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Major update that improves support for formulas specification #582
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- reintrocudes the square predictorMatrix - defines conversion functions p2f(), p2c(), f2p(), n2b(), b2n() - defines validate.blocks(), validate.predictorMatrix() - extends edit.setup() to formulas and blots - for reading ease, use "~ 1" for the empty model instead of "~ 0" - does not automatically set method = "" for variables that are not imputed - as far as possible, changes the leading argument to formulas (instead of blocks or predictorMatrix) - adds function typecodes() in sampler() to reduce multiple predictorMatrix lines to one (support for multivariate imputation methods) - implement new logic in samper.univ() - outcomments some tests that depend on hard-coded parameter estimates - sharpens test for equality between predictorMatrix and formulas specifications
Ideas for further development:
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…block. Update make.method() so that homogeneous types and nlevels within a block get an appropriate default method.
…, ] to zero when variable j is member of a block for which no imputations are needed.
Commits 5c6bee2 and 755c23a generalise the classic behaviour of the It works as follows:
This PR also removes the error message mice detected constant and/or collinear variables. No predictors were left after their removal. Imputations will be generated without predictors by the intercept-only imputation model (not recommended in general). WARNING: Setting |
Commit c2da03c cleans up the internal function |
- Stricter controls on input predictorMatrix - Output test of mids object
New behaviours
Changes
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Exit checks added:
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…ted issues in check.blocks(), make.method(), edit.predictorMatrix()
…o impute (ynames)
New behaviours and features thus far
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Three proposed changes to new behaviour
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@@ -82,9 +83,23 @@ check.predictorMatrix <- function(predictorMatrix, | |||
) | |||
} | |||
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# calculate ynames (variables to impute) for use in check.method() | |||
# NA-propagation prevention | |||
# find all dependent (imputed) variables | |||
hit <- apply(predictorMatrix, 1, function(x) any(x != 0)) |
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Can be simplified to: apply(predictorMatrix != 0, 1, any)
# find all variables in data that are not imputed | ||
notimputed <- setdiff(colnames(data), ynames) | ||
# select uip: unimputed incomplete predictors | ||
completevars <- colnames(data)[!apply(is.na(data), 2, sum)] |
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!apply(is.na(data), 2, any)
might be more efficient
@@ -157,6 +156,16 @@ check.blocks <- function(blocks, data, calltype = "pred") { | |||
)) | |||
} | |||
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# save ynames (variables to impute) for use in check.method() | |||
ynames <- unique(as.vector(unname(unlist(blocks)))) |
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Is as.vector
redundant for the return value from unlist
?
predictorMatrix
p2f()
,p2c()
,f2p()
,n2b()
,b2n()
validate.blocks()
,validate.predictorMatrix()
edit.setup()
toformulas
andblots
~ 1
for the empty predictor set instead of~ 0
method = ""
for variables that are not imputed (NOTE: DECISION REVERTED. SEE BELOW)formulas
(instead ofblocks
orpredictorMatrix
)typecodes()
insampler()
to reduce multiplepredictorMatrix
lines to one (support for multivariate imputation methods)samper.univ()
predictorMatrix
andformulas
specifications