In each step of the iteration, one variable is used as a response variable and the remaining variables serve as the regressors.
irmi(
x,
eps = 5,
maxit = 100,
mixed = NULL,
mixed.constant = NULL,
count = NULL,
step = FALSE,
robust = FALSE,
takeAll = TRUE,
noise = TRUE,
noise.factor = 1,
force = FALSE,
robMethod = "lmrob",
force.mixed = TRUE,
mi = 1,
addMixedFactors = FALSE,
trace = FALSE,
init.method = "kNN",
modelFormulas = NULL,
multinom.method = "multinom",
imp_var = TRUE,
imp_suffix = "imp"
)data.frame or matrix
threshold for convergency
maximum number of iterations
column index of the semi-continuous variables
vector with length equal to the number of semi-continuous variables specifying the point of the semi-continuous distribution with non-zero probability
column index of count variables
a stepwise model selection is applied when the parameter is set to TRUE
if TRUE, robust regression methods will be applied
takes information of (initialised) missings in the response as well for regression imputation.
irmi has the option to add a random error term to the imputed values, this creates the possibility for multiple imputation. The error term has mean 0 and variance corresponding to the variance of the regression residuals.
amount of noise.
if TRUE, the algorithm tries to find a solution in any case, possible by using different robust methods automatically.
regression method when the response is continuous. Default is
MM-regression with lmrob.
if TRUE, the algorithm tries to find a solution in any case, possible by using different robust methods automatically.
number of multiple imputations.
if TRUE add additional factor variable for each mixed variable as X variable in the regression
Additional information about the iterations when trace equals TRUE.
Method for initialization of missing values (kNN or median)
a named list with the name of variables for the rhs of the formulas, which must contain a rhs formula for each variable with missing values, it should look like `list(y1=c("x1","x2"),y2=c("x1","x3"))`` if factor variables for the mixed variables should be created for the regression models
Method for estimating the multinomial models (current default and only available method is multinom)
TRUE/FALSE if a TRUE/FALSE variables for each imputed variable should be created show the imputation status
suffix for the TRUE/FALSE variables showing the imputation status
the imputed data set.
The method works sequentially and iterative. The method can deal with a mixture of continuous, semi-continuous, ordinal and nominal variables including outliers.
A full description of the method can be found in the mentioned reference.
M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise regression imputation using standard and robust methods. Journal of Computational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.
A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16.
Other imputation methods:
hotdeck(),
impPCA(),
kNN(),
matchImpute(),
medianSamp(),
rangerImpute(),
regressionImp(),
sampleCat(),
xgboostImpute()
data(sleep)
irmi(sleep)
#> BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
#> 0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
#> 1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
#> 0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
#> 1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Dream Span Gest Pred Exp
#> 0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Dream Span Gest Pred
#> 1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
#> 0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
#> 1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp
#> 0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred
#> 1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000
#> Exp Danger
#> 5.000 5.000
data(testdata)
imp_testdata1 <- irmi(testdata$wna, mixed = testdata$mixed)
#> x2 x2
#> 4.599866 15.039669
#> x1 x1
#> 4.176996 15.235603
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
# mixed.constant != 0 (-10)
testdata$wna$m1[testdata$wna$m1 == 0] <- -10
testdata$wna$m2 <- log(testdata$wna$m2 + 0.001)
imp_testdata2 <- irmi(
testdata$wna,
mixed = testdata$mixed,
mixed.constant = c(-10,log(0.001))
)
#> x2 x2
#> 4.599866 15.039669
#> x1 x1
#> 4.176996 15.235603
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
#> x1 x2 x1 x2
#> 4.176996 4.599866 15.235603 15.039669
imp_testdata2$m2 <- exp(imp_testdata2$m2) - 0.001
#example with fixed formulas for the variables with missing
form = list(
NonD = c("BodyWgt", "BrainWgt"),
Dream = c("BodyWgt", "BrainWgt"),
Sleep = c("BrainWgt" ),
Span = c("BodyWgt" ),
Gest = c("BodyWgt", "BrainWgt")
)
irmi(sleep, modelFormulas = form, trace = TRUE)
#> Method for multinomial models:multinom
#> BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
#> 0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
#> 1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
#> 0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
#> 1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Dream Span Gest Pred Exp
#> 0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Dream Span Gest Pred
#> 1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
#> 0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
#> 1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp
#> 0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000
#> Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred
#> 1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000
#> Exp Danger
#> 5.000 5.000
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
#> 1 6654.000 5712.0 3.2 0.8 3.3 38.6 645 3 5 3
#> 2 1.000 6.6 6.3 2.0 8.3 4.5 42 3 1 3
#> 3 3.385 44.5 12.8 2.4 12.5 14.0 60 1 1 1
#> 4 0.920 5.7 10.4 2.4 16.5 3.2 25 5 2 3
#> 5 2547.000 4603.0 2.1 1.8 3.9 69.0 624 3 5 4
#> 6 10.550 179.5 9.1 0.7 9.8 27.0 180 4 4 4
#> Iteration1
#> [1] "inner loop: 3"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: NonD ~ BodyWgt+BrainWgt"
#> [1] "inner loop: 4"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Dream ~ BodyWgt+BrainWgt"
#> [1] "inner loop: 5"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Sleep ~ BrainWgt"
#> [1] "inner loop: 6"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Span ~ BodyWgt"
#> [1] "inner loop: 7"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Gest ~ BodyWgt+BrainWgt"
#> [1] "it = 1 , Wert = 28319.772541325"
#> [1] "eps 5"
#> [1] "test: TRUE"
#> Iteration2
#> [1] "inner loop: 3"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: NonD ~ BodyWgt+BrainWgt"
#> [1] "inner loop: 4"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Dream ~ BodyWgt+BrainWgt"
#> [1] "inner loop: 5"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Sleep ~ BrainWgt"
#> [1] "inner loop: 6"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Span ~ BodyWgt"
#> [1] "inner loop: 7"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Gest ~ BodyWgt+BrainWgt"
#> [1] "it = 2 , Wert = 10.6447267589895"
#> [1] "eps 5"
#> [1] "test: TRUE"
#> Iteration3
#> [1] "inner loop: 3"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: NonD ~ BodyWgt+BrainWgt"
#> [1] "inner loop: 4"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Dream ~ BodyWgt+BrainWgt"
#> [1] "inner loop: 5"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Sleep ~ BrainWgt"
#> [1] "inner loop: 6"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Span ~ BodyWgt"
#> [1] "inner loop: 7"
#> [1] "numeric"
#> [1] "numeric"
#> [1] "formula used: Gest ~ BodyWgt+BrainWgt"
#> [1] "it = 3 , Wert = 0.329034689483892"
#> [1] "eps 5"
#> [1] "test: FALSE"
#> [1] "0.329034689483892 < 5 = eps"
#> [1] " --> finished after 3 iterations"
#> Imputation performed on the following data set:
#> type #missing
#> BodyWgt "numeric" "0"
#> BrainWgt "numeric" "0"
#> NonD "numeric" "14"
#> Dream "numeric" "12"
#> Sleep "numeric" "4"
#> Span "numeric" "4"
#> Gest "numeric" "4"
#> Pred "integer" "0"
#> Exp "integer" "0"
#> Danger "integer" "0"
#> The variables NonD_imp,Dream_imp,Sleep_imp,Span_imp,Gest_imp are added to the data set.
# Example with ordered variable
td <- testdata$wna
td$c1 <- as.ordered(td$c1)
irmi(td)
#> Warning: The number of unique values in the ordinal variables in data.x
#> does not correspond to the values given in levOrders
#> Warning: The number of unique values in the ordinal variables in data.y
#> does not correspond to the values given in levOrders
#> x2 m1 m2 x2 m1 m2
#> 4.599866 -10.000000 -6.907755 15.039669 13.741169 2.625980
#> Warning: The number of unique values in the ordinal variables in data.x
#> does not correspond to the values given in levOrders
#> Warning: The number of unique values in the ordinal variables in data.y
#> does not correspond to the values given in levOrders
#> x1 m1 m2 x1 m1 m2
#> 4.176996 -10.000000 -6.907755 15.235603 13.741169 2.625980
#> x1 x2 m1 m2 x1 x2 m1
#> 4.176996 4.599866 -10.000000 -6.907755 15.235603 15.039669 13.741169
#> m2
#> 2.625980
#> x1 x2 m1 m2 x1 x2 m1
#> 4.176996 4.599866 -10.000000 -6.907755 15.235603 15.039669 13.741169
#> m2
#> 2.625980
#> x1 x2 m1 m2 x1 x2 m1
#> 4.176996 4.599866 -10.000000 -6.907755 15.235603 15.039669 13.741169
#> m2
#> 2.625980
#> x1 x2 m1 m2 x1 x2 m1
#> 4.176996 4.599866 -10.000000 -6.907755 15.235603 15.039669 13.741169
#> m2
#> 2.625980
#> x1 x2 m2 x1 x2 m2
#> 4.176996 4.599866 -6.907755 15.235603 15.039669 2.625980
#> x1 x2 m1 x1 x2 m1
#> 4.176996 4.599866 -10.000000 15.235603 15.039669 13.741169