R中的擬合指數

Question

我是R的新手，當我在「sem」模型上使用摘要時，我有以下輸出。然而，在R網上的大多數論述中，我發現RMSEA指數和第一行以下的附加擬合優度指數。爲什麼我沒有看到他們？我是否需要啓用一些庫或下載一些軟件包？

Answer 1

使用opt這樣。

opt <- options(fit.indices = c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", "CFI", "RNI", "IFI", "SRMR", "AIC", "AICc", "BIC", "CAIC")) 

例

library(sem) 
# The following examples use file input and may be executed via example(): 

etc <- file.path(.path.package(package="sem")[1], "etc") # path to data and model files 

# to get all fit indices (not recommended, but for illustration): 

opt <- options(fit.indices = c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", "CFI", "RNI", "IFI", "SRMR", "AIC", "AICc", "BIC", "CAIC")) 

# ------------- Duncan, Haller and Portes peer-influences model ---------------------- 
# A nonrecursive SEM with unobserved endogenous variables and fixed exogenous variables 

(R.DHP <- readMoments(file=file.path(etc, "R-DHP.txt"), 
       diag=FALSE, names=c("ROccAsp", "REdAsp", "FOccAsp", 
       "FEdAsp", "RParAsp", "RIQ", "RSES", "FSES", "FIQ", "FParAsp"))) 
(model.dhp <- specifyModel(file=file.path(etc, "model-DHP.txt"))) 
sem.dhp.1 <- sem(model.dhp, R.DHP, 329, 
    fixed.x=c('RParAsp', 'RIQ', 'RSES', 'FSES', 'FIQ', 'FParAsp')) 
summary(sem.dhp.1) 

輸出

Model Chisquare = 26.69722 Df = 15 Pr(>Chisq) = 0.03130238 
Goodness-of-fit index = 0.984387 
Adjusted goodness-of-fit index = 0.9427525 
RMSEA index = 0.04875944 90% CI: (0.01451664, 0.07830923) 
Bentler-Bonett NFI = 0.969384 
Tucker-Lewis NNFI = 0.9575676 
Bentler CFI = 0.9858559 
Bentler RNI = 0.9858559 
Bollen IFI = 0.986351 
SRMR = 0.02020441 
AIC = 64.69722 
AICc = 29.15676 
BIC = -60.24365 
CAIC = -75.24365 

Normalized Residuals 
    Min. 1st Qu. Median  Mean 3rd Qu.  Max. 
-0.79950 -0.11780 0.00000 -0.01201 0.03974 1.56500 

R-square for Endogenous Variables 
RGenAsp FGenAsp ROccAsp REdAsp FOccAsp FEdAsp 
0.5220 0.6170 0.5879 0.6639 0.6888 0.5954 

Parameter Estimates 
      Estimate Std Error z value Pr(>|z|)       
gam11  0.16122243 0.03879229 4.1560429 3.238070e-05 RGenAsp <--- RParAsp 
gam12  0.24964929 0.04398092 5.6763087 1.376323e-08 RGenAsp <--- RIQ  
gam13  0.21840307 0.04419737 4.9415399 7.750795e-07 RGenAsp <--- RSES 
gam14  0.07183948 0.04970692 1.4452610 1.483846e-01 RGenAsp <--- FSES 
gam23  0.06188722 0.05171967 1.1965895 2.314666e-01 FGenAsp <--- RSES 
gam24  0.22886655 0.04416219 5.1824090 2.190383e-07 FGenAsp <--- FSES 
gam25  0.34903584 0.04528981 7.7067195 1.290931e-14 FGenAsp <--- FIQ  
gam26  0.15953378 0.03882594 4.1089486 3.974645e-05 FGenAsp <--- FParAsp 
beta12  0.18423260 0.09488782 1.9415832 5.218758e-02 RGenAsp <--- FGenAsp 
beta21  0.23547774 0.11938936 1.9723511 4.856954e-02 FGenAsp <--- RGenAsp 
lam21  1.06267796 0.09013868 11.7893663 4.428606e-32 REdAsp <--- RGenAsp 
lam42  0.92972549 0.07028107 13.2286762 5.993366e-40 FEdAsp <--- FGenAsp 
ps12  -0.02260953 0.05119394 -0.4416447 6.587463e-01 FGenAsp <--> RGenAsp 
V[RGenAsp] 0.28098701 0.04623153 6.0778220 1.218259e-09 RGenAsp <--> RGenAsp 
V[FGenAsp] 0.26383553 0.04466689 5.9067359 3.489525e-09 FGenAsp <--> FGenAsp 
V[ROccAsp] 0.41214545 0.05122465 8.0458422 8.565431e-16 ROccAsp <--> ROccAsp 
V[REdAsp] 0.33614511 0.05209992 6.4519310 1.104339e-10 REdAsp <--> REdAsp 
V[FOccAsp] 0.31119482 0.04592713 6.7758385 1.236867e-11 FOccAsp <--> FOccAsp 
V[FEdAsp] 0.40460363 0.04618437 8.7606177 1.941833e-18 FEdAsp <--> FEdAsp 

Iterations = 32