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 0.795 0.879 0.836 0.717 0.885 0.843
#> 2 resp2 0.104 0.0781 0.149 0.0685 0.0214 0.143 0.101
#> 3 resp3 0.0417 0.00937 0.0521 0.0588 0.0185 0.140 0.0252
#> 4 resp4 0.00521 0 0.025 0.0233 0 0.0835 0
#> 5 age 70.8 69.4 71.6 71.6 68.0 72.5 71.3
#> 6 ageGrp1 0 0 0 0 0 0 0
#> 7 ageGrp2 0.536 0.483 0.601 0.493 0.429 0.701 0.504
#> 8 ageGrp3 0.224 0.175 0.285 0.259 0.134 0.297 0.257
#> 9 ageGrp4 0.193 0.152 0.252 0.167 0.0877 0.262 0.2
#> 10 ageGrp5 0.0417 0.0219 0.0729 0.05 0.00563 0.128 0.0175
#> # 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 0.0263 1.58e-3 0.0494 5.63e- 3 7.73e- 4 0.0248 0.0310
#> 2 MAM 0.0263 1.53e-3 0.0466 5.63e- 3 7.54e- 4 0.0248 0.0303
#> 3 SAM 0.0000706 3.09e-7 0.00282 1.19e-11 1.17e-13 0.000138 0.00132
#> # 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… 8.49e-1 7.95e-1 0.879 0.836 0.717
#> 2 resp2 Survey Resp… Prop… 1.04e-1 7.81e-2 0.149 0.0685 0.0214
#> 3 resp3 Survey Resp… Prop… 4.17e-2 9.37e-3 0.0521 0.0588 0.0185
#> 4 resp4 Survey Resp… Prop… 5.21e-3 0 0.025 0.0233 0
#> 5 age Demography… Mean… Mean 7.08e+1 6.94e+1 71.6 71.6 68.0
#> 6 ageGrp1 Demography… Self… Prop… 0 0 0 0 0
#> 7 ageGrp2 Demography… Self… Prop… 5.36e-1 4.83e-1 0.601 0.493 0.429
#> 8 ageGrp3 Demography… Self… Prop… 2.24e-1 1.75e-1 0.285 0.259 0.134
#> 9 ageGrp4 Demography… Self… Prop… 1.93e-1 1.52e-1 0.252 0.167 0.0877
#> 10 ageGrp5 Demography… Self… Prop… 4.17e-2 2.19e-2 0.0729 0.05 0.00563
#> # 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 0.7948 0.8792 0.8356 0.7171 0.8849 0.8430 0.7856 0.9252
#> 5 0.1042 0.0781 0.1490 0.0685 0.0214 0.1425 0.1009 0.0479 0.1986
#> 6 0.0417 0.0094 0.0521 0.0588 0.0185 0.1402 0.0252 0.0035 0.0539
#> 7 0.0052 0.0000 0.0250 0.0233 0.0000 0.0835 0.0000 0.0000 0.0223
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL UCL
#> 12 70.8229 69.3740 71.5917 71.5529 68.0355 72.4697 71.3119 69.1884 72.5154
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 0.5365 0.4833 0.6010 0.4930 0.4294 0.7005 0.5041 0.4358 0.5797
#> 15 0.2240 0.1750 0.2854 0.2588 0.1336 0.2969 0.2571 0.2186 0.3166
#> 16 0.1927 0.1521 0.2521 0.1667 0.0877 0.2625 0.2000 0.1434 0.2733
#> 17 0.0417 0.0219 0.0729 0.0500 0.0056 0.1278 0.0175 0.0000 0.0793
#> 18 0.4010 0.3823 0.4615 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000
#> 19 0.5990 0.5385 0.6177 0.0000 0.0000 0.0000 1.0000 1.0000 1.0000
#> 20 0.0365 0.0219 0.0667 0.0250 0.0000 0.0494 0.0182 0.0017 0.0474
#> 21 0.3177 0.2437 0.3927 0.5616 0.4872 0.6336 0.1345 0.0999 0.2162
#> 22 0.1042 0.0708 0.1323 0.1529 0.0974 0.2362 0.0877 0.0384 0.1222
#> 23 0.0990 0.0354 0.1250 0.0824 0.0388 0.1233 0.0583 0.0357 0.1082
#> 24 0.4219 0.4000 0.5073 0.1549 0.0866 0.2847 0.6807 0.6086 0.7353
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 0.1302 0.0906 0.1740 0.1507 0.1161 0.2123 0.1083 0.0503 0.1315
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL UCL
#> 31 2.5156 2.4000 2.6990 2.6076 2.2065 2.6978 2.5963 2.3566 2.7488
#> 32 4.5573 4.3771 4.6833 4.5634 4.1418 4.8449 4.6106 4.2988 4.8134
#> 33 0.9323 0.9031 0.9604 0.9250 0.7133 0.9605 0.9083 0.8414 0.9473
#> 34 0.5052 0.4542 0.5333 0.5316 0.3324 0.6185 0.5575 0.4689 0.6101
#> 35 0.5521 0.4802 0.6375 0.5500 0.4165 0.7201 0.6182 0.4942 0.7023
#> 36 0.0573 0.0365 0.0906 0.0256 0.0047 0.0575 0.0667 0.0340 0.1236
#> 37 0.0208 0.0010 0.0417 0.0548 0.0398 0.1064 0.0091 0.0000 0.0474
#> 38 0.3490 0.3115 0.4031 0.4177 0.3599 0.5232 0.2273 0.1554 0.3365
#> 39 0.4167 0.3458 0.4604 0.4419 0.3312 0.5178 0.4333 0.3709 0.5175
#> 40 0.0260 0.0031 0.0656 0.0000 0.0000 0.0245 0.0190 0.0086 0.0779
#> 41 0.2240 0.2094 0.2698 0.2442 0.1841 0.3557 0.1917 0.1299 0.3253
#> 42 0.5000 0.3896 0.5417 0.3882 0.2641 0.5294 0.5641 0.4613 0.7011
#> 43 0.9740 0.9583 0.9885 0.9651 0.9324 0.9976 0.9750 0.9163 1.0000
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL UCL
#> 48 0.4844 0.4021 0.5292 0.5000 0.3650 0.5499 0.4833 0.4538 0.5748
#> 49 0.4167 0.3458 0.4604 0.4419 0.3312 0.5178 0.4333 0.3709 0.5175
#> 50 0.1198 0.0635 0.1396 0.0976 0.0450 0.1519 0.1150 0.0586 0.2198
#> 51 0.5677 0.5323 0.6562 0.5732 0.4565 0.6466 0.6091 0.5515 0.7626
#> 52 0.0677 0.0125 0.0938 0.0588 0.0398 0.1081 0.0439 0.0182 0.1056
#> 53 0.6042 0.5427 0.6792 0.6026 0.4788 0.6729 0.6182 0.5853 0.7842
#> 54 0.6771 0.6312 0.7031 0.6279 0.5682 0.6924 0.6667 0.5634 0.7699
#> 55 0.0260 0.0031 0.0656 0.0000 0.0000 0.0245 0.0190 0.0086 0.0779
#> 56 0.6250 0.5938 0.6917 0.6761 0.5794 0.7661 0.5614 0.4845 0.6910
#> 57 0.6823 0.6292 0.7177 0.6962 0.6341 0.7688 0.6228 0.4976 0.7030
#> 58 0.8333 0.8010 0.8677 0.8028 0.7634 0.8367 0.8500 0.7739 0.8874
#> 59 0.6250 0.5938 0.6917 0.6761 0.5794 0.7661 0.5614 0.4845 0.6910
#> 60 0.8802 0.8354 0.9052 0.8659 0.8321 0.9535 0.8667 0.8360 0.9109
#> 61 0.4062 0.3583 0.4531 0.4872 0.3931 0.5630 0.3077 0.1906 0.3919
#> 62 0.4062 0.3563 0.4354 0.4872 0.3693 0.5507 0.3077 0.1887 0.3853
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL UCL
#> 67 0.7708 0.7271 0.8385 0.7529 0.6929 0.8548 0.7778 0.7249 0.8317
#> 68 0.1771 0.1073 0.2198 0.2000 0.1254 0.2971 0.1624 0.1076 0.2113
#> 69 0.0260 0.0062 0.0438 0.0128 0.0000 0.0594 0.0252 0.0169 0.0565
#> 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 0.9688 0.9229 0.9833 0.9625 0.8815 0.9977 1.0000 0.9638 1.0000
#> 108 0.9844 0.9583 0.9927 0.9872 0.9306 1.0000 1.0000 0.9910 1.0000
#> 109 0.9844 0.9583 0.9927 0.9872 0.9306 1.0000 1.0000 0.9910 1.0000
#> 110 0.9531 0.9167 0.9875 0.9756 0.9259 0.9977 0.9714 0.8989 1.0000
#> 111 0.7292 0.6740 0.7948 0.7765 0.7187 0.8415 0.7544 0.6182 0.8373
#> 112 0.9948 0.9760 0.9948 1.0000 0.9506 1.0000 1.0000 1.0000 1.0000
#> 113 5.6250 5.4562 5.6938 5.6795 5.3771 5.7957 5.7105 5.5582 5.8099
#> 114 0.9740 0.9187 0.9885 0.9872 0.9306 1.0000 0.9817 0.9155 1.0000
#> 115 0.0104 0.0052 0.0406 0.0000 0.0000 0.0000 0.0183 0.0000 0.0845
#> 116 0.0156 0.0062 0.0417 0.0128 0.0000 0.0694 0.0000 0.0000 0.0000
#> 117 0.5781 0.5271 0.6562 0.5349 0.4540 0.6821 0.5983 0.5075 0.6230
#> 118 0.1094 0.0635 0.1500 0.1375 0.0964 0.2029 0.0579 0.0365 0.1056
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL UCL
#> 123 12.0104 11.0906 12.9000 11.7808 9.8738 12.9251 12.3186 11.4357 13.6855
#> 124 0.4740 0.3969 0.5573 0.4872 0.3661 0.5855 0.5044 0.4356 0.5960
#> 125 0.2031 0.1625 0.2271 0.1412 0.1057 0.2377 0.2389 0.1524 0.3212
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL UCL
#> 130 0.4583 0.4042 0.4844 0.3500 0.2472 0.4573 0.4790 0.3485 0.5803
#> 131 0.7312 0.6741 0.8335 0.6562 0.5773 0.7965 0.8438 0.7579 0.8969
#> 132 0.1200 0.0000 0.5373 0.2727 0.1139 0.4889 0.0000 0.0000 0.2619
#> 133 0.5238 0.3103 0.5714 0.3333 0.0333 0.5253 0.5556 0.2100 0.7974
#> 134 0.0714 0.0000 0.2974 0.0000 0.0000 0.0000 0.1818 0.0222 0.7511
#> 135 0.0833 0.0000 0.2349 0.1250 0.0000 0.6000 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 0.0471 0.0000 0.0000 0.0000 0.0000 0.0000 0.3067
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 0.0909 0.0000 0.3784 0.2222 0.0182 0.4750 0.0000 0.0000 0.4364
#> 141 0.8854 0.8448 0.9156 0.8169 0.7690 0.9032 0.8947 0.7603 0.9150
#> 142 0.8276 0.7305 0.8816 0.7536 0.6905 0.8345 0.8586 0.7859 0.9010
#> 143 0.0952 0.0000 0.3823 0.2000 0.0000 0.4706 0.0000 0.0000 0.1515
#> 144 0.9048 0.4281 0.9422 0.7143 0.4794 0.9495 0.8571 0.6424 1.0000
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0000 0.0000 0.1507 0.0526 0.0000 0.1835 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 0.0278 0.0000 0.0978 0.0000 0.0000 0.0000 0.0909 0.0000 0.3380
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0467 0.0000 0.0000 0.0000 0.0000 0.0000 0.0845
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 0.0156 0.0052 0.0542 0.0000 0.0000 0.0540 0.0275 0.0123 0.0736
#> 153 0.4167 0.3115 0.5052 0.4390 0.3204 0.5226 0.3303 0.2395 0.4580
#> 154 0.2708 0.2344 0.3354 0.1977 0.1391 0.2339 0.3578 0.2755 0.4972
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL UCL
#> 159 0.5208 0.4760 0.5708 0.6860 0.5339 0.7302 0.5143 0.4880 0.5852
#> 160 0.3698 0.2865 0.4635 0.4521 0.3980 0.6002 0.2857 0.2197 0.3693
#> 161 0.0938 0.0677 0.1292 0.2439 0.1718 0.3422 0.0513 0.0000 0.0924
#> 162 0.0208 0.0156 0.0354 0.0488 0.0251 0.1064 0.0083 0.0000 0.0407
#> 163 0.0625 0.0312 0.0948 0.0137 0.0025 0.0249 0.0550 0.0192 0.1454
#> 164 0.0052 0.0000 0.0229 0.0000 0.0000 0.0000 0.0000 0.0000 0.0153
#> 165 0.0156 0.0052 0.0333 0.0375 0.0000 0.0575 0.0000 0.0000 0.0000
#> 166 0.0156 0.0010 0.0260 0.0250 0.0000 0.0698 0.0083 0.0000 0.0443
#> 167 0.3021 0.2281 0.3646 0.2907 0.1931 0.3862 0.3667 0.3032 0.4262
#> 168 0.0104 0.0000 0.0354 0.0000 0.0000 0.0128 0.0000 0.0000 0.0157
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL UCL
#> 173 0.6146 0.5802 0.7094 0.5753 0.4266 0.6375 0.6134 0.5063 0.7000
#> 174 0.7240 0.6479 0.7917 0.6164 0.5013 0.6875 0.6991 0.6210 0.8295
#> 175 0.2552 0.2458 0.3031 0.2073 0.1347 0.3146 0.2083 0.1715 0.2866
#> 176 0.2552 0.2365 0.2802 0.2073 0.1347 0.3146 0.1927 0.1518 0.2832
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL UCL
#> 181 0.0469 0.0219 0.0573 0.0250 0.0024 0.0878 0.0413 0.0036 0.0756
#> 182 0.0208 0.0104 0.0740 0.0471 0.0144 0.1194 0.0579 0.0155 0.1240
#> 183 0.0312 0.0021 0.0646 0.0253 0.0026 0.0399 0.0413 0.0053 0.0808
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL UCL
#> 188 0.0263 0.0016 0.0494 0.0056 0.0008 0.0248 0.0310 0.0224 0.0572
#> 189 0.0263 0.0015 0.0466 0.0056 0.0008 0.0248 0.0303 0.0165 0.0560
#> 190 0.0001 0.0000 0.0028 0.0000 0.0000 0.0001 0.0013 0.0001 0.0084The 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 86.9792 81.2500 91.2500 86.8421 78.5410 91.6000 86.0656 78.8769
#> 5 8.3333 6.9792 11.7708 7.0588 4.1639 12.4441 9.0909 6.7584
#> 6 4.1667 1.1458 7.6042 5.1282 0.0000 9.0807 3.2787 1.0781
#> 7 0.5208 0.0000 2.8125 2.5000 0.0000 7.1949 0.0000 0.0000
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL
#> 12 70.5469 69.3594 72.3781 70.4000 68.8506 72.1425 71.0727 68.7842
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 53.6458 44.2708 60.3125 53.9474 39.0008 63.2500 51.6393 41.5437
#> 15 25.0000 17.0833 26.0417 26.5060 19.7895 38.2143 22.1311 15.2189
#> 16 18.2292 12.7083 26.4583 12.0482 10.3228 21.5342 19.0083 12.2918
#> 17 4.1667 1.5625 6.3542 5.0000 2.8977 9.9211 4.1322 1.0781
#> 18 40.6250 32.9167 47.7083 100.0000 100.0000 100.0000 0.0000 0.0000
#> 19 59.3750 52.2917 67.0833 0.0000 0.0000 0.0000 100.0000 100.0000
#> 20 3.6458 1.6667 6.3542 0.0000 0.0000 4.3127 6.0870 1.9630
#> 21 30.2083 26.3542 33.2292 52.5641 46.6579 61.8824 16.5289 9.5115
#> 22 11.4583 8.3333 15.6250 18.7500 8.9412 30.2632 7.0175 4.7285
#> 23 6.2500 4.8958 11.5625 8.9744 1.8026 13.4859 5.9322 1.8775
#> 24 48.9583 41.6667 50.9375 18.4211 12.0784 22.2659 65.2542 55.1680
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 14.0625 6.6667 18.7500 15.0000 10.9247 19.2202 12.7119 7.3685
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL
#> 31 2.6198 2.4521 2.7198 2.5733 2.3005 2.7433 2.5868 2.4775
#> 32 4.5260 4.4542 4.6760 4.4487 3.9843 4.8542 4.7787 4.5527
#> 33 91.6667 87.1875 94.6875 92.9412 84.2056 95.6731 92.1053 87.8425
#> 34 52.0833 48.0208 57.0833 46.5753 38.5263 64.2721 55.9322 53.0519
#> 35 58.3333 49.7917 62.6042 51.7647 48.9744 61.4233 64.4068 55.1392
#> 36 4.6875 2.7083 6.2500 3.7500 1.1976 7.9235 6.0870 3.6119
#> 37 2.6042 1.5625 5.4167 4.0000 1.2564 8.8873 0.8264 0.0000
#> 38 31.7708 26.8750 35.3125 42.1687 29.5025 50.2769 29.4643 21.7425
#> 39 44.2708 38.7500 47.6042 39.7590 27.8762 49.3961 42.9825 34.0507
#> 40 2.6042 0.7292 5.1042 0.0000 0.0000 1.0000 4.7170 1.2043
#> 41 20.8333 17.2917 27.2917 25.0000 14.5123 29.7718 21.4876 14.9680
#> 42 48.9583 40.7292 58.5417 43.4211 31.1154 50.7609 60.1695 49.5695
#> 43 96.8750 93.8542 99.8958 96.0000 92.1842 100.0000 98.1818 93.0576
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL
#> 48 47.9167 46.3542 51.7708 44.8718 31.7399 54.9020 52.5424 46.7427
#> 49 44.2708 38.7500 47.6042 39.7590 27.8762 49.3961 42.9825 34.0507
#> 50 9.3750 8.9583 13.2292 6.2500 2.8408 16.8108 13.2075 8.0660
#> 51 59.8958 53.7500 62.5000 55.2632 49.7021 61.8852 67.2131 59.4582
#> 52 5.7292 3.7500 7.7083 4.0000 1.2888 8.8873 6.1404 1.5254
#> 53 61.9792 56.0417 67.3958 55.2941 53.9589 66.7045 70.4918 61.4921
#> 54 64.0625 60.1042 68.7500 56.5789 52.3707 68.5611 72.7273 61.6707
#> 55 2.6042 0.7292 5.1042 0.0000 0.0000 1.0000 4.7170 1.2043
#> 56 60.9375 58.0208 65.4167 65.7534 50.1300 77.3176 58.6777 50.7143
#> 57 65.6250 61.0417 70.5208 66.2500 54.8553 77.3176 65.7895 57.0408
#> 58 81.7708 80.3125 84.7917 79.4521 67.6842 80.9412 83.6364 78.7249
#> 59 60.9375 58.0208 65.4167 65.7534 50.1300 77.3176 58.6777 50.7143
#> 60 86.4583 84.0625 89.1667 87.0588 79.4079 90.6510 86.3636 81.2712
#> 61 38.0208 33.4375 41.5625 47.0588 32.9605 53.0281 31.8182 23.8771
#> 62 36.4583 32.2917 40.7292 47.0588 31.8638 52.7871 31.8182 23.5775
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL
#> 67 80.2083 73.2292 83.2292 74.6988 61.7500 83.6351 76.0331 67.4901
#> 68 15.6250 12.5000 18.3333 18.7500 10.8978 35.8676 18.1818 7.6044
#> 69 3.1250 0.7292 5.1042 1.3158 0.0000 7.7518 3.3058 0.0000
#> 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 93.5417 97.8125 95.0000 89.4829 97.1608 98.3051 95.9666
#> 108 98.4375 96.2500 98.9583 96.4706 90.8288 99.7647 99.1525 98.1627
#> 109 98.4375 96.2500 98.9583 96.4706 90.8288 99.7647 99.1525 98.1627
#> 110 96.3542 90.6250 98.3333 96.3855 90.5788 98.4627 97.5410 91.8570
#> 111 72.9167 67.8125 80.5208 78.3133 72.6704 86.3971 71.0526 65.5300
#> 112 99.4792 98.0208 100.0000 100.0000 92.9541 100.0000 100.0000 100.0000
#> 113 5.5833 5.4896 5.7365 5.6053 5.2998 5.7705 5.6518 5.5316
#> 114 95.8333 93.7500 98.8542 96.4706 90.8288 99.7647 98.1132 95.8438
#> 115 1.5625 0.0000 4.1667 0.0000 0.0000 0.0000 1.8868 0.0000
#> 116 1.0417 0.6250 2.9167 3.5294 0.2353 9.1712 0.0000 0.0000
#> 117 58.8542 54.5833 65.5208 55.0000 42.8297 63.1312 63.6364 54.4070
#> 118 10.4167 4.0625 13.8542 13.1579 7.3338 21.0508 7.0175 3.0744
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL
#> 123 12.3802 11.4354 13.5885 10.8875 9.6472 12.4736 11.8182 10.7346
#> 124 50.0000 43.0208 58.5417 41.0959 32.5897 56.2196 44.0678 42.7769
#> 125 21.3542 16.4583 26.3542 13.6986 5.2500 23.7477 19.4915 18.1818
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL
#> 130 46.8750 41.1458 55.1042 36.0000 30.1176 49.5479 50.0000 46.1258
#> 131 76.6667 69.3671 82.5823 70.8333 55.6484 81.4014 76.2712 65.1128
#> 132 15.0000 0.9524 42.0000 11.1111 0.0000 38.0000 7.1429 0.0000
#> 133 40.0000 16.0000 52.3048 40.0000 10.8571 73.6508 45.0000 17.6190
#> 134 5.0000 0.0000 33.0000 0.0000 0.0000 0.0000 13.3333 0.0000
#> 135 10.0000 0.9524 23.0476 23.0769 0.0000 48.8889 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 0.0000 0.0000 9.1005 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 19.0476 2.0000 39.7403 20.0000 0.0000 42.8571 21.4286 0.0000
#> 141 88.0208 80.5208 91.8750 82.5000 78.5480 93.3664 83.8983 80.4959
#> 142 79.8817 77.2955 89.2944 75.0000 68.5935 83.9276 85.5556 79.1871
#> 143 6.8966 2.8918 18.8846 9.5238 0.0000 25.3333 5.2632 0.0000
#> 144 82.7586 54.9288 95.8111 85.7143 63.3333 100.0000 90.9091 63.3333
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 6.2500 0.0000 17.6720 5.0000 0.0000 17.1053 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0000 7.6353 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 7.2967 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 2.6042 0.7292 5.2083 1.2821 0.0000 2.6039 1.7391 0.8836
#> 153 38.0208 30.9375 45.9375 50.6024 36.7333 57.7438 32.2034 23.1802
#> 154 29.6875 24.5833 34.4792 22.8916 18.6980 36.4737 31.3043 23.5971
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL
#> 159 55.7292 44.4792 63.6458 61.5385 46.7895 68.6471 51.7857 49.0973
#> 160 39.5833 27.9167 43.8542 47.4359 35.0658 56.4706 27.9661 23.0032
#> 161 11.4583 9.0625 17.5000 22.5000 9.8111 33.2851 2.6087 1.6732
#> 162 2.0833 0.6250 3.5417 6.8493 1.7331 9.8824 0.8929 0.0000
#> 163 5.2083 1.8750 9.2708 2.7397 0.0000 5.8782 4.9180 1.1997
#> 164 0.0000 0.0000 2.1875 0.0000 0.0000 0.0000 0.0000 0.0000
#> 165 1.0417 0.1042 3.1250 3.6145 1.1912 10.6471 0.0000 0.0000
#> 166 1.5625 0.5208 2.9167 2.6316 1.3406 4.7692 1.8182 0.8312
#> 167 33.3333 28.0208 39.2708 25.8824 17.6842 32.8854 35.4545 30.2666
#> 168 0.0000 0.0000 1.0417 1.3158 0.0000 2.4819 0.0000 0.0000
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL
#> 173 60.9375 55.9375 65.2083 62.6506 43.1077 70.2363 63.4783 52.1106
#> 174 70.3125 63.1250 76.5625 65.8824 47.8051 74.8622 72.1739 65.6348
#> 175 22.9167 17.9167 26.7708 27.5000 22.7487 33.5882 25.4098 17.7224
#> 176 21.8750 17.0833 26.3542 26.3158 22.7487 33.5882 23.7288 15.5795
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL
#> 181 3.6458 2.7083 5.2083 3.9474 0.2564 7.8253 3.6364 1.0826
#> 182 5.7292 3.2292 9.0625 3.7500 0.0000 9.9791 5.7377 0.8843
#> 183 3.1250 0.6250 6.6667 3.5294 1.2996 7.3333 4.1322 0.1754
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL
#> 188 4.0197 1.0584 7.2146 0.6286 0.0224 2.4600 3.5337 0.3472
#> 189 4.0178 1.0557 7.0158 0.6285 0.0224 2.4600 3.4664 0.1183
#> 190 0.0027 0.0001 0.8394 0.0000 0.0000 0.0029 0.1990 0.0037
#> X.10
#> 1
#> 2
#> 3 UCL
#> 4 90.2807
#> 5 16.6898
#> 6 4.9103
#> 7 2.8015
#> 8
#> 9
#> 10
#> 11 UCL
#> 12 72.9115
#> 13 0.0000
#> 14 61.4915
#> 15 32.9194
#> 16 32.6550
#> 17 8.1226
#> 18 0.0000
#> 19 100.0000
#> 20 9.4093
#> 21 26.9853
#> 22 10.1356
#> 23 6.3409
#> 24 74.0027
#> 25 0.0000
#> 26 18.7780
#> 27
#> 28
#> 29
#> 30 UCL
#> 31 2.7831
#> 32 4.9536
#> 33 97.2881
#> 34 65.7393
#> 35 69.5118
#> 36 13.7480
#> 37 4.4753
#> 38 34.3835
#> 39 51.7391
#> 40 10.1610
#> 41 32.0426
#> 42 64.6624
#> 43 99.8305
#> 44
#> 45
#> 46
#> 47 UCL
#> 48 59.1321
#> 49 51.7391
#> 50 20.4785
#> 51 73.5561
#> 52 10.4114
#> 53 74.5582
#> 54 81.1290
#> 55 10.1610
#> 56 68.3527
#> 57 73.1288
#> 58 90.3139
#> 59 68.3527
#> 60 91.2945
#> 61 40.4856
#> 62 39.9311
#> 63
#> 64
#> 65
#> 66 UCL
#> 67 89.0600
#> 68 24.3448
#> 69 4.2373
#> 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 99.8305
#> 111 82.2006
#> 112 100.0000
#> 113 5.7486
#> 114 100.0000
#> 115 4.1562
#> 116 0.0000
#> 117 75.2124
#> 118 14.1094
#> 119
#> 120
#> 121
#> 122 UCL
#> 123 13.3132
#> 124 57.3315
#> 125 30.8354
#> 126
#> 127
#> 128
#> 129 UCL
#> 130 53.5546
#> 131 90.1528
#> 132 23.0769
#> 133 80.0000
#> 134 49.2308
#> 135 0.0000
#> 136 0.0000
#> 137 0.0000
#> 138 36.5079
#> 139 0.0000
#> 140 33.3333
#> 141 90.7702
#> 142 89.6115
#> 143 27.8571
#> 144 100.0000
#> 145 0.0000
#> 146 0.0000
#> 147 0.0000
#> 148 28.2051
#> 149 0.0000
#> 150 7.4038
#> 151 0.0000
#> 152 4.3979
#> 153 41.2484
#> 154 43.1081
#> 155
#> 156
#> 157
#> 158 UCL
#> 159 58.3996
#> 160 41.2546
#> 161 4.6827
#> 162 4.1372
#> 163 9.8207
#> 164 3.1548
#> 165 0.0000
#> 166 3.2911
#> 167 42.1126
#> 168 2.3573
#> 169
#> 170
#> 171
#> 172 UCL
#> 173 67.8661
#> 174 81.2831
#> 175 36.2435
#> 176 34.1602
#> 177
#> 178
#> 179
#> 180 UCL
#> 181 7.9367
#> 182 9.2131
#> 183 5.2565
#> 184
#> 185
#> 186
#> 187 UCL
#> 188 9.7385
#> 189 9.5488
#> 190 0.8612Piped 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:
