dscore {dscore} | R Documentation |
D-score estimation
Description
The function dscore()
function estimates the D-score,
a numeric score that measures child development, from PASS/FAIL
observations on milestones.
Usage
dscore(
data,
items = names(data),
xname = "age",
xunit = c("decimal", "days", "months"),
key = NULL,
itembank = dscore::builtin_itembank,
metric = c("dscore", "logit"),
prior_mean = NULL,
prior_sd = NULL,
transform = NULL,
qp = -10:100,
population = NULL,
dec = c(2L, 3L),
relevance = c(-Inf, Inf)
)
dscore_posterior(
data,
items = names(data),
xname = "age",
xunit = c("decimal", "days", "months"),
key = NULL,
itembank = dscore::builtin_itembank,
metric = c("dscore", "logit"),
prior_mean = NULL,
prior_sd = NULL,
transform = NULL,
qp = -10:100,
population = NULL,
dec = c(2L, 3L),
relevance = c(-Inf, Inf)
)
Arguments
data |
A |
items |
A character vector containing names of items to be
included into the D-score calculation. Milestone scores are coded
numerically as |
xname |
A string with the name of the age variable in
|
xunit |
A string specifying the unit in which age is measured
(either |
key |
A string that selects a subset in the itembank that
makes up the key, the set of difficulty
estimates from a fitted Rasch model.
The built-in keys are: |
itembank |
A |
metric |
A string, either |
prior_mean |
A string specifying where the mean of the
prior for the D-score calculation should come from. It could be
a column name in |
prior_sd |
A string specifying a column name in |
transform |
Vector of length 2, signalling the intercept
and slope respectively of the linear transform that converts an
observation in the logit scale to the the D-score scale. Only
needed if |
qp |
Numeric vector of equally spaced quadrature points.
This vector should span the range of all D-score values. The default
( |
population |
A string describing the population. Currently
supported are |
dec |
A vector of two integers specifying the number of
decimals for rounding the D-score and DAZ, respectively.
The default is |
relevance |
A numeric vector of length with the lower and
upper bounds of the relevance interval. The procedure calculates
a dynamic EAP for each item. If the difficulty level (tau) of the
next item is outside the relevance interval around EAP, the procedure
ignore the score on the item. The default is |
Details
The algorithm is based on the method by Bock and Mislevy (1982). The method uses Bayes rule to update a prior ability into a posterior ability.
The item names should correspond to the "gsed"
lexicon.
A key is defined by the set of estimated item difficulties.
Key | Model | Quadrature | Instruments | Direct/Caregiver | Reference |
"dutch" | 75_0 | -10:80 | 1 | direct | Van Buuren, 2014/2020 |
"gcdg" | 565_18 | -10:100 | 14 | direct | Weber, 2019 |
"gsed1912" | 807_17 | -10:100 | 20 | mixed | GSED Team, 2019 |
"gsed2206" | 818_17 | -10:100 | 22 | mixed | GSED Team, 2022 |
"gsed2208" | 818_6 | -10:100 | 22 | mixed | GSED Team, 2022 |
"gsed2212" | 818_6 | -10:100 | 22 | mixed | GSED Team, 2022 |
"lf2206" | 155_0 | -10:100 | 1 | direct | GSED Team, 2022 |
"sf2206" | 139_0 | -10:100 | 1 | caregiver | GSED Team, 2022 |
As a general rule, one should only compare D-scores
that are calculated using the same key and the same
set of quadrature points. For calculating D-scores on new data,
the advice is to use the default, which currently links to
"gsed2212"
.
The default starting prior is a mean calculated from a so-called
"Count model" that describes mean D-score as a function of age. The
Count models are stored as internal functions
dscore:::count_mu_phase1()
, dscore:::count_mu_gcdg()
and
dscore:::count_mu_dutch()
. The spread of the starting prior
is 5 D-score points around this mean D-score, which corresponds to
approximately 1.5 to 2 times the normal spread of child of a given age. The
starting prior is thus somewhat informative for low numbers of
valid items, and uninformative for large number of items (say >10 items).
Value
The dscore()
function returns a data.frame
with
nrow(data)
rows and the following columns:
Name | Label |
a | Decimal age |
n | Number of items with valid (0/1) data |
p | Percentage of passed milestones |
d | Ability estimate, mean of posterior |
sem | Standard error of measurement, standard deviation of the posterior |
daz | D-score corrected for age, calculated in Z-scale |
The dscore_posterior()
function returns a numeric matrix with
nrow(data)
rows and length(qp)
columns with the
density at each quadrature point. The vector represents the full
posterior ability distribution. If no valid responses were obtained,
dscore_posterior()
returns the prior.
Author(s)
Stef van Buuren, Iris Eekhout, Arjan Huizing (2022)
References
Bock DD, Mislevy RJ (1982). Adaptive EAP Estimation of Ability in a Microcomputer Environment. Applied Psychological Measurement, 6(4), 431-444.
Van Buuren S (2014). Growth charts of human development. Stat Methods Med Res, 23(4), 346-368. https://stefvanbuuren.name/publication/van-buuren-2014-gc/
Weber AM, Rubio-Codina M, Walker SP, van Buuren S, Eekhout I, Grantham-McGregor S, Caridad Araujo M, Chang SM, Fernald LCH, Hamadani JD, Hanlon A, Karam SM, Lozoff B, Ratsifandrihamanana L, Richter L, Black MM (2019). The D-score: a metric for interpreting the early development of infants and toddlers across global settings. BMJ Global Health, BMJ Global Health 4: e001724. https://gh.bmj.com/content/bmjgh/4/6/e001724.full.pdf
See Also
get_tau()
,
builtin_itembank()
, posterior()
,
builtin_references()
Examples
data <- data.frame(
age = rep(round(21 / 365.25, 4), 10),
ddifmd001 = c(NA, NA, 0, 0, 0, 1, 0, 1, 1, 1),
ddicmm029 = c(NA, NA, NA, 0, 1, 0, 1, 0, 1, 1),
ddigmd053 = c(NA, 0, 0, 1, 0, 0, 1, 1, 0, 1)
)
items <- names(data)[2:4]
# third item is not part of default key
get_tau(items)
# calculate D-score
dscore(data)
# calculate full posterior
p <- dscore_posterior(data)
# plot posterior for row 7
plot(x = -10:100, y = p[7, ], type = "l", xlab = "D-score",
ylab = "Density", xlim = c(0, 30))