Dear R-Community,

I have just started using R (3.0.0) for my statistics and I find it the best programm for running my regressions.

However, as I am still a junior user I soon came to the limit of my technical knowhow.

Here's my situation:

My dataset consists of 30 observations and 100 variables (numeric and factors). Some of the observations have missing values and as a result these observations will be omitted when running the regressions, which in turn reduces the sample. In order to counteract this, I have installed the package 'mi' for applying the method of multiple imputation.

To reduce the structural problems in the data, I have corrected the variable types (i.e. numeric to factors and vice versa) and eliminated one observation, as it was an outlier and led to collinearity amongst some variables.
To control for the problem of  “contrasts can be applied only to factors with 2 or more levels” all factorvariables with only 1 level where excluded. Here's the information matrix:



names

include

order

number.mis

all.mis

type

collinear

1

InvestmentYear

Yes

1

1

No

positive-continuous

No

2

InvestmentSize

Yes

NA

0

No

positive-continuous

No

3

InvestmentSyndication

Yes

NA

0

No

binary

No

4

InvestmentRole

Yes

NA

0

No

binary

No

5

InvestmentExitYearAgreed

Yes

2

7

No

nonnegative

No

6

InvestmentExitYearActual

Yes

3

3

No

nonnegative

No

7

InvestmentExitStrategyAgreed

Yes

4

5

No

unordered-categorical

No

8

InvestmentExitStrategyActual

Yes

5

3

No

unordered-categorical

No

9

InvestmentAmountRL

Yes

6

7

No

continuous

No

10

Sector

Yes

NA

0

No

unordered-categorical

No

11

BusinessStage

Yes

NA

0

No

unordered-categorical

No

12

Origin

Yes

7

2

No

unordered-categorical

No

13

ManagementFoundingMember

Yes

NA

0

No

binary

No

14

ManagementFormerPC

Yes

NA

0

No

binary

No

15

ManagementFormerPCQuantity

Yes

NA

0

No

ordered-categorical

No

16

ManagementFormerPCSuccess

Yes

8

1

No

ordered-categorical

No

17

ManagementCompositionCEO

Yes

9

2

No

ordered-categorical

No

18

ManagementCompositionTechnical

Yes

10

2

No

ordered-categorical

No

19

ManagementCompositionFinancial

Yes

11

2

No

binary

No

20

ManagementCompositionScientific

Yes

12

2

No

ordered-categorical

No

21

ManagementCompositionMarketing

Yes

13

2

No

fixed

No

22

ManagementCompositionSales

Yes

14

2

No

fixed

No

23

ManagementExperienceCEO

Yes

15

6

No

nonnegative

No

24

ManagementExperienceTechnical

Yes

16

7

No

nonnegative

No

25

ManagementExperienceFinancial

Yes

17

6

No

nonnegative

No

26

ManagementExperienceScientific

Yes

18

7

No

nonnegative

No

27

ManagementExperienceMarketing

Yes

19

6

No

binary

No

28

ManagementExperienceSales

Yes

20

6

No

fixed

No

29

ManagementExperienceStartup

Yes

21

6

No

ordered-categorical

No

30

ManagementJointExperience

Yes

22

6

No

ordered-categorical

No

31

ManagementDegreesNSBachelors

Yes

23

2

No

binary

No

32

ManagementDegreesNSMasters

Yes

24

2

No

binary

No

33

ManagementDegreesNSPhD

Yes

25

2

No

ordered-categorical

No

34

ManagementDegreesNSProf

Yes

26

2

No

ordered-categorical

No

35

ManagementDegreesEBachelors

Yes

27

2

No

fixed

No

36

ManagementDegreesEMasters

Yes

28

2

No

binary

No

37

ManagementDegreesEPhD

Yes

29

2

No

binary

No

38

ManagementDegreesEProf

Yes

30

2

No

fixed

No

39

ManagementAverageCompensation

Yes

31

5

No

nonnegative

No

40

ManagementStockCompensation

Yes

NA

0

No

unordered-categorical

No

41

BoDMembers

Yes

NA

0

No

nonnegative

No

42

BoDCompsitionFounder

Yes

32

1

No

ordered-categorical

No

43

BoDCompsitionInvestor

Yes

33

1

No

nonnegative

No

44

BoDCompsitionExecutive

Yes

34

1

No

ordered-categorical

No

45

BoDCompsitionOther

Yes

NA

0

No

ordered-categorical

No

46

ScientificBoardMembers

Yes

35

6

No

nonnegative

No

47

FundsTotal

Yes

NA

0

No

positive-continuous

No

48

FundsResearch

Yes

NA

0

No

positive-continuous

No

49

FundsManufacturing

Yes

NA

0

No

ordered-categorical

No

50

FundsMarketing

Yes

NA

0

No

fixed

No

51

FundsOther

Yes

NA

0

No

binary

No

52

FundsCommittedCorporate

Yes

NA

0

No

nonnegative

No

53

FundsCommittedCorporateAmount

Yes

36

3

No

nonnegative

No

54

FundsCommittedPrivate

Yes

NA

0

No

nonnegative

No

55

FundsCommittedPrivateAmount

Yes

37

6

No

nonnegative

No

56

FundsCommittedFounder

Yes

38

1

No

ordered-categorical

No

57

FundsCommittedFounderAmount

Yes

39

2

No

ordered-categorical

No

58

FundsCommittedNone

Yes

NA

0

No

binary

No

59

GovernmentalAward

Yes

40

2

No

binary

No

60

GovernmentalAwardAmount

Yes

41

9

No

nonnegative

No

61

NonGovernmentalAward

Yes

42

3

No

binary

No

62

NonGovernmentalAwardAmount

Yes

43

3

No

ordered-categorical

No

63

Label

Yes

44

2

No

binary

No

64

AdditionalFundsSource

Yes

NA

0

No

fixed

No

65

AdditionalFundsAmount

Yes

NA

0

No

fixed

No

66

Compounds

Yes

45

1

No

ordered-categorical

No

67

DevelopmentStage1

Yes

46

2

No

unordered-categorical

No

68

Technology1

Yes

47

1

No

binary

No

69

DevelopmentStage2

Yes

48

1

No

unordered-categorical

No

70

Technology2

Yes

49

1

No

binary

No

71

DevelopmentStage3

Yes

50

2

No

unordered-categorical

No

72

Technology3

Yes

51

2

No

binary

No

73

DevelopmentStage4

Yes

52

1

No

fixed

No

74

Technology4

Yes

53

1

No

fixed

No

75

PendingPatents

Yes

54

4

No

nonnegative

No

76

GrantedPatents

Yes

55

1

No

ordered-categorical

No

77

Competition

Yes

56

4

No

nonnegative

No

78

MarketSales

Yes

57

4

No

positive-continuous

No

79

MarketPatients

Yes

58

17

No

nonnegative

No

80

MarketGrowthRate

Yes

59

8

No

fixed

No

81

CustomerBase

Yes

60

1

No

binary

No

82

CustomerBaseAmount

Yes

NA

0

No

ordered-categorical

No

83

Collaboration

Yes

NA

0

No

binary

No

84

CollaborationOutsourcing

Yes

NA

0

No

ordered-categorical

No

85

CollaborationCooperation

Yes

NA

0

No

ordered-categorical

No

86

CollaborationStrategicAlliance

Yes

NA

0

No

ordered-categorical

No

87

CollaborationJointVenture

Yes

NA

0

No

fixed

No

88

CollaborationMerger

Yes

NA

0

No

fixed

No

89

CollaborationUniversity

Yes

NA

0

No

ordered-categorical

No

90

CollaborationCompany

Yes

NA

0

No

ordered-categorical

No

91

CollaborationConsulting

Yes

NA

0

No

ordered-categorical

No

92

CollaborationOther

Yes

NA

0

No

ordered-categorical

No



Unfortunately I was not able to impute semi-continous data with transformation. When using the following code:

>newdata<-mi.preprocess(ISC)

and

>IMP <- mi(newdata, preprocess = FALSE)

the following error message resulted:

"Fehler in .local(object, ...) : unbenutztes Argument (preprocess = FALSE)"

I have searched for an answer, but until now I didn't found any. It seems like the argument does not apply to this objecte, but as mentioned in the package description it should be a valid argument.

Anyhow, I have performed the default function of mi() to check my data:

>IMP <- mi(ISC) , where ISC is the data frame. Furthermore, the defaul arguments of the function are as follows (source: http://127.0.0.1:31868/library/mi/html/mi.html):

object

                A data frame or an mi object that contains an incomplete data. mi identifies NAs as the missing data.


info


                The mi.info<http://127.0.0.1:31868/library/mi/help/mi.info> object.


n.imp

                The number of multiple imputations. Default is 3 chains.


n.iter

                The maximum number of imputation iterations. Default is 30 iterations.


R.hat

                The value of the R.hat statistic used as a convergence criterion. Default is 1.1.


max.minutes

                The maximum minutes to operate the whole imputation process. Default is 20 minutes.


rand.imp.method

                The methods for random imputation. Currently, mi implements only the boostrap method.


run.past.convergence

   Default is FALSE. If the value is set to be TRUE, mi will run until the values of either n.iter or max.minutes are reached even if the imputation is converged.


seed

   The random number seed.


check.coef.convergence

   Default is FALSE. If the value is set to be TRUE, mi will check the convergence of the coefficients of imputation models.


add.noise

  A list of parameters for controlling the process of adding noise to mi via noise.control<http://127.0.0.1:31868/library/mi/help/noise.control>.



Unfortuntely, the imputation stops after the first chain (imputation) of the first iteration and does not show an error message (by default the imputation process shoud run over 30 iterations including 3 chains each):

"> IMP <- mi(ISC)
Beginning Multiple Imputation ( Sun Jun 02 16:44:41 2013 ):
Iteration 1
 Chain 1 : InvestmentYear*  InvestmentExitYearAgreed*  InvestmentExitYearActual*  InvestmentExitStrategyAgreed*  InvestmentExitStrategyActual*  InvestmentAmountRL*  Origin*  ManagementFormerPCSuccess*  ManagementCompositionCEO*  ManagementCompositionTechnical*  ManagementCompositionFinancial*  ManagementCompositionScientific*  ManagementCompositionMarketing*  ManagementCompositionSales*  ManagementExperienceCEO*  ManagementExperienceTechnical*  ManagementExperienceFinancial*  ManagementExperienceScientific*  ManagementExperienceMarketing*  ManagementExperienceSales*  ManagementExperienceStartup*  ManagementJointExperience*  ManagementDegreesNSBachelors*  ManagementDegreesNSMasters*  ManagementDegreesNSPhD*  ManagementDegreesNSProf*  ManagementDegreesEBachelors*  ManagementDegreesEMasters*  ManagementDegreesEPhD*  ManagementDegreesEProf*  ManagementAverageCompensation*  BoDCompsitionFounder*  BoDCompsitionInvestor*  BoDCompsitionExecutive*  ScientificBoardMembers*  FundsCommittedCorporateAmount*  FundsCommittedPrivateAmount*  FundsCommittedFounder*  FundsCommittedFounderAmount*  GovernmentalAward*  GovernmentalAwardAmount*  NonGovernmentalAward*  NonGovernmentalAwardAmount*  Label*  Compounds*  DevelopmentStage1*  Technology1*  DevelopmentStage2*  Technology2*  DevelopmentStage3*  Technology3*  DevelopmentStage4*  Technology4*  PendingPatents*  GrantedPatents*  Competition*  MarketSales*  MarketPatients*  MarketGrowthRate*  CustomerBase*
>"

My questions:

Does the mi(ISC) need the specification of the mi.preprocess(ISC)? If so, how can i fix the error "Fehler in .local(object, ...) : unbenutztes Argument (preprocess = FALSE)"?

Did I mispecify the mi(ISC) or why does it stop without providing an error message and how can i solve this?

Many thanks in advance for your highly appreciated support on this matter. So far, I have intensivelly searched for solving this problem, but could not find any solution.

Best regards,

Sascha













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