我是 R 新手，当我在“sem”模型上使用摘要时，我有以下输出。然而，在网上关于 R 的大多数论述中，我在第一行下方发现了一个 RMSEA 指数和其他拟合优度指数。为什么我没有看到他们？我需要启用一些库或下载一些包吗？
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1 回答
4
像这样使用 opt 。
opt <- options(fit.indices = c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", "CFI", "RNI", "IFI", "SRMR", "AIC", "AICc", "BIC", "CAIC"))
例子
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)
输出
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
于 2013-02-16T12:38:10.583 回答