Step1Measures computes up to 18 measures for each
longitudinal trajectory. See Details for the list of measures.
Step1Measures(
Data,
Time = NULL,
ID = FALSE,
measures = c(1:17),
midpoint = NULL,
cap.outliers = FALSE
)# S3 method for trajMeasures
print(x, ...)
# S3 method for trajMeasures
summary(object, ...)
An object of class trajMeasures; a list containing the values
of the measures, a table of the outliers which have been capped, as well as
a curated form of the function's arguments.
a matrix or data frame in which each row contains the longitudinal data (trajectories).
either NULL, a vector or a matrix/data frame of the same dimension
as Data. If a vector, matrix or data frame is supplied, its entries
are assumed to be measured at the times of the corresponding cells in
Data. When set to NULL (the default), the times are assumed
equidistant.
logical. Set to TRUE if the first columns of Data and
Time corresponds to an ID variable identifying the
trajectories. Defaults to FALSE.
a vector containing the numerical identifiers of the measures to compute. The default, 1:17, corresponds to measures 1-17 and thus excludes the measures which require specifying a midpoint.
specifies which column of Time to use as the midpoint
in measure 18. Can be NULL, an integer or a vector of integers of length
the number of rows in Time. The default is NULL, in which case the
midpoint is the time closest to the median of the Time vector specific to
each trajectory.
logical. If TRUE, extreme values of the measures will be capped. If FALSE, only the infinite values will be capped. Defaults to FALSE.
object of class trajMeasures.
further arguments passed to or from other methods.
object of class trajMeasures.
Each trajectory must have a minimum of 3 observations otherwise it will be omitted from the analysis.
The 18 measures and their numerical identifiers are listed below. Please refer to the vignette for the specific formulas used to compute them.
Maximum
Range (max - min)
Mean value
Standard deviation
Slope of the linear model
\(R^2\): Proportion of variance explained by the linear model
Curve length (total variation)
Rate of intersection with the mean
Proportion of time spent above the mean
Minimum of the first derivative
Maximum of the first derivative
Mean of the first derivative
Standard deviation of the first derivative
Minimum of the second derivative
Maximum of the second derivative
Mean of the second derivative
Standard deviation of the second derivative
Later change/Early change
If 'cap.outliers' is set to TRUE, or if some measures are infinite as a result of division by 0, Nishiyama's improved Chebychev bound for continuous distributions
is used to determine extreme values for each measure, corresponding to
a 0.3% probability threshold. Extreme values beyond the threshold are then capped
to the 0.3% probability threshold (see vignette for more details). If applicable, the values which
would be of the form 0/0 are set to 1.
Leffondre K, Abrahamowicz M, Regeasse A, Hawker GA, Badley EM, McCusker J, Belzile E. Statistical measures were proposed for identifying longitudinal patterns of change in quantitative health indicators. J Clin Epidemiol. 2004 Oct;57(10):1049-62. doi: 10.1016/j.jclinepi.2004.02.012. PMID: 15528056.
Nishiyama T, Improved Chebyshev inequality: new probability bounds with known supremum of PDF, arXiv:1808.10770v2 stat.ME https://doi.org/10.48550/arXiv.1808.10770
if (FALSE) {
data("trajdata")
trajdata.noGrp <- trajdata[, -which(colnames(trajdata) == "Group")] #remove the Group column
m1 = Step1Measures(trajdata.noGrp, ID = TRUE, measures = 18, midpoint = NULL)
m2 = Step1Measures(trajdata.noGrp, ID = TRUE, measures = 18, midpoint = 3)
identical(m1$measures, m2$measures)
}
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