Description Usage Arguments Value References Examples
Fit a Renshaw and Haberman (Lee-Carter with cohorts) mortality model using the iterative Newton-Raphson procedure presented in Algorithm 1 of Hunt and Villegas (2015). This approach helps solve the well-known robustness and converges issues of the Lee-Carter model with cohort-effects.
1 2 3 4 5 6 | ## S3 method for class 'rh'
fit(object, data = NULL, Dxt = NULL, Ext = NULL,
ages = NULL, years = NULL, ages.fit = NULL, years.fit = NULL,
oxt = NULL, wxt = NULL, start.ax = NULL, start.bx = NULL,
start.kt = NULL, start.b0x = NULL, start.gc = NULL, verbose = TRUE,
tolerance = 1e-04, iterMax = 10000, ...)
|
object |
an object of class |
data |
an optional object of type StMoMoData containing information on
deaths and exposures to be used for fitting the model. This is typically created
with function |
Dxt |
optional matrix of deaths data. |
Ext |
optional matrix of observed exposures of the same dimension of
|
ages |
optional vector of ages corresponding to rows of |
years |
optional vector of years corresponding to rows of |
ages.fit |
optional vector of ages to include in the fit. Must be a
subset of |
years.fit |
optional vector of years to include in the fit. Must be a
subset of |
oxt |
optional matrix/vector or scalar of known offset to be used in fitting the model. This can be used to specify any a priori known component to be added to the predictor during fitting. |
wxt |
optional matrix of 0-1 weights to be used in the fitting process.
This can be used, for instance, to zero weight some cohorts in the data.
See |
start.ax |
optional vector with starting values for α_x. |
start.bx |
optional matrix with starting values for β_x^{(i)}. |
start.kt |
optional matrix with starting values for κ_t^{(i)}. |
start.b0x |
optional vector with starting values for β_x^{(0)}. |
start.gc |
optional vector with starting values for γ_c. |
verbose |
a logical value. If |
tolerance |
a positive numeric value specifying the tolerance level for convergence. |
iterMax |
a positive integer specifying the maximum number of iterations to perform. |
... |
arguments to be passed to or from other methods. |
model |
the object of class |
ax |
vector with the fitted values of the static age function
α_x. If the model does not have a static age function or
failed to fit this is set to |
bx |
matrix with the values of the period age-modulating functions
β_x^{(i)}, i=1, ..., N. If the i-th age-modulating
function is non-parametric (e.g. as in the Lee-Carter model)
|
kt |
matrix with the values of the fitted period indexes
κ_t^{(i)}, i=1, ..., N. |
b0x |
vector with the values of the cohort age-modulating function
β_x^{(0)}. If the age-modulating function is non-parametric
|
gc |
vector with the fitted cohort index γ_{c}.
If the model does not have a cohort effect or failed to fit this is set
to |
data |
StMoMoData object provided for fitting the model. |
Dxt |
matrix of deaths used in the fitting. |
Ext |
matrix of exposures used in the fitting. |
oxt |
matrix of known offset values used in the fitting. |
wxt |
matrix of 0-1 weights used in the fitting. |
ages |
vector of ages used in the fitting. |
years |
vector of years used in the fitting. |
cohorts |
vector of cohorts used in the fitting. |
fittingModel |
output from the iterative fitting algorithm. |
loglik |
log-likelihood of the model. If the fitting failed to
converge this is set to |
deviance |
deviance of the model. If the fitting failed to
converge this is set to |
npar |
effective number of parameters in the model. If the fitting
failed to converge this is set to |
nobs |
number of observations in the model fit. If the fitting
failed to converge this is set to |
fail |
|
conv |
|
Hunt, A., & Villegas, A. M. (2015). Robustness and convergence in the Lee-Carter model with cohorts. Insurance: Mathematics and Economics, 64, 186-202.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | LCfit <- fit(lc(), data = EWMaleData, ages.fit = 55:89)
wxt <- genWeightMat(55:89, EWMaleData$years, clip = 3)
RHfit <- fit(rh(), data = EWMaleData, ages.fit = 55:89,
wxt = wxt, start.ax = LCfit$ax,
start.bx = LCfit$bx, start.kt = LCfit$kt)
plot(RHfit)
#Impose approximate constraint as in Hunt and Villegas (2015)
## Not run:
RHapprox <- rh(approxConst = TRUE)
RHapproxfit <- fit(RHapprox, data = EWMaleData, ages.fit = 55:89,
wxt = wxt)
plot(RHapproxfit)
## End(Not run)
|
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