| Title: | Missing Data Imputation and Model Checking |
|---|---|
| Description: | The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty. |
| Authors: | Andrew Gelman [ctb], Jennifer Hill [ctb], Yu-Sung Su [aut], Masanao Yajima [ctb], Maria Pittau [ctb], Ben Goodrich [cre, aut], Yajuan Si [ctb], Jon Kropko [aut] |
| Maintainer: | Ben Goodrich <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1 |
| Built: | 2024-11-28 06:46:12 UTC |
| Source: | CRAN |
The mi package performs multiple imputation for data with missing values. The algorithm iteratively draws imputed values from the conditional distribution for each variable given the observed and imputed values of the other variables in the data. The process approximates a Bayesian framework; multiple chains are run and convergence is assessed after a pre-specified number of iterations within each chain. The package allows customization of the conditional model and the treatment of missing values for each variable. In addition, the package provides graphics to visualize missing data patterns, to diagnose the models used to generate the imputations, and to assess convergence. Functions are included to run statistical models post-imputation with the appropriate degree of sampling uncertainty.
| Package: | mi |
| Type: | Package |
| Version: | 1.0 |
| Date: | Thu Nov 28 06:45:57 2024 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
See the vignette for an example of typical usage.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima,Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_data.frame, change, mi, Rhats,
pool, complete
The missing_variable class is essentially the data comprising a variable plus all
the metadata needed to understand how its missing values will be imputed. However, no variable is
merely of missing_variable class; rather every variable is of a class that inherits from the
missing_variable class. Even if a variable has no missing values, it needs to be coerced to a class
that inherits from the missing_variable class before it can be used to impute values of other
missing_variables. Understanding the properties of different subclasses of the missing_variable class
is essential for modeling and imputing them. The missing_data.frame-class is essentially
a list of objects that inherit from the missing_variable class, plus metadata need to understand how
these missing_variables relate to each other. Most users will never need to call missing_variable directly since it is called by missing_data.frame.
missing_variable(y, type, ...) ## Hidden arugments not included in the signature: ## favor_ordered = TRUE, favor_positive = FALSE, ## variable_name = deparse(substitute(y))missing_variable(y, type, ...) ## Hidden arugments not included in the signature: ## favor_ordered = TRUE, favor_positive = FALSE, ## variable_name = deparse(substitute(y))
y |
Can be any vector, some of whose values may be |
type |
Missing or a character string among the classes that inherit from the missing_variable
class. If missing, the constructor will guess (sometimes incorrectly) based on the characteristics
of the variable. The best way to improve the guessing of categorical variables is to
use the |
... |
Further hidden arguments that are not in the signature. The |
The missing_variable function returns an object that inherits from the missing_variable class.
The missing_variable class is virtual, so no objects
may be created from it. However, the missing_variable generic function can be used to
instantiate an object that inherits from the missing_variable class by specifying its
type argument. A user would call the missing_data.frame
function on a data.frame, which in turn calls the missing_variable function
on each column of the data.frame using various heuristics to guess the
type argument.
In the following table, indentation indicates inheritance from the class with less indentation, and
italics indicates that the class is virtual so no variables can be created with that class. Inherited
classes inherit the transformations, families, link functions, and fit_model-methods
from their parent class, although these are often superceeded by analogues that are tailored for the
inherited class. Also note, the default transformation for the continuous class is a standardization
using twice the standard deviation of the observed values.
The distinction between the transformation entailed by the family and the transformation
entailed by the function in the tranformation slot may be confusing at this point. The former pertains
to how the linear predictor of a variable is mapped to the space of a variable when it is on the left-hand
side of a generalized linear model. The latter pertains — for continuous variables only — to how the
values in the raw_data slot are mapped into those in the data and thus affects how a continuous
variable enters into the model whether it is on the left or right-hand side. The classes are discussed in
much more detail below.
| Class name [transformation] | Default family and link | Default fit_model |
| missing_variable | none | throws error |
| categorical | none | throws error |
| unordered-categorical | binomial(link = 'logit') |
multinom |
| ordered-categorical | binomial(link = 'logit') |
bayespolr |
| binary | binomial(link = 'logit') |
bayesglm |
| interval | gaussian{link = 'identity'} |
survreg |
| continuous[standardize] | gaussian{link = 'identity'} |
bayesglm |
| semi-continuous[identity] | ||
| nonnegative-continuous[logshift] | ||
| SC_proportion[squeeze] | binomial(link = 'logit') |
betareg |
positive-continuous[log] |
||
| proportion[identity] | binomial(link = 'logit') |
betareg |
| bounded-continuous[identity] | ||
| count | quasipoisson{link = 'log'} |
bayesglm |
| irrelevant | throws error | |
| fixed | throws error | |
The missing_variable class is virtual and has the following slots (this information is primarily directed at developeRs):
variable_name:Object of class character of length one naming the variable
raw_data:Object of class "ANY" representing the observations
on a variable, some of which may be NA. No method should ever change
this slot at all. Instead, methods should change the data slot.
data:Object of class "ANY", which is initially a copy of the
raw_data slot — transformed by the function in the transformation slot
for continuous variables only — and whose NA values are replaced during
the multiple imputation process. See mi
n_total:Object of class "integer" which is the length
of the data slot
all_obs:Object of class "logical" of length one indicating whether
all values of the data slot are observed and thus not NA
n_obs:Object of class "integer" of length one indicating the number
of values of the data slot that are observed and thus not NA
which_obs:Object of class "integer", which is a vector indicating
the positions of the observed values in the data slot
all_miss:Object of class "logical" of length one indicating whether
all values of the data slot are NA
n_miss:Object of class "integer" of length one indicating the number
of values of the data slot that are NA
which_miss:Object of class "integer", which is a vector indicating
the positions of the missing values in the data slot
n_extra:Object of class "integer" of length one indicating how many
(missing) observations have been added to the end of the data slot that are not
included in the raw_data slot. Although the extra values will be imputed, they
are not considered to be “missing” for the purposes of defining the previous
three slots
which_extra:Object of class "integer", which is a vector indicating
the positions of the extra values at the end of the data slot
n_unpossible:Object of class "integer" of length one indicating the
number of values that are logically or structurally unobservable
which_unpossible:Object of class "integer" indicating the positions
of the unpossible values in the data slot
n_drawn:Object of class "integer" of length one which is the sum of
the n_miss and n_extra slots
which_drawn:Object of class "integer" which is a vector concatinating
the which_miss and which_extra slots
imputation_method:Object of class "character" of length one indicating
how the NA values are to be imputed. Possibilities include “ppd” for
imputation from the posterior predictive distribution, “pmm” for imputation via
predictive mean matching, “mean” for mean-imputation, “median” for
median-imputation, “expectation” for conditional mean-imputation. With enough
programming effort, other kinds of imputation can be defined and specified here.
family:Object of class "WeAreFamily" that will typically be passed to
glm and similar functions during the multiple imputation process
known_families:Object of class character indicating the families
that are known to be supported for a class; see family
known_links:Object of class character indicating what link functions
are known to be supported by the elements of the known_families slot; see
family
imputations:Object of class "MatrixTypeThing" with rows equal to the number
of iterations (initially zero) of the multiple imputation algorithm and columns equal to the
n_drawn slot. The rows are appropriately extended and then filled by the
mi function
done:Object of class "logical" of length one indicating whether the
NA values in the data slot have been replaced by imputed values
parameters:Object of class "MatrixTypeThing" with rows equal to the number
of iterations (initially zero) of the multiple imputation algorithm and columns equal to the number
of estimated parameters when modeling the data slot. The rows are appropriately extended
and then filled by the mi function
model:Object of class "ANY" which can be filled by an object that is output
by one of the fit_model-methods, which is done by default by mi
when all the iterations have completed
fitted:Object of class "ANY" although typically a vector or matrix that
contains the fitted values of the model in the slot immediately above. Note that the
fitted slot is filled by default by mi, although the model slot
is left empty by default to save RAM.
estimator:Object of class "character" of length one indicating which pre-existing
fit_model to use for an unordered-categorical variable. Options are "mnl", in which
multinom from the nnet package is used to fit the values of the unordered
categorical variable; and "rnl", in which each category is separately modeled as the positive
binary outcome against all other categories using a bayesglm fit_model and
the probabilities of each category are normalized to sum to 1 after each model is run. In general,
"rnl" is slightly less accurate than "mnl", but runs much more quickly especially when
the unordered categorical variable has many unique categories.
The WeAreFamily class is a class union of character and family, while the
MatrixTypeThing class is a class union of matrix only at the moment.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_data.frame, categorical-class, unordered-categorical-class,
ordered-categorical-class, binary-class, interval-class,
continuous-class, semi-continuous-class, nonnegative-continuous-class,
SC_proportion-class, censored-continuous-class,
truncated-continuous-class, bounded-continuous-class,
positive-continuous-class, proportion-class, count-class
# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "continuous") show(income) # STEP 1: CONVERT IT TO A missing_data.frame mdf <- missing_data.frame(nlsyV) # this calls missing_variable() internally show(mdf)# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "continuous") show(income) # STEP 1: CONVERT IT TO A missing_data.frame mdf <- missing_data.frame(nlsyV) # this calls missing_variable() internally show(mdf)
This class is similar to a data.frame but is customized for the situation in
which variables with missing data are being modeled for multiple imputation. This class primarily
consists of a list of missing_variables plus slots containing metadata indicating how the
missing_variables relate to each other. Most operations that work for a
data.frame also work for a missing_data.frame.
missing_data.frame(y, ...) ## Hidden arguments not included in the signature ## favor_ordered = TRUE, favor_positive = FALSE, ## subclass = NA_character_, ## include_missingness = TRUE, skip_correlation_check = FALSEmissing_data.frame(y, ...) ## Hidden arguments not included in the signature ## favor_ordered = TRUE, favor_positive = FALSE, ## subclass = NA_character_, ## include_missingness = TRUE, skip_correlation_check = FALSE
y |
Usually a |
... |
Hidden arguments. The Any further arguments are passed to the |
In most cases, the first step of an analysis is for a useR to call the
missing_data.frame function on a data.frame whose variables
have some NA values, which will call the missing_variable
function on each column of the data.frame and return the list
that fills the variable slot. The classes of the list elements will depend on the
nature of the column of the data.frame and various fallible heuristics. The
success rate can be enhanced by making sure that columns of the original
data.frame that are intended to be categorical variables are
(ordered if appropriate) factors with labels. Even in the best case
scenario, it will often be necessary to utlize the change function to
modify various discretionary aspects of the missing_variables in the
variables slot of the missing_data.frame. The show method for
a missing_data.frame should be utilized to get a quick overview of the
missing_variables in a missing_data.frame and recognized what needs
to be changed.
The missing_data.frame constructor function returns an object of class missing_data.frame
or that inherits from the missing_data.frame class.
Objects can be created by calls of the form new("missing_data.frame", ...).
However, useRs almost always will pass a data.frame to the
missing_data.frame constructor function to produce an object of missing_data.frame class.
This section is primarily aimed at developeRs. A missing_data.frame inherits from
data.frame but has the following additional slots:
variables:Object of class "list" and each list element
is an object that inherits from the missing_variable-class
no_missing:Object of class "logical", which is a vector
whose length is the same as the length of the variables slot indicating
whether the corresponding missing_variable is fully observed
patterns:Object of class factor whose length is equal
to the number of observation and whose elements indicate the missingness pattern
for that observation
DIM:Object of class "integer" of length two indicating
first the number of observations and second the length of the variables
slot
DIMNAMES:Object of class "list" of length two providing
the appropriate number rownames and column names
postprocess:Object of class "function" used to create
additional variables from existing variables, such as interactions between
two missing_variables once their missing values have been
imputed. Does not work at the moment
index:Object of class "list" whose length is equal to
the number of missing_variables with some missing values. Each
list element is an integer vector indicating which columns of the X
slot must be dropped when modeling the corresponding missing_variable
X:Object of MatrixTypeThing-class with rows equal to the
number of observations and is loosely related to a model.matrix. Rather
than repeatedly parsing a formula during the multiple imputation process,
this X matrix is created once and some of its columns are dropped when
modeling a missing_variable utilizing the index slot.
The columns of the X matrix consists of numeric representations of the
missing_variables plus (by default) the unique missingness patterns
weights:Object of class "list" whose length is equal to one
or the number of missing_variables with some missing values. Each
list element is passed to the corresponding argument of bayesglm
and similar functions. In particular, some observations can be given a weight
of zero, which should drop them when modeling some missing_variables
priors:Object of class "list" whose length is equal to the number
of missing_variables and whose elements give appropriate values for
the priors used by the model fitting function wraped by the fit_model-methods;
see, e.g., bayesglm
correlations:Object of class "matrix" with rows and
columns equal to the length of the variables slot. Its strict upper
triangle contains Spearman correlations between pairs of
variables (ignoring missing values), and its strict lower triangle contains
Squared Multiple Correlations (SMCs) between a variable and all other
variables (ignoring missing values). If either a Spearman correlation or
a SMC is very close to unity, there may be difficulty or error messages
during the multiple imputation process.
done:Object of class "logical" of length one indicating
whether the missing values have been imputed
workpath:Object of class character of length one indicating
the path to a working directory that is used to store some objects
There are many methods that are defined for a missing_data.frame, although some are primarily intended for developers. The most relevant ones for users are:
signature(data = "missing_data.frame", y = "ANY", what = "character", to = "ANY")
which is used to change discretionary aspects of the missing_variables
in the variables slot of a missing_data.frame
signature(x = "missing_data.frame") which shows histograms
of the observed variables that have missingness
signature(x = "missing_data.frame") which plots
an image of the missingness slot to visualize the pattern of missingness
when grayscale = FALSE or the pattern of missingness in light of the
observed values (grayscale = TRUE, the default)
signature(y = "missing_data.frame", model = "missing") which
multiply imputes the missing values
signature(object = "missing_data.frame") which gives an overview
of the salient characteristics of the missing_variables in the
variables slot of a missing_data.frame
signature(object = "missing_data.frame") which produces the same
result as the summary method for a data.frame
There are also S3 methods for the dim, dimnames, and names
generics, which allow functions like nrow, ncol, rownames,
colnames, etc. to work as expected on missing_data.frames. Also, accessing
and changing elements for a missing_data.frame mostly works the same way as for a
data.frame
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
change, missing_variable, mi,
experiment_missing_data.frame, multilevel_missing_data.frame
# STEP 0: Get data data(CHAIN, package = "mi") # STEP 1: Convert to a missing_data.frame mdf <- missing_data.frame(CHAIN) # warnings about missingness patterns show(mdf) # STEP 2: change things mdf <- change(mdf, y = "log_virus", what = "transformation", to = "identity") # STEP 3: look deeper summary(mdf) hist(mdf) image(mdf) # STEP 4: impute ## Not run: imputations <- mi(mdf) ## End(Not run) ## An example with subsetting on a fully observed variable data(nlsyV, package = "mi") mdfs <- missing_data.frame(nlsyV, favor_positive = TRUE, favor_ordered = FALSE, by = "first") mdfs <- change(mdfs, y = "momed", what = "type", to = "ord") show(mdfs)# STEP 0: Get data data(CHAIN, package = "mi") # STEP 1: Convert to a missing_data.frame mdf <- missing_data.frame(CHAIN) # warnings about missingness patterns show(mdf) # STEP 2: change things mdf <- change(mdf, y = "log_virus", what = "transformation", to = "identity") # STEP 3: look deeper summary(mdf) hist(mdf) image(mdf) # STEP 4: impute ## Not run: imputations <- mi(mdf) ## End(Not run) ## An example with subsetting on a fully observed variable data(nlsyV, package = "mi") mdfs <- missing_data.frame(nlsyV, favor_positive = TRUE, favor_ordered = FALSE, by = "first") mdfs <- change(mdfs, y = "momed", what = "type", to = "ord") show(mdfs)
These methods change the family, imputation method, size, type, and
so forth of a missing_variable. They are typically
called immediately before calling mi because they
affect how the conditional expectation of each missing_variable
is modeled.
change(data, y, to, what, ...) change_family(data, y, to, ...) change_imputation_method(data, y, to, ...) change_link(data, y, to, ...) change_model(data, y, to, ...) change_size(data, y, to, ...) change_transformation(data, y, to, ...) change_type(data, y, to, ...)change(data, y, to, what, ...) change_family(data, y, to, ...) change_imputation_method(data, y, to, ...) change_link(data, y, to, ...) change_model(data, y, to, ...) change_size(data, y, to, ...) change_transformation(data, y, to, ...) change_type(data, y, to, ...)
data |
A |
y |
A character vector (typically) naming one or more |
what |
Typically a character string naming what is to be changed, such
as |
to |
Typically a character string naming what |
... |
Other arguments, not currently utilized |
In order to run mi correctly, data must first be specified to be ready for multiple imputation using the missing_data.frame function. For each variable, missing_data.frame will record information required by mi: the variable's type, distribution family, and link function; whether a variable should be standardized or tranformed by a log function or square root; what specific model to use for the conditional distribution of the variable in the mi algorithm and how to draw imputed values from this model; and whether additional rows (for the purposes of prediction) are required. missing_data.frame will attempt to guess the correct type, family, and link for each variable based on its class in a regular data.frame. These guesses can be checked with show and adjusted if necessary with change. Any further additions to the model in regards to variable transformations, custom conditional models, or extra non-observed predictive cases must be specified with change before mi is run.
In general, most users will only use the change command. change will then call change_family, change_imputation_method, change_link, change_model, change_size, change_transformation, or change_type depending on what characteristic is specified with the what option. The other change_* functions can be called directly but are primarily intended to be called indirectly by the change function.
what = "type"Change the subclass of variable(s) y. to should be a character vector whose elements are subclasses of the missing_variable-class and are documented further there. Among the most commonly used subclasses are "unordered-categorical", "ordered-categorical", "binary", "interval", "continuous", "count", and "irrelevant".
what = "family"Change the distribution family for variable(s) y. to must be of class family or a list where each element is of class family. If a variable is of binary-class, then the family must be binomial (the default) or possibly quasibinomial. If a variable is of ordered-categorical-class or unordered-categorical-class, use the multinomial family. If a variable is of count-class, then the family must be quasipoisson (the default) or poisson. If a variable is continuous, there are more choices for its family, but gaussian is the default and the others are not supported yet.
what = "link"Change the link function for variable(s) y. to can be any of the supported link functions for the existing family. See family for details; however, not all of these link functions have appropriate fit_model and mi-methods yet.
what = "model"Change the conditional model for variable y. It usually is not necessary to change the model, since it is actually determined by the class, family, and link function of the variable. This option can be used, however, to employ models that are not among those listed above.to should be a character vector of length one indicating what model should be used during the imputation process. Valid choices for binary variables include "logit", "probit" "cauchit", "cloglog", or quasilikelihoods "qlogit", "qprobit", "qcauchit", "qcloglog". For ordinal variables, valid choices include "ologit", "oprobit", "ocauchit", and "ocloglog". For count variables, valid choices include "qpoisson" and "poisson". Currently the only valid option for gaussian variables is "linear". To change the model for unordered-categorical variables, see the estimator slot in missing_variable.
what = "imputation_method"Change the method for drawing imputed values from the conditional model specified for variable(s) y. to should be a character vector of length one or of the same length as y naming one of the following imputation methods: "ppd" (posterior predictive distribution), "pmm" (predictive mean matching), "mean" (mean imputation), "median" (median imputation), "expectation" (conditional expectation imputation).
what = "size"Optionally add additional rows for the purposes of prediction. to should be a single integer. If to is non-negative but less than the number of rows in the missing_data.frame given by the data argument, then missing_data.frame is augmented with to more rows, where all the additional observations are missing. If to is greater than the number of rows in the missing_data.framegiven by the data argument, then the missing_data.frame is extended to have to rows, where the observations in the surplus rows are missing. If to is negative, then any additional rows in the missing_data.frame given by the data argument are removed to restore it to its original size.
what = "transformation"Specify a particular transformation to be applied to variable(s) y. to should be a character vector of length one or of
the same length as y indicating what transformation function to use. Valid choices are "identity" for no transformation, "standardize" for standardization (using twice the standard deviation of the observed values), "log" for natural logarithm transformation, "logshift" for a log(y + a) transformation where a is a small constant, or "sqrt" for square-root transformation. Changing the transformation will also change the inverse transformation in the appropriate way. Any other value of to will produce an informative error message indicating that the transformation and inverse transformation need to be changed manually.
Finally, if both what and to are values then the former is recoded to the latter for all
occurances within the missing variable indicated by y.
If the data argument is not missing, then the method returns this argument with the
specified changes. If data is missing, then the method returns an object that inherits
from the missing_variable-class with the specified changes.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, missing_data.frame
# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 1: CONVERT IT TO A missing_data.frame mdf <- missing_data.frame(nlsyV) show(mdf) # STEP 2: CHANGE WHATEVER IS WRONG WITH IT mdf <- change(mdf, y = "momrace", what = "type", to = "un") mdf <- change(mdf, y = "income", what = "imputation_method", to = "pmm") mdf <- change(mdf, y = "binary", what = "family", to = binomial(link = "probit")) mdf <- change(mdf, y = 5, what = "transformation", to = "identity") show(mdf)# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 1: CONVERT IT TO A missing_data.frame mdf <- missing_data.frame(nlsyV) show(mdf) # STEP 2: CHANGE WHATEVER IS WRONG WITH IT mdf <- change(mdf, y = "momrace", what = "type", to = "un") mdf <- change(mdf, y = "income", what = "imputation_method", to = "pmm") mdf <- change(mdf, y = "binary", what = "family", to = binomial(link = "probit")) mdf <- change(mdf, y = 5, what = "transformation", to = "identity") show(mdf)
The mi function cannot be run in isolation. It is the most important step of a multi-step process to perform multiple imputation. The data must be specified as a missing_data.frame before mi is used to impute missing values for one or more missing_variables. An iterative algorithm is used where each missing_variable is modeled (using fit_model) as a function of all the other missing_variables and their missingness patterns. This documentation outlines the technical uses of the mi function. For a more general discussion of how to use mi for multiple imputation, see mi-package.
mi(y, model, ...) ## Hidden arguments: ## n.iter = 30, n.chains = 4, max.minutes = Inf, seed = NA, verbose = TRUE, ## save_models = FALSE, parallel = .Platform$OS.type != "windows"mi(y, model, ...) ## Hidden arguments: ## n.iter = 30, n.chains = 4, max.minutes = Inf, seed = NA, verbose = TRUE, ## save_models = FALSE, parallel = .Platform$OS.type != "windows"
y |
Typically an object that inherits from the |
model |
Missing when |
... |
Further arguments, the most important of which are
|
It is important to distinguish the two mi methods that are most relevant to users from the many mi methods that are less relevant. The primary mi method is that where y inherits from the missing_data.frame-class and model is omitted. This method “does” the imputation according to the additional arguments described under ... above and returns an object of class "mi". Executing two or more independent chains is important for monitoring the convergence
of each chain, see Rhats.
If the chains have not converged in the amount of iterations or time specified, the second important mi method is that where y is an object of class "mi" and model is omitted, which continues a previous run of the iterative imputation algorithm. All the arguments described under ... above remain applicable, except for n.chains and save_RAM because these are established by the previous run that is being continued.
The numerous remaining methods are of less importance to users. One mi method is called when y = "parallel" and model is omitted. This method merely sets up the parallel backend so that the chains can be executed in parallel on the local machine. We use the mclapply function in the parallel package to implement parallel processing on non-Windows machines, and we use the snow package to implement parallel processing on Windows machines; we refer users to the documentation for these packages for more detail about parallel processing. Parallel processing is used by default on machines with multiple processors, but sequential processing can be used instead by using the parallel=FALSE option. If the user is not using a mulitcore computer, sequential processing is used instead of parallel processing.
The first two mi methods described above in turn call a mi method where y inherits from the missing_data.frame-class and model is that which is returned by one of the fit_model-methods. The methods impute values for the originally missing values of a missing_variable given a fitted model, according to the imputation_method slot of the missing_variable in question. Advanced users could define new subclasses of the missing_variable-class in which case it may be necessary to write such a mi method for the new class. It will almost certainly be necessary to add to the
fit_model-methods. The existing mi and fit-model-methods should provide a template for doing so.
If model is missing and n.chains is positive, then the mi method will return an object of
class "mi", which has the following slots:
the call to mi
a list of missing_data.frames, one for each chain
an integer vector that records how many iterations have been performed
There are a few methods for such an object, such as show, summary,
dimnames, nrow, ncol, etc.
If mi is called on a missing_data.frame with model missing and a nonpositive
n.chains, then the missing_data.frame will be returned after allocating storeage.
If model is not missing, then the mi method will impute missing values for the y
argument and return it.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
# STEP 0: Get data data(CHAIN, package = "mi") # STEP 1: Convert to a missing_data.frame mdf <- missing_data.frame(CHAIN) # warnings about missingness patterns show(mdf) # STEP 2: change things mdf <- change(mdf, y = "log_virus", what = "transformation", to = "identity") # STEP 3: look deeper summary(mdf) # STEP 4: impute ## Not run: imputations <- mi(mdf) ## End(Not run)# STEP 0: Get data data(CHAIN, package = "mi") # STEP 1: Convert to a missing_data.frame mdf <- missing_data.frame(CHAIN) # warnings about missingness patterns show(mdf) # STEP 2: change things mdf <- change(mdf, y = "log_virus", what = "transformation", to = "identity") # STEP 3: look deeper summary(mdf) # STEP 4: impute ## Not run: imputations <- mi(mdf) ## End(Not run)
These functions are used to gauge whether mi has converged.
Rhats(imputations, statistic = c("moments", "imputations", "parameters")) mi2BUGS(imputations, statistic = c("moments", "imputations", "parameters"))Rhats(imputations, statistic = c("moments", "imputations", "parameters")) mi2BUGS(imputations, statistic = c("moments", "imputations", "parameters"))
imputations |
an object of |
statistic |
single character string among |
If statistic = "moments" (the default), then the mean and standard deviation of
each variable will be monitored over the iterations. If statistic = "imputations", then
the imputed values will be monitored, which may be quite large and quite slow and is not
possible if the save_RAM = TRUE flag was set in the call to the mi function.
If statistic = "parameters", then the estimated coefficients and ancillary parameters
extracted by the get_parameters-methods will be monitored.
Rhats produces a vector of R-hat convergence statistics that compare the variance between chains to the variance across chains. Values closer to 1.0 indicate little is to be gained by running the chains longer, and in general, values greater than 1.1 indicate that the chains should be run longer. See Gelman, Carlin, Stern, and Rubin, "Bayesian Data Analysis", Second Edition, 2009, p.304 for more information about the R-hat statistic.
mi2BUGS outputs the history of the indicated statistic
mi2BUGS returns an array while Rhats a vector of R-hat convergence statistics.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } dim(mi2BUGS(imputations)) Rhats(imputations)if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } dim(mi2BUGS(imputations)) Rhats(imputations)
This function estimates a chosen model, taking into account the additional uncertainty that arises due to a finite number of imputations of the missing data.
pool(formula, data, m = NULL, FUN = NULL, ...)pool(formula, data, m = NULL, FUN = NULL, ...)
formula |
|
data |
an object of |
m |
number of completed datasets to average over, which if |
FUN |
Function to estimate models or |
... |
further arguments passed to |
FUN is estimated on each of the m completed datasets according to the given
formula and the results are combined using the Rubin Rules.
An object of class "pooled" whose definition is subject to change but it has a
summary and display method.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace, data = imputations) display(analysis)if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace, data = imputations) display(analysis)
This function extracts several multiply imputed data.frames
from an object of mi-class.
complete(y, m, ...)complete(y, m, ...)
y |
An object of |
m |
If y is an object of |
... |
Other arguments, not currently utilized |
Several functions within mi use complete, although the only reason
in principle why a user should need to call complete is to create
data.frames to export to another program. For analysis, it is
better to use the pool function, although currently pool
might not offer all the necessary functionality.
If y is an object of mi-class and m > 1, a list
of m data.frames is returned. Otherwise, a single data.frame
is returned.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } data.frames <- complete(imputations, 3) lapply(data.frames, summary)if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } data.frames <- complete(imputations, 3) lapply(data.frames, summary)
This class inherits from the missing_data.frame-class but is customized for the situation where all the variables are categorical.
The fit_model-methods for the allcategorical_missing_data.frame class
implement a Gibbs sampler. However, it does not utilize any ordinal information that
may be available. Continuous variables should be made into factors using the
cut command before calling missing_data.frame.
Objects can be created by calls of the form new("allcategorical_missing_data.frame", ...).
However, its users almost always will pass a data.frame to the
missing_data.frame function and specify the subclass argument.
The allcategorical_missing_data.frame class inherits from the missing_data.frame-class and
has three additional slots
Positive integer indicating the maximum number of latent classes
A list that holds the current realization of the unknown parameters
An object of unordered-categorical-class that contains
the current realization of the latent classes
Sophie Si for the algorithm and Ben Goodrich for the R implementation
rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "stratified", types = "ord") mdf <- missing_data.frame(rdf$obs, subclass = "allcategorical")rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "stratified", types = "ord") mdf <- missing_data.frame(rdf$obs, subclass = "allcategorical")
The bounded-continuous class inherits from the continuous-class and is intended for variables whose observations
fall within open intervals that have known boundaries. Although proportions satisfy this definition, the
proportion-class should be used in that case. At the moment, a bounded continuous variable is modeled as if it were
simply a continuous variable, but its mi-methods impute the missing values from a truncated normal distribution using
the rtruncnorm function in the truncnorm package. Note that the default transformation is the identity
so if another transformation is used, the bounds must be specified on the transformed data. Aside from these facts, the rest of the
documentation here is primarily directed toward developers.
Objects can be created that are of bounded-continuous class via the
the missing_variable generic function by specifying type = "bounded-continuous"
as well as lower and / or upper
The bounded-continuous class inherits from the continuous class and is intended for variables that are supported on a known
interval. Its default transformation function is the identity transformation and its imputation_method must be
"ppd". It has two additional slots:
a numeric vector whose length is either one or the value of the n_total slot giving the upper bound for
every observation; NAs are not allowed
a numeric vector whose length is either one or the value of the n_total slot giving the lower bound for
every observation; NAs are not allowed
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, continuous-class, positive-continuous-class,
proportion-class
# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) lo_bound <- 0 hi_bound <- rep(Inf, nrow(CHAIN)) hi_bound[CHAIN$log_virus == 0] <- 6 log_virus <- missing_variable(ifelse(CHAIN$log_virus == 0, NA, CHAIN$log_virus), type = "bounded-continuous", lower = lo_bound, upper = hi_bound) show(log_virus)# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) lo_bound <- 0 hi_bound <- rep(Inf, nrow(CHAIN)) hi_bound[CHAIN$log_virus == 0] <- 6 log_virus <- missing_variable(ifelse(CHAIN$log_virus == 0, NA, CHAIN$log_virus), type = "bounded-continuous", lower = lo_bound, upper = hi_bound) show(log_virus)
The categorical class is a virtual class that inherits from the missing_variable-class
and is the parent of the unordered-categorical and ordered-categorical classes. The ordered-categorical
class is the parent of both the binary and interval classes. Aside from these facts, the rest of the
documentation here is primarily directed toward developers.
The categorical class is virtual, so no objects
may be created from it. However, the missing_variable generic function can be used to
instantiate an object that inherits from the categorical class by specifying
type = "unordered-categorical", type = "ordered-categorical",
type = "binary", type = "grouped-binary", or type = "interval".
The unordered-categorical class inherits from the categorical class and has no additional slots
but must have more than two uniquely observed values in its raw_data slot. The default fit_model
method is a wrapper for the multinom function in the nnet package. The ordered-categorical
class inherits from the categorical class and has one additional slot:
Object of class "numeric" which is a vector of thresholds (sometimes estimated) that
govern how an assumed latent variable is divided into observed ordered categories
The fit_model method for an ordered-categorical variable is, by default, a wrapper for
bayespolr. The binary class inherits from the ordered-categorical class and has no additional slots.
It must have exactly two uniquely observed values in its raw_data slot and its fit_model method is,
by default, a wrapper for bayespolr. The grouped-binary class inherits from the binary class and
has one additional slot:
Object of class "character" which is a vector (possibly of length one) of variable names that
group the observations into strata. The named external variables should also be categorical.
The default fit_model method for a grouped-binary variable is a wrapper for the clogit
function in the survival package and the variables named in the strata slot are passed to the
strata function.
The interval class inherits from the ordered-categorical class, has no additional slots, and is intended for variables
whose observed values are only known up to orderable intervals. Its fit_model method is, by default, a
wrapper for survreg even though it may or may not be a “survival” model in any meaningful sense.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) momrace <- missing_variable(as.factor(nlsyV$momrace), type = "unordered-categorical") show(momrace)# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) momrace <- missing_variable(as.factor(nlsyV$momrace), type = "unordered-categorical") show(momrace)
The censored-continuous class and the truncated-continuous class are both virtual and both inherit from the continuous-class
and each is the parent of four classes that differ depending on whether the lower and upper bounds are numeric vectors or functions. A
censored observation is one whose exact value is not observed. A truncated observation is one whose exact value is not observed and which
implies that values on some other variables are not observed for that unit of observation. An example of truncation might be that
some taxation forms are not required when a person's income falls below a certain threshold. The methods for these classes are not
working yet. Aside from these facts, the rest of the documentation here is primarily directed toward developeRs.
Both the censored-continuous class and the truncated-continuous class are virtual, so no objects can be
created with these classes. However, the missing_variable generic function can be used to create an object that inherits
from one of their subclasses by specifying type = "NNcensored-continuous", type = "NFcensored-continuous",
type = "FNcensored-continuous", type = "FFcensored-continuous", type = "NNtruncated-continuous", type = "NFtruncated-continuous",
type = "FNtruncated-continuous", type = "FFtruncated-continuous". When doing so, the lower and upper slots need to be
specified appropriately.
The censored-continuous class and the truncated-continuous class are both virtual, both inherit from the continuous class, both use the identity transformation by default, and both have two additional slots:
The upper bound for each observation
The lower bound for each observation
Both the censored-continuous class and the truncated-continuous class have four subclasses that differ depending
on whether the upper and / or lower bounds are numeric vectors or functions that output numeric
vectors (scalars are recycled and can be Inf). These subclasses are
where both the lower and upper bounds are numeric vectors
where the lower bound is a function and the upper bound is a numeric vector
where the lower bound is a numeric vector and the upper bound is a function
where both the lower and upper bounds are functions
where both the lower and upper bounds are numeric vectors
where the lower bound is a function and the upper bound is a numeric vector
where the lower bound is a numeric vector and the upper bound is a function
where both the lower and upper bounds are functions
Ben Goodrich, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, continuous-class
# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) #log_virus <- missing_variable(CHAIN$log_virus, type = "NN_censored-continuous", # lower = 0, upper = Inf) #show(log_virus)# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) #log_virus <- missing_variable(CHAIN$log_virus, type = "NN_censored-continuous", # lower = 0, upper = Inf) #show(log_virus)
The CHAIN project was a longitudinal cohort study of people living with HIV in New York City, which was recruited in 1994 from a large number of medical care and social service agencies serving HIV in New York City. This subset of data pertain to the sixth round of interviews.
data(CHAIN)data(CHAIN)
A data.frame with 532 observations on the following 8 variables.
log_viruslog of self reported viral load level, where zero represents an undetectable level.
ageage at time of the interview
incomeannual family income in 10 intervals
healthya continuous scale of physical health with a theoretical range between 0 and 100 where better health is associated with higher scale values
mentala binary measure of poor mental health ( 1=Yes, 0=No )
damageordered interval for the CD4 count, which is an indicator of how much damage HIV has caused to the immune system
treatmenta three-level ordered variable: 0=Not currently taking HAART (Highly Active AntiretRoviral Therapy) 1=taking HAART but nonadherent, 2=taking HAART and adherent
A missing value in the log virus load level was assigned to individuals who either could not recall their viral load level, did not have a viral load test in the six month preceding the interview, or reported their viral loads as "good" or "bad".
http://cchps.columbia.edu/research.cfm
Messeri P, Lee G, Abramson DA, Aidala A, Chiasson MA, Jones JD. (2003). “Antiretroviral therapy and declining AIDS mortality in New York City”. Medical Care 41:512–521.
The continuous class inherits from the missing_variable-class and is the parent of the following
classes: semi-continuous, censored-continuous, truncated-continuous,
and bounded-continuous. The distinctions
among these subclasses are given on their respective help pages. Aside from these facts, the rest of the
documentation here is primarily directed toward developers.
Objects can be created that are of class continuous via
the missing_variable generic function by specifying type = "continuous"
The continuous class inherits from the missing_variable class and has the following additional slots:
Object of class "function" which is passed the raw_data slot and
whose returned value is assigned to the data slot. By default, this function is the
“standardize” transformation, using the mean and twice the standard deviation of the
observed values
Object of class "function" which is the inverse of the function
in the transformation slot.
Object of class "logical" of length one indicating whether the
data slot is in the “transformed” state or the “untransformed” state
Object of class "character" indicating which transformations
are possible for this variable
The fit_model method for a continuous variable is, by default, a wrapper for
bayesglm and its family slot is, by default, gaussian
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, semi-continuous-class, censored-continuous-class,
truncated-continuous-class, bounded-continuous-class
# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "continuous") show(income)# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "continuous") show(income)
The count class inherits from the missing_variable-class and is intended for count data.
Aside from these facts, the rest of the documentation here is primarily directed toward developers.
Objects can be created that are of count class via the
missing_variable generic function by specifying type = "count"
The count class inherits from the missing_variable class and its raw_data slot must consist of nonnegative
integers. Its default family is quasipoisson and its default fit_model method is
a wrapper for bayesglm. The other possibility for the family is poisson but is
not recommended due to its overly-restrictive nature.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, continuous-class, positive-continuous-class,
proportion-class
# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) age <- missing_variable(as.integer(CHAIN$age), type = "count") show(age)# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) age <- missing_variable(as.integer(CHAIN$age), type = "count") show(age)
This class inherits from the missing_data.frame-class but is customized for the situation
where the sample is a randomized experiment.
The fit_model-methods for the experiment_missing_data.frame class take into account the
special nature of a randomized experiment. At the moment, the treatment variable must be binary and
fully observed.
Objects can be created by calls of the form new("experiment_missing_data.frame", ...).
However, its users almost always will pass a data.frame to the
missing_data.frame function and specify the subclass and concept arguments.
The experiment_missing_data.frame class inherits from the missing_data.frame-class and
has two additional slots
Object of class factor whose length is equal to the number of variables
and whose levels are "treatment", "covariate" and "outcome"
Object of class character of length one, indicating whether the missingness
is in the outcomes only, in the covariates only, or in both the outcomes and covariates. This slot
is filled automatically by the initialize method
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "stratified", experiment = TRUE, types = c("t", "ord", "con", "pos"), treatment_cor = c(0, 0, NA, 0, NA)) Sigma <- tcrossprod(rdf$L) rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2", "missing_y_1", "missing_Y_2") print(round(Sigma, 3)) concept <- as.factor(c("treatment", "covariate", "covariate", "outcome")) mdf <- missing_data.frame(rdf$obs, subclass = "experiment", concept = concept)rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "stratified", experiment = TRUE, types = c("t", "ord", "con", "pos"), treatment_cor = c(0, 0, NA, 0, NA)) Sigma <- tcrossprod(rdf$L) rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2", "missing_y_1", "missing_Y_2") print(round(Sigma, 3)) concept <- as.factor(c("treatment", "covariate", "covariate", "outcome")) mdf <- missing_data.frame(rdf$obs, subclass = "experiment", concept = concept)
The methods are called by the mi function to model a given
missing_variable as a function of all the other
missing_variables and also their missingness pattern.
By overwriting these methods, users can change the way a
missing_variable is modeled for the purposes of imputing
its missing values. See also the table in missing_variable.
fit_model(y, data, ...)fit_model(y, data, ...)
y |
An object that inherits from |
data |
|
... |
Additional arguments, not currently utilized |
In mi, each missing_variable is modeled as a function of
all the other missing_variables plus their missingness pattern. The
fit_model methods are typically short wrappers around a statistical model fitting
function and return the estimated model. The model is then passed to one of the
mi-methods to impute the missing values of that missing_variable.
Users can easily overwrite these methods to estimate a different model, such as wrapping
glm instead of bayesglm. See the source code for examples,
but the basic outline is to first extract the X slot of the
missing_data.frame, then drop some of its columns using the index slot
of the missing_data.frame, next pass the result along with the data slot
of y to a statistical fitting function, and finally returned the appropriately classed
result (along with the subset of X used in the model).
Many of the optional arguments to a statistical fitting function can be specified using the
slots of y (e.g. its family slot) or the slots of data (e.g. its
weights slot).
The exception is the method where y is missing, which is used internally by
mi, and should not be overwritten unless great care is taken to understand
its role.
If y is missing, then the modified missing_data.frame passed to
data is returned. Otherwise, the estimated model is returned as a classed
list object.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, mi, get_parameters
getMethod("fit_model", signature(y = "binary", data = "missing_data.frame")) setMethod("fit_model", signature(y = "binary", data = "missing_data.frame"), def = function(y, data, ...) { to_drop <- data@index[[y@variable_name]] X <- data@X[, -to_drop] start <- NULL # using glm.fit() instead of bayesglm.fit() out <- glm.fit(X, y@data, weights = data@weights[[y@variable_name]], start = start, family = y@family, Warning = FALSE, ...) out$x <- X class(out) <- c("glm", "lm") # not "bayesglm" class anymore return(out) }) ## Not run: if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } imputations <- mi(imputations) # will use new fit_model() method for binary variables ## End(Not run)getMethod("fit_model", signature(y = "binary", data = "missing_data.frame")) setMethod("fit_model", signature(y = "binary", data = "missing_data.frame"), def = function(y, data, ...) { to_drop <- data@index[[y@variable_name]] X <- data@X[, -to_drop] start <- NULL # using glm.fit() instead of bayesglm.fit() out <- glm.fit(X, y@data, weights = data@weights[[y@variable_name]], start = start, family = y@family, Warning = FALSE, ...) out$x <- X class(out) <- c("glm", "lm") # not "bayesglm" class anymore return(out) }) ## Not run: if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } imputations <- mi(imputations) # will use new fit_model() method for binary variables ## End(Not run)
This function is not intended to be called directly by users.
During the multiple imputation process, the mi
function estimates models and stores the estimated parameters in the
parameters slot of an object that inherits from the
missing_variable-class. The get_parameter
function simply extracts these parameters for storeage, which are
usually the estimated coefficients but may also include ancillary
parameters.
get_parameters(object, ...)get_parameters(object, ...)
object |
Usually an estimated model, such as that produced by |
... |
Additional arguments, currently not used |
There is method for the object produced by polr, which
also returns the estimated cutpoints in a proportional odds model. However,
the default method simply calls coef and returns the result.
If users implement their own models, it may be necessary to write a short
get_parameters method.
A numeric vector of estimated parameters
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
showMethods("get_parameters")showMethods("get_parameters")
This function creates a histogram from an object of
missing_data.frame-class or
mi-class
hist(x, ...)hist(x, ...)
x |
an object of |
... |
further arguments passed to |
When called on an object of missing_data.frame-class, the
histograms of the observed data are generated, one for each missing_variable
but grouped on a single page. When called on an object of mi-class,
the histograms of the observed, imputed, and completed data are generated, one for each
missing_variable, grouped on a single page for each chain.
An invisible NULL is returned with a side-effect of creating a plot
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } hist(imputations)if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } hist(imputations)
The irrelevant class inherits from the missing_variable-class and is used to
designate variables that are excluded from the models used to impute the missing values of
“relevant” variables. For example, if a survey has an “id” variable that
simply distinguishes observations, the user should designate it as irrelevant, although it
will automatically be classified so if its name is either “id” or starts with punctuation
(including underscores). The fixed class inherits from the irrelevant class and is used
for variables that are constant (within a sample). A variable that is instantiated from the
fixed class cannot have any missing values. The group class inherits from the fixed
class and is used like a factor to spit samples in multilevel modeling; see
multilevel_missing_data.frame-class. None of these classes have an additional
slots. Aside from these facts, the rest of the documentation here is primarily directed toward developeRs.
The missing_variable generic function can be used to
instantiate an object that inherits from the irrelevant class by specifying
type = "irrelevant", type = "fixed", or type = "group".
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) first <- missing_variable(as.factor(nlsyV$first), type = "group") show(first)# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) first <- missing_variable(as.factor(nlsyV$first), type = "group") show(first)
This function exports completed data from an object of mi-class in which m completed
data.frames are appended to the end of the raw data. Two additional variables are added which indicate the row number and distinguish the data.frames. The outputed file is either Stata
(.dta) or comma-separated (.csv) format, and can be easily registered in Stata as multiply imputed data.
mi2stata(imputations, m, file, missing.ind=FALSE, ...)mi2stata(imputations, m, file, missing.ind=FALSE, ...)
imputations |
Object of |
m |
The number of completed datasets to append onto the raw data |
file |
The filename, either a full path or relative to the working directory, where the file will be saved. Filenames must end in either '.dta' or '.csv'. Files with names ending in '.dta' will be saved as a Stata data file, and files with names ending in '.csv' will be saved as a comma-separated file. |
missing.ind |
If |
... |
Further arguments passed to |
The function calls complete to construct m completed data.frames, and uses
rbind to append them to the bottom of the raw data that still contains all of the missing values.
Two new variables are added: _mi, which contains the observation numbers; and _mj, which indexes the
data.frames.
To save a Stata .dta file, end the filename with '.dta'. To save a comma-separated file, end the filename with
.csv'. Stata files are loaded into Stata using Stata's use command, and comma-separated files can be loaded
by typing insheet using filename, comma names clear. Once the file is loaded into Stata, the
data must be registered as multiply imputed before any subsequent analyses can be performed. In Stata version 11 or
later, type mi import mice to register the data. The _mi and _mj variables will be replaced
by variables named _mi_id and _mi_m respectively. In Stata version 10 or earlier, install the
MIM package by typing findit mim and installing package st0139_1. Then the prefix mim:
must be added to any command using the multiply imputed data.
Any observations which are unpossible (legitimately skipped, and are not imputed, see
missing_variable) will remain missing in the complete data, but will not be indicated as missing by these variables. If there are
any unpossible values, missing indicators are included automatically.
NULL
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
complete, mi, write.dta, write.table
fn <- paste(tempfile(), "dta", sep = ".") if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } mi2stata(imputations, m=5, file=fn , missing.ind=TRUE)fn <- paste(tempfile(), "dta", sep = ".") if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } mi2stata(imputations, m=5, file=fn , missing.ind=TRUE)
This function is a wrapper around sapply that is invoked on the
data slot of an object of mi-class and / or on an object
of missing_data.frame-class after being coerced to a
data.frame
mipply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE, columnwise = TRUE, to.matrix = FALSE)mipply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE, columnwise = TRUE, to.matrix = FALSE)
X |
Object of |
FUN |
Function to call |
... |
Further arguments passed to |
simplify |
If |
USE.NAMES |
ignored but included for compatibility with |
columnwise |
logical indicating whether to invoke |
to.matrix |
Logical indicating whether to coerce each |
The columnwise and to.matrix arguments are the only additions to the argument list
in sapply, see the Examples section for an illustration of their use. Note that
functions such as mean only accept numeric inputs, which can produce
errors or warnings when to.matrix = FALSE.
A list, vector, or matrix depending on the arguments
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } round(mipply(imputations, mean, to.matrix = TRUE), 3) mipply(imputations, summary, columnwise = FALSE)if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } round(mipply(imputations, mean, to.matrix = TRUE), 3) mipply(imputations, summary, columnwise = FALSE)
This class inherits from the missing_data.frame-class but is customized for the situation
where the sample has a multilevel structure.
The fit_model-methods for the multilevel_missing_data.frame class will, by default, utilize
multilevel modeling techniques that shrink the estimated parameters for each group toward their global
means.
Objects can be created by calls of the form new("multilevel_missing_data.frame", ...).
However, its users almost always will pass a data.frame to the
missing_data.frame function and specify the subclass and groups arguments.
The multilevel_missing_data.frame class inherits from the missing_data.frame-class and
has two additional slots
Object of class character indicating which variables define the
multilevel structure
Object of class mdf_list whose elements contain a missing_data.frame
for each group. This slot is filled automatically by the initialize method.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
## Write example## Write example
This function is a returns a family and is a generalization of binomial.
users would only need to call it when calling change with
what = "family", to = multinomial(link = 'logit')
multinomial(link = "logit")multinomial(link = "logit")
link |
character string among those supported by |
This function is mostly cosmetic. The family slot for an object of
unordered-categorical-class must be multinomial(link = 'logit'). For
an object of ordered-categorical-class but not its subclasses, the family
slot must be multinomial() but the link function can differ from its default ("logit")
A family object
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
multinomial()multinomial()
This dataset pertains to children and their families in the United States and is intended to illustrate missing data issues. Note that although the original data are longitudinal, this extract is not.
data(nlsyV)data(nlsyV)
A data frame with 400 randomly subsampled observations on the following 7 variables.
ppvtr.36a numeric vector with data on the Peabody Picture Vocabulary Test (Revised) administered at 36 months
firstindicator for whether child was first-born
b.marrindicator for whether mother was married when child was born
incomea numeric vector with data on family income in year after the child was born
momagea numeric vector with data on the age of the mother when the child was born
momededucational status of mother when child was born (1 = less than high school, 2 = high school graduate, 3 = some college, 4 = college graduate)
momracerace of mother (1 = black, 2 = Hispanic, 3 = white)
Note that momed would typically be an ordered factor while momrace
would typically be an unorderd factor but both are numeric in this
data.frame in order to illustrate the mechanism to change the
type of a missing_variable
National Longitudinal Survey of Youth, 1997, https://www.bls.gov/nls/nlsy97.htm
data(nlsyV) summary(nlsyV)data(nlsyV) summary(nlsyV)
The positive-continuous class inherits from the continuous-class and is the parent of the proportion class.
In both cases, no observations can be zero, and in the case of the proportion class, no observations can be one. The
nonnegative-continuous-class and the SC_proportion-class are appropriate for those situations.
Aside from these facts, the rest of the
documentation here is primarily directed toward developeRs.
Objects can be created that are of positive-continuous or proportion class via the
missing_variable generic function by specifying type = "positive-continuous" or
type = "proportion"
The default transformation for the positive-continuous class is the log function. The proportion class inherits
from the positive-continuous class and has the identity transformation and the binomial family as defaults, in
which case the fit_model-methods call the betareg function in the betareg package.
Alternatively, the transformation could be an inverse CDF like the qnorm function and the family could be gaussian,
in which case the fit_model-methods call the bayesglm function in the arm package.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, continuous-class, positive-continuous-class,
proportion-class
# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) healthy <- missing_variable(CHAIN$healthy / 100, type = "proportion") show(healthy)# STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) healthy <- missing_variable(CHAIN$healthy / 100, type = "proportion") show(healthy)
This function generates a random data.frame with a
missingness mechanism that is used to impose a missingness pattern. The primary
purpose of this function is for use in simulations
rdata.frame(N = 1000, restrictions = c("none", "MARish", "triangular", "stratified", "MCAR"), last_CPC = NA_real_, strong = FALSE, pr_miss = .25, Sigma = NULL, alpha = NULL, experiment = FALSE, treatment_cor = c(rep(0, n_full - 1), rep(NA, 2 * n_partial)), n_full = 1, n_partial = 1, n_cat = NULL, eta = 1, df = Inf, types = "continuous", estimate_CPCs = TRUE)rdata.frame(N = 1000, restrictions = c("none", "MARish", "triangular", "stratified", "MCAR"), last_CPC = NA_real_, strong = FALSE, pr_miss = .25, Sigma = NULL, alpha = NULL, experiment = FALSE, treatment_cor = c(rep(0, n_full - 1), rep(NA, 2 * n_partial)), n_full = 1, n_partial = 1, n_cat = NULL, eta = 1, df = Inf, types = "continuous", estimate_CPCs = TRUE)
N |
integer indicating the number of observations |
restrictions |
character string indicating what restrictions to impose on the the missing data mechansim, see the Details section |
last_CPC |
a numeric scalar between |
strong |
Integer among 0, 1, and 2 indicating how strong to
make the instruments with multiple partially observed variables,
in which case the missingness indicators for each partially observed variable
can be used as instruments when predicting missingness on other partially
observed variables. Only applies when |
pr_miss |
numeric scalar on the (0,1) interval or vector
of length |
Sigma |
Either |
alpha |
Either |
experiment |
logical indicating whether to simulate a randomized experiment |
treatment_cor |
Numeric vector of appropriate length indicating the
correlations between the treatment variable and the other variables, which
is only relevant if |
n_full |
integer indicating the number of fully observed variables |
n_partial |
integer indicating the number of partially observed variables |
n_cat |
Either |
eta |
Positive numeric scalar which serves as a hyperparameter in the data-generating process. The default value of 1 implies that the correlation matrix among the variables is jointly uniformally distributed, using essentially the same logic as in the clusterGeneration package |
df |
positive numeric scalar indicating the degress of freedom for the
(possibly skewed) multivariate t distribution, which defaults to
|
types |
a character vector (possibly of length one, in which case it
is recycled) indicating the type for each fully observed and partially
observed variable, which currently can be among |
estimate_CPCs |
A logical indicating whether the canonical partial correlations
between the partially observed variables and the latent missingnesses should
be estimated. The default is |
By default, the correlation matrix among the variables and missingness indicators
is intended to be close to uniform, although it is often not possible to achieve
exactly. If restrictions = "none", the data will be Not Missing At Random
(NMAR). If restrictions = "MARish", the departure from Missing At Random
(MAR) will be minimized via a call to optim, but generally will
not fully achieve MAR. If restrictions = "triangular", the MAR assumption
will hold but the missingness of each partially observed variable will only
depend on the fully observed variables and the other latent missingness indicators.
If restrictions = "stratified", the MAR assumption will hold but the
missingness of each partially observed variable will only depend on the fully
observed variables. If restrictions = "MCAR", the Missing Completely At
Random (MCAR) assumption holds, which is much more restrictive than MAR.
There are some rules to follow, particularly when specifying types.
First, if experiment = TRUE, there must be exactly one treatment
variable (taken to be binary) and it must come first to ensure that the
elements of treatment_cor are handled properly. Second, if there are any
partially observed nominal variables, they must come last; this is to ensure
that they are conditionally uncorrelated with each other. Third, fully observed
nominal variables are not supported, but they can be made into ordinal variables
and then converted to nominal after the fact. Fourth, including both ordinal and
nominal partially observed variables is not supported yet, Finally, if any
variable is specified as a count, it will not be exactly consistent with the
data-generating process. Essentially, a count variable is constructed from a
continuous variable by evaluating pt on it and passing that to
qpois with an intensity parameter of 5. The other non-continuous
variables are constructed via some transformation or discretization of a continuous
variable.
If some partially observed variables are either ordinal or nominal (but not both),
then the n_cat argument governs how many categories there are. If n_cat
is NULL, then the number of categories defaults to three. If
n_cat has length one, then that number of categories will be used for all
categorical variables but must be greater than two. Otherwise, the length of
n_cat must match the number of partially observed categorical variables and
the number of categories for the th such variable will be the th element
of n_cat.
A list with the following elements:
true a data.frame containing no NA values
obs a data.frame derived from the previous with some
NA values that represents a dataset that could be observed
empirical_CPCs a numeric vector of empirical Canonical Partial
Correlations, which should differ only randomly from zero iff
MAR = TRUE and the data-generating process is multivariate normal
L a Cholesky factor of the correlation matrix used to generate the true data
In addition, if alpha is not NULL, then the following
elements are also included:
alpha the alpha vector utilized
sn_skewness the skewness of the multivariate skewed normal distribution
in the population; note that this value is only an approximation of the
skewness when df < Inf
sn_kurtosis the kurtosis of the multivariate skewed normal distribution
in the population; note that this value is only an approximation of the
kurtosis when df < Inf
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
data.frame, missing_data.frame
rdf <- rdata.frame(n_partial = 2, df = 5, alpha = rnorm(5)) print(rdf$empirical_CPCs) # not zero rdf <- rdata.frame(n_partial = 2, restrictions = "triangular", alpha = NA) print(rdf$empirical_CPCs) # only randomly different from zero print(rdf$L == 0) # some are exactly zero by construction mdf <- missing_data.frame(rdf$obs) show(mdf) hist(mdf) image(mdf) # a randomized experiment rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "triangular", experiment = TRUE, types = c("t", "ord", "con", "pos"), treatment_cor = c(0, 0, NA, 0, NA)) Sigma <- tcrossprod(rdf$L) rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2", "missing_y_1", "missing_Y_2") print(round(Sigma, 3))rdf <- rdata.frame(n_partial = 2, df = 5, alpha = rnorm(5)) print(rdf$empirical_CPCs) # not zero rdf <- rdata.frame(n_partial = 2, restrictions = "triangular", alpha = NA) print(rdf$empirical_CPCs) # only randomly different from zero print(rdf$L == 0) # some are exactly zero by construction mdf <- missing_data.frame(rdf$obs) show(mdf) hist(mdf) image(mdf) # a randomized experiment rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "triangular", experiment = TRUE, types = c("t", "ord", "con", "pos"), treatment_cor = c(0, 0, NA, 0, NA)) Sigma <- tcrossprod(rdf$L) rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2", "missing_y_1", "missing_Y_2") print(round(Sigma, 3))
The semi-continuous class inherits from the continuous-class and is the parent of the nonnegative-continuous
class, which in turn is the parent of the SC_proportion class for semi-continuous variables. A semi-continuous variable has support on one or more point
masses and a continuous interval. The semi-continuous class differs from the censored-continuous-class and
the truncated-continuous-class in that observations that fall on the point masses are bonafide data, rather than
indicators of censoring or truncation. If there are no observations that fall on a point mass, then either the
continuous-class or one of its other subclasses should be used. Aside from these facts, the rest of the
documentation here is primarily directed toward developers.
Objects can be created that are of semi-continuous, nonnegative-continuous, or
SC_proportion class via the missing_variable generic function by specifying type = "semi-continuous"
type = "nonnegative-continuous", type = "SC_proportion".
The semi-continuous class inherits from the continuous class and is intended for variables that, for example have a point mass at certain points and are continuous in between. Thus, its default transformation is the identity transformation, which is to say no transformation in practice. It has one additional slot.
Object of class "ordered-categorical" that indicates whether an observed
value falls on a point mass or the continuous interval in between. By convention, zero signifies an
observation that falls within the continuous interval
At the moment, there are no methods for the semi-continuous class. However, the basic approach to modeling a semi-continuous variable has two steps. First, the indicator is modeled using the methods that are defined for it and its missing values are imputed. Second, the continuous part of the semi-continuous variable is modeled using the same techniques that are used when modeling continuous variables. Note that in the second step, only a subset of the observations are modeled, although this subset possibly includes values that were originally missing in which case they are imputed.
The nonnegative-continuous class inherits from the semi-continuous class, which has its point mass at zero and
is continuous over the positive real line. By default, the transformation for the positive part of a
nonnegative-continuuos variable is log(y + a), where a is a small constant determined by the
observed data. If a variable is strictly positive, the positive-continuous-class should be used instead.
The SC_proportion class inherits from the nonnegative-continuous class. It has no additional slots, and the only supported
transformation function is the (y * (n - 1) + .5) / n function. Its default fit_model method is a
wrapper for the betareg function in the betareg package. Its family must be
binomial so that its link function can be passed to betareg
If the observed values fall strictly on the open unit interval, the proportion-class should be used instead.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
missing_variable, continuous-class, positive-continuous-class,
proportion-class
# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "nonnegative-continuous") show(income)# STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "nonnegative-continuous") show(income)