INTERNAL FUNCTION: Repeated regression imputation.
LmImputeMulti.RdLmImpute is run several times using different versions of input parameters. Output from each run of LmImpute is in output of this function. In each run it is possible to update the interest variable (y) with available imputed values. Thus the function can be used to impute using a primary x-variable and a secondary x-variable for cases where the primary is missing.
Usage
LmImputeMulti(
data,
yName = "y",
xModel = c("x1", "x2"),
yModel = yName,
weights = NULL,
limitModel = 2.5,
limitIterate = 4.5,
limitImpute = 50,
maxiter = 10,
returnIter = TRUE,
returnYHat = FALSE,
returnFirst = FALSE,
returnLast = TRUE,
returnFinal = FALSE,
MultiFuction = function(x) {
max(abs(x))
},
estimationGroup = TRUE,
unfoldCoef = FALSE,
BackTransform = list(NULL),
warningEstimate = vector("list", n),
removeEmpty = FALSE,
replaceByImputed = TRUE,
imputedInModel = FALSE,
category123FromFirst = FALSE,
cvPercent = TRUE,
returnSameType = FALSE
)
LmImpute2(
data,
warningEstimate = list(NULL, "estimate: Missing yImputed replaced by zero"),
replaceByImputed = TRUE,
cvPercent = TRUE,
...
)
LmImputeOne2Many(
data,
yName = "y",
xModel = "x",
yModel = c(yName, "yMany"),
category123FromFirst = TRUE,
keepSinge = NULL,
keepMulti = NULL,
...
)Arguments
- data
Input data set (data.frame, data.table or list)
- yName
Name of interest variable in data set
- xModel
Vector of strings with the right part model formula.
- yModel
String with left part model formula (vector possible)
- weights
NULL or string with weight expression (vector possible)
- limitModel
Studentized residuals limit. Above limit -> category 2.(vector possible)
- limitIterate
Studentized residuals limit for iterative calculation of studentized residuals.(vector possible)
- limitImpute
Studentized residuals limit. Above limit -> category 3. No imputation when 0.(vector possible)
- maxiter
Maximum number of iterations.(vector possible)
- returnIter
When TRUE, iteration when observation was thrown outin output.(vector possible)
- returnYHat
When TRUE, fitted values and corresponding estimates in output. (vector possible)
- returnFirst
When TRUE, studentized residuals from first iteration in output. (vector possible)
- returnLast
When TRUE, some results from last iteration in output.(vector possible)
- returnFinal
When TRUE, extra results from final model in output. (vector possible)
- MultiFuction
Transforming rStud for several responses into a single positive value.
- estimationGroup
Total estimates will be computed within each group. Default (and TRUE) is a single group (estimationGroup <- rep(1, N) ).
- unfoldCoef
When TRUE several elements of coef will be spilt as several ouput elements.
- BackTransform
When model contains transformation of y (e.g: "log(y)~x") a function (e.g: exp) can be supplied to transform back to original scale before calculation of leaveOutResid, yHat, yImputed, estimate, estimateYHat, estimateOrig and seRobust. (list possible)
- warningEstimate
Warning text when missing values. Use NULL to avoid warning. (list possible)
- removeEmpty
When TRUE empty elements will be removed from output.
- replaceByImputed
When TRUE missing values of the interest variable (y) is replaced by imputed values in each round.
- imputedInModel
When FALSE above imputed values are omitted from subsequent models (category 2 forced).
- category123FromFirst
Whene TRUE category123 from first run is input to subsequent LmImpute calls.
- cvPercent
When TRUE (default) cv output is in percent
- returnSameType
When TRUE and when the type of input y variable(s) is integer, the output type of yImputed and estimate is also integer. Estimates/sums are then calculated from rounded imputed values.
- keepSinge
When non-NULL only output elements with names in keepSinge are kept from first LmImputeOne2Many run.
- keepMulti
When non-NULL only output elements with names in keepMulti are kept from second LmImputeOne2Many run.
Value
Output of LmImputeMulti is a list where each element is the output of LmImpute.
Output of LmImpute2 is not such a list. Instead the names are changed using "A" and "B".
Output of LmImputeOne2Many is not such a list. Instead the names from second round are changed using "M".
Details
LmImputeMulti performs several calls to LmImpute and the number of calls is the length of xModel. Other parameters can also change between calls by specifying them as vectors or lists.
LmImpute2 is a specialized variant for two LmImpute runs only and combined estimates of seRobust, seEStimate and cv are calculated when replaceByImputed = TRUE.
LmImputeOne2Many is another specialized variant meant for two runs using the same model except that the first run has a single y and the next run several y's. Category123 from the first run is used in the second.
Examples
# ----- LmImpute2 and LmImputeMulti -----
set.seed(123) # same results each time
z = data.frame( # Small test data set
x1 = c(NA,2:19,NA),
x2 = rep(1:20),
y = runif(20)+c(rep(0,15),NA,1E3,1E5,NA,NA))
LmImpute2(z)
#> $Acategory123
#> [1] 0 1 1 1 1 2 1 1 1 1 1 1 1 1 1 3 3 3 3 0
#>
#> $N
#> [1] 20
#>
#> $AnImputed
#> [1] 4
#>
#> $AestimateOrig
#> [1] 101008.8
#>
#> $Acoef
#> (Intercept) x1
#> 0.74619158 -0.01839066
#>
#> $AnModel
#> [1] 14
#>
#> $ArStud
#> [1] NA 3.028524e-01 -1.097380e+00 7.770173e-01 1.058450e+00
#> [6] -2.605458e+00 -3.084761e-01 1.052993e+00 -1.000843e-01 -3.654455e-01
#> [11] 1.589354e+00 -2.549938e-01 6.236896e-01 3.113201e-01 -1.550398e+00
#> [16] NA 2.919388e+03 2.840154e+05 NA NA
#>
#> $Adffits
#> [1] NaN 0.17818274 -0.55621711 0.33967255 0.40055866 -0.86317413
#> [7] -0.09177708 0.29445999 -0.02798766 -0.10872649 0.52654429 -0.09649960
#> [13] 0.27264544 0.15779543 -0.91217413 NaN NaN NaN
#> [19] NaN NaN
#>
#> $Ahii
#> [1] NaN 0.25714286 0.20439560 0.16043956 0.12527473 0.09890110
#> [7] 0.08131868 0.07252747 0.07252747 0.08131868 0.09890110 0.12527473
#> [13] 0.16043956 0.20439560 0.25714286 NaN NaN NaN
#> [19] NaN NaN
#>
#> $AleaveOutResid
#> [1] NA 1.062046e-01 -3.545012e-01 2.505936e-01 3.272216e-01
#> [6] -6.550792e-01 -9.726058e-02 3.162926e-01 -3.152723e-02 -1.150238e-01
#> [11] 4.582616e-01 -8.250536e-02 2.030320e-01 1.054683e-01 -4.945864e-01
#> [16] NA 9.998125e+02 9.999963e+04 NA NA
#>
#> $AsigmaHat
#> [1] 0.2905855
#>
#> $AseEstimate
#> [1] 0.9567087
#>
#> $AseRobust
#> [1] 1.011323
#>
#> $Aiter
#> [1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 2 1 1
#>
#> $Bcategory123
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 3
#>
#> $yImputed
#> 1 2 3 4 5 6 7 8
#> 0.2875775 0.7883051 0.4089769 0.8830174 0.9404673 0.0455565 0.5281055 0.8924190
#> 9 10 11 12 13 14 15 16
#> 0.5514350 0.4566147 0.9568333 0.4533342 0.6775706 0.5726334 0.1029247 0.4447424
#> 17 18 19 20
#> 0.4191530 0.3935637 0.3679744 0.4810972
#>
#> $BnImputed
#> [1] 1
#>
#> $estimate
#> [1] 10.6523
#>
#> $BestimateOrig
#> [1] 10.1712
#>
#> $Bcoef
#> (Intercept) x2
#> 0.628798670 -0.007385073
#>
#> $BnModel
#> [1] 15
#>
#> $BrStud
#> [1] -1.31925666 0.63518863 -0.70616697 1.01598050 1.25619058 -2.11948316
#> [7] -0.16360244 1.12993545 -0.03634973 -0.33137954 1.51296885 -0.29948296
#> [13] 0.51231079 0.16950439 -1.70948215 -0.19380991 -0.24175089 -0.28698726
#> [19] -0.32948813 NA
#>
#> $Bdffits
#> [1] -0.744745002 0.312860902 -0.303542798 0.381911850 0.415955279
#> [6] -0.629036399 -0.044966623 0.301987952 -0.009990832 -0.098349351
#> [11] 0.500980814 -0.112577056 0.220214561 0.083489051 -0.965034572
#> [16] NaN NaN NaN NaN NaN
#>
#> $Bhii
#> [1] 0.24166667 0.19523810 0.15595238 0.12380952 0.09880952 0.08095238
#> [7] 0.07023810 0.06666667 0.07023810 0.08095238 0.09880952 0.12380952
#> [13] 0.15595238 0.19523810 0.24166667 NaN NaN NaN
#> [19] NaN NaN
#>
#> $BleaveOutResid
#> [1] -0.44022340 0.21655674 -0.23418884 0.32385541 0.38681498 -0.58640240
#> [7] -0.05269916 0.34575103 -0.01172128 -0.10699468 0.45414426 -0.09911502
#> [13] 0.17152814 0.05868289 -0.54738183 -0.06589513 -0.08409938 -0.10230364
#> [19] -0.12050790 NA
#>
#> $BsigmaHat
#> [1] 0.2987463
#>
#> $BseEstimate
#> [1] 0.3756314
#>
#> $BseRobust
#> [1] 0.4064078
#>
#> $Biter
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1
#>
#> $nImputed
#> [1] 5
#>
#> $seRobust
#> [1] 1.089928
#>
#> $seEstimate
#> [1] 1.027809
#>
#> $cv
#> [1] 9.6487
#>
LmImputeMulti(z)
#> [[1]]
#> [[1]]$category123
#> [1] 0 1 1 1 1 2 1 1 1 1 1 1 1 1 1 3 3 3 3 0
#>
#> [[1]]$N
#> [1] 20
#>
#> [[1]]$yImputed
#> 1 2 3 4 5 6 7 8
#> 0.2875775 0.7883051 0.4089769 0.8830174 0.9404673 0.0455565 0.5281055 0.8924190
#> 9 10 11 12 13 14 15 16
#> 0.5514350 0.4566147 0.9568333 0.4533342 0.6775706 0.5726334 0.1029247 0.4447424
#> 17 18 19 20
#> 0.4191530 0.3935637 0.3679744 NA
#>
#> [[1]]$nImputed
#> [1] 4
#>
#> [[1]]$estimate
#> [1] 10.1712
#>
#> [[1]]$cv
#> [1] 9.406051
#>
#> [[1]]$estimateOrig
#> [1] 101008.8
#>
#> [[1]]$coef
#> (Intercept) x1
#> 0.74619158 -0.01839066
#>
#> [[1]]$nModel
#> [1] 14
#>
#> [[1]]$rStud
#> [1] NA 3.028524e-01 -1.097380e+00 7.770173e-01 1.058450e+00
#> [6] -2.605458e+00 -3.084761e-01 1.052993e+00 -1.000843e-01 -3.654455e-01
#> [11] 1.589354e+00 -2.549938e-01 6.236896e-01 3.113201e-01 -1.550398e+00
#> [16] NA 2.919388e+03 2.840154e+05 NA NA
#>
#> [[1]]$dffits
#> [1] NaN 0.17818274 -0.55621711 0.33967255 0.40055866 -0.86317413
#> [7] -0.09177708 0.29445999 -0.02798766 -0.10872649 0.52654429 -0.09649960
#> [13] 0.27264544 0.15779543 -0.91217413 NaN NaN NaN
#> [19] NaN NaN
#>
#> [[1]]$hii
#> [1] NaN 0.25714286 0.20439560 0.16043956 0.12527473 0.09890110
#> [7] 0.08131868 0.07252747 0.07252747 0.08131868 0.09890110 0.12527473
#> [13] 0.16043956 0.20439560 0.25714286 NaN NaN NaN
#> [19] NaN NaN
#>
#> [[1]]$leaveOutResid
#> [1] NA 1.062046e-01 -3.545012e-01 2.505936e-01 3.272216e-01
#> [6] -6.550792e-01 -9.726058e-02 3.162926e-01 -3.152723e-02 -1.150238e-01
#> [11] 4.582616e-01 -8.250536e-02 2.030320e-01 1.054683e-01 -4.945864e-01
#> [16] NA 9.998125e+02 9.999963e+04 NA NA
#>
#> [[1]]$sigmaHat
#> [1] 0.2905855
#>
#> [[1]]$seEstimate
#> [1] 0.9567087
#>
#> [[1]]$seRobust
#> [1] 1.011323
#>
#> [[1]]$iter
#> [1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 2 1 1
#>
#>
#> [[2]]
#> [[2]]$category123
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 3
#>
#> [[2]]$N
#> [1] 20
#>
#> [[2]]$yImputed
#> 1 2 3 4 5 6 7 8
#> 0.2875775 0.7883051 0.4089769 0.8830174 0.9404673 0.0455565 0.5281055 0.8924190
#> 9 10 11 12 13 14 15 16
#> 0.5514350 0.4566147 0.9568333 0.4533342 0.6775706 0.5726334 0.1029247 0.4447424
#> 17 18 19 20
#> 0.4191530 0.3935637 0.3679744 0.4810972
#>
#> [[2]]$nImputed
#> [1] 1
#>
#> [[2]]$estimate
#> [1] 10.6523
#>
#> [[2]]$cv
#> [1] 3.526293
#>
#> [[2]]$estimateOrig
#> [1] 10.1712
#>
#> [[2]]$coef
#> (Intercept) x2
#> 0.628798670 -0.007385073
#>
#> [[2]]$nModel
#> [1] 15
#>
#> [[2]]$rStud
#> [1] -1.31925666 0.63518863 -0.70616697 1.01598050 1.25619058 -2.11948316
#> [7] -0.16360244 1.12993545 -0.03634973 -0.33137954 1.51296885 -0.29948296
#> [13] 0.51231079 0.16950439 -1.70948215 -0.19380991 -0.24175089 -0.28698726
#> [19] -0.32948813 NA
#>
#> [[2]]$dffits
#> [1] -0.744745002 0.312860902 -0.303542798 0.381911850 0.415955279
#> [6] -0.629036399 -0.044966623 0.301987952 -0.009990832 -0.098349351
#> [11] 0.500980814 -0.112577056 0.220214561 0.083489051 -0.965034572
#> [16] NaN NaN NaN NaN NaN
#>
#> [[2]]$hii
#> [1] 0.24166667 0.19523810 0.15595238 0.12380952 0.09880952 0.08095238
#> [7] 0.07023810 0.06666667 0.07023810 0.08095238 0.09880952 0.12380952
#> [13] 0.15595238 0.19523810 0.24166667 NaN NaN NaN
#> [19] NaN NaN
#>
#> [[2]]$leaveOutResid
#> [1] -0.44022340 0.21655674 -0.23418884 0.32385541 0.38681498 -0.58640240
#> [7] -0.05269916 0.34575103 -0.01172128 -0.10699468 0.45414426 -0.09911502
#> [13] 0.17152814 0.05868289 -0.54738183 -0.06589513 -0.08409938 -0.10230364
#> [19] -0.12050790 NA
#>
#> [[2]]$sigmaHat
#> [1] 0.2987463
#>
#> [[2]]$seEstimate
#> [1] 0.3756314
#>
#> [[2]]$seRobust
#> [1] 0.4064078
#>
#> [[2]]$iter
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1
#>
#>
LmImputeMulti(z,xModel=c("x1","x2","x2"),limitImpute=c(50,50,3), replaceByImputed=FALSE)
#> [[1]]
#> [[1]]$category123
#> [1] 0 1 1 1 1 2 1 1 1 1 1 1 1 1 1 3 3 3 3 0
#>
#> [[1]]$N
#> [1] 20
#>
#> [[1]]$yImputed
#> 1 2 3 4 5 6 7 8
#> 0.2875775 0.7883051 0.4089769 0.8830174 0.9404673 0.0455565 0.5281055 0.8924190
#> 9 10 11 12 13 14 15 16
#> 0.5514350 0.4566147 0.9568333 0.4533342 0.6775706 0.5726334 0.1029247 0.4447424
#> 17 18 19 20
#> 0.4191530 0.3935637 0.3679744 NA
#>
#> [[1]]$nImputed
#> [1] 4
#>
#> [[1]]$estimate
#> [1] 10.1712
#>
#> [[1]]$cv
#> [1] 9.406051
#>
#> [[1]]$estimateOrig
#> [1] 101008.8
#>
#> [[1]]$coef
#> (Intercept) x1
#> 0.74619158 -0.01839066
#>
#> [[1]]$nModel
#> [1] 14
#>
#> [[1]]$rStud
#> [1] NA 3.028524e-01 -1.097380e+00 7.770173e-01 1.058450e+00
#> [6] -2.605458e+00 -3.084761e-01 1.052993e+00 -1.000843e-01 -3.654455e-01
#> [11] 1.589354e+00 -2.549938e-01 6.236896e-01 3.113201e-01 -1.550398e+00
#> [16] NA 2.919388e+03 2.840154e+05 NA NA
#>
#> [[1]]$dffits
#> [1] NaN 0.17818274 -0.55621711 0.33967255 0.40055866 -0.86317413
#> [7] -0.09177708 0.29445999 -0.02798766 -0.10872649 0.52654429 -0.09649960
#> [13] 0.27264544 0.15779543 -0.91217413 NaN NaN NaN
#> [19] NaN NaN
#>
#> [[1]]$hii
#> [1] NaN 0.25714286 0.20439560 0.16043956 0.12527473 0.09890110
#> [7] 0.08131868 0.07252747 0.07252747 0.08131868 0.09890110 0.12527473
#> [13] 0.16043956 0.20439560 0.25714286 NaN NaN NaN
#> [19] NaN NaN
#>
#> [[1]]$leaveOutResid
#> [1] NA 1.062046e-01 -3.545012e-01 2.505936e-01 3.272216e-01
#> [6] -6.550792e-01 -9.726058e-02 3.162926e-01 -3.152723e-02 -1.150238e-01
#> [11] 4.582616e-01 -8.250536e-02 2.030320e-01 1.054683e-01 -4.945864e-01
#> [16] NA 9.998125e+02 9.999963e+04 NA NA
#>
#> [[1]]$sigmaHat
#> [1] 0.2905855
#>
#> [[1]]$seEstimate
#> [1] 0.9567087
#>
#> [[1]]$seRobust
#> [1] 1.011323
#>
#> [[1]]$iter
#> [1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 2 1 1
#>
#>
#> [[2]]
#> [[2]]$category123
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3
#>
#> [[2]]$N
#> [1] 20
#>
#> [[2]]$yImputed
#> 1 2 3 4 5 6 7 8
#> 0.2875775 0.7883051 0.4089769 0.8830174 0.9404673 0.0455565 0.5281055 0.8924190
#> 9 10 11 12 13 14 15 16
#> 0.5514350 0.4566147 0.9568333 0.4533342 0.6775706 0.5726334 0.1029247 0.5106375
#> 17 18 19 20
#> 0.5032524 0.4958674 0.4884823 0.4810972
#>
#> [[2]]$nImputed
#> [1] 5
#>
#> [[2]]$estimate
#> [1] 11.02511
#>
#> [[2]]$cv
#> [1] 10.70079
#>
#> [[2]]$estimateOrig
#> [1] 101008.8
#>
#> [[2]]$coef
#> (Intercept) x2
#> 0.628798670 -0.007385073
#>
#> [[2]]$nModel
#> [1] 15
#>
#> [[2]]$rStud
#> [1] -1.319257e+00 6.351886e-01 -7.061670e-01 1.015980e+00 1.256191e+00
#> [6] -2.119483e+00 -1.636024e-01 1.129935e+00 -3.634973e-02 -3.313795e-01
#> [11] 1.512969e+00 -2.994830e-01 5.123108e-01 1.695044e-01 -1.709482e+00
#> [16] NA 2.873846e+03 2.805237e+05 NA NA
#>
#> [[2]]$dffits
#> [1] -0.744745002 0.312860902 -0.303542798 0.381911850 0.415955279
#> [6] -0.629036399 -0.044966623 0.301987952 -0.009990832 -0.098349351
#> [11] 0.500980814 -0.112577056 0.220214561 0.083489051 -0.965034572
#> [16] NaN NaN NaN NaN NaN
#>
#> [[2]]$hii
#> [1] 0.24166667 0.19523810 0.15595238 0.12380952 0.09880952 0.08095238
#> [7] 0.07023810 0.06666667 0.07023810 0.08095238 0.09880952 0.12380952
#> [13] 0.15595238 0.19523810 0.24166667 NaN NaN NaN
#> [19] NaN NaN
#>
#> [[2]]$leaveOutResid
#> [1] -4.402234e-01 2.165567e-01 -2.341888e-01 3.238554e-01 3.868150e-01
#> [6] -5.864024e-01 -5.269916e-02 3.457510e-01 -1.172128e-02 -1.069947e-01
#> [11] 4.541443e-01 -9.911502e-02 1.715281e-01 5.868289e-02 -5.473818e-01
#> [16] NA 9.997428e+02 9.999955e+04 NA NA
#>
#> [[2]]$sigmaHat
#> [1] 0.2987463
#>
#> [[2]]$seEstimate
#> [1] 1.179773
#>
#> [[2]]$seRobust
#> [1] 1.30235
#>
#> [[2]]$iter
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 2 1 1
#>
#>
#> [[3]]
#> [[3]]$category123
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3
#>
#> [[3]]$N
#> [1] 20
#>
#> [[3]]$yImputed
#> 1 2 3 4 5 6 7 8
#> 0.2875775 0.7883051 0.4089769 0.8830174 0.9404673 0.0455565 0.5281055 0.8924190
#> 9 10 11 12 13 14 15 16
#> 0.5514350 0.4566147 0.9568333 0.4533342 0.6775706 0.5726334 0.1029247 0.5106375
#> 17 18 19 20
#> 0.5032524 0.4958674 0.4884823 0.4810972
#>
#> [[3]]$nImputed
#> [1] 5
#>
#> [[3]]$estimate
#> [1] 11.02511
#>
#> [[3]]$cv
#> [1] 10.70079
#>
#> [[3]]$estimateOrig
#> [1] 101008.8
#>
#> [[3]]$coef
#> (Intercept) x2
#> 0.628798670 -0.007385073
#>
#> [[3]]$nModel
#> [1] 15
#>
#> [[3]]$rStud
#> [1] -1.319257e+00 6.351886e-01 -7.061670e-01 1.015980e+00 1.256191e+00
#> [6] -2.119483e+00 -1.636024e-01 1.129935e+00 -3.634973e-02 -3.313795e-01
#> [11] 1.512969e+00 -2.994830e-01 5.123108e-01 1.695044e-01 -1.709482e+00
#> [16] NA 2.873846e+03 2.805237e+05 NA NA
#>
#> [[3]]$dffits
#> [1] -0.744745002 0.312860902 -0.303542798 0.381911850 0.415955279
#> [6] -0.629036399 -0.044966623 0.301987952 -0.009990832 -0.098349351
#> [11] 0.500980814 -0.112577056 0.220214561 0.083489051 -0.965034572
#> [16] NaN NaN NaN NaN NaN
#>
#> [[3]]$hii
#> [1] 0.24166667 0.19523810 0.15595238 0.12380952 0.09880952 0.08095238
#> [7] 0.07023810 0.06666667 0.07023810 0.08095238 0.09880952 0.12380952
#> [13] 0.15595238 0.19523810 0.24166667 NaN NaN NaN
#> [19] NaN NaN
#>
#> [[3]]$leaveOutResid
#> [1] -4.402234e-01 2.165567e-01 -2.341888e-01 3.238554e-01 3.868150e-01
#> [6] -5.864024e-01 -5.269916e-02 3.457510e-01 -1.172128e-02 -1.069947e-01
#> [11] 4.541443e-01 -9.911502e-02 1.715281e-01 5.868289e-02 -5.473818e-01
#> [16] NA 9.997428e+02 9.999955e+04 NA NA
#>
#> [[3]]$sigmaHat
#> [1] 0.2987463
#>
#> [[3]]$seEstimate
#> [1] 1.179773
#>
#> [[3]]$seRobust
#> [1] 1.30235
#>
#> [[3]]$iter
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 2 1 1
#>
#>
# ----- LmImputeOne2Many -----
# Create data fram z with matrix embedded in one variable
# The single y is also the first variable in the matrix yMany
set.seed(123) # same results each time
y = runif(20)+c(rep(0,15),NA,1E3,1E5,NA,NA)
z = data.frame( # Small test data set
x = c(1,1,1:10,3,3,3,4,4,5,5,5),
y = y,
yMany = I(cbind(y,matrix(1:60,20,3,dimnames=list(NULL,c("A","B","C"))))))
a=LmImputeOne2Many(z,limitModel=1)
print(a)
#> $category123
#> [1] 1 1 1 2 2 2 1 2 1 1 2 1 1 1 2 3 3 3 3 3
#>
#> $N
#> [1] 20
#>
#> $yImputed
#> 1 2 3 4 5 6 7 8
#> 0.2875775 0.7883051 0.4089769 0.8830174 0.9404673 0.0455565 0.5281055 0.8924190
#> 9 10 11 12 13 14 15 16
#> 0.5514350 0.4566147 0.9568333 0.4533342 0.6775706 0.5726334 0.1029247 0.5269902
#> 17 18 19 20
#> 0.5269902 0.5208706 0.5208706 0.5208706
#>
#> $nImputed
#> [1] 5
#>
#> $estimate
#> [1] 11.16236
#>
#> $cv
#> [1] 6.915114
#>
#> $estimateOrig
#> [1] 101008.8
#>
#> $coef
#> (Intercept) x
#> 0.52111299 0.01104661
#>
#> $nModel
#> [1] 15
#>
#> $rStud
#> [1] -8.858747e-01 9.307077e-01 -4.356614e-01 1.231600e+00 1.398263e+00
#> [6] -1.998724e+00 -1.609987e-01 1.075081e+00 -1.614298e-01 -5.467762e-01
#> [11] 1.326493e+00 -7.097364e-01 4.169977e-01 6.171800e-02 -1.684959e+00
#> [16] NA 3.237769e+03 3.236335e+05 NA NA
#>
#> $dffits
#> [1] -0.38690666 0.40648755 -0.19027556 0.44017817 0.41935559 -0.53971725
#> [7] -0.04402737 0.33265848 -0.05999492 -0.24878693 0.74105963 -0.48730970
#> [13] 0.12506254 0.01850996 -0.50533899 NaN NaN NaN
#> [19] NaN NaN
#>
#> $hii
#> [1] 0.16019417 0.16019417 0.16019417 0.11326861 0.08252427 0.06796117
#> [7] 0.06957929 0.08737864 0.12135922 0.17152104 0.23786408 0.32038835
#> [13] 0.08252427 0.08252427 0.08252427 NaN NaN NaN
#> [19] NaN NaN
#>
#> $leaveOutResid
#> [1] -2.912365e-01 3.050057e-01 -1.466800e-01 3.832177e-01 4.209533e-01
#> [6] -5.576409e-01 -5.184812e-02 3.342310e-01 -5.349656e-02 -1.845203e-01
#> [11] 4.412610e-01 -2.622747e-01 1.344099e-01 2.003385e-02 -4.919238e-01
#> [16] NA 9.996808e+02 9.999947e+04 NA NA
#>
#> $sigmaHat
#> [1] 0.2987705
#>
#> $seEstimate
#> [1] 0.7718902
#>
#> $seRobust
#> [1] 0.8181103
#>
#> $iter
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 2 1 1
#>
#> $Mcategory123
#> [1] 1 1 1 2 2 2 1 2 1 1 2 1 1 1 2 3 3 3 3 3
#>
#> $MN
#> [1] 20
#>
#> $MyImputed
#> y A B C
#> 1 0.2875775 1.000000 21.00000 41.00000
#> 2 0.7883051 2.000000 22.00000 42.00000
#> 3 0.4089769 3.000000 23.00000 43.00000
#> 4 0.8830174 4.000000 24.00000 44.00000
#> 5 0.9404673 5.000000 25.00000 45.00000
#> 6 0.0455565 6.000000 26.00000 46.00000
#> 7 0.5281055 7.000000 27.00000 47.00000
#> 8 0.8924190 8.000000 28.00000 48.00000
#> 9 0.5514350 9.000000 29.00000 49.00000
#> 10 0.4566147 10.000000 30.00000 50.00000
#> 11 0.9568333 11.000000 31.00000 51.00000
#> 12 0.4533342 12.000000 32.00000 52.00000
#> 13 0.6775706 13.000000 33.00000 53.00000
#> 14 0.5726334 14.000000 34.00000 54.00000
#> 15 0.1029247 15.000000 35.00000 55.00000
#> 16 0.5269902 7.602469 27.60247 47.60247
#> 17 0.5269902 7.602469 27.60247 47.60247
#> 18 0.5208706 8.461728 28.46173 48.46173
#> 19 0.5208706 8.461728 28.46173 48.46173
#> 20 0.5208706 8.461728 28.46173 48.46173
#>
#> $MnImputed
#> [1] 5
#>
#> $Mestimate
#> y A B C
#> 1 11.16236 160.5901 560.5901 960.5901
#>
#> $Mcv
#> y A B C
#> 1 3.919745 7.39013 2.117023 1.235472
#>
#> $MestimateOrig
#> y A B C
#> 1 101008.8 210 610 1010
#>
#> $Mcoef
#> y A B C
#> (Intercept) 0.551468378 4.1654321 24.1654321 44.1654321
#> x -0.006119548 0.8592593 0.8592593 0.8592593
#>
#> $MnModel
#> [1] 9
#>
#> $MrStud
#> y A B C
#> [1,] -2.474696e+00 -1.0982147 -1.0982147 -1.0982147
#> [2,] 2.209948e+00 -0.7914529 -0.7914529 -0.7914529
#> [3,] -9.939421e-01 -0.5151551 -0.5151551 -0.5151551
#> [4,] 2.027052e+00 -0.4095308 -0.4095308 -0.4095308
#> [5,] 2.444753e+00 -0.3857022 -0.3857022 -0.3857022
#> [6,] -2.913273e+00 -0.3575011 -0.3575011 -0.3575011
#> [7,] 4.547594e-02 -0.3419624 -0.3419624 -0.3419624
#> [8,] 2.255518e+00 -0.2908560 -0.2908560 -0.2908560
#> [9,] 2.829034e-01 -0.2876550 -0.2876550 -0.2876550
#> [10,] -3.180068e-01 -0.2648589 -0.2648589 -0.2648589
#> [11,] 2.526246e+00 -0.1817989 -0.1817989 -0.1817989
#> [12,] -3.014682e-01 -0.2273443 -0.2273443 -0.2273443
#> [13,] 9.871350e-01 1.8221212 1.8221212 1.8221212
#> [14,] 2.518184e-01 2.3496347 2.3496347 2.3496347
#> [15,] -2.581753e+00 1.8268952 1.8268952 1.8268952
#> [16,] NA 1.8734381 1.8734381 1.8734381
#> [17,] 6.049544e+03 2.0965320 2.0965320 2.0965320
#> [18,] 6.041166e+05 2.1243966 2.1243966 2.1243966
#> [19,] NA 2.3471201 2.3471201 2.3471201
#> [20,] NA 2.5698435 2.5698435 2.5698435
#>
#> $Mdffits
#> y A B C
#> [1,] -1.36994408 -0.6079505 -0.6079505 -0.6079505
#> [2,] 1.22338446 -0.4381331 -0.4381331 -0.4381331
#> [3,] -0.55022717 -0.2851799 -0.2851799 -0.2851799
#> [4,] NaN NaN NaN NaN
#> [5,] NaN NaN NaN NaN
#> [6,] NaN NaN NaN NaN
#> [7,] 0.01647741 -0.1239041 -0.1239041 -0.1239041
#> [8,] NaN NaN NaN NaN
#> [9,] 0.13707132 -0.1393735 -0.1393735 -0.1393735
#> [10,] -0.18874011 -0.1571963 -0.1571963 -0.1571963
#> [11,] NaN NaN NaN NaN
#> [12,] -0.28269786 -0.2131891 -0.2131891 -0.2131891
#> [13,] 0.38303914 0.7070398 0.7070398 0.7070398
#> [14,] 0.09771340 0.9117315 0.9117315 0.9117315
#> [15,] NaN NaN NaN NaN
#> [16,] NaN NaN NaN NaN
#> [17,] NaN NaN NaN NaN
#> [18,] NaN NaN NaN NaN
#> [19,] NaN NaN NaN NaN
#> [20,] NaN NaN NaN NaN
#>
#> $Mhii
#> [1] 0.2345679 0.2345679 0.2345679 NaN NaN NaN 0.1160494
#> [8] NaN 0.1901235 0.2604938 NaN 0.4679012 0.1308642 0.1308642
#> [15] NaN NaN NaN NaN NaN NaN
#>
#> $MleaveOutResid
#> y A B C
#> [1,] -3.367657e-01 -5.2580645 -5.2580645 -5.2580645
#> [2,] 3.174107e-01 -3.9516129 -3.9516129 -3.9516129
#> [3,] -1.781633e-01 -2.6451613 -2.6451613 -2.6451613
#> [4,] 3.437881e-01 -1.8839506 -1.8839506 -1.8839506
#> [5,] 4.073576e-01 -1.7432099 -1.7432099 -1.7432099
#> [6,] -4.814337e-01 -1.6024691 -1.6024691 -1.6024691
#> [7,] 8.184680e-03 -1.6536313 -1.6536313 -1.6536313
#> [8,] 3.776680e-01 -1.3209877 -1.3209877 -1.3209877
#> [9,] 5.285185e-02 -1.4573171 -1.4573171 -1.4573171
#> [10,] -6.206473e-02 -1.4056761 -1.4056761 -1.4056761
#> [11,] 4.604409e-01 -0.8987654 -0.8987654 -0.8987654
#> [12,] -6.942083e-02 -1.4245940 -1.4245940 -1.4245940
#> [13,] 1.662121e-01 7.1988636 7.1988636 7.1988636
#> [14,] 4.547468e-02 8.3494318 8.3494318 8.3494318
#> [15,] -4.301850e-01 8.2567901 8.2567901 8.2567901
#> [16,] NA 8.3975309 8.3975309 8.3975309
#> [17,] 9.997191e+02 9.3975309 9.3975309 9.3975309
#> [18,] 9.999952e+04 9.5382716 9.5382716 9.5382716
#> [19,] NA 10.5382716 10.5382716 10.5382716
#> [20,] NA 11.5382716 11.5382716 11.5382716
#>
#> $MsigmaHat
#> y A B C
#> [1,] 0.1566879 4.250034 4.250034 4.250034
#>
#> $MseEstimate
#> y A B C
#> 1 0.4375362 11.86782 11.86782 11.86782
#>
#> $MseRobust
#> [,1] [,2] [,3] [,4]
#> [1,] 0.4727081 12.11742 12.11742 12.11742
#>
a$MrStud[, 1] - a$rStud # Not equal since no iteration and no "last" when multiple y
#> [1] -1.588821e+00 1.279240e+00 -5.582807e-01 7.954514e-01 1.046490e+00
#> [6] -9.145482e-01 2.064747e-01 1.180437e+00 4.443332e-01 2.287694e-01
#> [11] 1.199753e+00 4.082682e-01 5.701373e-01 1.901004e-01 -8.967939e-01
#> [16] NA 2.811775e+03 2.804831e+05 NA NA
a=LmImputeOne2Many(z,limitModel=1,returnFinal=TRUE)
a$MrStud[, 1] - a$rStud # Now equal
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0 0 NA NA