Calculates estimates, standard errors and confidence intervals for regression coefficients in subpopulations.
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Object of class
Formula giving a symbolic description of the linear model.
Formula specifying the variables that define the "estimation domains". If
Probability specifying the desired confidence level: the default value is
This function calculates weighted estimates of linear regression coefficients using suitable weights depending on the class of
deskott: calibrated weights for class
kott.cal.design and direct weights otherwise. Standard errors are calculated using the extended DAGJK method [Kott 99-01].
The mandatory argument
model specifies, by means of a symbolic
formula, the linear regression model whose coefficients are to be estimated.
model must have the form
response ~ terms where
response is the (numeric) response variable and
terms represents a series of terms which specifies a linear predictor for
response. Variables referenced by
model must not contain any missing value (
The optional argument
by specifies the variables that define the "estimation domains", that is the subpopulations for which the estimates are to be calculated. If
by=NULL (the default option), the estimates produced by
kottby refer to the whole population. Estimation domains must be defined by a formula: for example the statement
by=~B1:B2 selects as estimation domains the subpopulations determined by crossing the modalities of variables
deskott variables referenced by
by (if any) must be
factor and must not contain any missing value (
conf.int argument allows to request the confidence intervals for the estimates. By default
conf.int=FALSE, that is the confidence intervals are not provided.
Whenever confidence intervals are requested (i.e.
conf.int=TRUE), the desired confidence level can be specified by means of the
conf.lev argument. The
conf.lev value must represent a probability (
0<=conf.lev<=1) and its default is chosen to be
0.95. Given an input
kott.design object with
nrg random groups and a regression
model with p predictors plus an intercept term,
kott.regcoef builds the confidence intervals making use of a t distribution with
nrg-p-1 degrees of freedom.
The return value depends on the value of the input parameters. In the most general case, the function returns an object of class
list (typically a list made up of data frames).
Kott, Phillip S. (1999) "The Extended Delete-A-Group Jackknife". Bulletin of the International Statistical Instititute. 52nd Session. Contributed Papers. Book 2, pp. 167-168.
Kott, Phillip S. (2001) "The Delete-A-Group Jackknife". Journal of Official Statistics, Vol.17, No.4, pp. 521-526.
kottby for estimating totals and means,
kott.ratio for estimating ratios between totals,
kott.quantile for estimating quantiles and
kottby.user for calculating estimates based on user-defined estimators.
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data(data.examples) # Creation of a kott.design object: kdes<-kottdesign(data=example,ids=~towcod+famcod,strata=~SUPERSTRATUM, weights=~weight,nrg=15) # A model with one predictor and no intercept: kott.regcoef(kdes,income~z-1) # ...compare with ratio estimator: kott.ratio(kott.addvars(kdes,income.mult.z=income*z,z2=z^2),~income.mult.z,~z2) # A model with a factor term and no intercept: kott.regcoef(kdes,income~age5c-1) # ...compare with mean estimator in subpopulations: kottby(kdes,~income,~age5c,estimator="mean") # ...and with regression coefficients (for a different model) # in subpopulations: kott.regcoef(kdes,income~1,~age5c) # An awkward model with many coefficients: kott.regcoef(kdes,income~z:age5c+x3+marstat-1)
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