2
나는 CFA (괜찮 았던)를하고 싶었고, 모델의 적합성을 높이기 위해 가장 낮은 R2를 가진 두 항목을 지웠다. 문제는 그 중 하나에 대해 효과가 있다는 것입니다. 물론 적합성이 이전보다 향상되었습니다. 그러나 두 번째 항목 (첫 번째 항목 외에)을 삭제하려고 시도했을 때 위의 경고가 나타났습니다. 처음 삭제 된 항목은 "External7"이고 두 번째 항목은 "Negative2"입니다. 아이디어 나 지원이 필요하십니까?R (CFA)의 SEM : 헤 시안의 QR 분해를 계산할 수 없습니다. 최적화는 아마도 수렴하지 않았습니다.
install.packages("sem")
library(sem)
cov.matrix<-cov(na.omit(verbesserungfit))
View(cov.matrix)
cfa.model11 <- specifyModel() #definition welche items zu welchen latenten variablen gehören #und benennung der pfade von latenter variable zu item
EXTERNAL -> External1, external1
EXTERNAL -> External2, external2
EXTERNAL -> External3, external3
EXTERNAL -> External4, external4
EXTERNAL -> External5, external5
EXTERNAL -> External6, external6
Here, I deleted the row EXTERNAL -> External7, external7
SELF -> Self1, self1
SELF -> Self2, self2
SELF -> Self3, self3
SELF -> Self4, self4
POSITIVE -> Positive1, positive1
POSITIVE -> Positive2, positive2
POSITIVE -> Positive3, positive3
POSITIVE -> Positive4, positive4
NEGATIVE -> Negative1, negative1
Here, i deleted the row NEGATIVE -> Negative2, negative2
NEGATIVE -> Negative3, negative3
NEGATIVE -> Negative4, negative4
PROMOTION -> Promotion1, promotion1
PROMOTION -> Promotion2, promotion2
PROMOTION -> Promotion3, promotion3
PROMOTION -> Promotion4, promotion4
PROMOTION -> Promotion5, promotion5
GENERAL -> General1, general1
GENERAL -> General2, general2
GENERAL -> General3, general3
GENERAL -> General4, general4
GENERAL -> General5, general5
CAREER -> Career1, career1
CAREER -> Career2, career2
CAREER -> Career3, career3
CAREER -> Career4, career4
CAREER -> Career5, career5
FINANCIAL -> Financial1, financial1
FINANCIAL -> Financial2, financial2
FINANCIAL -> Financial3, financial3
FINANCIAL -> Financial4, financial4
FINANCIAL -> Financial5, financial5
ENTERTAINMENT -> Entertainment1, entertainment1
ENTERTAINMENT -> Entertainment2, entertainment2
ENTERTAINMENT -> Entertainment3, entertainment3
ENTERTAINMENT -> Entertainment4, entertainment4
ENTERTAINMENT -> Entertainment5, entertainment5
EXTERNAL<->EXTERNAL,NA,1 #Varianz der latenten Variablen definieren # NA und 1 für idendification des modells (standardisierte Variablen) # fixierung der varianzen #varExternal
SELF<->SELF, NA, 1 #varSelf
POSITIVE<->POSITIVE, NA, 1 #varPositive
NEGATIVE<->NEGATIVE, NA, 1 #varNegative
PROMOTION<->PROMOTION, NA, 1 #varPromotion
GENERAL<->GENERAL, NA, 1 #varGeneral
CAREER<->CAREER, NA, 1 #varCarrer
FINANCIAL<->FINANCIAL, NA, 1 #varFinancial
ENTERTAINMENT<->ENTERTAINMENT, NA, 1 #varEnterntainment
External1<->External1, error01 #hinzufuegen von stoerterm zu items, unerklärter teil pro item
External2<->External2, error02
External3<->External3, error03
External4<->External4, error04
External5<->External5, error05
External6<->External6, error06
Here, i deleted the row External7<->External7, error07
Self1<->Self1, error08
Self2<->Self2, error09
Self3<->Self3, error10
Self4<->Self4, error11
Positive1<->Positive1, error12
Positive2<->Positive2, error13
Positive3<->Positive3, error14
Positive4<->Positive4, error15
Negative1<->Negative1, error16
Here, i deleted the row Negative2<->Negative2, error17
Negative3<->Negative3, error18
Negative4<->Negative4, error19
Promotion1<->Promotion1, error20
Promotion2<->Promotion2, error21
Promotion3<->Promotion3, error22
Promotion4<->Promotion4, error23
Promotion5<->Promotion5, error24
General1<->General1, error25
General2<->General2, error26
General3<->General3, error27
General4<->General4, error28
General5<->General5, error29
Career1<->Career1, error30
Career2<->Career2, error31
Career3<->Career3, error32
Career4<->Career4, error33
Career5<->Career5, error34
Financial1<->Financial1, error35
Financial2<->Financial2, error36
Financial3<->Financial3, error37
Financial4<->Financial4, error38
Financial5<->Financial5, error39
Entertainment1<->Entertainment1, error40
Entertainment2<->Entertainment2, error41
Entertainment3<->Entertainment3, error42
Entertainment4<->Entertainment4, error43
Entertainment5<->Entertainment5, error44
EXTERNAL<->SELF, cov12 #covarianz zwischen den latenten variablen
EXTERNAL<->POSITIVE, cov13
EXTERNAL<->NEGATIVE, cov14
EXTERNAL<->PROMOTION, cov15
EXTERNAL<->GENERAL, cov16
EXTERNAL<->CAREER, cov17
EXTERNAL<->FINANCIAL, cov18
EXTERNAL<->ENTERTAINMENT, cov19
SELF<->POSITIVE, cov23
SELF<->NEGATIVE, cov24
SELF<->PROMOTION, cov25
SELF<->GENERAL, cov26
SELF<->CAREER, cov27
SELF<->FINANCIAL, cov28
SELF<->ENTERTAINMENT, cov29
POSITIVE<->NEGATIVE, cov34
POSITIVE<->PROMOTION, cov35
POSITIVE<->GENERAL, cov36
POSITIVE<->CAREER, cov37
POSITIVE<->FINANCIAL, cov38
POSITIVE<->ENTERTAINMENT, cov39
NEGATIVE<->PROMOTION, cov45
NEGATIVE<->GENERAL, cov46
NEGATIVE<->CAREER, cov47
NEGATIVE<->FINANCIAL, cov48
NEGATIVE<->ENTERTAINMENT, cov49
PROMOTION<->GENERAL, cov56
PROMOTION<->CAREER, cov57
PROMOTION<->FINANCIAL, cov58
PROMOTION<->ENTERTAINMENT, cov59
GENERAL<->CAREER, cov67
GENERAL<->FINANCIAL, cov68
GENERAL<->ENTERTAINMENT, cov69
CAREER<->FINANCIAL, cov78
CAREER<->ENTERTAINMENT, cov79
FINANCIAL<->ENTERTAINMENT, cov89
cfa11 <- sem(cfa.model11, cov.matrix, nrow(verbesserungfit)) # was muss alles an cfa gesendet werden #modellname #name für covarianzmatrix #wie viele participants in dataset mit n row function
summary(cfa11,fit.indices=c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", "CFI", "RNI", "IFI", "SRMR", "AIC", "AICc", "BIC", "CAIC"))
감사합니다.