signifAutoSmoothAPC: Smooths demographic data using automatically estimated parameters and
taking into account only significant period and cohort effects
Description
It is a heuristic procedure which tries to figure out positions of
period and cohort effects in the data. It also uses a few steps to estimate
model's parameters. The procedure is supposed to outperform autoSmoothAPC slightly.
Demographic data (log mortality) presented as a matrix.
Row numbers represent ages and column numbers represet time.
p.value
P-value used to test the period and the cohort effects for significance.
The lower the value the fewer diagonals and years will be used to find cohort and period effects.
cornerLength
Minimal length of a diagonal to be considered for cohort effects.
lower
Lowest possible values for the optimization procedure.
upper
Highest possible values for the optimization procedure.
init
Initial values for the optimization procedure.
reltol
Relative tolerance parameter to be supplied to optim function.
trace
Controls if tracing is on.
control
The control data passed directly to rq.fit.sfn function.
weights
Define how much every observation effect the resulting smooth surface.
The parameter must have same dimentions as data parameter.
Weights can be set to reciprocal of estimated standard deviation of the data.
Value
A list of six components: smooth surface, period effects, cohort effects, parameters
used for smoothing, diagonals used for cohort effects and years used for period effects.