pattPC.fit {prefmod} | R Documentation |
Function to fit a pattern model for paired comparisons allowing for missing values using a CL approach.
pattPC.fit(obj, nitems, formel = ~1, elim = ~1, resptype = "paircomp", obj.names = NULL, undec = FALSE, ia = FALSE, NItest = FALSE, NI = FALSE, MISalpha = NULL, MIScommon = FALSE, MISbeta = NULL, pr.it = FALSE)
obj |
either a dataframe or the path/name of the datafile to be read. |
nitems |
the number of compared objects, not the number of comparisons |
formel |
the formula for subject covariates to fit different preference scales for the objects (see below). |
elim |
the formula for the subject covariates that specify the table
to be analysed. If ommitted and formel is not ~1 then
elim will be set to the highest interaction between all terms
contained in formel . If elim is specified, the terms
must be separated by the * operator. |
resptype |
is "paircomp" by default and is reserved for future usage.
Any other specification will not change the behaviour of pattPC.fit
|
obj.names |
character vector with names for objects. |
undec |
for paired comparisons with a undecided/neutral category,
a common parameter will be estimated if undec = TRUE . |
ia |
interaction parameters between comparisons that have one object
in common if ia = TRUE .
|
NItest |
separate estimation of object parameters for complete and
incomplete patterns if NItest = TRUE . Currently,
NItest is set to FALSE if subject covariates are specified.
|
NI |
if TRUE , fits large table (crossclassification with NA patterns), for comparison
. with models including MISalpha (and MISbeta ). |
MISalpha |
if not NULL , specification to fit parameters for
NA indicators using a
logical vector, where TRUE means that the NA indicator parameter
for the corresponding object should be estimated (see example below).
Currently each comparison is reparameterized with
alpha_i + alpha_j. |
MIScommon |
if TRUE , fits a common parameter for NA indicators,
i.e.,
alpha = alpha_i = alpha_j = .... |
MISbeta |
if not NULL , fits parameters for MNAR model, i.e., interactions
between outcome model object parameters and NA indicator parameters.
Currently each comparison is reparameterized with
beta_i + beta_j. The specification is the same as
for MISalpha (see example below). Usually, the specification for
MISbeta is the same as for MISalpha , but any subset is
reasonable. If MISalpha = NULL but MISbeta is not, then
MISalpha is set to MISbeta .
|
pr.it |
a dot is printed at each iteration cycle if set to TRUE
|
Models including categorical subject covariates can be fitted using the
formel
and elim
arguments. formel
specifies the
actual model to be fitted. For instance, if specified as
formel=~SEX
different preference scale for the objects will be
estimated for males and females. For two or more covariates,
the operators +
or *
can be used to model main or interaction
effects, respectively. The operator :
is not allowed. See also
formula
.
The spcification for elim
follows the same rules as for
formel
. However, elim
specifies the basic contingency
table to be set up but does not specify any covariates to be fitted.
This is done using formel
.
If, e.g., elim=SEX
but formel=~1
,
then the table is set up as if SEX
would be fitted but only one global
preference scale is computed. This feature
allows for the succesive fitting of nested models to enable the use of
deviance differences for model selection (see example below).
On return the function provides a list with elements
result |
a list of results from the fitting routine (see Value of
nlm .
|
partsList |
a list of the basic data structures for each subgroup
defined by crossing all covariate levels and different missing value
patterns. Each element of partsList is again a list containing
counts, missing value pattern, the CL matrix represented as a vector, and
the specification of the covariates. Use str to inspect
the elements and see example below.
|
The responses have to be coded as 0/1 for paired comparisons without undecided category (0 means first object in a comparison preferred) or 0/1/2 for paired comparisons with an undecided category (where 1 is the undecided category). Optional subject covariates have to be specified such that the categories are represented by consecutive integers starting with 1. Rows with missing values for subject covariates are removed from the data and a message is printed. The leftmost columns in the data must be the responses to the paired comparisons (where the mandatory order of comparisons is (12) (13) (23) (14) (24) (34) (15) (25) etc.), optionally followed by columns for categorical subject covariates.
The data specified via obj
are supplied using either a data frame
or a datafile in which case obj
is a path/filename. The input
data file if specified must be a plain text file with variable names in
the first row as readable via the command read.table(datafilename,
header = TRUE)
.
For an example see cemspc
or the file cemspc.dat
in
the package's data/
directory.
The size of the table to be analysed increases dramatically with the number of objects. For paired comparisons with two response categories the number of rows of the table is 2 ^ (number of comparisons), e.g., with six objects this is 32768, for three response categories this is 14348907. A reasobale maximum number of objects to be analysed with pattern models is 6 in the case of two response categories and 5 when an additional undecided/neutral category has been observed).
Reinhold Hatzinger
patt.design
, checkMIS
, pattL.fit
,
pattR.fit
## fit only first three objects with undecided parameter data(cemspc) pattPC.fit(cemspc, nitems=3, undec=TRUE) ## check for ignorable missing pattPC.fit(cemspc, nitems=3, undec=TRUE, NItest=TRUE) ## check if SEX has an effect m1<-pattPC.fit(cemspc, nitems=3, formel=~1,elim=~SEX, undec=TRUE) m2<-pattPC.fit(cemspc, nitems=3, formel=~SEX, elim=~SEX, undec=TRUE) ## calculate LR test for SEX ll1<-m1$result$minimum ll2<-m2$result$minimum df1<-length(m1$result$estimate) df2<-length(m2$result$estimate) lr<-2*(ll1-ll2) df<-df2-df1 cat("LR test is",lr,"on df =",df," ( p =",round(1-pchisq(lr,df),digits=4),")\n") ## generates data set with three items and some missing values ## in comparison (23), column 3, then there are no NAs for ## object 1 data(dat4) data3<-dat4[,1:3] idx3<-sample(1:100,10) data3[idx3,3]<-NA checkMIS(data3,nitems=3,verbose=TRUE) ## estimate MNAR PC pattern model for data3 without alpha1 and beta1 pattPC.fit(data3, nitems=3, MISalpha=c(FALSE,TRUE,TRUE), MISbeta=c(FALSE,TRUE,TRUE))