The anthro package allows you to perform comprehensive analysis of anthropometric survey data based on the method developed by the Department of Nutrition for Health and Development at the World Health Organization.
The package is modeled after the original R macros provided by WHO. In addition to z-scores, the package adds more accurate calculations of confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs, whenever is the case by using the survey package.
This function calculates z-scores for the eight anthropometric indicators, weight-for- age, length/height-for-age, weight-for-length/height, body mass index (BMI)-for-age, head circumference-for-age, arm circumference-for-age, triceps skinfold-for-age and subscapular skinfold-for-age based on the WHO Child Growth Standards.
anthro_zscores(
sex = c(1, 2, 1, 1),
age = c(1001, 1000, 1010, 1000),
weight = c(18, 15, 10, 15),
lenhei = c(120, 80, 100, 100)
)
#> clenhei cbmi cmeasure csex zlen flen zwei fwei zwfl fwfl zbmi fbmi zhc
#> 1 120 12.5000 <NA> 1 7.31 1 2.20 0 -2.39 0 -3.01 0 NA
#> 2 80 23.4375 <NA> 2 -3.50 0 0.95 0 4.13 0 4.66 0 NA
#> 3 100 10.0000 <NA> 1 1.62 0 -2.76 0 -5.19 1 -5.61 1 NA
#> 4 100 15.0000 <NA> 1 1.70 0 0.69 0 -0.29 0 -0.58 0 NA
#> fhc zac fac zts fts zss fss
#> 1 NA NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA NA
#> 4 NA NA NA NA NA NA NAThe returned value is a data.frame that can further be processed or saved as a .csv file as in the original function.
You can also use the function with a given dataset with with
your_data_set <- read.csv("my_survey.csv")
with(
your_data_set,
anthro_zscores(
sex = sex, age = age_in_days,
weight = weight, lenhei = lenhei
)
)To look at all parameters, type ?anthro_zscores.
The prevalence estimates are similar to anthro_zscores: again they take vectors instead of a data frame and column names for the aforementioned reasons.
anthro_prevalence(
sex = c(1, 2, 2, 1),
age = c(1001, 1000, 1010, 1000),
weight = c(18, 15, 10, 15),
lenhei = c(100, 80, 100, 100)
)[, 1:5]
#> Group HAZ_pop HAZ_unwpop HA_3_r HA_3_se
#> 1 All 4 4 25 25.00000
#> 2 Age group: 00-05 mo 0 0 NA NA
#> 3 Age group: 06-11 mo 0 0 NA NA
#> 4 Age group: 12-23 mo 0 0 NA NA
#> 5 Age group: 24-35 mo 4 4 25 25.00000
#> 6 Age group: 36-47 mo 0 0 NA NA
#> 7 Age group: 48-59 mo 0 0 NA NA
#> 8 Sex: Female 2 2 50 40.82483
#> 9 Sex: Male 2 2 0 0.00000
#> 10 Age + sex: 00-05 mo.Female 0 0 NA NA
#> 11 Age + sex: 06-11 mo.Female 0 0 NA NA
#> 12 Age + sex: 12-23 mo.Female 0 0 NA NA
#> 13 Age + sex: 24-35 mo.Female 2 2 50 40.82483
#> 14 Age + sex: 36-47 mo.Female 0 0 NA NA
#> 15 Age + sex: 48-59 mo.Female 0 0 NA NA
#> 16 Age + sex: 00-05 mo.Male 0 0 NA NA
#> 17 Age + sex: 06-11 mo.Male 0 0 NA NA
#> 18 Age + sex: 12-23 mo.Male 0 0 NA NA
#> 19 Age + sex: 24-35 mo.Male 2 2 0 0.00000
#> 20 Age + sex: 36-47 mo.Male 0 0 NA NA
#> 21 Age + sex: 48-59 mo.Male 0 0 NA NAUsing the function with it is easy to apply anthro_prevalence to a full dataset.
To look at all parameters, type ?anthro_prevalence.