Fit naive General Location Model
Description
Fit a naive General Location Model, only supporting cells that are present in the data.
Usage
1 
Arguments
dfr 

weights 
vector of weights attributed to each row in 
uniqueIdentifiersPerRow 
List of uids (see 
separator 
Only relevant if 
pooledCov 
If 
verbosity 
The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output) 
... 
Ignored for now 
Details
Finds all 'cells' defined by the combinations of categorical variables in
dfr
, and finds their (weighted) probability. Then it finds the mean per
cell for all continuous variables, and a covariance matrix (which is typically
pooled, although theoretically it should be unpooled)
Value
List of class "GLoMo".
(NOTE: where I write dataset, this could either be a data.frame
or numdfr
object):

character vector: each item is a unique identifier of a cell. These get longer with more factor columns in the dataset 

probability of each cell (numerical vector) 

matrix (named) of the continuous columns within cells (note: homoscedastic) 

dimensions of the dataset used to create this 

for each uid, the matching factor levels + the means in that cell for the continuous columns. Note: the column and row order is the same as the column order in the original dataset 

named vector of column indices of the factor columns 

character used as separator in creating the uids. 

inverse of omegahat — often used for prediction 
Note
The dfr
passed is supposed to not contain any NA
values!
Further more, in the return value, the order of the columns (in e.g.
omegahat
and uniqueFactorCombinationsAndContinuousMeans
) is the
same as in the original dfr
.
The length of uid
and pihat
is the same as the number of rows in
uniqueFactorCombinationsAndContinuousMeans
(i.e. the number of unique
'cells' in dfr
). Their order also matches (i.e. first item of uid
matches the first row of uniqueFactorCombinationsAndContinuousMeans
etc.)
The number of columns/rows in omegahat
and the number of items in
factorCols
is also the total number of columns in dfr
and thus
also in uniqueFactorCombinationsAndContinuousMeans
.
Author(s)
Nick Sabbe (nick.sabbe@ugent.be)
References
"Statistical Analysis with Missing Values"
See Also
GLoMopackage
, NumDfr
, predict
, GLoMoclass
Examples
1 2 3 4 5 6 