| Title: | Generate MUAC and BMI z-Scores and Percentiles for Children and Adolescents |
|---|---|
| Description: | Generates mid upper arm circumference (MUAC) and body mass index (BMI) for age z-scores and percentiles based on LMS method for children and adolescents up to 19 years that can be used to assess nutritional and health status and define risk of adverse health events. |
| Authors: | Lazarus Mramba [aut, cre], James Berkley [aut] |
| Maintainer: | Lazarus Mramba <[email protected]> |
| License: | GPL-3 |
| Version: | 2.1.0 |
| Built: | 2025-02-19 02:42:44 UTC |
| Source: | https://github.com/cran/MUACz |
Generates Body Mass Index (BMI) for age z-scores and percentiles based on LMS method for children and adolescents aged 0 to 19 years.
bmizs( Datafm, age_range = "0-24", digits.zscore = 2, digits.perc = 2, Notes = FALSE )bmizs( Datafm, age_range = "0-24", digits.zscore = 2, digits.perc = 2, Notes = FALSE )
Datafm |
A DataFrame with variables age (in months), sex (1, 2 or "Male", "Female"), bmi (numeric: in kilograms per meter squared (kg/m^2)). |
age_range |
age range in months. Input has to be characters. It allows "0-24" by default. Other inputs allowed are "24-60" or "61-228". |
digits.zscore |
The number of digits for z-score variable |
digits.perc |
The number of digits for percentile variable |
Notes |
Is FALSE by default. If set to TRUE, 'notes' will be printed on the console about the nature, range of variables allowed and number of records processed. |
A DataFrame with BMI z scores for age and percentiles.
<https://www.who.int/childgrowth/standards/bmi_for_age/en/>
<https://www.who.int/growthref/who2007_bmi_for_age/en/>
<https://www.who.int/childgrowth/standards/Technical_report.pdf>
indivmuaczs, muaczs, muacz.bmiz, plotmuac and plotbmi.
## Example 1: for younger age range = "0-24" months ## No need to specify age_range ## creating a hypothetical dataset dat1 <- data.frame(age = c(5, 6, 12, 12, 18, 18, 23, 23), sex = c(1, 2, 1, 2, 1, 2, 1, 2), bmi = c(17.3, 18.6, 18.2, 12.7, 20.8, 20.8, 13.6, 18.4)) ans1 <- bmizs(Datafm = dat1) ans1 <- bmizs(Datafm = dat1, Notes = TRUE) # Will also print notes # ans1 ## Example 2: specify age range = "24-60" months ## creating a hypothetical dataset dat2 <- data.frame(age = c(25, 36, 48, 60), sex = 2, bmi = c(15.7, 16.8, 20.6, 12.7)) ans2 <- bmizs(Datafm = dat2, age_range = "24-60") # ans2 ## Example 3: specify age range = "61-228" months ## creating a hypothetical dataset dat3 <- data.frame(age = c(61, 73, 181, 217), sex = 1, bmi = c(12.1, 14.1, 27.1, 35.4)) ans3 <- bmizs(Datafm = dat3, age_range = "61-228") # ans3## Example 1: for younger age range = "0-24" months ## No need to specify age_range ## creating a hypothetical dataset dat1 <- data.frame(age = c(5, 6, 12, 12, 18, 18, 23, 23), sex = c(1, 2, 1, 2, 1, 2, 1, 2), bmi = c(17.3, 18.6, 18.2, 12.7, 20.8, 20.8, 13.6, 18.4)) ans1 <- bmizs(Datafm = dat1) ans1 <- bmizs(Datafm = dat1, Notes = TRUE) # Will also print notes # ans1 ## Example 2: specify age range = "24-60" months ## creating a hypothetical dataset dat2 <- data.frame(age = c(25, 36, 48, 60), sex = 2, bmi = c(15.7, 16.8, 20.6, 12.7)) ans2 <- bmizs(Datafm = dat2, age_range = "24-60") # ans2 ## Example 3: specify age range = "61-228" months ## creating a hypothetical dataset dat3 <- data.frame(age = c(61, 73, 181, 217), sex = 1, bmi = c(12.1, 14.1, 27.1, 35.4)) ans3 <- bmizs(Datafm = dat3, age_range = "61-228") # ans3
Generates MUAC z-Scores and percentiles for individual subjects by entering their age (in months), sex and muac (in cm) directly. This is useful for children and adolescents aged 3 months to 19 years and to assess their nutritional and health status, and define risk of adverse health events.
indivmuaczs( age = 60, sex = 1, muac = 10, age_range = "3-60", digits.zscore = 2, digits.perc = 2, Notes = FALSE )indivmuaczs( age = 60, sex = 1, muac = 10, age_range = "3-60", digits.zscore = 2, digits.perc = 2, Notes = FALSE )
age |
a numeric value (in months) between 3 and 228 depending on the age_range. |
sex |
preferably numeric (1 = Male, 2 = Female). Strings can also be used. |
muac |
a numeric mid upper arm circumference value in cm. |
age_range |
age range in months. Input has to be characters. Options allowed are: "3-60" the default, or "60-228". |
digits.zscore |
The number of digits for z-score variable |
digits.perc |
The number of digits for percentile variable |
Notes |
Is FALSE by default. If set to TRUE, 'notes' will be printed on the console about the nature, range of variables allowed and number of records processed. |
A DataFrame with MUAC z-scores and percentiles.
Mramba L., Ngari M., Mwangome M., Muchai L., Bauni E., Walker A.S., Gibb D.M., Fegan G. and Berkley J.A. (2017) A growth reference for mid upper arm circumference for age among school age children and adolescents, and validation for mortality: growth curve construction and longitudinal cohort study BMJ 2017;358:j3423 <doi:10.1136/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423/related#datasupp>
<https://www.who.int/childgrowth/standards/Technical_report.pdf>
<https://www.who.int/childgrowth/standards/ac_for_age/en/>
muaczs, bmizs, muacz.bmiz, plotmuac and plotbmi.
test0 <- indivmuaczs(age = 70, sex = "Female", muac = 15.8, age_range = "60-228") # test0 test1 <- indivmuaczs(age = c(4, 40, 60), sex = "Male", muac = c(17.2, 17.2, 19.8), age_range = "3-60") # test1 test2 <- indivmuaczs(age = c(4, 40, 60), sex = 2, muac = c(14.9, 17.7, 19)) # test2 res2 <- indivmuaczs(age = 70, sex = c(1, 1, 2, 2, 2, 1), muac = c(23.1, 15.2, 18.4, 13.9, 19.5, 14.6), age_range = "60-228") # res2test0 <- indivmuaczs(age = 70, sex = "Female", muac = 15.8, age_range = "60-228") # test0 test1 <- indivmuaczs(age = c(4, 40, 60), sex = "Male", muac = c(17.2, 17.2, 19.8), age_range = "3-60") # test1 test2 <- indivmuaczs(age = c(4, 40, 60), sex = 2, muac = c(14.9, 17.7, 19)) # test2 res2 <- indivmuaczs(age = 70, sex = c(1, 1, 2, 2, 2, 1), muac = c(23.1, 15.2, 18.4, 13.9, 19.5, 14.6), age_range = "60-228") # res2
Generates both MUAC and BMI z-scores for age z-scores and percentiles based on LMS method for children and adolescents aged 3 months to 19 years olds.
muacz.bmiz( Datafm, age_range.muac = "60-228", age_range.bmi = "61-228", digits.zscore = 2, digits.perc = 2, Notes = FALSE )muacz.bmiz( Datafm, age_range.muac = "60-228", age_range.bmi = "61-228", digits.zscore = 2, digits.perc = 2, Notes = FALSE )
Datafm |
A DataFrame with variables including, age (in months), sex (1, 2 or "Male", "Female"), muac (numeric: in cm). |
age_range.muac |
MUAC age range in months: This can be "3-60" or "60-228". |
age_range.bmi |
BMI age range in months: This can be "0-24", "24-60" or "61-228". |
digits.zscore |
The number of digits for z-score variable |
digits.perc |
The number of digits for percentile variable |
Notes |
Is FALSE by default. If set to TRUE, 'notes' will be printed on the console about the nature, range of variables allowed and number of records processed. |
A DataFrame with MUAC and BMI z-scores and their percentiles
Mramba L., Ngari M., Mwangome M., Muchai L., Bauni E., Walker A.S., Gibb D.M., Fegan G. and Berkley J.A. (2017) A growth reference for mid upper arm circumference for age among school age children and adolescents, and validation for mortality: growth curve construction and longitudinal cohort study BMJ 2017;358:j3423 <doi:10.1136/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423/related#datasupp>
<https://www.who.int/childgrowth/standards/bmi_for_age/en/>
<https://www.who.int/growthref/who2007_bmi_for_age/en/>
<https://www.who.int/childgrowth/standards/Technical_report.pdf>
<https://www.who.int/childgrowth/standards/ac_for_age/en/>
indivmuaczs, muaczs, bmizs, plotmuac and plotbmi.
## Example 1 ## creating a hypothetical dataset dat1 <- data.frame(age = c(61, 73, 181, 217), sex = 1, muac = c(13.0, 15.7, 34.1, 43.9), bmi = c(12.1, 14.1, 27.1, 35.4)) ans1 <- muacz.bmiz(Datafm = dat1, age_range.muac = "60-228", age_range.bmi = "61-228") # ans1 ## Example 1 ## creating a hypothetical dataset dat2 <- data.frame(age = c(25, 36, 48, 60), sex = 2, muac = c(15, 17, 21.3, 14), bmi = c(15.7, 16.8, 20.6, 12.7)) ans2 <- muacz.bmiz(Datafm = dat2, age_range.muac = "3-60", age_range.bmi = "24-60") # ans2## Example 1 ## creating a hypothetical dataset dat1 <- data.frame(age = c(61, 73, 181, 217), sex = 1, muac = c(13.0, 15.7, 34.1, 43.9), bmi = c(12.1, 14.1, 27.1, 35.4)) ans1 <- muacz.bmiz(Datafm = dat1, age_range.muac = "60-228", age_range.bmi = "61-228") # ans1 ## Example 1 ## creating a hypothetical dataset dat2 <- data.frame(age = c(25, 36, 48, 60), sex = 2, muac = c(15, 17, 21.3, 14), bmi = c(15.7, 16.8, 20.6, 12.7)) ans2 <- muacz.bmiz(Datafm = dat2, age_range.muac = "3-60", age_range.bmi = "24-60") # ans2
Generates mid upper arm circumference (MUAC) for age z-scores and percentiles based on LMS-method for children and adolescents aged 3 months to 19 years.
muaczs( Datafm, age_range = "3-60", digits.zscore = 2, digits.perc = 2, Notes = FALSE )muaczs( Datafm, age_range = "3-60", digits.zscore = 2, digits.perc = 2, Notes = FALSE )
Datafm |
A DataFrame with variables including pid (unique subject identification), age (in months), sex (1, 2 or "Male", "Female"), muac (numeric: in cm). |
age_range |
age range in months. Input has to be characters. Options allowed are: "3-60" which is the default, or "60-228" |
digits.zscore |
The number of digits for z-score variable |
digits.perc |
The number of digits for percentile variable |
Notes |
Is FALSE by default. If set to TRUE, 'notes' will be printed on the console about the nature, range of variables allowed and number of records processed. |
A DataFrame with MUAC z scores for age and percentiles.
Mramba L., Ngari M., Mwangome M., Muchai L., Bauni E., Walker A.S., Gibb D.M., Fegan G. and Berkley J.A. (2017) A growth reference for mid upper arm circumference for age among school age children and adolescents, and validation for mortality: growth curve construction and longitudinal cohort study BMJ 2017;358:j3423 <doi:10.1136/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423/related#datasupp>
<https://www.who.int/childgrowth/standards/Technical_report.pdf>
<https://www.who.int/childgrowth/standards/ac_for_age/en/>
indivmuaczs, bmizs, muacz.bmiz, plotmuac and plotbmi.
## Example 1: younger age range is the default: 3 months - 5 years # Creating a hypothetical dataset dat1 <- data.frame(age = c(3, 5, 12, 18, 23, 24, 36, 48, 60, 24, 36, 48, 60), sex = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2), muac = c(15.6, 14.1, 15.8, 18.7, 12.9, 13, 14.5, 16.1, 21.7, 12.7, 14.4, 16.2, 22.5)) # Run the function ans1 <- muaczs(Datafm = dat1) # save the output # ans1 ## Example 2: For children 5-19 years old, specify their age range "61-228" # Creating a hypothetical dataset dat1 <- data.frame(age = c(60, 65, 90, 120, 220), sex = c("Male","Female","Male","Male","Female"), muac = c(20, 14.3, 15.4, 17.8, 25.1)) ans1 <- muaczs(Datafm = dat1, age_range = "60-228") # ans1## Example 1: younger age range is the default: 3 months - 5 years # Creating a hypothetical dataset dat1 <- data.frame(age = c(3, 5, 12, 18, 23, 24, 36, 48, 60, 24, 36, 48, 60), sex = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2), muac = c(15.6, 14.1, 15.8, 18.7, 12.9, 13, 14.5, 16.1, 21.7, 12.7, 14.4, 16.2, 22.5)) # Run the function ans1 <- muaczs(Datafm = dat1) # save the output # ans1 ## Example 2: For children 5-19 years old, specify their age range "61-228" # Creating a hypothetical dataset dat1 <- data.frame(age = c(60, 65, 90, 120, 220), sex = c("Male","Female","Male","Male","Female"), muac = c(20, 14.3, 15.4, 17.8, 25.1)) ans1 <- muaczs(Datafm = dat1, age_range = "60-228") # ans1
Plots BMI z-scores and percentiles on standardized growth references using the LMS method for children and adolescents aged 0 to 19 years.
plotbmi( age, sex, bmi, lwd = 1, age_range = "0-24", line.color = "skyblue", graphtype = "z-scores", size.label = 2, size.score = 4, line.type = "solid", shape = 2, Notes = FALSE )plotbmi( age, sex, bmi, lwd = 1, age_range = "0-24", line.color = "skyblue", graphtype = "z-scores", size.label = 2, size.score = 4, line.type = "solid", shape = 2, Notes = FALSE )
age |
a numeric value (in months) from 0 to 228 depending on the age_range. |
sex |
preferably numeric (1 = Male, 2 = Female). Strings (such as "Male", "Female") can also be used. |
bmi |
a numeric value in kilograms per meter squared (kg/m^2). |
lwd |
Line width allows you to specify a numeric value that control the width of the line plots. By dafult, it is set to 1. |
age_range |
age range in months. Input has to be characters. It allows "0-24" by default. Other inputs allowed are "24-60" or "61-228". |
line.color |
The color of the lines. It is set to "skyblue" by default. |
graphtype |
A character input is required: "z-scores" or "percentiles". |
size.label |
size of the label for calculated z-score or percentile |
size.score |
refers to the size of the text for the calculated z-scores (-3,-2,-1,0,1,2,3) or the percentiles. |
line.type |
Type of line such as: "solid", "dotted", "dashed", "blank", "dotdash", "twodash", "longdash" |
shape |
of the individual point (marker) |
Notes |
Is FALSE by default. If set to TRUE, 'notes' will be printed on the console about the nature, range of variables allowed and number of records processed. |
Plots z-scores or percentiles with a mark indicating where the individual person lies within the standardized reference curves.
<https://www.who.int/childgrowth/standards/bmi_for_age/en/>
<https://www.who.int/growthref/who2007_bmi_for_age/en/>
<https://www.who.int/childgrowth/standards/Technical_report.pdf>
indivmuaczs, muaczs, bmizs, muacz.bmiz and plotmuac.
g1 <- plotbmi(age = 6, sex = 2, bmi = 18.5) # plots Z-scores g2 <- plotbmi(age = 6, sex = 2, bmi = 18.5, graphtype = "percentiles") g3 <- plotbmi(age = c(25, 36, 48, 60), sex = 2, bmi = c(15.7, 16.8, 20.6, 12.7), age_range = "24-60") g4 <- plotbmi(age = c(61, 73, 181, 217), sex = 1, bmi = c(12.1, 14.1, 27.1, 35.4), age_range = "61-228", graphtype = "percentiles")g1 <- plotbmi(age = 6, sex = 2, bmi = 18.5) # plots Z-scores g2 <- plotbmi(age = 6, sex = 2, bmi = 18.5, graphtype = "percentiles") g3 <- plotbmi(age = c(25, 36, 48, 60), sex = 2, bmi = c(15.7, 16.8, 20.6, 12.7), age_range = "24-60") g4 <- plotbmi(age = c(61, 73, 181, 217), sex = 1, bmi = c(12.1, 14.1, 27.1, 35.4), age_range = "61-228", graphtype = "percentiles")
Plots individual MUAC z-scores and percentiles on standardized growth references using the LMS method for children and adolescents aged 3 months to 19 years.
plotmuac( age, sex, muac, age_range = "3-60", graphtype = "z-scores", lwd = 1, line.color = "skyblue", line.type = "solid", shape = 2, size.label = 2, size.score = 4, Notes = FALSE )plotmuac( age, sex, muac, age_range = "3-60", graphtype = "z-scores", lwd = 1, line.color = "skyblue", line.type = "solid", shape = 2, size.label = 2, size.score = 4, Notes = FALSE )
age |
a numeric value (in months) between 3 and 228 depending on the age_range. |
sex |
preferably numeric (1 = Male, 2 = Female). Strings (such as "Male", "Female") can also be used. |
muac |
a numeric mid upper arm circumference value in cm. |
age_range |
age range in months. Input has to be characters. Options allowed are: "3-60" which is the default, or "60-228". |
graphtype |
A character input is required: "z-scores" or "percentiles". |
lwd |
Line width allows you to specify a numeric value that control the width of the line plots. By dafult, it is set to 1. |
line.color |
The color of the lines. It is set to "skyblue" by default. |
line.type |
Type of line such as: "solid", "dotted", "dashed", "blank", "dotdash", "twodash", "longdash" |
shape |
of the individual point (marker) |
size.label |
size of the label for calculated z-score or percentile |
size.score |
refers to the size of the text for the calculated z-scores (-3,-2,-1,0,1,2,3) or the percentiles. |
Notes |
Is FALSE by default. If set to TRUE, 'notes' will be printed on the console about the nature, range of variables allowed and number of records processed. |
Plots z-scores or percentiles with a mark indicating where the individual person lies within the standardized reference curves.
Mramba L., Ngari M., Mwangome M., Muchai L., Bauni E., Walker A.S., Gibb D.M., Fegan G. and Berkley J.A. (2017) A growth reference for mid upper arm circumference for age among school age children and adolescents, and validation for mortality: growth curve construction and longitudinal cohort study BMJ 2017;358:j3423 <doi:10.1136/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423>
<https://www.bmj.com/content/358/bmj.j3423/related#datasupp>
<https://www.who.int/childgrowth/standards/Technical_report.pdf>
<https://www.who.int/childgrowth/standards/ac_for_age/en/>
indivmuaczs, muaczs, bmizs, muacz.bmiz and plotbmi.
g1 <- plotmuac(age = 48, sex = 2, muac = 16.2, line.color = "orange") g2 <- plotmuac(age = 48, sex = 2, muac = 16.2, graphtype = "percentiles", line.color = "orange") g3 <- plotmuac(age = c(24, 36, 48, 59), sex = 1, muac = c(13, 14.5, 16.1, 21.7)) g4 <- plotmuac(age = c(24, 36, 48, 59), sex = 1, muac = c(13, 14.5, 16.1, 21.7), graphtype = "percentiles") g5 <- plotmuac(age = c(61, 73, 181, 217), sex = 1, muac = c(13, 15.7, 34.1, 43.9), age_range = "60-228") g6 <- plotmuac(age = c(61, 73, 181, 217), sex = 1, muac = c(13, 15.7, 34.1, 43.9), age_range = "60-228", graphtype = "percentiles")g1 <- plotmuac(age = 48, sex = 2, muac = 16.2, line.color = "orange") g2 <- plotmuac(age = 48, sex = 2, muac = 16.2, graphtype = "percentiles", line.color = "orange") g3 <- plotmuac(age = c(24, 36, 48, 59), sex = 1, muac = c(13, 14.5, 16.1, 21.7)) g4 <- plotmuac(age = c(24, 36, 48, 59), sex = 1, muac = c(13, 14.5, 16.1, 21.7), graphtype = "percentiles") g5 <- plotmuac(age = c(61, 73, 181, 217), sex = 1, muac = c(13, 15.7, 34.1, 43.9), age_range = "60-228") g6 <- plotmuac(age = c(61, 73, 181, 217), sex = 1, muac = c(13, 15.7, 34.1, 43.9), age_range = "60-228", graphtype = "percentiles")