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 0.854 0.809 0.9 0.84 0.798 0.904 0.878
#> 2 resp2 0.0885 0.0531 0.134 0.1 0.0675 0.200 0.0957
#> 3 resp3 0.0417 0.0167 0.0708 0.0256 0 0.0871 0.0196
#> 4 resp4 0.00521 0 0.025 0 0 0.0246 0
#> 5 age 70.9 68.5 72.9 72.1 70.2 73.8 69.9
#> 6 ageGrp1 0 0 0 0 0 0 0
#> 7 ageGrp2 0.547 0.401 0.624 0.466 0.385 0.548 0.546
#> 8 ageGrp3 0.234 0.179 0.264 0.229 0.155 0.347 0.214
#> 9 ageGrp4 0.188 0.107 0.314 0.247 0.149 0.327 0.21
#> 10 ageGrp5 0.0312 0.0208 0.0844 0.0533 0.0188 0.104 0.0336
#> # 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 0.0409 1.75e-2 0.0581 1.12e-2 2.02e- 3 2.43e-2 0.0216
#> 2 MAM 0.0406 1.75e-2 0.0577 1.12e-2 2.02e- 3 2.43e-2 0.0199
#> 3 SAM 0.0000172 2.18e-9 0.00210 7.55e-7 5.42e-11 2.61e-6 0.000320
#> # 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… 8.54e-1 0.809 0.9 0.84 0.798
#> 2 resp2 Survey Resp… Prop… 8.85e-2 0.0531 0.134 0.1 0.0675
#> 3 resp3 Survey Resp… Prop… 4.17e-2 0.0167 0.0708 0.0256 0
#> 4 resp4 Survey Resp… Prop… 5.21e-3 0 0.025 0 0
#> 5 age Demography… Mean… Mean 7.09e+1 68.5 72.9 72.1 70.2
#> 6 ageGrp1 Demography… Self… Prop… 0 0 0 0 0
#> 7 ageGrp2 Demography… Self… Prop… 5.47e-1 0.401 0.624 0.466 0.385
#> 8 ageGrp3 Demography… Self… Prop… 2.34e-1 0.179 0.264 0.229 0.155
#> 9 ageGrp4 Demography… Self… Prop… 1.88e-1 0.107 0.314 0.247 0.149
#> 10 ageGrp5 Demography… Self… Prop… 3.12e-2 0.0208 0.0844 0.0533 0.0188
#> # 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 0.8542 0.8094 0.9000 0.8400 0.7977 0.9035 0.8783 0.7805 0.8927
#> 5 0.0885 0.0531 0.1344 0.1000 0.0675 0.2004 0.0957 0.0819 0.1806
#> 6 0.0417 0.0167 0.0708 0.0256 0.0000 0.0871 0.0196 0.0018 0.0763
#> 7 0.0052 0.0000 0.0250 0.0000 0.0000 0.0246 0.0000 0.0000 0.0160
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL UCL
#> 12 70.9010 68.5115 72.9260 72.1081 70.2018 73.7538 69.8571 68.8107 72.5464
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 0.5469 0.4010 0.6240 0.4658 0.3848 0.5485 0.5462 0.4689 0.6437
#> 15 0.2344 0.1792 0.2635 0.2286 0.1551 0.3467 0.2143 0.1533 0.2928
#> 16 0.1875 0.1073 0.3135 0.2468 0.1494 0.3271 0.2100 0.1219 0.3152
#> 17 0.0312 0.0208 0.0844 0.0533 0.0188 0.1039 0.0336 0.0035 0.0421
#> 18 0.4115 0.3417 0.4813 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000
#> 19 0.5885 0.5187 0.6583 0.0000 0.0000 0.0000 1.0000 1.0000 1.0000
#> 20 0.0208 0.0062 0.0500 0.0128 0.0000 0.0382 0.0476 0.0020 0.0884
#> 21 0.3438 0.2875 0.3792 0.5641 0.3884 0.6566 0.1373 0.0684 0.2223
#> 22 0.1146 0.0990 0.1604 0.1757 0.1201 0.2505 0.0756 0.0409 0.1243
#> 23 0.0469 0.0302 0.0719 0.0641 0.0165 0.1439 0.0536 0.0281 0.0947
#> 24 0.4479 0.4240 0.5396 0.1918 0.0819 0.3851 0.6786 0.6142 0.7468
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 0.1510 0.1073 0.1833 0.1667 0.0926 0.2928 0.0982 0.0619 0.1556
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL UCL
#> 31 2.5677 2.4490 2.5938 2.4444 2.2523 2.5362 2.5700 2.4508 2.8256
#> 32 4.4323 4.3604 4.6583 4.3506 3.9319 4.6506 4.6545 4.4376 4.8990
#> 33 0.9115 0.8740 0.9260 0.8904 0.7575 0.9866 0.9127 0.8710 0.9582
#> 34 0.5104 0.4771 0.5698 0.4429 0.3083 0.4935 0.5635 0.4919 0.6483
#> 35 0.5938 0.5375 0.6323 0.5714 0.4502 0.6436 0.5952 0.5216 0.7196
#> 36 0.0469 0.0219 0.0760 0.0405 0.0257 0.0769 0.0696 0.0434 0.1089
#> 37 0.0260 0.0115 0.0458 0.0260 0.0027 0.0855 0.0159 0.0000 0.0552
#> 38 0.3333 0.2792 0.4000 0.4267 0.3045 0.5846 0.2609 0.2504 0.3419
#> 39 0.3906 0.3500 0.4448 0.4306 0.2406 0.4715 0.4182 0.3777 0.4795
#> 40 0.0260 0.0073 0.0490 0.0000 0.0000 0.0248 0.0490 0.0114 0.0799
#> 41 0.2031 0.1313 0.2448 0.1644 0.1078 0.2332 0.2261 0.1446 0.2753
#> 42 0.4896 0.4229 0.5198 0.4286 0.2809 0.4998 0.5588 0.4511 0.6221
#> 43 0.9635 0.9531 0.9792 0.9726 0.9499 0.9974 0.9748 0.9289 0.9983
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL UCL
#> 48 0.4479 0.3917 0.4948 0.4722 0.3263 0.5301 0.4870 0.4485 0.5470
#> 49 0.3906 0.3500 0.4448 0.4306 0.2406 0.4715 0.4182 0.3777 0.4795
#> 50 0.0990 0.0792 0.1396 0.0811 0.0423 0.1352 0.1304 0.0868 0.2062
#> 51 0.5885 0.5333 0.6615 0.5769 0.4393 0.6308 0.6270 0.5882 0.7731
#> 52 0.0573 0.0333 0.0667 0.0260 0.0134 0.0947 0.0490 0.0274 0.1352
#> 53 0.6198 0.5500 0.6823 0.5974 0.4421 0.6640 0.6746 0.5924 0.7837
#> 54 0.6615 0.6042 0.7177 0.6438 0.5443 0.8149 0.7000 0.6329 0.7994
#> 55 0.0260 0.0073 0.0490 0.0000 0.0000 0.0248 0.0490 0.0114 0.0799
#> 56 0.5885 0.5302 0.6688 0.6286 0.5537 0.7773 0.5882 0.5176 0.6563
#> 57 0.6146 0.5656 0.7073 0.6400 0.6053 0.7801 0.6471 0.5819 0.6996
#> 58 0.7917 0.7615 0.8625 0.8000 0.7052 0.8699 0.8636 0.7861 0.8887
#> 59 0.5885 0.5302 0.6688 0.6286 0.5537 0.7773 0.5882 0.5176 0.6563
#> 60 0.8490 0.8208 0.9083 0.8846 0.8287 0.9451 0.8655 0.7901 0.9181
#> 61 0.3802 0.3396 0.4385 0.4658 0.3430 0.6094 0.3400 0.2756 0.4095
#> 62 0.3542 0.3354 0.4292 0.4400 0.3430 0.6094 0.3304 0.2678 0.4003
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL UCL
#> 67 0.7500 0.7167 0.8479 0.8082 0.6359 0.8440 0.8000 0.7413 0.8389
#> 68 0.1719 0.1354 0.2323 0.1507 0.0937 0.3036 0.1478 0.0780 0.2114
#> 69 0.0156 0.0021 0.0542 0.0411 0.0290 0.0949 0.0174 0.0000 0.0598
#> 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 0.9740 0.9594 0.9938 0.9333 0.8990 0.9740 0.9841 0.9345 1.0000
#> 108 0.9948 0.9896 0.9990 0.9583 0.9317 0.9743 1.0000 0.9911 1.0000
#> 109 0.9948 0.9896 0.9990 0.9583 0.9317 0.9743 1.0000 0.9911 1.0000
#> 110 0.9635 0.9583 0.9927 0.9359 0.9114 0.9740 0.9664 0.9336 0.9911
#> 111 0.7292 0.6792 0.8198 0.7808 0.6775 0.8498 0.7054 0.6276 0.7752
#> 112 1.0000 0.9948 1.0000 0.9733 0.9635 0.9863 1.0000 1.0000 1.0000
#> 113 5.6510 5.6167 5.7479 5.5200 5.3783 5.6503 5.6569 5.5004 5.7591
#> 114 0.9896 0.9635 0.9948 0.9583 0.9317 0.9743 0.9800 0.9504 1.0000
#> 115 0.0052 0.0000 0.0313 0.0000 0.0000 0.0000 0.0200 0.0000 0.0496
#> 116 0.0052 0.0010 0.0104 0.0417 0.0257 0.0683 0.0000 0.0000 0.0000
#> 117 0.5729 0.5312 0.6333 0.4865 0.3978 0.6620 0.6273 0.5112 0.6986
#> 118 0.1094 0.0792 0.1552 0.1558 0.1051 0.2515 0.0909 0.0555 0.1643
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL UCL
#> 123 11.8333 11.2385 12.5500 11.8133 9.4803 13.0517 12.8000 11.7890 13.2000
#> 124 0.4844 0.4344 0.5469 0.5000 0.3552 0.6004 0.5179 0.4555 0.5448
#> 125 0.2083 0.1687 0.2563 0.1507 0.1040 0.2899 0.2000 0.1411 0.2253
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL UCL
#> 130 0.4115 0.3333 0.4823 0.4324 0.2782 0.4759 0.4902 0.4160 0.6060
#> 131 0.7711 0.6726 0.8670 0.6667 0.4935 0.7531 0.7959 0.7324 0.8782
#> 132 0.1818 0.0095 0.3737 0.0833 0.0000 0.4857 0.0000 0.0000 0.1600
#> 133 0.4167 0.0917 0.6234 0.4000 0.1333 0.4444 0.4167 0.2349 0.8711
#> 134 0.1364 0.0182 0.2667 0.0000 0.0000 0.0000 0.2000 0.0867 0.6500
#> 135 0.1538 0.0000 0.2048 0.3000 0.1139 0.4889 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 0.0476 0.0000 0.0894 0.0000 0.0000 0.0000 0.0000 0.0000 0.1933
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 0.1250 0.0000 0.2636 0.1818 0.0167 0.6000 0.1000 0.0000 0.3395
#> 141 0.8646 0.7750 0.9385 0.8784 0.7690 0.9193 0.8922 0.8339 0.9205
#> 142 0.7952 0.7489 0.9032 0.7812 0.6667 0.8328 0.8696 0.7729 0.9403
#> 143 0.0870 0.0309 0.1670 0.0000 0.0000 0.2655 0.0000 0.0000 0.1453
#> 144 0.8387 0.7129 0.9644 0.6667 0.5397 0.9857 0.8636 0.8067 1.0000
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0294 0.0000 0.0954 0.1739 0.0143 0.3576 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 0.0294 0.0000 0.1287 0.0000 0.0000 0.0000 0.0000 0.0000 0.1933
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0727
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 0.0208 0.0062 0.0354 0.0130 0.0000 0.0498 0.0174 0.0000 0.0464
#> 153 0.4115 0.2646 0.4792 0.4805 0.4269 0.6112 0.3482 0.2317 0.4297
#> 154 0.2917 0.2406 0.3531 0.2821 0.2062 0.4231 0.3217 0.2166 0.3885
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL UCL
#> 159 0.5938 0.5219 0.6135 0.6286 0.5578 0.6703 0.4957 0.3934 0.6312
#> 160 0.4115 0.3198 0.4667 0.4545 0.3649 0.5362 0.2870 0.2025 0.5277
#> 161 0.1094 0.0698 0.1719 0.1944 0.1128 0.3151 0.0397 0.0052 0.0789
#> 162 0.0312 0.0031 0.0552 0.0519 0.0136 0.0850 0.0079 0.0000 0.0153
#> 163 0.0521 0.0271 0.1031 0.0128 0.0000 0.0256 0.0588 0.0348 0.0856
#> 164 0.0000 0.0000 0.0146 0.0000 0.0000 0.0000 0.0087 0.0000 0.0469
#> 165 0.0156 0.0021 0.0208 0.0400 0.0026 0.0776 0.0000 0.0000 0.0000
#> 166 0.0156 0.0052 0.0365 0.0133 0.0000 0.0515 0.0000 0.0000 0.0360
#> 167 0.3021 0.2510 0.3729 0.2714 0.2334 0.3565 0.3393 0.3040 0.3712
#> 168 0.0052 0.0000 0.0156 0.0256 0.0000 0.0424 0.0000 0.0000 0.0245
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL UCL
#> 173 0.5990 0.5354 0.6771 0.5769 0.4509 0.6947 0.6607 0.5744 0.7251
#> 174 0.7031 0.6115 0.7417 0.6622 0.4845 0.7189 0.7451 0.6317 0.8131
#> 175 0.2604 0.1813 0.2896 0.2800 0.1799 0.3385 0.2522 0.1589 0.3416
#> 176 0.2604 0.1792 0.2792 0.2800 0.1799 0.3385 0.2522 0.1307 0.3295
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL UCL
#> 181 0.0365 0.0104 0.0833 0.0400 0.0164 0.0634 0.0357 0.0187 0.0497
#> 182 0.0521 0.0198 0.0760 0.0267 0.0000 0.1178 0.0455 0.0020 0.1092
#> 183 0.0312 0.0115 0.0667 0.0256 0.0000 0.0983 0.0273 0.0020 0.0612
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL UCL
#> 188 0.0409 0.0175 0.0581 0.0112 0.0020 0.0243 0.0216 0.0115 0.0707
#> 189 0.0406 0.0175 0.0577 0.0112 0.0020 0.0243 0.0199 0.0084 0.0700
#> 190 0.0000 0.0000 0.0021 0.0000 0.0000 0.0000 0.0003 0.0000 0.0051The 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 84.8958 81.2500 90.0000 80.2632 75.1282 88.7640 84.8739 79.7118
#> 5 10.4167 5.0000 13.2292 10.4651 7.0179 15.4887 10.7143 6.8991
#> 6 3.6458 1.6667 5.1042 5.4795 1.3369 11.1585 4.2017 0.0000
#> 7 1.0417 0.0000 2.5000 1.3333 0.0000 4.9245 0.0000 0.0000
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL
#> 12 71.3281 69.7958 72.5844 71.5814 69.5815 72.9915 69.5902 68.2916
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 47.9167 45.1042 60.7292 48.6842 38.1353 53.2691 56.7797 53.0672
#> 15 25.5208 20.0000 28.7500 26.7442 20.6899 32.9524 19.2661 15.1917
#> 16 18.2292 14.6875 26.3542 21.9178 9.5179 28.3835 16.3934 11.8893
#> 17 4.6875 2.2917 10.6250 5.2632 0.4651 7.8563 0.9174 0.0000
#> 18 40.1042 34.1667 42.1875 100.0000 100.0000 100.0000 0.0000 0.0000
#> 19 59.8958 57.8125 65.8333 0.0000 0.0000 0.0000 100.0000 100.0000
#> 20 2.6042 0.7292 5.1042 2.4096 0.0000 5.7471 3.5714 2.1236
#> 21 28.1250 24.1667 40.7292 52.3256 43.3198 65.5761 16.8067 8.0467
#> 22 11.4583 5.6250 15.1042 19.7368 14.1358 21.2772 6.0870 4.2827
#> 23 6.7708 3.5417 8.8542 9.2105 5.2916 16.0654 7.2727 4.4328
#> 24 50.5208 36.1458 57.3958 18.9189 7.1909 24.7233 66.1017 60.1739
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 12.5000 9.6875 16.6667 16.6667 8.1825 22.9474 7.8947 4.9208
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL
#> 31 2.5625 2.4344 2.7573 2.5698 2.2803 2.6773 2.6066 2.5036
#> 32 4.5938 4.2469 4.8010 4.3837 4.1502 4.5866 4.7411 4.6048
#> 33 93.2292 90.2083 95.1042 90.6667 83.2720 96.7859 92.4370 88.3159
#> 34 52.6042 46.2500 57.6042 47.3684 33.9459 59.6765 59.0164 55.4561
#> 35 58.3333 47.7083 66.5625 52.6316 45.4683 61.1977 62.1849 57.3506
#> 36 6.7708 3.3333 9.5833 3.4884 1.3668 7.1177 8.0357 3.9175
#> 37 3.6458 1.0417 5.1042 3.8462 0.2632 8.0620 1.7391 0.1681
#> 38 31.7708 25.8333 39.7917 42.1687 28.1053 46.9645 27.1930 21.3070
#> 39 40.1042 36.3542 47.5000 34.8837 19.7083 47.1671 45.7627 32.8533
#> 40 2.6042 2.0833 3.1250 0.0000 0.0000 5.1243 4.3478 1.2191
#> 41 21.3542 16.3542 27.7083 23.2877 17.4175 27.9129 22.0339 14.0212
#> 42 49.4792 38.4375 54.1667 40.6977 33.5160 54.5714 51.7544 45.5997
#> 43 96.8750 93.3333 98.3333 98.6301 93.5690 100.0000 97.4576 95.6089
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL
#> 48 48.4375 41.6667 55.1042 40.5405 29.3342 53.4702 52.1739 44.9638
#> 49 40.1042 36.3542 47.5000 34.8837 19.7083 47.1671 45.7627 32.8533
#> 50 13.5417 7.5000 16.3542 10.0000 4.1065 11.3994 14.4068 9.4039
#> 51 61.4583 50.9375 70.9375 53.4884 46.5718 60.0140 67.2269 60.6674
#> 52 6.7708 4.1667 7.1875 6.0241 0.5263 9.2819 6.0870 4.2871
#> 53 63.0208 53.2292 75.6250 57.8313 49.6450 63.9927 70.3390 62.8048
#> 54 66.6667 59.0625 73.2292 59.4595 53.5338 69.7188 71.8182 67.3419
#> 55 2.6042 2.0833 3.1250 0.0000 0.0000 5.1243 4.3478 1.2191
#> 56 62.5000 53.6458 66.9792 66.2651 56.1024 69.1653 57.3913 47.4115
#> 57 65.1042 59.1667 70.7292 66.6667 58.9474 69.7571 66.9643 57.9929
#> 58 82.2917 78.1250 85.3125 77.6316 71.6563 80.1860 88.2353 79.3693
#> 59 62.5000 53.6458 66.9792 66.2651 56.1024 69.1653 57.3913 47.4115
#> 60 86.9792 84.1667 87.9167 86.0465 78.9860 88.9472 89.9160 81.9282
#> 61 38.0208 31.7708 46.5625 46.5753 32.4060 53.1974 33.9130 28.0839
#> 62 38.0208 30.9375 43.5417 45.3333 32.4060 52.9564 31.3559 28.0688
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL
#> 67 73.9583 68.9583 83.0208 76.3158 67.8221 83.5985 82.3529 73.7813
#> 68 18.2292 12.0833 25.4167 21.0526 9.8533 30.9577 13.1148 7.9148
#> 69 2.6042 0.2083 6.1458 2.3256 0.2410 6.4060 2.5210 0.8541
#> 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 97.9167 95.2083 98.9583 94.6667 92.1457 98.6771 98.2456 95.5151
#> 108 98.9583 97.0833 100.0000 96.5116 93.7289 99.7368 100.0000 100.0000
#> 109 98.9583 97.0833 100.0000 96.5116 93.7289 99.7368 100.0000 100.0000
#> 110 97.9167 95.2083 98.8542 94.6667 92.4022 98.2497 95.5357 92.6438
#> 111 73.9583 69.5833 79.8958 73.9726 66.4154 85.1779 74.7899 66.5860
#> 112 100.0000 98.9583 100.0000 98.7179 95.2180 100.0000 100.0000 100.0000
#> 113 5.6979 5.5698 5.7396 5.6164 5.4504 5.6838 5.6639 5.6087
#> 114 98.4375 96.9792 98.9583 96.5116 93.7289 99.7368 98.3193 95.1481
#> 115 1.0417 0.0000 1.9792 0.0000 0.0000 0.0000 1.6807 0.0000
#> 116 0.5208 0.0000 1.9792 3.4884 0.2632 6.2711 0.0000 0.0000
#> 117 58.8542 51.5625 68.6458 51.4286 43.5420 62.4592 61.8182 52.5962
#> 118 9.8958 7.1875 13.9583 15.7895 8.3546 24.5946 7.8261 5.8629
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL
#> 123 11.9635 10.9740 12.9427 11.0822 10.2787 14.0088 12.5818 10.7356
#> 124 45.8333 42.5000 53.8542 42.1687 33.7900 67.3514 47.8992 41.4039
#> 125 21.8750 15.1042 27.5000 15.7143 9.9266 22.5579 21.8487 16.0458
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL
#> 130 44.2708 41.7708 54.5833 40.6977 33.1651 56.2406 49.1228 46.4663
#> 131 73.2558 69.8480 85.9340 61.7647 49.2571 84.8387 82.1429 73.0645
#> 132 14.2857 0.8696 29.7667 10.0000 0.0000 49.0769 14.2857 0.0000
#> 133 43.4783 24.2000 69.1014 40.0000 4.0000 80.1923 53.8462 23.5000
#> 134 8.6957 0.0000 30.8261 0.0000 0.0000 0.0000 14.2857 0.0000
#> 135 12.0000 1.3333 17.4510 20.0000 1.5385 51.1538 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 4.3478 0.0000 21.6000 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 12.0000 0.0000 50.9317 20.0000 1.2500 55.6923 7.6923 0.0000
#> 141 84.8958 80.8333 90.8333 85.3333 75.7752 90.8173 88.2353 82.5486
#> 142 81.1688 76.2975 85.3953 76.5625 71.6366 88.3185 85.7143 81.9048
#> 143 9.0909 3.2278 18.5641 0.0000 0.0000 27.1429 10.0000 0.0000
#> 144 80.6452 76.0230 93.7557 80.0000 65.7143 100.0000 83.3333 69.0000
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0000 0.0000 6.7294 0.0000 0.0000 25.8182 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 4.5455 0.0000 9.1212 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 6.8205 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 0.6250 5.3125 0.0000 0.0000 3.9765 2.7273 0.0000
#> 153 40.6250 31.3542 46.5625 48.6486 40.7356 55.0359 32.1429 20.6916
#> 154 30.2083 23.9583 41.8750 29.0698 22.4731 35.9233 32.7731 21.2605
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL
#> 159 53.6458 48.9583 63.1250 61.8421 57.3147 68.2156 50.4202 43.7681
#> 160 37.5000 24.7917 43.6458 45.7831 43.2186 51.0256 32.4561 28.4170
#> 161 9.8958 7.9167 13.6458 20.4819 10.8629 34.8864 5.0420 0.0000
#> 162 1.5625 1.0417 5.0000 5.1282 1.3193 10.1078 0.0000 0.0000
#> 163 6.2500 2.7083 10.5208 2.6316 0.0000 5.9621 8.2569 2.4749
#> 164 0.0000 0.0000 1.4583 0.0000 0.0000 0.0000 0.9174 0.0000
#> 165 0.5208 0.0000 4.2708 2.3256 0.2632 6.7253 0.0000 0.0000
#> 166 2.0833 1.1458 5.1042 2.6316 0.0000 5.2923 1.6949 0.1681
#> 167 31.7708 24.1667 36.6667 27.7108 19.5368 40.7714 33.0275 28.1429
#> 168 1.0417 0.0000 2.9167 2.3256 0.0000 5.6615 0.0000 0.0000
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL
#> 173 60.4167 58.0208 63.9583 61.6279 52.5589 82.1353 61.6071 51.6892
#> 174 70.8333 65.3125 78.4375 65.7895 60.1622 82.9925 74.7899 61.8241
#> 175 25.5208 21.7708 32.8125 32.0513 16.2653 39.0072 23.6842 16.7075
#> 176 25.5208 21.5625 32.8125 32.0513 16.0022 39.0072 22.8070 14.6095
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL
#> 181 3.1250 1.7708 6.0417 2.7397 0.2410 5.2632 4.2017 1.9120
#> 182 6.2500 2.1875 9.6875 2.6316 0.4651 5.4646 2.6316 0.8519
#> 183 2.6042 0.7292 6.6667 1.3699 0.2632 5.6768 0.9174 0.1695
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL
#> 188 2.9311 1.1723 5.0757 1.0689 0.0443 2.1145 4.7824 2.5532
#> 189 2.9300 1.0343 5.0528 1.0689 0.0338 2.1132 4.2341 2.5371
#> 190 0.0088 0.0002 0.1957 0.0000 0.0000 0.1039 0.0680 0.0000
#> X.10
#> 1
#> 2
#> 3 UCL
#> 4 89.3434
#> 5 16.9442
#> 6 9.1686
#> 7 1.7314
#> 8
#> 9
#> 10
#> 11 UCL
#> 12 70.8413
#> 13 0.0000
#> 14 71.0680
#> 15 26.5974
#> 16 27.0970
#> 17 5.0580
#> 18 0.0000
#> 19 100.0000
#> 20 8.4901
#> 21 19.0543
#> 22 9.1576
#> 23 10.2053
#> 24 75.0351
#> 25 0.0000
#> 26 15.2731
#> 27
#> 28
#> 29
#> 30 UCL
#> 31 2.8892
#> 32 4.8547
#> 33 96.6498
#> 34 65.7208
#> 35 70.8630
#> 36 10.0902
#> 37 4.5136
#> 38 32.8413
#> 39 57.1744
#> 40 7.7130
#> 41 30.0420
#> 42 62.4384
#> 43 99.1538
#> 44
#> 45
#> 46
#> 47 UCL
#> 48 61.4327
#> 49 57.1744
#> 50 18.4862
#> 51 76.4204
#> 52 10.0840
#> 53 77.4050
#> 54 76.1474
#> 55 7.7130
#> 56 74.7899
#> 57 75.8450
#> 58 92.6166
#> 59 74.7899
#> 60 92.6166
#> 61 38.3506
#> 62 37.2121
#> 63
#> 64
#> 65
#> 66 UCL
#> 67 84.6773
#> 68 21.7002
#> 69 3.5589
#> 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 99.6610
#> 111 76.9121
#> 112 100.0000
#> 113 5.7471
#> 114 100.0000
#> 115 4.8519
#> 116 0.0000
#> 117 70.1863
#> 118 10.4394
#> 119
#> 120
#> 121
#> 122 UCL
#> 123 13.2754
#> 124 56.7857
#> 125 26.6917
#> 126
#> 127
#> 128
#> 129 UCL
#> 130 53.8577
#> 131 89.1058
#> 132 37.8824
#> 133 72.9412
#> 134 62.1538
#> 135 0.0000
#> 136 0.0000
#> 137 0.0000
#> 138 28.2051
#> 139 0.0000
#> 140 25.0000
#> 141 95.4838
#> 142 90.8583
#> 143 26.0526
#> 144 100.0000
#> 145 0.0000
#> 146 0.0000
#> 147 0.0000
#> 148 19.7500
#> 149 0.0000
#> 150 5.0000
#> 151 0.0000
#> 152 7.5332
#> 153 39.8182
#> 154 35.5238
#> 155
#> 156
#> 157
#> 158 UCL
#> 159 59.0426
#> 160 41.3865
#> 161 10.8317
#> 162 3.9384
#> 163 11.4412
#> 164 4.5136
#> 165 0.0000
#> 166 3.7250
#> 167 36.3657
#> 168 0.8757
#> 169
#> 170
#> 171
#> 172 UCL
#> 173 66.0393
#> 174 83.3005
#> 175 33.2016
#> 176 33.2016
#> 177
#> 178
#> 179
#> 180 UCL
#> 181 9.9627
#> 182 7.3004
#> 183 4.7479
#> 184
#> 185
#> 186
#> 187 UCL
#> 188 5.3604
#> 189 5.2808
#> 190 0.6302Piped 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:
