Description Usage Arguments Details Value Author(s) References See Also Examples
This function performs nonparametric graduation of mortality rates using discrete beta kernel smoothing techniques.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | dbkGrad(obsq, limx, limy, exposures = NULL, transformation = c("none", "log", "logit",
"Gompertz"), bwtypex = c("FX", "VC", "EX"), bwtypey = c("FX", "VC", "EX"),
adaptx = c("a", "b", "ab"), adapty = c("a", "b", "ab"), hx = 0.002, hy = 0.002,
sx = 0.2, sy = 0.2, cvres = c("propres", "res"), cvhx = FALSE, cvhy = FALSE,
cvsx = FALSE, cvsy = FALSE, alpha = 0.05)
## S3 method for class 'dbkGrad'
print(x, ...)
## S3 method for class 'dbkGrad'
as.data.frame(x, row.names = x$limx[1]:x$limx[2], optional = FALSE, ...)
## S3 method for class 'dbkGrad'
residuals(object, type = c("working", "proportional", "response",
"deviance", "pearson"), ...)
|
obsq |
a matrix (or an object which can be coerced to a matrix using |
limx, limy |
optional vector of two integers; if provided, |
exposures |
an optional matrix containing the exposed to the risk of death for each age and year. Dimensions of |
transformation |
an optional character string; the transformation specified is applied to the observed data before graduation. Graduated data are then back-transformed. Possible values are
|
bwtypex, bwtypey |
an optional character string. It specifies the type of bandwidth to be adopted by row (by column) and must be:
|
adaptx, adapty |
an optional character string. It is the type of adaptive bandwidth to be adopted by row (by column) and must be:
|
hx, hy |
an optional scalar. It is the global bandwidth used for the variable on the rows (columns).
Default value is 0.002.
If |
sx, sy |
an optional scalar.
It is the sensitive parameter used for the variable on the rows (columns).
Default value is 0.2.
If |
cvhx, cvhy |
an optional logical; if |
cvsx, cvsy |
an optional logical; if |
cvres |
an optional character string; if |
alpha |
an optional scalar.
When the |
x |
a |
row.names |
a NULL or a character vector giving the row names for the data frame. Missing values are not allowed. Default value is |
optional |
logical. If TRUE, setting row names and converting column names (to syntactic names: see make.names) is optional. |
... |
additional arguments to be passed to or from methods. |
type |
|
object |
a |
In the cross-validation routine, minimization is performed using the Levenberg-Marquardt algorithm (More 1978) in the minpack.lm package (Elzhov, Mullen, and Bolker 2010).
Returned from this function is an dbkGrad
object which is a list with the following components:
fitted.values |
a matrix containing the graduated values. |
residuals |
a matrix containing the working residuals |
kernels |
a matrix.
|
cvRSS |
a scalar.
It is the cross-validation residual sum of squares (RSS) computed over the fitted values, using the residuals specified in |
hx (hy) |
a scalar. It is the global bandwidth used for the variable on the rows (columns). |
sx (sy) |
a scalar.
It is the sensitive parameter used for the variable on the rows (columns). It is returned when |
upperbound,lowerbound |
pointwise confidence interval. Returned when |
bonferroniupperbound, bonferronilowerbound |
limits of the Bonferroni confidence bands. Returned when |
sidakupperbound, sidaklowerbound |
limits of the Sidak confidence bands. Returned when |
obsq |
a matrix containing the observed mortality rates with dimensions set by |
exposures |
a matrix containing the exposures with dimensions set by |
limx (limy) |
a vector with lower and upper row (column) limits. Only data within these interval are graduated. |
call |
an object of class |
Angelo Mazza and Antonio Punzo
Elzhov TV, Mullen KM, Bolker B (2010) minpack.lm: R Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK. R package version 1.1-5. URL http://CRAN.R-project.org/package=minpack.lm.
Mazza A, Punzo A (2011) Discrete Beta Kernel Graduation of Age-Specific Demographic Indicators. In S Ingrassia, R Rocci, M Vichi (eds.), New Perspectives in Statistical Modeling and Data Analysis, Studies in Classification, Data Analysis and Knowledge Organization, pp. 127-134. Springer-Verlag, Berlin-Heidelberg.
Mazza A, Punzo A (2013a) Graduation by Adaptive Discrete Beta Kernels. In A Giusti, G Ritter, M Vichi (eds.), Classification and Data Mining, Studies in Classification, Data Analysis and Knowledge Organization, pp. 77-84. Springer-Verlag, Berlin-Heidelberg.
Mazza A, Punzo A (2013b) Using the Variation Coefficient for Adaptive Discrete Beta Kernel Graduation. In P Giudici, S Ingrassia, M Vichi (eds.), Advances in Statistical Modelling for Data Analysis, Studies in Classification, Data Analysis and Knowledge Organization, pp. 225-232, Springer International Publishing, Switzerland.
Mazza A, Punzo A (2014) DBKGrad: An R Package for Mortality Rates Graduation by Discrete Beta Kernel Techniques. Journal of Statistical Software, Code Snippets, 572, 1-18.
More J (1978) The Levenberg-Marquardt Algorithm: Implementation and Theory. In G Watson (ed.), Numerical Analysis, volume 630 of Lecture Notes in Mathematics, pp. 104-116. Springer- Verlag, Berlin-Heidelberg.
Punzo A (2010) Discrete Beta-type Models. In H Locarek-Junge, C Weihs (eds.), Classification as a Tool for Research, Studies in Classification, Data Analysis and Knowledge Organization, pp. 253-261. Springer-Verlag, Berlin-Heidelberg.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data("ItalyM")
# unidimensional analysis
res1 <- dbkGrad(obsq=obsq, limx=c(6,71), limy=104, exposure=population, bwtypex="EX", adaptx="ab")
plot(res1, plottype="obsfit", CI=FALSE, CBBonf=TRUE)
plot(res1, plottype="residuals", restype="pearson")
plot(res1, plottype="checksd", restype="pearson")
residuals(res1, type="pearson")
# bidimensional analysis
res2 <- dbkGrad(obsq=obsq, limx=c(6,46), limy=c(60,80), exposure=population,
transformation="logit", bwtypex="VC", bwtypey="EX", hx=0.01, hy=0.008, adaptx="ab", adapty="b")
plot(res2, plottype="obsfit")
plot(res2, plottype="obsfit", plotstyle="persp", col="black")
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