Cellwise-robust iterative regression imputation for mixed data
Source:R/imputeCellwise.R
imputeCellIRMI.RdExtends IRMI (Templ, Kowarik, and Filzmoser, 2011) with cellwise contamination handling. Each conditional regression uses a cell-weighted IRWLS engine where per-cell weights in the design matrix downweight contaminated cells without discarding entire observations.
imputeCellIRMI(
data,
method = "tukey",
alpha = NULL,
maxit = 100,
maxit_irwls = 50,
eps = 0.005,
eps_irwls = 1e-06,
uncert = "pmm",
weight_update = "multivariate",
init_weights = "mcd",
hard_threshold = 0.5,
trace = FALSE
)Arguments
- data
a
data.framewith missing values (mixed continuous and categorical variables are supported).- method
weight function:
"tukey"(default, Tukey bisquare) or"huber"(Huber).- alpha
tuning constant.
NULL(default) uses 1.345 for Huber and 4.685 for Tukey, giving 95% efficiency at the normal model.- maxit
maximum number of outer IRMI iterations (default: 100).
- maxit_irwls
maximum number of inner IRWLS iterations per regression (default: 50).
- eps
convergence tolerance for the outer loop (default: 5e-3). Convergence is declared when the relative change in imputed values falls below this threshold.
- eps_irwls
convergence tolerance for the inner IRWLS (default: 1e-6).
- uncert
imputation uncertainty method:
"pmm"(predictive mean matching, default),"normalerror"(add normal noise), or"resid"(bootstrap residual).- weight_update
strategy for updating cell weights between outer iterations:
"multivariate"(default) uses an MCD-based multivariate update for weight coherence across variables, or"univariate"updates each variable independently from its residuals.- init_weights
method for initialising cell weights:
"mcd"(default) uses the minimum covariance determinant on the continuous block, or"marginal"uses univariate median/MAD standardisation.- hard_threshold
numeric in \([0, 1]\). After convergence, cells with weight below this value are flagged as contaminated (default: 0.5).
- trace
logical; if
TRUE, print progress information.
Value
A list with components:
- data_imputed
the imputed
data.frame.- cellweights
\(n \times p\) matrix of final cell weights (1 = clean, 0 = fully downweighted). Categorical columns always have weight 1.
- converged
logical indicating whether the outer loop converged.
- iterations
number of outer iterations used.
Details
The algorithm works iteratively: in each outer iteration, every variable
with missing values is used as response in a conditional regression on
all remaining variables. For continuous responses, the custom
cellIRWLS() engine fits a weighted regression where each cell in
the design matrix receives its own weight reflecting potential cellwise
contamination. For categorical responses, a weighted multinomial model
is used. After each regression, cell weights for the response variable
are updated from the residuals.
The algorithm proceeds as follows:
Missing values are initialised using
initialise.Initial cell weights are computed with
cellWeights()on all continuous variables in the initialised data.Outer loop (up to
maxititerations):For each variable \(j\) with missing values:
Form predictor matrix \(X\) (all other variables) and response \(y\) (variable \(j\)).
If \(j\) is continuous: fit
cellIRWLS(X, y, w_cell, w_response)and impute missing values in \(j\) using the fitted model plus uncertainty.If \(j\) is categorical: fit
nnet::multinom()with row weights derived from the cell weight matrix and impute by sampling from predicted probabilities.Update cell weights for \(j\) from residuals via
cellWeightsFromResiduals().
Check convergence: relative change in imputed values falls below
eps.
References
Templ, M., Kowarik, A. and Filzmoser, P. (2011). Iterative stepwise regression imputation using standard and robust methods. Computational Statistics & Data Analysis, 55(10), 2793–2806.
See also
imputeCellM, imputeCellEM,
initialise, irmi
Other imputation methods:
hotdeck(),
impPCA(),
imputeCellEM(),
imputeCellM(),
imputeCellMCD(),
imputeCellwise(),
imputeRobust(),
imputeRobustChain(),
irmi(),
kNN(),
matchImpute(),
medianSamp(),
rangerImpute(),
regressionImp(),
sampleCat(),
vimmi,
vimpute(),
xgboostImpute()
Examples
# \donttest{
data(sleep, package = "VIM")
result <- imputeCellIRMI(sleep)
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in coefficient 'Overall'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in coefficient 'Overall'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
head(result$data_imputed)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
#> 1 0.023 406.0 12.8 0.9 6.2 50.0 645 1 5 1
#> 2 1.000 6.6 6.3 2.0 8.3 4.5 42 3 1 3
#> 3 3.385 44.5 12.0 2.4 13.7 14.0 60 1 1 1
#> 4 0.920 5.5 13.8 5.6 14.4 19.0 46 4 2 3
#> 5 27.660 325.0 10.4 3.9 10.9 40.0 252 3 5 4
#> 6 10.550 179.5 9.1 0.7 9.8 27.0 180 4 4 4
image(result$cellweights, main = "Cell weights")
# With Huber weights (less aggressive downweighting)
result2 <- imputeCellIRMI(sleep, method = "huber", trace = TRUE)
#> hard thresholding removed 71 outlying cells (set to NA)
#> --------------------------------------
#> cellIRMI: start of iteration 1
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in coefficient 'Overall'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 4
#> convergence criterion: 12.443471
#> --------------------------------------
#> cellIRMI: start of iteration 2
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in coefficient 'Overall'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> multivariate weight update: flagged cells = 13
#> convergence criterion: 0.694692
#> --------------------------------------
#> cellIRMI: start of iteration 3
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 39
#> convergence criterion: 0.084367
#> --------------------------------------
#> cellIRMI: start of iteration 4
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 50
#> convergence criterion: 0.075971
#> --------------------------------------
#> cellIRMI: start of iteration 5
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 54
#> convergence criterion: 0.103992
#> --------------------------------------
#> cellIRMI: start of iteration 6
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 59
#> convergence criterion: 0.045517
#> --------------------------------------
#> cellIRMI: start of iteration 7
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in coefficient 'Overall'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 34
#> convergence criterion: 0.026946
#> --------------------------------------
#> cellIRMI: start of iteration 8
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> multivariate weight update: flagged cells = 30
#> convergence criterion: 0.015232
#> --------------------------------------
#> cellIRMI: start of iteration 9
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 31
#> convergence criterion: 0.013398
#> --------------------------------------
#> cellIRMI: start of iteration 10
#> imputing variable: 1 ( BodyWgt ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 28
#> convergence criterion: 0.013798
#> --------------------------------------
#> cellIRMI: start of iteration 11
#> imputing variable: 1 ( BodyWgt ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 30
#> convergence criterion: 0.010333
#> --------------------------------------
#> cellIRMI: start of iteration 12
#> imputing variable: 1 ( BodyWgt ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 37
#> convergence criterion: 0.009459
#> --------------------------------------
#> cellIRMI: start of iteration 13
#> imputing variable: 1 ( BodyWgt ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 35
#> convergence criterion: 0.008289
#> --------------------------------------
#> cellIRMI: start of iteration 14
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 34
#> convergence criterion: 0.006104
#> --------------------------------------
#> cellIRMI: start of iteration 15
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 40
#> convergence criterion: 0.005782
#> --------------------------------------
#> cellIRMI: start of iteration 16
#> imputing variable: 1 ( BodyWgt ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 45
#> convergence criterion: 0.005238
#> --------------------------------------
#> cellIRMI: start of iteration 17
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 51
#> convergence criterion: 0.007089
#> --------------------------------------
#> cellIRMI: start of iteration 18
#> imputing variable: 1 ( BodyWgt ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in 2 coefficients 'Overall', ''.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> multivariate weight update: flagged cells = 40
#> convergence criterion: 0.011392
#> --------------------------------------
#> cellIRMI: start of iteration 19
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 44
#> convergence criterion: 0.006841
#> --------------------------------------
#> cellIRMI: start of iteration 20
#> imputing variable: 1 ( BodyWgt ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> multivariate weight update: flagged cells = 42
#> convergence criterion: 0.025436
#> --------------------------------------
#> cellIRMI: start of iteration 21
#> imputing variable: 1 ( BodyWgt ) - continuous
#> imputing variable: 2 ( BrainWgt ) - continuous
#> imputing variable: 3 ( NonD ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 4 ( Dream ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 5 ( Sleep ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 6 ( Span ) - continuous
#> imputing variable: 7 ( Gest ) - continuous
#> imputing variable: 8 ( Pred ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> imputing variable: 9 ( Exp ) - continuous
#> imputing variable: 10 ( Danger ) - continuous
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> multivariate weight update: flagged cells = 42
#> convergence criterion: 0.003849
#> cellIRMI converged.
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: Detected possible local breakdown of S-estimate in coefficient 'Overall'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
#> Warning: S-estimated scale == 0: Probably exact fit; check your data
# Mixed data example
data(testdata)
result3 <- imputeCellIRMI(testdata$wna)
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm2'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm1'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm2'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm1'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm2'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm1'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: find_scale() did not converge in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0
#> Warning: S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm2'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
#> Warning: Detected possible local breakdown of S-estimate in 3 coefficients 'c1b', 'c2b', 'm1'.
#> Use lmrob argument 'setting="KS2014"' to avoid this problem.
# }