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 backtransformed. 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 crossvalidation routine, minimization is performed using the LevenbergMarquardt 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 crossvalidation 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 LevenbergMarquardt Nonlinear LeastSquares Algorithm Found in MINPACK. R package version 1.15. URL http://CRAN.Rproject.org/package=minpack.lm.
Mazza A, Punzo A (2011) Discrete Beta Kernel Graduation of AgeSpecific 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. 127134. SpringerVerlag, BerlinHeidelberg.
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. 7784. SpringerVerlag, BerlinHeidelberg.
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. 225232, 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, 118.
More J (1978) The LevenbergMarquardt Algorithm: Implementation and Theory. In G Watson (ed.), Numerical Analysis, volume 630 of Lecture Notes in Mathematics, pp. 104116. Springer Verlag, BerlinHeidelberg.
Punzo A (2010) Discrete Betatype Models. In H LocarekJunge, C Weihs (eds.), Classification as a Tool for Research, Studies in Classification, Data Analysis and Knowledge Organization, pp. 253261. SpringerVerlag, BerlinHeidelberg.
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")

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.