Description Usage Arguments Value References Examples
Fit a Renshaw and Haberman (LeeCarter with cohorts) mortality model using the iterative NewtonRaphson procedure presented in Algorithm 1 of Hunt and Villegas (2015). This approach helps solve the wellknown robustness and converges issues of the LeeCarter model with cohorteffects.
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 = 1e04, 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 01 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 agemodulating functions
β_x^{(i)}, i=1, ..., N. If the ith agemodulating
function is nonparametric (e.g. as in the LeeCarter 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 agemodulating function
β_x^{(0)}. If the agemodulating function is nonparametric

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 01 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 
loglikelihood 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 LeeCarter model with cohorts. Insurance: Mathematics and Economics, 64, 186202.
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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