anthro_prevalence {anthro}  R Documentation 
Prevalence estimates according to the WHO recommended standard analysis: includes prevalence estimates with corresponding standard errors and confidence intervals, and zscore summary statistics (mean and standard deviation) with most common cutoffs describing the full index distribution (3, 2, 1, +1, +2, +3), and at disaggregated levels for all available factors (age, sex, type of residence, geographical regions, wealth quintiles, mother education and one additional factor the user is interested in and for which data are available).
anthro_prevalence(
sex,
age = NA_real_,
is_age_in_month = FALSE,
weight = NA_real_,
lenhei = NA_real_,
measure = NA_character_,
oedema = "n",
sw = NULL,
cluster = NULL,
strata = NULL,
typeres = NA_character_,
gregion = NA_character_,
wealthq = NA_character_,
mothered = NA_character_,
othergr = NA_character_
)
sex 
A numeric or text variable containing gender information. If it is numeric, its values must be: 1 for males and 2 for females. If it is character, it must be "m" or "M" for males and "f" or "F" for females. No zscores will be calculated if sex is missing. 
age 
A numeric variable containing age information; age can be in either days or months (if optional argument is_age_in_month is set to TRUE). An exact age in days is expected and should not be rounded if age is in months. Agerelated zscores will NOT be calculated if age is missing (NA). 
is_age_in_month 
A logical flag; if TRUE, variable age unit will be treated as months. The function converts it to days by dividing age by 30.4375 and rounding it to integer so that reference tables can be used. When unspecified, the default value FALSE is used and age unit is treated as days. 
weight 
A numeric variable containing body weight information, which must be in kilograms. Weightrelated zscores are not calculated if body weight is missing. 
lenhei 
A numeric variable containing length (recumbent length) or height (standing height) information, which must be in centimeters. Length/heightrelated zscores will not be calculated if lenhei is missing. For children with age below 24 months (i.e. below 731 days) and standing height measured, the function converts it to recumbent length by adding 0.7 cm; and for children with age equal and above 24 months and measured in recumbent length, the function converts it to standing height by subtracting 0.7 cm. This way all the zscores calculated by this function are lengthbased for children below 24 months, and heightbased otherwise. This converted length/height according to age is assigned to the variable clenhei in the resulting data.frame. 
measure 
A character variable indicating whether recumbent length or standing height was measured for each observation. The values of this variable must be "L" or "l" for recumbent length, and "H" or "h" for standing height. Although it is highly recommended that this variable is provided according to the measurements taken in the survey, it is possible to run the analysis without specifying this variable. If unspecified, the default vector with all missing values is used. The function imputes the missing values according to the following algorithm:

oedema 
The values of this character variable must be "n", "N" or "2"
for nonoedema, and "y", "Y", "1" for oedema. Although it is highly
recommended that this variable is provided by the survey,
it is possible to run
the analysis without specifying this variable. If unspecified,
the default vector of all "n" with values considered as
nonoedema is used. Missing values will be
treated as nonoedema. For oedema, weight related zscores
(zwei, zwfl and zbmi) are NOT calculated (set to missing),
BUT they are treated as being < 3 SD in the weightrelated
indicator prevalence ( 
sw 
An optional numeric vector containing the sampling weights. If NULL, no sampling weights are used. 
cluster 
An optional integer vector representing clusters. If the value is NULL this is treated as a survey without a cluster. This is also the case if all values are equal, then we assume there is also no cluster. 
strata 
An optional integer vector representing strata. Pass NULL to indicate that there are no strata. 
typeres 
An optional integer or character vector representing a type of residence. Any values are accepted, however, “Rural” or “Urban” are preferable for outputs purposes. 
gregion 
An optional integer or character vector representing a geographical region. 
wealthq 
An optional integer or character vector representing wealth quintiles where (1=poorest; 2,3,4,5=richest). All values can either be NA, or 1, 2, 3, 4, 5 or Q1, Q2, Q3, Q4, Q5. 
mothered 
An optional integer or character vector representing the education of the mother. Any number of categories is accepted for the analysis, provided sample sizes are sufficient in all categories. However, the common, standard recommended categories are no education, primary school, and secondary school or higher (“None”, “Primary” and “Secondary”). Note: Mother education refers to the highest level of schooling attained by the mother 
othergr 
An optional integer or character vector that is of interest for stratified analysis. 
In this function, all available (nonmissing and nonflagged) zscore values are used for each indicatorspecific prevalence estimation (standard analysis).
Note: the function temporarily sets the survey
option
survey.lonely.psu
to "adjust" and then restores the original values.
The function is a wrapper around the survey
package to compute
estimates for the different groups (e.g. by age or sex).
If not all parameter values have equal length, parameter values will be
repeated to match the maximum length of all arguments except
is_age_in_month
using rep_len
. This happens without warnings.
Returns a data.frame with prevalence estimates for the various groups.
The output data frame includes prevalence estimates with corresponding standard errors and confidence intervals, and zscore summary statistics (mean and standard deviation) with most common cutoffs describing the full index distribution (3, 2, 1, +1, +2, +3), and at disaggregated levels for all available factors (age, sex, type of residence, geographical regions, wealth quintiles, mother education and one additional factor the user is interested in and for which data are available).
The resulting columns are coded with a prefix, a prevalence indicator and a suffix:
Prefix:
Heightforage
Weightforage
Underweight
Bodymassindexforage
Weightforheight
Heightforage and weightforheight combined
Prevalence indicator:
Prevalence corresponding to < 3 SD
Prevalence corresponding to < 2 SD
Prevalence corresponding to < 1 SD
Prevalence corresponding to > +1 SD
Prevalence corresponding to > +2 SD
Prevalence corresponding to > +3 SD
Suffix:
Weighted sample size
Unweighted sample size
Mean/prevalence
lower 95% confidence interval limit
upper 95% confidence interval limit
Standard Deviation
Standard error
For example:
Weightforheight weighted sample size
Heightforage zscore mean
Weightforage zscore Standard Deviation
Prevalence of weightforheight >+2 SD (overweight )
Mean weightforheight zscore
Prevalence of BMIforage <2 SD standard error
Prevalence of BMIforage <3 SD lower 95% confidence interval limit
Prevalence of children Heightforage and weightforheight combined (stunted & wasted) lower 95% confidence interval limit
## Not run:
# because it takes too long for CRAN checks
library(anthro)
# compute the prevalence estimates for 100 random children
# with weight around 15kg and height around 100cm
res < anthro_prevalence(
sex = c(1, 2),
age = 1000, # days
weight = rnorm(100, 15, 1),
lenhei = rnorm(100, 100, 10)
)
# Heightforage
# We extract prevalence estimates for <3SD, <2SD (Stunting)
# and the zscore mean
col_names < c("Group", "HAZ_unwpop", "HA_3_r", "HA_2_r", "HA_r")
res < res[, col_names]
# rename the columns
colnames(res) < c("Group", "Unweighted N", "3SD", "2SD", "zscore mean ")
# note that we only generated data for one age group
res
## End(Not run)