kottdesign.RdAdds to a data frame of survey data the replicate weights calculated according to the "Delete-A-Group Jackknife" (DAGJK) method.
kottdesign(data, ids, strata = FALSE, weights, nrg, self.rep.str = FALSE, check.data = FALSE, aux = FALSE)
| data | Data frame of survey data. |
|---|---|
| ids | Formula identifying clusters selected at subsequent sampling stages (PSUs, SSUs, …). |
| strata | Formula identifying the stratification variable; |
| weights | Formula identifying the initial weights for the sampling units. |
| nrg | Number of "random groups" (and replicate weights) you want to create. |
| self.rep.str | Formula identifying self-representing strata (SR), if any; |
| check.data | Boolean ( |
| aux | If |
This function creates an object of class kott.design. A kott.design object is made up by the union of the replicated survey data and the metadata describing the sampling design. The metadata (stored as attributes of the object) are used to enable and guide processing and analyses provided by other functions in the EVER package (such as kottcalibrate, kottby, desc, …).
The data, ids, weights and nrg arguments are mandatory, while strata, check.data and aux arguments are optional. The data variables that are referenced by ids, weights and strata (if specified) must not contain any missing value (NA).
The ids argument specifies the cluster identifiers. It is possible to specify a multi-stage sampling design by simply using a formula with the identifiers of clusters selected at subsequent sampling stages. For example, ids=~id.PSU+id.SSU declares a two-stage sampling in which the first stage units are identified by the id.PSU variable and second stage ones by the id.SSU variable.
The strata argument identifies the stratification variable. The data variable referenced by strata (if specified) must be a factor. By default the sample is assumed to be non-stratified.
The weights argument identifies the initial (or direct) weights for the units included in the sample. The data variable referenced by weights must be numeric.
The nrg argument selects the number of "random groups" (and replicate weights) you want to create by means of the DAGJK method [Kott 98-99-01]. The value of nrg must be greater than 1 and less than or equal to the number of sampled PSUs (otherwise the function stops and prints an error message). If nrg equals the number of sampled PSUs, the DAGJK method "reduces" to (that is, it provides identical results to) the traditional stratified jackknife method. The advantage of the DAGJK method over the traditional jackknife is that, unlike the latter, it remains computationally manageable even when dealing with "complex and big" surveys (tens of thousands of PSUs arranged in a large number of strata with widely varying sizes). In fact, the DAGJK method is known to provide, for a broad range of sampling designs and estimators, (near) unbiased standard error estimates even with a "small" number (e.g. a few tens) of replicate weights.
When dealing with a multistage, stratified sampling design that includes self-representing (SR) strata (i.e. strata containing PSUs selected with probability 1), the main contribution to the variance of the SR strata arises from the second stage units ("variance PSUs"). In this instance, the user can exploit the self.rep.str argument to specify, by a formula, the data variable identifying the SR strata: as a result the function will build the variance PSUs and take care of them. When choosing this option, the user must ensure that the variable referenced by self.rep.str is logical (with value TRUE for SR strata and FALSE otherwise) or numeric (with value 1 for SR strata and 0 otherwise).
As an alternative, the user can attend to develop by himself the appropriate identifiers for the sampling units in ids. To be precise, the identifier for the PSUs (say id.PSU) must have, in the SR strata, values in correspondence 1:1 (for example they can be equal, provided this does not cause undesired duplications) with those of the SSUs identifier (say id.SSU).
The optional argument check.data allows to check the correct nesting of data clusters (PSUs, SSUs, …). If check.data=TRUE the function checks that every unit selected at stage k+1 is associated to one and only one unit selected at stage k. For a stratified design the function checks also the correct nesting of clusters within strata.
The optional argument aux can usually be ignored: its default value selects the standard behaviour of the function. Invoking kottdesign with aux=TRUE can, on the other hand, prove useful for any user who wants to fully understand how the DAGJK method builds the replicate weights. If aux=TRUE, the output data frame contains auxiliary columns that provide: the number of PSUs per stratum, the number of PSUs per stratum and random group and the multiplicative coefficients that transform the initial weights into replicate weights.
An object of class kott.design. The data frame it contains includes (in addition to the original survey data):
A new column named rgi (random group index) giving the random group to which each sample unit belongs.
The replicate weights columns (one per random group, nrg in all), the names of which are obtained by pasting the name of the initial weights column with the indices 1, 2, …, nrg.
The EVER package implements the extended version of the DAGJK method [Kott 99-01]. It guarantees unbiased estimates of standard errors even when the number of PSUs sampled in some strata is small (that is, less than nrg).
The rigorous [Kott 98-99-01] results were derived under the hypothesis of with replacement selection of PSUs. This means that the DAGJK method cannot include finite population corrections (fpc): this restriction is fully reflected in the EVER package.
If only one PSU (lonely PSU) has been selected in some non-self-representative strata (NSR), the kottdesign function does not report an error message, rather a warning one. In fact, the extended DAGJK method automatically removes the contribution of strata containing lonely PSUs from the estimation of standard errors (obviously, their contribution remains when calculating the estimates). This is all the users have to remember, if they come across the warning message produced by kottdesign. Whenever the described behaviour seems to be undesirable, a viable alternative in order to eliminate the lonely PSUs is to collapse strata in a suitable manner. In such a case, the price to pay is the possibility of ending up with an over-estimation of the standard errors. As far as the strata collapsing strategie is concerned, the EVER package does not provide (in the current version) any support to the user.
Unlike the conventional jackknife method, the DAGJK is a stochastic replication method. If, having fixed the sampling design and the number of replicates, it is applied a number of times to the same sample data frame, generally a different random groups composition results. This means that repeated invocations of the kottdesign function, even if run with identical actual parameters, generate different kott.design objects (and, consequently, different standard error estimates). What has been stated obviously does not apply when nrg equals the number of sampled PSUs. If you really need it, you can however generate exactly the same results for subsequent applications of kottdesign: you have only to keep fixed the seed of R's random numbers generator (using the set.seed function).
Kott, Phillip S. (1998) "Using the Delete-A-Group Jackknife Variance Estimator in NASS Surveys", RD Research Report No. RD-98-01, USDA, NASS: Washington, DC.
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.
desc for a concise description of kott.design objects, kottby, kott.ratio, kott.regcoef, kott.quantile and kottby.user for calculating estimates and standard errors, kottcalibrate for calibrating replicate weights.
# Creation of kott.design objects starting with survey data sampled # with different sampling designs (actually the survey data frame is # always the same: the examples serve the purpose of illustrating # the syntax). data(data.examples) # Two-stage stratified cluster sampling design (notice the presence of # lonely PSUs): kdes<-kottdesign(data=example,ids=~towcod+famcod,strata=~stratum, weights=~weight,nrg=15)#> Warning: Lonely PSUs in strata: 902, 903, 904, 905, 906, 907, 1004, 1005, 1006, 1007, 1008, 1102, 1103, 1104, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3105, 3106, 3107, 5407, 5408, 5409, 5410, 5411, 5412, 5413, 5415, 5416, 5502, 5503, 9304, 9305, 9306, 9307, 9308, 9309, 9310, 9312desc(kdes)#> DAGJK replicated survey data #> > 15 random groups #> > Stratified 2 - Stage Cluster Sampling design #> - [80] strata #> - [1307,2372] clusters #> #> Call: #> kottdesign(data = example, ids = ~towcod + famcod, strata = ~stratum, #> weights = ~weight, nrg = 15) #># The same using collapsed strata (SUPERSTRATUM variable) to remove # lonely PSUs: kdes<-kottdesign(data=example,ids=~towcod+famcod,strata=~SUPERSTRATUM, weights=~weight,nrg=15) desc(kdes)#> DAGJK replicated survey data #> > 15 random groups #> > Stratified 2 - Stage Cluster Sampling design #> - [55] strata #> - [1307,2372] clusters #> #> Call: #> kottdesign(data = example, ids = ~towcod + famcod, strata = ~SUPERSTRATUM, #> weights = ~weight, nrg = 15) #># Same design, but using the self.rep.str argument to identify # the SR strata (actually towcod identifies the # "variance PSUs" by construction): kdes<-kottdesign(data=example,ids=~towcod+famcod,strata=~SUPERSTRATUM, weights=~weight,nrg=15,self.rep.str=~sr) desc(kdes)#> DAGJK replicated survey data #> > 15 random groups #> > Stratified 2 - Stage Cluster Sampling design #> - [55] strata #> - [1307,2372] clusters #> - [1307] variance PSUs #> #> Call: #> kottdesign(data = example, ids = ~towcod + famcod, strata = ~SUPERSTRATUM, #> weights = ~weight, nrg = 15, self.rep.str = ~sr) #># Two stage cluster sampling (no stratification): kdes<-kottdesign(data=example,ids=~towcod+famcod,weights=~weight,nrg=15) desc(kdes)#> DAGJK replicated survey data #> > 15 random groups #> > 2 - Stage Cluster Sampling design #> - [1307,2372] clusters #> #> Call: #> kottdesign(data = example, ids = ~towcod + famcod, weights = ~weight, #> nrg = 15) #># One-stage stratified cluster sampling: kdes<-kottdesign(data=example,ids=~towcod,strata=~SUPERSTRATUM, weights=~weight,nrg=15) desc(kdes)#> DAGJK replicated survey data #> > 15 random groups #> > Stratified 1 - Stage Cluster Sampling design #> - [55] strata #> - [1307] clusters #> #> Call: #> kottdesign(data = example, ids = ~towcod, strata = ~SUPERSTRATUM, #> weights = ~weight, nrg = 15) #># Stratified independent sampling design: kdes<-kottdesign(data=example,ids=~key,strata=~SUPERSTRATUM, weights=~weight,nrg=15) desc(kdes)#> DAGJK replicated survey data #> > 15 random groups #> > Stratified Independent Unit Sampling design #> - [55] strata #> #> Call: #> kottdesign(data = example, ids = ~key, strata = ~SUPERSTRATUM, #> weights = ~weight, nrg = 15) #>