Description Usage Arguments Details Value Author(s) Examples
Various estimators that handle missing data via the Available Cases Method
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xy 
Matrix or data frame, X values in the first columns, Y in the last column. 
indata 
Matrix or data frame. 
x 
Matrix or data frame, one column per variable. 
nboot 
If positive, number of bootstrap samples to take. 
probna 
Probability that an element will be NA. 
scale 
If TRUE, call 

Matrix or data frame, one column per variable. 
tbl 
An R table. 
m 
Number of synthetic NAs to insert. 
object 
Output from 
... 
Needed for consistency with generic function. Not used. 
margin 
A list of vectors specifying the model, as in

The Available Cases (AC) approach applies to statistical methods that depend only on products of k of the variables, so that cases having nonNA values for those k variables can be used, as opposed to using only cases that are fully intact in all variables, the Complete Cases (CC) approach. In the case of linear regression, for instance, the estimated coefficients depend only on covariances between the variables (both predictors and response). This approach assumes thst the cases with missing values have the same distribution as the intact cases.
The lmac
function forms OLS estimates as with lm
, but
applying AC, in contrast to lm
, which uses the CC method.
The pcac
function is an AC substitute for prcomp
. The
data is centered, corresponding to a fixed value of center =
TRUE
in prcomp
. It is also scaled if scale
is TRUE,
corresponding scale. = TRUE
in prcomp;.
Due to AC,
there is a small chance of negative eigenvalues, in which case
stop
will be called.
The loglinac
function is an AC substitute for loglin
.
The latter takes tables as input, but loglinac
takes the raw
data. If you have just the table, use tbltofakedf
to
regenerate a usable data frame.
The makeNA
function is used to insert random NA values into
data, for testing purposes.
For lmac
, an object of class 'lmac'
, with components
coefficients, as with lm
;
accessible directly or by calling coef
, as with lm
fitted.values, as with lm
residuals, as with lm
r2, (unadjusted) Rsquared
cov, for nboot >0
the estimated covariance matrix
of the vector of estimated regression coefficients; accessible
directly or by calling vcov
, as with lm
For pcac
, an R list, with components
sdev, as with prcomp
rotation, as with prcomp
For loglinac
, an R list, with components
param, estimated coefficients, as in loglin
fit, estimated expected call counts, as in loglin
Norm Matloff
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