adjustcarryforward {growthcleanr}

adjustcarryforward adjustcarryforward Uses absolute height velocity to identify values excluded as carried forward values for reinclusion.

Description

adjustcarryforward adjustcarryforward Uses absolute height velocity to identify values excluded as carried forward values for reinclusion.

Usage

adjustcarryforward(
  subjid,
  param,
  agedays,
  sex,
  measurement,
  orig.exclude,
  exclude_opt = 0,
  sd.recenter = NA,
  ewma.exp = -1.5,
  ref.data.path = "",
  quietly = TRUE,
  minfactor = 0.5,
  maxfactor = 2,
  banddiff = 3,
  banddiff_plus = 5.5,
  min_ht.exp_under = 2,
  min_ht.exp_over = 0,
  max_ht.exp_under = 0.33,
  max_ht.exp_over = 1.5
)

Arguments

subjid

Vector of unique identifiers for each subject in the database.

param

Vector identifying each measurement, may be 'WEIGHTKG', 'HEIGHTCM', or 'LENGTHCM' 'HEIGHTCM' vs. 'LENGTHCM' only affects z-score calculations between ages 24 to 35 months (730 to 1095 days). All linear measurements below 731 days of life (age 0-23 months) are interpreted as supine length, and all linear measurements above 1095 days of life (age 36+ months) are interpreted as standing height. Note: at the moment, all LENGTHCM will be converted to HEIGHTCM. In the future, the algorithm will be updated to consider this difference.

agedays

Numeric vector containing the age in days at each measurement.

sex

Vector identifying the gender of the subject, may be 'M', 'm', or 0 for males, vs. 'F', 'f' or 1 for females.

measurement

Numeric vector containing the actual measurement data. Weight must be in kilograms (kg), and linear measurements (height vs. length) in centimeters (cm).

orig.exclude

Vector of exclusion assessment results from cleangrowth()

exclude_opt

Number from 0 to 3 indicating which option to use to handle strings of carried-forwards: 0. no change.

1. when deciding to exclude values, if we have a string of carried forwards, drop the most deviant value, and all CFs in the same string, and move on as normal.

2. when deciding to exclude values, if the most deviant in a string of carried forwards is flagged, check all the CFs in that string from 1:N. Exclude all after the first that is flagged for exclusion when comparing to the Include before and after. Do not remove things designated as include.

3. when deciding to exclude values, if the most deviant in a string of carried forwards is flagged, check all the CFs in that string from 1:N. Exclude all after the first that is flagged for exclusion when comparing to the Include before and after. Make sure remove things designated as include.

sd.recenter

Data frame or table with median SD-scores per day of life

ewma.exp

Exponent to use for weighting measurements in the exponentially weighted moving average calculations. Defaults to -1.5. This exponent should be negative in order to weight growth measurements closer to the measurement being evaluated more strongly. Exponents that are further from zero (e.g. -3) will increase the relative influence of measurements close in time to the measurement being evaluated compared to using the default exponent.

ref.data.path

Path to reference data. If not supplied, the year 2000 Centers for Disease Control (CDC) reference data will be used.

quietly

Determines if function messages are to be displayed and if log files (parallel only) are to be generated. Defaults to TRUE.

minfactor

Sweep variable for computing mindiff.next.ht in 15f, default 0.5

maxfactor

Sweep variable for computing maxdiff.next.ht in 15f, default 2

banddiff

Sweep variable for computing mindiff.next.ht in 15f, default 3

banddiff_plus

Sweep variable for computing maxdiff.next.ht in 15, default 5.5

min_ht.exp_under

Sweep variable for computing ht.exp in 15f, default 2

min_ht.exp_over

Sweep variable for computing ht.exp in 15f, default 0

max_ht.exp_under

Sweep variable for computing ht.exp in 15f, default 0.33

max_ht.exp_over

Sweep variable for computing ht.exp in 15f, default 1.5

Value

Re-evaluated exclusion assessments based on height velocity.

Examples

# Run on a small subset of given data
df <- as.data.frame(syngrowth)
df <- df[df$subjid %in% unique(df[, "subjid"])[1:2], ]
clean_df <- cbind(df,
                  "gcr_result" = cleangrowth(df$subjid,
                                             df$param,
                                             df$agedays,
                                             df$sex,
                                             df$measurement))

# Adjust carry forward values in cleaned data
adj_clean <- adjustcarryforward(subjid = clean_df$subjid,
                                param = clean_df$param,
                                agedays = clean_df$agedays,
                                sex = clean_df$sex,
                                measurement = clean_df$measurement,
                                orig.exclude = clean_df$gcr_result)