbl>, EST.FEMALES <dbl>, LCL.FEMALES <dbl>,
#> # UCL.FEMALES <dbl>Creating charts
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:
The resulting PNG file can be found in the temporary directory
and will look something like this:
Reporting estimates
Finally, estimates can be reported through report tables. The
report_op_table function facilitates this through the
following syntax:
The resulting CSV file is found in the temporary directory
and will look something like this:
#> X X.1
#> 1 Survey
#> 2
#> 3 INDICATOR TYPE
#> 4 Respondent : SUBJECT 2
#> 5 Respondent : FAMILY CARER 2
#> 6 Respondent : OTHER CARER 2
#> 7 Respondent : OTHER 2
#> 8
#> 9 Demography and situation
#> 10
#> 11 INDICATOR TYPE
#> 12 Mean self-reported age of subject (years) 1
#> 13 Self-reported age between 50 and 59 years 2
#> 14 Self-reported age between 60 and 69 years 2
#> 15 Self-reported age between 70 and 79 years 2
#> 16 Self-reported age between 80 and 89 years 2
#> 17 Self-reported age 90 years or older 2
#> 18 Sex : MALE 2
#> 19 Sex : FEMALE 2
#> 20 Marital status : SINGLE (NEVER MARRIED) 2
#> 21 Marital status : MARRIED 2
#> 22 Marital status : LIVING TOGETHER 2
#> 23 Marital status : DIVORCED 2
#> 24 Marital status : WIDOWED 2
#> 25 Marital status : OTHER 2
#> 26 Subject lives alone 2
#> 27
#> 28 Diet
#> 29
#> 30 INDICATOR TYPE
#> 31 Meal frequency (i.e. number of meals and snacks in previous 24 hours) 1
#> 32 Dietary diversity (count from 11 food groups) 1
#> 33 Consumed CEREALS (in previous 24 hours) 2
#> 34 Consumed ROOTS / TUBERS (in previous 24 hours) 2
#> 35 Consumed FRUITS / VEGETABLES (in previous 24 hours) 2
#> 36 Consumed MEAT (in previous 24 hours) 2
#> 37 Consumed EGGS (in previous 24 hours) 2
#> 38 Consumed FISH (in previous 24 hours) 2
#> 39 Consumed LEGUMES / NUTS / SEEDS (in previous 24 hours) 2
#> 40 Consumed MILK / MILK PRODUCTS (in previous 24 hours) 2
#> 41 Consumed FATS (in previous 24 hours) 2
#> 42 Consumed SUGARS (in previous 24 hours) 2
#> 43 Consumed OTHER (in previous 24 hours) 2
#> 44
#> 45 Nutrients
#> 46
#> 47 INDICATOR TYPE
#> 48 PROTEIN rich foods in diet 2
#> 49 Protein rich plant sources of protein in diet 2
#> 50 Protein rich animal sources of protein in diet 2
#> 51 Plant sources of Vitamin A in diet 2
#> 52 Animal sources of Vitamin A in diet 2
#> 53 Any source of Vitamin A 2
#> 54 IRON rich foods in diet 2
#> 55 CALCIUM rich foods in diet 2
#> 56 ZINC rich foods in diet 2
#> 57 Vitamin B1 rich foods in diet 2
#> 58 Vitamin B2 rich foods in diet 2
#> 59 Vitamin B3 rich foods in diet 2
#> 60 Vitamin B6 rich foods in diet 2
#> 61 Vitamin B12 rich foods in diet 2
#> 62 Vitamin B1 / B2 / B3 / B6 / B12 rich foods in diet 2
#> 63
#> 64 Food Security
#> 65
#> 66 INDICATOR TYPE
#> 67 Little or no hunger in household (HHS = 0 / 1) 2
#> 68 Moderate hunger in household (HHS = 2 / 3) 2
#> 69 Severe hunger in household (HHS = 4 / 5 / 6) 2
#> 70
#> 71 Disability (WG)
#> 72
#> 73 INDICATOR TYPE
#> 74 Vision : D0 : None 2
#> 75 Vision : D1 : Any 2
#> 76 Vision : D2 : Moderate or severe 2
#> 77 Vision : D3: Severe 2
#> 78 Hearing : D0 : None 2
#> 79 Hearing : D1 : Any 2
#> 80 Hearing : D2 : Moderate or severe 2
#> 81 Hearing : D3: Severe 2
#> 82 Mobility : D0 : None 2
#> 83 Mobility : D1 : Any 2
#> 84 Mobility : D2 : Moderate or severe 2
#> 85 Mobility : D3: Severe 2
#> 86 Remembering : D0 : None 2
#> 87 Remembering : D1 : Any 2
#> 88 Remembering : D2 : Moderate or severe 2
#> 89 Remembering : D3: Severe 2
#> 90 Self-care : D0 : None 2
#> 91 Self-care : D1 : Any 2
#> 92 Self-care : D2 : Moderate or severe 2
#> 93 Self-care : D3: Severe 2
#> 94 Communicating : D0 : None 2
#> 95 Communicating : D1 : Any 2
#> 96 Communicating : D2 : Moderate or severe 2
#> 97 Communicating : D3: Severe 2
#> 98 No disability in Washington Group domains 2
#> 99 At least 1 domain with any disability (P1) 2
#> 100 At least 1 domain with moderate or severe disability (P2) 2
#> 101 At least 1 domain with severe disability (P3) 2
#> 102 Multiple disability : More than one domain with any disability (PM) 2
#> 103
#> 104 Activities of daily living
#> 105
#> 106 INDICATOR TYPE
#> 107 Independent : Bathing 2
#> 108 Independent : Dressing 2
#> 109 Independent : Toileting 2
#> 110 Independent : Transferring (mobility) 2
#> 111 Independent : Continence 2
#> 112 Independent : Feeding 2
#> 113 Katz ADL score 1
#> 114 Independent (Katz ADL score = 5/6) 2
#> 115 Partial dependency (Katz ADL score = 3/4) 2
#> 116 Severe dependency (Katz ADL score = 0/1/2) 2
#> 117 Subject has someone to help them with activities of daily living 2
#> 118 Subject has ADL needs (ADL < 6) but has no helper 2
#> 119
#> 120 Mental health
#> 121
#> 122 INDICATOR TYPE
#> 123 K6 psychological distress score 1
#> 124 Serious psychological distress (K6 > 12) 2
#> 125 Probable dementia by brief CSID screen 2
#> 126
#> 127 Health
#> 128
#> 129 INDICATOR TYPE
#> 130 Long term disease requiring regular medication 2
#> 131 Takes medication for long term disease requiring regular medication 2
#> 132 Not taking drugs for long term disease : NO DRUGS AVAILABLE 2
#> 133 Not taking drugs for long term disease : TOO EXPENSIVE / NO MONEY 2
#> 134 Not taking drugs for long term disease : TOO OLD TO LOOK FOR CARE 2
#> 135 Not taking drugs for long term disease : USE OF TRADITIONAL MEDICINE 2
#> 136 Not taking drugs for long term disease : DRUGS DON'T HELP 2
#> 137 Not taking drugs for long term disease : NO-ONE TO HELP ME 2
#> 138 Not taking drugs for long term disease : NO NEED 2
#> 139 Not taking drugs for long term disease : OTHER 2
#> 140 Not taking drugs for long term disease : NO REASON GIVEN 2
#> 141 Recent illness (i.e. in the previous 2 weeks) 2
#> 142 Accessed care for recent illness 2
#> 143 Not accessing care for recent illness : NO DRUGS AVAILABLE 2
#> 144 Not accessing care for recent illness : TOO EXPENSIVE / NO MONEY 2
#> 145 Not accessing care for recent illness : TOO OLD TO LOOK FOR CARE 2
#> 146 Not accessing care for recent illness : USE OF TRADITIONAL MEDICINE 2
#> 147 Not accessing care for recent illness : DRUGS DON'T HELP 2
#> 148 Not accessing care for recent illness : NO-ONE TO HELP ME 2
#> 149 Not accessing care for recent illness : NO NEED 2
#> 150 Not accessing care for recent illness : OTHER 2
#> 151 Not accessing care for recent illness : NO REASON GIVEN 2
#> 152 Bilateral pitting oedema (may not be nutritional) 2
#> 153 Visual impairment (visual acuity < 6 / 12) by tumbling E method 2
#> 154 Problems chewing food (self-report) 2
#> 155
#> 156 Income
#> 157
#> 158 INDICATOR TYPE
#> 159 Has a personal source of income 2
#> 160 Source of income : Agriculture / fishing / livestock 2
#> 161 Source of income : Wages / salary 2
#> 162 Source of income : Sale of charcoal / bricks / etc. 2
#> 163 Source of income : Trading (e.g. market or shop) 2
#> 164 Source of income : Investments 2
#> 165 Source of income : Spending savings / sales of assets 2
#> 166 Source of income : Charity 2
#> 167 Source of income : Cash transfer / social security / welfare 2
#> 168 Source of income : Other source(s) of income 2
#> 169
#> 170 WASH
#> 171
#> 172 INDICATOR TYPE
#> 173 Improved source of drinking water 2
#> 174 Safe drinking water 2
#> 175 Improved sanitation facility 2
#> 176 Improved non-shared sanitation facility 2
#> 177
#> 178 Relief
#> 179
#> 180 INDICATOR TYPE
#> 181 Previously screened (MUAC or oedema) 2
#> 182 Anyone in household receives a ration 2
#> 183 Received non-food relief items in previous month 2
#> 184
#> 185 Anthropometry
#> 186
#> 187 INDICATOR TYPE
#> 188 Global acute malnutrition : GAM 2
#> 189 Moderate acute malnutrition : MAM 2
#> 190 Severe acute malnutrition : SAM 2
#> X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10
#> 1
#> 2 ALL MALES FEMALES
#> 3 EST LCL UCL EST LCL UCL EST LCL UCL
#> 4 0.8490 0.8177 0.8719 0.8537 0.7999 0.8917 0.8870 0.8520 0.9018
#> 5 0.1146 0.0573 0.1240 0.0976 0.0600 0.1183 0.1019 0.0893 0.1109
#> 6 0.0469 0.0177 0.0833 0.0441 0.0377 0.0775 0.0093 0.0000 0.0395
#> 7 0.0104 0.0010 0.0146 0.0000 0.0000 0.0376 0.0000 0.0000 0.0070
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL UCL
#> 12 70.5417 68.9250 72.5552 71.8243 69.7957 72.5983 71.3190 68.8664 73.7658
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 0.5521 0.4271 0.6083 0.5000 0.4340 0.5249 0.5391 0.4209 0.6638
#> 15 0.2240 0.1875 0.2854 0.2927 0.1907 0.3870 0.1897 0.1421 0.2668
#> 16 0.1719 0.1469 0.2719 0.1500 0.0442 0.2279 0.2119 0.1510 0.3093
#> 17 0.0365 0.0094 0.0698 0.0658 0.0398 0.1149 0.0345 0.0000 0.0880
#> 18 0.4375 0.3490 0.4844 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000
#> 19 0.5625 0.5156 0.6510 0.0000 0.0000 0.0000 1.0000 1.0000 1.0000
#> 20 0.0365 0.0083 0.0813 0.0130 0.0000 0.0476 0.0278 0.0018 0.0510
#> 21 0.3542 0.2958 0.4260 0.5000 0.4491 0.5948 0.1574 0.0910 0.1790
#> 22 0.1042 0.0604 0.1760 0.1463 0.0752 0.1891 0.0783 0.0571 0.1335
#> 23 0.0625 0.0365 0.0771 0.1169 0.0546 0.1668 0.0339 0.0109 0.0677
#> 24 0.4427 0.3594 0.5260 0.2059 0.1195 0.3032 0.7119 0.5874 0.7888
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 0.1146 0.0896 0.1385 0.1750 0.1298 0.2621 0.1019 0.0440 0.1241
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL UCL
#> 31 2.5521 2.4708 2.6917 2.5714 2.2153 2.7273 2.7130 2.5114 2.8505
#> 32 4.4323 4.2917 4.7990 4.5244 4.1949 4.7024 4.6983 4.5481 4.9672
#> 33 0.9062 0.8812 0.9406 0.9268 0.8403 0.9720 0.9074 0.8648 0.9572
#> 34 0.5156 0.4615 0.6083 0.5455 0.3955 0.5976 0.5804 0.5222 0.6224
#> 35 0.6094 0.5125 0.7094 0.5000 0.4491 0.5911 0.6296 0.5904 0.6946
#> 36 0.0469 0.0281 0.0750 0.0260 0.0026 0.0631 0.0862 0.0500 0.1269
#> 37 0.0156 0.0052 0.0406 0.0294 0.0129 0.0388 0.0185 0.0086 0.0498
#> 38 0.3229 0.3021 0.4177 0.4390 0.3503 0.5023 0.2542 0.1953 0.4502
#> 39 0.4010 0.3406 0.4500 0.4051 0.3247 0.4688 0.4537 0.3413 0.5619
#> 40 0.0260 0.0208 0.0406 0.0000 0.0000 0.0287 0.0259 0.0173 0.0442
#> 41 0.2031 0.1562 0.2437 0.2195 0.1831 0.3108 0.2051 0.1401 0.2786
#> 42 0.4375 0.4167 0.4885 0.3797 0.3178 0.5407 0.5641 0.4757 0.6238
#> 43 0.9740 0.9344 0.9896 0.9706 0.9296 0.9976 0.9722 0.9398 0.9965
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL UCL
#> 48 0.4583 0.3917 0.4927 0.4459 0.3532 0.5496 0.5259 0.4167 0.6397
#> 49 0.4010 0.3406 0.4500 0.4051 0.3247 0.4688 0.4537 0.3413 0.5619
#> 50 0.0938 0.0573 0.1417 0.0779 0.0258 0.1144 0.1441 0.0907 0.1826
#> 51 0.6302 0.5302 0.7375 0.5128 0.4293 0.5840 0.6724 0.6321 0.7339
#> 52 0.0417 0.0271 0.0760 0.0375 0.0129 0.0574 0.0536 0.0278 0.0707
#> 53 0.6458 0.5354 0.7594 0.5366 0.4593 0.6104 0.7069 0.6441 0.7500
#> 54 0.6875 0.6375 0.7750 0.6176 0.5183 0.6930 0.7179 0.6613 0.7463
#> 55 0.0260 0.0208 0.0406 0.0000 0.0000 0.0287 0.0259 0.0173 0.0442
#> 56 0.6198 0.5177 0.6719 0.6951 0.6079 0.7833 0.6339 0.5806 0.7705
#> 57 0.6458 0.5823 0.7000 0.7195 0.6219 0.7853 0.6667 0.6230 0.7866
#> 58 0.8177 0.7917 0.8667 0.7875 0.7616 0.8642 0.8718 0.8149 0.9671
#> 59 0.6198 0.5177 0.6719 0.6951 0.6079 0.7833 0.6339 0.5806 0.7705
#> 60 0.8802 0.8417 0.9115 0.8571 0.7971 0.9135 0.9043 0.8637 0.9690
#> 61 0.3854 0.3458 0.4448 0.4868 0.4007 0.5366 0.3220 0.2956 0.5183
#> 62 0.3490 0.3385 0.4313 0.4737 0.3985 0.5366 0.3162 0.2937 0.5183
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL UCL
#> 67 0.7500 0.7094 0.8573 0.7125 0.6587 0.7955 0.8000 0.7313 0.8722
#> 68 0.1875 0.1010 0.2229 0.2597 0.1851 0.3303 0.1368 0.0937 0.2006
#> 69 0.0312 0.0104 0.0729 0.0128 0.0000 0.0755 0.0179 0.0000 0.0568
#> 70
#> 71
#> 72 ALL MALES FEMALES
#> 73 EST LCL UCL EST LCL UCL EST LCL UCL
#> 74 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 79 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 99 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 ALL MALES FEMALES
#> 106 EST LCL UCL EST LCL UCL EST LCL UCL
#> 107 0.9635 0.9250 0.9823 0.9620 0.9148 0.9870 0.9828 0.9511 1.0000
#> 108 0.9792 0.9490 0.9938 0.9868 0.9517 1.0000 1.0000 0.9911 1.0000
#> 109 0.9792 0.9490 0.9938 0.9868 0.9517 1.0000 1.0000 0.9911 1.0000
#> 110 0.9531 0.9073 0.9833 0.9740 0.9279 1.0000 0.9722 0.9197 0.9966
#> 111 0.7396 0.6958 0.7917 0.8228 0.7031 0.8428 0.6897 0.6102 0.8521
#> 112 0.9896 0.9531 1.0000 1.0000 0.9743 1.0000 1.0000 1.0000 1.0000
#> 113 5.6354 5.3823 5.6760 5.6471 5.5439 5.8071 5.6410 5.5625 5.7877
#> 114 0.9740 0.9146 0.9844 0.9868 0.9517 1.0000 0.9828 0.9479 1.0000
#> 115 0.0104 0.0021 0.0344 0.0000 0.0000 0.0000 0.0172 0.0000 0.0521
#> 116 0.0156 0.0010 0.0510 0.0132 0.0000 0.0483 0.0000 0.0000 0.0000
#> 117 0.6042 0.5073 0.6896 0.5366 0.4822 0.5723 0.6525 0.5299 0.7093
#> 118 0.1250 0.0594 0.1802 0.1266 0.0879 0.2048 0.0763 0.0552 0.1567
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL UCL
#> 123 12.1146 11.1750 12.8063 11.2468 9.7202 12.2838 12.3248 11.4336 12.8077
#> 124 0.4740 0.3792 0.5271 0.4304 0.3442 0.5191 0.4957 0.3569 0.5312
#> 125 0.2240 0.1562 0.2552 0.1341 0.0747 0.1833 0.2119 0.1604 0.2627
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL UCL
#> 130 0.4427 0.3813 0.5010 0.3117 0.2961 0.4032 0.4615 0.4356 0.5752
#> 131 0.6824 0.6087 0.8396 0.6333 0.5464 0.8028 0.8627 0.6782 0.9144
#> 132 0.1111 0.0000 0.2652 0.2500 0.0927 0.3576 0.0000 0.0000 0.2063
#> 133 0.5185 0.2952 0.5717 0.3000 0.0000 0.4800 0.5000 0.2571 0.7089
#> 134 0.0909 0.0000 0.2210 0.0000 0.0000 0.0000 0.2857 0.0250 0.6857
#> 135 0.1143 0.0071 0.2179 0.2727 0.1086 0.4909 0.0000 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 0.0000 0.0000 0.1937 0.0000 0.0000 0.0000 0.0000 0.0000 0.2508
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 0.1429 0.0111 0.3831 0.1818 0.0000 0.5481 0.0625 0.0000 0.2508
#> 141 0.8750 0.8615 0.8958 0.8415 0.8133 0.8812 0.8333 0.7583 0.9352
#> 142 0.8084 0.7704 0.8558 0.7742 0.7383 0.8140 0.8218 0.7712 0.9073
#> 143 0.1111 0.0062 0.1596 0.0714 0.0000 0.1250 0.0000 0.0000 0.1453
#> 144 0.8333 0.7103 0.9875 0.8750 0.7214 0.9286 0.8462 0.6983 0.9818
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0513 0.0000 0.0827 0.0625 0.0000 0.2429 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0000 0.0732 0.0000 0.0000 0.0000 0.0769 0.0000 0.2522
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0647 0.0000 0.0000 0.0000 0.0000 0.0000 0.0982
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 0.0208 0.0052 0.0510 0.0000 0.0000 0.0594 0.0254 0.0087 0.0735
#> 153 0.4219 0.3688 0.4854 0.4595 0.3766 0.5988 0.3426 0.2774 0.4690
#> 154 0.2708 0.2365 0.4052 0.2703 0.1781 0.3172 0.3661 0.2791 0.3953
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL UCL
#> 159 0.5625 0.4750 0.6198 0.6410 0.5206 0.6831 0.5357 0.4342 0.6027
#> 160 0.3646 0.2979 0.4865 0.4500 0.3675 0.5636 0.2906 0.1741 0.3429
#> 161 0.0990 0.0792 0.1302 0.1951 0.1066 0.2342 0.0603 0.0142 0.0780
#> 162 0.0260 0.0125 0.0500 0.0395 0.0254 0.1201 0.0179 0.0000 0.0585
#> 163 0.0729 0.0344 0.0979 0.0256 0.0124 0.0845 0.0948 0.0549 0.1393
#> 164 0.0052 0.0010 0.0104 0.0000 0.0000 0.0000 0.0089 0.0017 0.0240
#> 165 0.0104 0.0052 0.0292 0.0380 0.0148 0.0498 0.0000 0.0000 0.0000
#> 166 0.0156 0.0052 0.0375 0.0390 0.0160 0.0512 0.0179 0.0000 0.0400
#> 167 0.2865 0.2313 0.3490 0.3382 0.2226 0.3727 0.3839 0.2754 0.4321
#> 168 0.0104 0.0000 0.0323 0.0122 0.0000 0.0289 0.0000 0.0000 0.0241
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL UCL
#> 173 0.6094 0.5542 0.6771 0.5882 0.5546 0.6927 0.5741 0.5487 0.6507
#> 174 0.7292 0.6417 0.8094 0.6410 0.6102 0.7728 0.7407 0.6144 0.7985
#> 175 0.2708 0.2313 0.3167 0.2875 0.2167 0.3280 0.2143 0.1722 0.2534
#> 176 0.2656 0.2167 0.3167 0.2875 0.2167 0.3280 0.2143 0.1704 0.2453
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL UCL
#> 181 0.0469 0.0219 0.0948 0.0250 0.0027 0.0525 0.0179 0.0000 0.0629
#> 182 0.0417 0.0271 0.0927 0.0260 0.0024 0.0727 0.0431 0.0189 0.1581
#> 183 0.0260 0.0083 0.0729 0.0244 0.0000 0.0401 0.0171 0.0000 0.0457
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL UCL
#> 188 0.0203 0.0098 0.0529 0.0066 0.0007 0.0191 0.0390 0.0207 0.0581
#> 189 0.0189 0.0097 0.0529 0.0065 0.0005 0.0191 0.0379 0.0200 0.0571
#> 190 0.0000 0.0000 0.0013 0.0000 0.0000 0.0005 0.0007 0.0001 0.0045The RAM-OP workflow in R using pipe operators
The 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 |>.
Piped operation to get output estimates table
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
with the following structure:
#> X X.1
#> 1 Survey
#> 2
#> 3 INDICATOR TYPE
#> 4 Respondent : SUBJECT 2
#> 5 Respondent : FAMILY CARER 2
#> 6 Respondent : OTHER CARER 2
#> 7 Respondent : OTHER 2
#> 8
#> 9 Demography and situation
#> 10
#> 11 INDICATOR TYPE
#> 12 Mean self-reported age of subject (years) 1
#> 13 Self-reported age between 50 and 59 years 2
#> 14 Self-reported age between 60 and 69 years 2
#> 15 Self-reported age between 70 and 79 years 2
#> 16 Self-reported age between 80 and 89 years 2
#> 17 Self-reported age 90 years or older 2
#> 18 Sex : MALE 2
#> 19 Sex : FEMALE 2
#> 20 Marital status : SINGLE (NEVER MARRIED) 2
#> 21 Marital status : MARRIED 2
#> 22 Marital status : LIVING TOGETHER 2
#> 23 Marital status : DIVORCED 2
#> 24 Marital status : WIDOWED 2
#> 25 Marital status : OTHER 2
#> 26 Subject lives alone 2
#> 27
#> 28 Diet
#> 29
#> 30 INDICATOR TYPE
#> 31 Meal frequency (i.e. number of meals and snacks in previous 24 hours) 1
#> 32 Dietary diversity (count from 11 food groups) 1
#> 33 Consumed CEREALS (in previous 24 hours) 2
#> 34 Consumed ROOTS / TUBERS (in previous 24 hours) 2
#> 35 Consumed FRUITS / VEGETABLES (in previous 24 hours) 2
#> 36 Consumed MEAT (in previous 24 hours) 2
#> 37 Consumed EGGS (in previous 24 hours) 2
#> 38 Consumed FISH (in previous 24 hours) 2
#> 39 Consumed LEGUMES / NUTS / SEEDS (in previous 24 hours) 2
#> 40 Consumed MILK / MILK PRODUCTS (in previous 24 hours) 2
#> 41 Consumed FATS (in previous 24 hours) 2
#> 42 Consumed SUGARS (in previous 24 hours) 2
#> 43 Consumed OTHER (in previous 24 hours) 2
#> 44
#> 45 Nutrients
#> 46
#> 47 INDICATOR TYPE
#> 48 PROTEIN rich foods in diet 2
#> 49 Protein rich plant sources of protein in diet 2
#> 50 Protein rich animal sources of protein in diet 2
#> 51 Plant sources of Vitamin A in diet 2
#> 52 Animal sources of Vitamin A in diet 2
#> 53 Any source of Vitamin A 2
#> 54 IRON rich foods in diet 2
#> 55 CALCIUM rich foods in diet 2
#> 56 ZINC rich foods in diet 2
#> 57 Vitamin B1 rich foods in diet 2
#> 58 Vitamin B2 rich foods in diet 2
#> 59 Vitamin B3 rich foods in diet 2
#> 60 Vitamin B6 rich foods in diet 2
#> 61 Vitamin B12 rich foods in diet 2
#> 62 Vitamin B1 / B2 / B3 / B6 / B12 rich foods in diet 2
#> 63
#> 64 Food Security
#> 65
#> 66 INDICATOR TYPE
#> 67 Little or no hunger in household (HHS = 0 / 1) 2
#> 68 Moderate hunger in household (HHS = 2 / 3) 2
#> 69 Severe hunger in household (HHS = 4 / 5 / 6) 2
#> 70
#> 71 Disability (WG)
#> 72
#> 73 INDICATOR TYPE
#> 74 Vision : D0 : None 2
#> 75 Vision : D1 : Any 2
#> 76 Vision : D2 : Moderate or severe 2
#> 77 Vision : D3: Severe 2
#> 78 Hearing : D0 : None 2
#> 79 Hearing : D1 : Any 2
#> 80 Hearing : D2 : Moderate or severe 2
#> 81 Hearing : D3: Severe 2
#> 82 Mobility : D0 : None 2
#> 83 Mobility : D1 : Any 2
#> 84 Mobility : D2 : Moderate or severe 2
#> 85 Mobility : D3: Severe 2
#> 86 Remembering : D0 : None 2
#> 87 Remembering : D1 : Any 2
#> 88 Remembering : D2 : Moderate or severe 2
#> 89 Remembering : D3: Severe 2
#> 90 Self-care : D0 : None 2
#> 91 Self-care : D1 : Any 2
#> 92 Self-care : D2 : Moderate or severe 2
#> 93 Self-care : D3: Severe 2
#> 94 Communicating : D0 : None 2
#> 95 Communicating : D1 : Any 2
#> 96 Communicating : D2 : Moderate or severe 2
#> 97 Communicating : D3: Severe 2
#> 98 No disability in Washington Group domains 2
#> 99 At least 1 domain with any disability (P1) 2
#> 100 At least 1 domain with moderate or severe disability (P2) 2
#> 101 At least 1 domain with severe disability (P3) 2
#> 102 Multiple disability : More than one domain with any disability (PM) 2
#> 103
#> 104 Activities of daily living
#> 105
#> 106 INDICATOR TYPE
#> 107 Independent : Bathing 2
#> 108 Independent : Dressing 2
#> 109 Independent : Toileting 2
#> 110 Independent : Transferring (mobility) 2
#> 111 Independent : Continence 2
#> 112 Independent : Feeding 2
#> 113 Katz ADL score 1
#> 114 Independent (Katz ADL score = 5/6) 2
#> 115 Partial dependency (Katz ADL score = 3/4) 2
#> 116 Severe dependency (Katz ADL score = 0/1/2) 2
#> 117 Subject has someone to help them with activities of daily living 2
#> 118 Subject has ADL needs (ADL < 6) but has no helper 2
#> 119
#> 120 Mental health
#> 121
#> 122 INDICATOR TYPE
#> 123 K6 psychological distress score 1
#> 124 Serious psychological distress (K6 > 12) 2
#> 125 Probable dementia by brief CSID screen 2
#> 126
#> 127 Health
#> 128
#> 129 INDICATOR TYPE
#> 130 Long term disease requiring regular medication 2
#> 131 Takes medication for long term disease requiring regular medication 2
#> 132 Not taking drugs for long term disease : NO DRUGS AVAILABLE 2
#> 133 Not taking drugs for long term disease : TOO EXPENSIVE / NO MONEY 2
#> 134 Not taking drugs for long term disease : TOO OLD TO LOOK FOR CARE 2
#> 135 Not taking drugs for long term disease : USE OF TRADITIONAL MEDICINE 2
#> 136 Not taking drugs for long term disease : DRUGS DON'T HELP 2
#> 137 Not taking drugs for long term disease : NO-ONE TO HELP ME 2
#> 138 Not taking drugs for long term disease : NO NEED 2
#> 139 Not taking drugs for long term disease : OTHER 2
#> 140 Not taking drugs for long term disease : NO REASON GIVEN 2
#> 141 Recent illness (i.e. in the previous 2 weeks) 2
#> 142 Accessed care for recent illness 2
#> 143 Not accessing care for recent illness : NO DRUGS AVAILABLE 2
#> 144 Not accessing care for recent illness : TOO EXPENSIVE / NO MONEY 2
#> 145 Not accessing care for recent illness : TOO OLD TO LOOK FOR CARE 2
#> 146 Not accessing care for recent illness : USE OF TRADITIONAL MEDICINE 2
#> 147 Not accessing care for recent illness : DRUGS DON'T HELP 2
#> 148 Not accessing care for recent illness : NO-ONE TO HELP ME 2
#> 149 Not accessing care for recent illness : NO NEED 2
#> 150 Not accessing care for recent illness : OTHER 2
#> 151 Not accessing care for recent illness : NO REASON GIVEN 2
#> 152 Bilateral pitting oedema (may not be nutritional) 2
#> 153 Visual impairment (visual acuity < 6 / 12) by tumbling E method 2
#> 154 Problems chewing food (self-report) 2
#> 155
#> 156 Income
#> 157
#> 158 INDICATOR TYPE
#> 159 Has a personal source of income 2
#> 160 Source of income : Agriculture / fishing / livestock 2
#> 161 Source of income : Wages / salary 2
#> 162 Source of income : Sale of charcoal / bricks / etc. 2
#> 163 Source of income : Trading (e.g. market or shop) 2
#> 164 Source of income : Investments 2
#> 165 Source of income : Spending savings / sales of assets 2
#> 166 Source of income : Charity 2
#> 167 Source of income : Cash transfer / social security / welfare 2
#> 168 Source of income : Other source(s) of income 2
#> 169
#> 170 WASH
#> 171
#> 172 INDICATOR TYPE
#> 173 Improved source of drinking water 2
#> 174 Safe drinking water 2
#> 175 Improved sanitation facility 2
#> 176 Improved non-shared sanitation facility 2
#> 177
#> 178 Relief
#> 179
#> 180 INDICATOR TYPE
#> 181 Previously screened (MUAC or oedema) 2
#> 182 Anyone in household receives a ration 2
#> 183 Received non-food relief items in previous month 2
#> 184
#> 185 Anthropometry
#> 186
#> 187 INDICATOR TYPE
#> 188 Global acute malnutrition : GAM 2
#> 189 Moderate acute malnutrition : MAM 2
#> 190 Severe acute malnutrition : SAM 2
#> X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9
#> 1
#> 2 ALL MALES FEMALES
#> 3 EST LCL UCL EST LCL UCL EST LCL
#> 4 84.3750 79.7917 88.2292 85.0746 76.1618 88.9723 82.9268 80.3462
#> 5 10.9375 6.6667 13.4375 7.7922 3.8080 13.4177 12.3894 7.3971
#> 6 3.1250 1.7708 6.6667 4.4776 2.5063 11.4489 3.6364 0.1818
#> 7 0.5208 0.0000 2.6042 3.7037 0.0000 4.9702 0.0000 0.0000
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL
#> 12 70.8021 69.9688 72.7042 70.8395 69.7658 73.6961 71.1667 69.6330
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 52.6042 44.8958 56.7708 49.3671 37.1369 57.2840 54.6296 46.8986
#> 15 22.9167 20.4167 33.1250 26.5823 15.1907 34.0651 19.0909 13.0137
#> 16 20.3125 12.6042 28.4375 20.2532 11.1424 29.6461 24.1667 13.8639
#> 17 4.6875 1.2500 7.1875 6.1728 1.7919 8.6642 4.0650 0.5000
#> 18 36.9792 34.7917 49.3750 100.0000 100.0000 100.0000 0.0000 0.0000
#> 19 63.0208 50.6250 65.2083 0.0000 0.0000 0.0000 100.0000 100.0000
#> 20 3.6458 1.0417 5.1042 1.2987 0.0000 8.0837 4.1667 1.0779
#> 21 32.2917 23.4375 34.3750 53.2468 42.1954 65.1852 14.2857 10.7847
#> 22 11.4583 8.3333 12.9167 17.2840 9.5785 30.5746 6.1404 3.7409
#> 23 6.7708 5.2083 10.9375 5.0633 0.7407 15.1271 4.4643 2.0269
#> 24 47.9167 43.7500 54.6875 17.5000 10.5804 25.6068 69.2982 62.5932
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 11.4583 9.2708 14.5833 16.0494 5.8043 19.2626 9.6491 4.3377
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL
#> 31 2.5365 2.4188 2.6604 2.5287 2.2816 2.7010 2.5865 2.4176
#> 32 4.5781 4.2885 4.7760 4.4179 4.0886 4.8948 4.6818 4.5697
#> 33 91.1458 88.7500 93.1250 91.0448 88.9606 96.0123 92.5926 87.0347
#> 34 52.6042 45.3125 58.4375 53.5211 32.0519 55.5914 53.0973 48.0390
#> 35 60.4167 51.1458 68.1250 56.9620 51.0448 62.0602 63.3333 55.4545
#> 36 6.2500 3.7500 9.1667 2.8169 0.0000 8.2826 5.3571 3.0011
#> 37 2.0833 1.0417 6.9792 3.7037 1.5063 8.7648 1.9231 0.0000
#> 38 33.8542 27.9167 41.1458 42.8571 33.8308 47.7139 29.1667 20.5923
#> 39 42.7083 36.9792 46.6667 44.7761 32.4323 52.4465 41.8182 34.7832
#> 40 2.6042 2.0833 3.5417 0.0000 0.0000 1.3799 4.8077 1.0844
#> 41 22.3958 17.3958 29.6875 22.2222 10.8571 30.5066 20.5357 16.6620
#> 42 45.3125 40.0000 54.5833 33.3333 27.6410 47.8672 56.2500 48.7003
#> 43 96.3542 92.8125 99.4792 97.4684 93.9491 100.0000 97.3451 91.6835
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL
#> 48 50.0000 42.9167 55.8333 50.7463 34.9270 56.5676 48.1481 41.9860
#> 49 42.7083 36.9792 46.6667 44.7761 32.4323 52.4465 41.8182 34.7832
#> 50 12.5000 8.1250 15.6250 7.4074 4.2725 11.4739 11.6071 9.3966
#> 51 61.4583 57.9167 67.7083 59.4937 51.8861 64.0868 66.9643 58.9091
#> 52 5.2083 3.7500 10.3125 3.7037 1.5063 9.0465 6.7308 2.5928
#> 53 63.5417 59.6875 72.6042 62.9630 52.3433 67.0924 67.8571 58.9091
#> 54 67.7083 56.7708 73.4375 66.6667 54.7068 73.0479 74.1667 64.4180
#> 55 2.6042 2.0833 3.5417 0.0000 0.0000 1.3799 4.8077 1.0844
#> 56 61.9792 57.3958 69.5833 63.7500 58.6051 75.7876 60.0000 52.5954
#> 57 66.6667 62.2917 72.6042 65.0000 60.8154 76.8205 69.0265 58.7930
#> 58 83.3333 80.7292 86.5625 78.8732 72.9254 86.6752 86.6071 82.8490
#> 59 61.9792 57.3958 69.5833 63.7500 58.6051 75.7876 60.0000 52.5954
#> 60 88.0208 83.5417 91.6667 89.8734 81.8725 95.8876 89.0909 86.7917
#> 61 38.5417 37.6042 45.7292 45.4545 36.0641 51.2559 38.0531 27.7747
#> 62 38.0208 36.6667 45.4167 42.8571 36.0641 49.4168 35.1852 27.0604
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL
#> 67 77.0833 72.2917 83.3333 77.2152 68.5107 86.2154 81.8182 71.1636
#> 68 16.6667 12.8125 23.6458 20.0000 11.3495 28.9249 13.6364 9.9957
#> 69 2.6042 1.1458 6.0417 2.4691 0.2299 4.8966 2.6786 0.1770
#> 70
#> 71
#> 72 ALL MALES FEMALES
#> 73 EST LCL UCL EST LCL UCL EST LCL
#> 74 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 79 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 99 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 ALL MALES FEMALES
#> 106 EST LCL UCL EST LCL UCL EST LCL
#> 107 96.8750 91.7708 98.3333 96.2025 90.6201 98.5866 99.0909 97.4642
#> 108 98.4375 94.2708 99.8958 97.5309 96.5176 99.7701 100.0000 99.0973
#> 109 98.4375 94.2708 99.8958 97.5309 96.5176 99.7701 100.0000 99.0973
#> 110 96.3542 93.3333 98.7500 96.5517 94.0617 99.7468 96.4912 92.1283
#> 111 72.9167 67.1875 76.7708 75.9494 68.3543 86.0563 73.3333 62.4868
#> 112 99.4792 96.1458 100.0000 98.7342 96.5176 100.0000 100.0000 100.0000
#> 113 5.6146 5.4385 5.6792 5.6420 5.5088 5.7308 5.6500 5.5778
#> 114 97.3958 93.8542 98.8542 97.5309 96.5176 99.7701 99.1228 97.2323
#> 115 0.5208 0.0000 2.6042 0.0000 0.0000 0.0000 0.8772 0.0000
#> 116 1.0417 0.0000 5.3125 2.4691 0.2299 3.4824 0.0000 0.0000
#> 117 59.3750 50.3125 70.1042 61.9718 36.9964 66.1692 62.8319 51.2195
#> 118 9.8958 7.1875 15.1042 12.6582 4.6901 24.5767 9.8214 5.0909
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL
#> 123 12.4167 11.2354 13.0375 11.4366 10.7316 12.5289 12.2833 11.5459
#> 124 48.4375 40.2083 55.6250 46.8354 43.2615 53.2068 49.0909 41.6077
#> 125 18.2292 14.1667 26.7708 19.7183 11.1674 30.4156 22.7273 18.8462
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL
#> 130 42.7083 41.7708 51.1458 36.2500 23.3446 47.9938 46.0177 42.3666
#> 131 75.6098 68.9745 83.7118 70.2703 41.5948 75.9524 78.4314 68.3283
#> 132 17.3913 9.7727 35.4737 20.0000 9.7727 78.2857 10.0000 0.0000
#> 133 35.7143 16.0000 47.3518 27.2727 0.0000 61.4286 50.0000 33.9394
#> 134 14.2857 4.3478 30.4545 0.0000 0.0000 0.0000 25.0000 15.4286
#> 135 10.5263 4.7283 17.8571 20.0000 0.0000 57.5758 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 5.2632 0.0000 14.0373 0.0000 0.0000 0.0000 0.0000 0.0000
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 9.3750 0.0000 29.0000 22.2222 0.0000 32.6667 0.0000 0.0000
#> 141 86.9792 82.1875 92.1875 85.7143 77.2630 92.4810 85.4545 78.3462
#> 142 80.3371 76.3057 84.5135 78.4615 68.3006 82.1918 82.6923 75.4078
#> 143 14.2857 2.9981 23.7161 0.0000 0.0000 14.1259 8.3333 0.0000
#> 144 77.1429 71.1742 96.2429 87.5000 72.5275 100.0000 88.8889 71.6667
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0000 0.0000 5.9305 7.6923 0.0000 27.4725 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 2.9412 0.0000 10.0654 0.0000 0.0000 0.0000 0.0000 0.0000
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 3.7000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 2.0833 0.1042 3.9583 1.2658 0.0000 5.0383 2.7273 1.0013
#> 153 42.1875 34.4792 47.6042 48.1013 39.7875 60.3254 35.3982 29.5499
#> 154 27.6042 23.4375 39.8958 23.4568 18.5985 33.2687 34.2593 23.1700
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL
#> 159 57.2917 46.1458 60.8333 63.2911 56.1791 73.7420 54.8673 47.7643
#> 160 33.8542 30.3125 46.7708 51.9481 34.4627 59.2811 30.3571 28.2254
#> 161 10.9375 6.0417 18.2292 23.9437 8.4701 31.8231 5.3097 3.0220
#> 162 3.1250 0.3125 5.1042 2.5000 0.2299 10.9321 0.0000 0.0000
#> 163 6.7708 5.4167 8.6458 1.1494 0.0000 3.6803 7.2727 3.8479
#> 164 0.5208 0.0000 1.4583 0.0000 0.0000 0.0000 0.9091 0.0000
#> 165 1.5625 0.0000 3.4375 3.7500 0.2985 6.1023 0.0000 0.0000
#> 166 1.5625 0.0000 3.0208 1.2346 0.0000 6.1143 1.9231 0.1667
#> 167 31.7708 27.1875 40.3125 29.6296 19.2476 44.2011 37.2727 31.5569
#> 168 0.5208 0.0000 1.9792 0.0000 0.0000 4.7595 0.8772 0.0000
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL
#> 173 60.9375 56.8750 66.9792 56.7901 38.1408 64.5657 62.2807 51.4161
#> 174 72.9167 66.2500 78.2292 64.1975 45.5493 69.2263 72.8070 67.9660
#> 175 23.9583 18.1250 32.2917 22.7848 20.1791 28.4909 20.9091 16.8415
#> 176 22.9167 17.9167 32.0833 21.8391 18.1772 27.9898 20.9091 14.7236
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL
#> 181 2.0833 0.1042 9.6875 3.7037 1.1695 6.9649 1.8519 0.1786
#> 182 4.1667 2.1875 7.6042 3.7975 0.2500 12.1662 3.8462 0.8787
#> 183 3.1250 0.6250 4.1667 1.4925 0.0000 3.7407 1.8182 0.8787
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL
#> 188 3.2807 1.1078 6.2232 0.4288 0.0104 2.4428 3.0036 0.5524
#> 189 3.0739 0.8468 6.1554 0.4288 0.0104 2.4428 2.8264 0.3752
#> 190 0.0023 0.0000 0.3260 0.0000 0.0000 0.0003 0.1469 0.0124
#> X.10
#> 1
#> 2
#> 3 UCL
#> 4 89.0909
#> 5 16.9312
#> 6 5.8205
#> 7 2.3252
#> 8
#> 9
#> 10
#> 11 UCL
#> 12 72.8036
#> 13 0.0000
#> 14 61.7073
#> 15 27.7924
#> 16 27.1279
#> 17 9.8756
#> 18 0.0000
#> 19 100.0000
#> 20 10.6070
#> 21 19.0298
#> 22 13.9947
#> 23 8.0839
#> 24 72.6818
#> 25 0.0000
#> 26 14.1652
#> 27
#> 28
#> 29
#> 30 UCL
#> 31 2.8059
#> 32 4.9175
#> 33 97.1488
#> 34 64.4744
#> 35 73.6427
#> 36 13.4472
#> 37 4.4486
#> 38 45.6364
#> 39 49.2035
#> 40 7.9614
#> 41 28.9091
#> 42 63.1364
#> 43 99.8374
#> 44
#> 45
#> 46
#> 47 UCL
#> 48 54.0476
#> 49 49.2035
#> 50 19.7037
#> 51 74.1707
#> 52 9.0438
#> 53 77.7561
#> 54 80.4964
#> 55 7.9614
#> 56 70.2360
#> 57 72.5678
#> 58 92.8038
#> 59 70.2360
#> 60 95.8884
#> 61 48.1818
#> 62 46.9091
#> 63
#> 64
#> 65
#> 66 UCL
#> 67 86.3968
#> 68 21.1078
#> 69 5.1111
#> 70
#> 71
#> 72
#> 73 UCL
#> 74 100.0000
#> 75 0.0000
#> 76 0.0000
#> 77 0.0000
#> 78 100.0000
#> 79 0.0000
#> 80 0.0000
#> 81 0.0000
#> 82 100.0000
#> 83 0.0000
#> 84 0.0000
#> 85 0.0000
#> 86 100.0000
#> 87 0.0000
#> 88 0.0000
#> 89 0.0000
#> 90 100.0000
#> 91 0.0000
#> 92 0.0000
#> 93 0.0000
#> 94 100.0000
#> 95 0.0000
#> 96 0.0000
#> 97 0.0000
#> 98 100.0000
#> 99 0.0000
#> 100 0.0000
#> 101 0.0000
#> 102 0.0000
#> 103
#> 104
#> 105
#> 106 UCL
#> 107 100.0000
#> 108 100.0000
#> 109 100.0000
#> 110 99.5122
#> 111 80.0540
#> 112 100.0000
#> 113 5.7678
#> 114 100.0000
#> 115 2.7677
#> 116 0.0000
#> 117 70.1212
#> 118 15.2475
#> 119
#> 120
#> 121
#> 122 UCL
#> 123 13.1684
#> 124 56.8868
#> 125 29.0000
#> 126
#> 127
#> 128
#> 129 UCL
#> 130 51.8386
#> 131 89.5290
#> 132 32.1212
#> 133 75.6667
#> 134 54.4444
#> 135 0.0000
#> 136 0.0000
#> 137 0.0000
#> 138 18.2540
#> 139 0.0000
#> 140 21.1429
#> 141 93.3862
#> 142 89.1978
#> 143 17.5455
#> 144 98.8889
#> 145 0.0000
#> 146 0.0000
#> 147 0.0000
#> 148 15.3333
#> 149 0.0000
#> 150 16.6667
#> 151 0.0000
#> 152 7.0909
#> 153 39.8846
#> 154 43.6302
#> 155
#> 156
#> 157
#> 158 UCL
#> 159 58.2114
#> 160 36.3704
#> 161 10.0281
#> 162 1.7287
#> 163 15.7825
#> 164 3.6772
#> 165 0.0000
#> 166 5.6575
#> 167 42.1388
#> 168 2.8387
#> 169
#> 170
#> 171
#> 172 UCL
#> 173 70.2963
#> 174 79.3571
#> 175 30.7209
#> 176 28.4987
#> 177
#> 178
#> 179
#> 180 UCL
#> 181 8.3089
#> 182 11.8182
#> 183 5.4788
#> 184
#> 185
#> 186
#> 187 UCL
#> 188 5.6506
#> 189 5.4661
#> 190 0.6615Piped operation to get output an HTML report
If 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:
