Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates estimates, standard errors and confidence intervals for regression coefficients in subpopulations.
1 2 3  kott.regcoef(deskott, model, by = NULL,
vartype = c("se", "cv", "cvpct", "var"),
conf.int = FALSE, conf.lev = 0.95)

deskott 
Object of class 
model 
Formula giving a symbolic description of the linear model. 
by 
Formula specifying the variables that define the "estimation domains". If 
vartype 

conf.int 
Boolean ( 
conf.lev 
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 9901].
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 (NA
).
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 B1
and B2
. The deskott
variables referenced by by
(if any) must be factor
and must not contain any missing value (NA
).
The 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 nrgp1
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).
Diego Zardetto
Kott, Phillip S. (1999) "The Extended DeleteAGroup Jackknife". Bulletin of the International Statistical Instititute. 52nd Session. Contributed Papers. Book 2, pp. 167168.
Kott, Phillip S. (2001) "The DeleteAGroup Jackknife". Journal of Official Statistics, Vol.17, No.4, pp. 521526.
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 userdefined estimators.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  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~z1)
# ...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~age5c1)
# ...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+marstat1)

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