error_checks() checks for incorrect data entry in autoMrP()
call.
error_checks(
y,
L1.x,
L2.x,
L2.unit,
L2.reg,
L2.x.scale,
pcs,
folds,
bin.proportion,
bin.size,
survey,
census,
ebma.size,
k.folds,
cv.sampling,
loss.unit,
loss.fun,
best.subset,
lasso,
pca,
gb,
svm,
mrp,
best.subset.L2.x,
lasso.L2.x,
gb.L2.x,
svm.L2.x,
mrp.L2.x,
gb.L2.unit,
gb.L2.reg,
lasso.lambda,
lasso.n.iter,
uncertainty,
boot.iter
)No return value, called for detection of errors in autoMrP() call.
Outcome variable. A character vector containing the column names of
the outcome variable. A character scalar containing the column name of
the outcome variable in survey.
Individual-level covariates. A character vector containing the
column names of the individual-level variables in survey and
census used to predict outcome y. Note that geographic unit
is specified in argument L2.unit.
Context-level covariates. A character vector containing the
column names of the context-level variables in survey and
census used to predict outcome y. To exclude context-level
variables, set L2.x = NULL.
Geographic unit. A character scalar containing the column
name of the geographic unit in survey and census at which
outcomes should be aggregated.
Geographic region. A character scalar containing the column
name of the geographic region in survey and census by which
geographic units are grouped (L2.unit must be nested within
L2.reg). Default is NULL.
Scale context-level covariates. A logical argument
indicating whether the context-level covariates should be normalized.
Default is TRUE. Note that if set to FALSE, then the
context-level covariates should be normalized prior to calling
auto_MrP().
Principal components. A character vector containing the column
names of the principal components of the context-level variables in
survey and census. Default is NULL.
EBMA and cross-validation folds. A character scalar containing
the column name of the variable in survey that specifies the fold
to which an observation is allocated. The variable should contain integers
running from \(1\) to \(k + 1\), where \(k\) is the number of
cross-validation folds. Value \(k + 1\) refers to the EBMA fold. Default
is NULL. Note: if folds is NULL, then
ebma.size, k.folds, and cv.sampling must be specified.
Proportion of ideal types. A character scalar
containing the column name of the variable in census that indicates
the proportion of individuals by ideal type and geographic unit. Default is
NULL. Note: if bin.proportion is NULL, then
bin.size must be specified.
Bin size of ideal types. A character scalar containing the
column name of the variable in census that indicates the bin size of
ideal types by geographic unit. Default is NULL. Note:
ignored if bin.proportion is provided, but must be specified
otherwise.
Survey data. A data.frame whose column names include
y, L1.x, L2.x, L2.unit, and, if specified,
L2.reg, pcs, and folds.
Census data. A data.frame whose column names include
L1.x, L2.x, L2.unit, if specified, L2.reg and
pcs, and either bin.proportion or bin.size.
EBMA fold size. A number in the open unit interval
indicating the proportion of respondents to be allocated to the EBMA fold.
Default is \(1/3\). Note: ignored if folds is provided, but
must be specified otherwise.
Number of cross-validation folds. An integer-valued scalar
indicating the number of folds to be used in cross-validation. Default is
\(5\). Note: ignored if folds is provided, but must be
specified otherwise.
Cross-validation sampling method. A character-valued
scalar indicating whether cross-validation folds should be created by
sampling individual respondents (individuals) or geographic units
(L2 units). Default is L2 units. Note: ignored if
folds is provided, but must be specified otherwise.
Loss function unit. A character-valued scalar indicating
whether performance loss should be evaluated at the level of individual
respondents (individuals), geographic units (L2 units) or at
both levels. Default is c("individuals", "L2 units"). With multiple
loss units, parameters are ranked for each loss unit and the loss unit with
the lowest rank sum is chosen. Ties are broken according to the order in
the search grid.
Loss function. A character-valued scalar indicating whether
prediction loss should be measured by the mean squared error (MSE),
the mean absolute error (MAE), binary cross-entropy
(cross-entropy), mean squared false error (msfe), the f1
score (f1), or a combination thereof. Default is c("MSE",
"cross-entropy","msfe", "f1"). With multiple loss functions, parameters
are ranked for each loss function and the parameter combination with the
lowest rank sum is chosen. Ties are broken according to the order in the
search grid.
Best subset classifier. A logical argument indicating
whether the best subset classifier should be used for predicting outcome
y. Default is TRUE.
Lasso classifier. A logical argument indicating whether the
lasso classifier should be used for predicting outcome y. Default is
TRUE.
PCA classifier. A logical argument indicating whether the PCA
classifier should be used for predicting outcome y. Default is
TRUE.
GB classifier. A logical argument indicating whether the GB
classifier should be used for predicting outcome y. Default is
TRUE.
SVM classifier. A logical argument indicating whether the SVM
classifier should be used for predicting outcome y. Default is
TRUE.
MRP classifier. A logical argument indicating whether the standard
MRP classifier should be used for predicting outcome y. Default is
FALSE.
Best subset context-level covariates. A character
vector containing the column names of the context-level variables in
survey and census to be used by the best subset classifier.
If NULL and best.subset is set to TRUE, then best
subset uses the variables specified in L2.x. Default is NULL.
Lasso context-level covariates. A character vector
containing the column names of the context-level variables in
survey and census to be used by the lasso classifier. If
NULL and lasso is set to TRUE, then lasso uses the
variables specified in L2.x. Default is NULL.
GB context-level covariates. A character vector containing the
column names of the context-level variables in survey and
census to be used by the GB classifier. If NULL and gb
is set to TRUE, then GB uses the variables specified in L2.x.
Default is NULL.
SVM context-level covariates. A character vector containing
the column names of the context-level variables in survey and
census to be used by the SVM classifier. If NULL and
svm is set to TRUE, then SVM uses the variables specified in
L2.x. Default is NULL.
MRP context-level covariates. A character vector containing
the column names of the context-level variables in survey and
census to be used by the MRP classifier. The character vector
empty if no context-level variables should be used by the MRP
classifier. If NULL and mrp is set to TRUE, then MRP
uses the variables specified in L2.x. Default is NULL. Note:
For the empty MrP model, set L2.x = NULL and mrp.L2.x = "".
GB L2.unit. A logical argument indicating whether
L2.unit should be included in the GB classifier. Default is
FALSE.
GB L2.reg. A logical argument indicating whether
L2.reg should be included in the GB classifier. Default is
FALSE.
Lasso penalty parameter. A numeric vector of
non-negative values. The penalty parameter controls the shrinkage of the
context-level variables in the lasso model. Default is a sequence with
minimum 0.1 and maximum 250 that is equally spaced on the log-scale. The
number of values is controlled by the lasso.n.iter parameter.
Lasso number of lambda values. An integer-valued scalar
specifying the number of lambda values to search over. Default is
\(100\). Note: Is ignored if a vector of lasso.lambda
values is provided.
Uncertainty estimates. A logical argument indicating
whether uncertainty estimates should be computed. Default is FALSE.
Number of bootstrap iterations. An integer argument
indicating the number of bootstrap iterations to be computed. Will be
ignored unless uncertainty = TRUE. Default is 200 if
uncertainty = TRUE and NULL if uncertainty = FALSE.