BIFIE.data | R Documentation |
BIFIEdata
This function creates an object of class BIFIEdata
.
Finite sampling correction of statistical inferences can be
conducted by specifying appropriate input in the fayfac
argument.
BIFIE.data(data.list, wgt=NULL, wgtrep=NULL, fayfac=1, pv_vars=NULL, pvpre=NULL, cdata=FALSE, NMI=FALSE) ## S3 method for class 'BIFIEdata' summary(object,...) ## S3 method for class 'BIFIEdata' print(x,...)
data.list |
List of multiply imputed datasets. Can be also a list of list of imputed
datasets in case of nested multiple imputation. Then, the argument
|
wgt |
A string indicating the label of case weight or a vector containing all case weights. |
wgtrep |
Optional vector of replicate weights |
fayfac |
Fay factor for calculating standard errors, a numeric value. If finite sampling correction is requested, an appropriate vector input can be used (see Example 3). |
pv_vars |
Optional vector for names of plausible values, see
|
pvpre |
Optional vector for prefixes of plausible values, see
|
cdata |
An optional logical indicating whether the |
NMI |
Optional logical indicating whether |
object |
Object of class |
x |
Object of class |
... |
Further arguments to be passed |
An object of class BIFIEdata
saved in a non-compact
or compact way, see value cdata
. The following entries are
included in the list:
datalistM |
Stacked list of imputed datasets (if |
wgt |
Vector with case weights |
wgtrep |
Matrix with replicate weights |
Nimp |
Number of imputed datasets |
N |
Number of observations in a dataset |
dat1 |
Last imputed dataset |
varnames |
Vector with variable names |
fayfac |
Fay factor. |
RR |
Number of replicate weights |
NMI |
Logical indicating whether the dataset is nested multiply imputed. |
cdata |
Logical indicating whether the |
Nvars |
Number of variables |
variables |
Data frame including some informations about variables.
All transformations are saved in the column |
datalistM_ind |
Data frame with response indicators
(if |
datalistM_imputed |
Data frame with imputed values
(if |
See BIFIE.data.transform
for data transformations on
BIFIEdata
objects.
For saving and loading BIFIEdata
objects see
save.BIFIEdata
.
For converting PIRLS/TIMSS or PISA datasets into BIFIEdata
objects see BIFIE.data.jack
.
See the BIFIEdata2svrepdesign
function for converting
BIFIEdata
objects to objects used in the survey package.
############################################################################# # EXAMPLE 1: Create BIFIEdata object with multiply-imputed TIMSS data ############################################################################# data(data.timss1) data(data.timssrep) bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) summary(bdat) # create BIFIEdata object in a compact way bdat2 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ], cdata=TRUE) summary(bdat2) ## Not run: ############################################################################# # EXAMPLE 2: Create BIFIEdata object with one dataset ############################################################################# data(data.timss2) # use first dataset with missing data from data.timss2 bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss2[[1]], wgt=data.timss2[[1]]$TOTWGT) ## End(Not run) ############################################################################# # EXAMPLE 3: BIFIEdata objects with finite sampling correction ############################################################################# data(data.timss1) data(data.timssrep) #----- # BIFIEdata object without finite sampling correction bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) summary(bdat1) #----- # generate BIFIEdata object with finite sampling correction by adjusting # the "fayfac" factor bdat2 <- bdat1 #-- modify "fayfac" constant fayfac0 <- bdat1$fayfac # set fayfac=.75 for the first 50 replication zones (25% of students in the # population were sampled) and fayfac=.20 for replication zones 51-75 # (meaning that 80% of students were sampled) fayfac <- rep( fayfac0, bdat1$RR ) fayfac[1:50] <- fayfac0 * .75 fayfac[51:75] <- fayfac0 * .20 # include this modified "fayfac" factor in bdat2 bdat2$fayfac <- fayfac summary(bdat2) summary(bdat1) #---- compare some univariate statistics # no finite sampling correction res1 <- BIFIEsurvey::BIFIE.univar( bdat1, vars="ASMMAT") summary(res1) # finite sampling correction res2 <- BIFIEsurvey::BIFIE.univar( bdat2, vars="ASMMAT") summary(res2) ## Not run: ############################################################################# # EXAMPLE 4: Create BIFIEdata object with nested multiply imputed dataset ############################################################################# data(data.timss4) data(data.timssrep) # nested imputed dataset, save it in compact format bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss4, wgt=data.timss4[[1]][[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ], NMI=TRUE, cdata=TRUE ) summary(bdat) ## End(Not run)
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