拟合优度指数 "NA"
Goodness-of-fit indices "NA"
我是 运行 Lavaan 的非递归模型。但是，发生了两件事，我不太明白。首先，拟合优度指数和一些标准误差为 "NA"。第二，不同方向的两个变量之间的两个系数不一致（非递归部分：ResidentialMobility--作者）：一个为正，一个为负（至少应该是同向的，否则如何解释？）。有人可以帮我吗？如果您想让我进一步澄清，请告诉我。谢谢！
model01<-'ResidentialMobility~a*Coun
SavingMotherPercentage~e*Affect
SavingMotherPercentage~f*Author
SavingMotherPercentage~g*Recipro
Affect~b*ResidentialMobility
Author~c*ResidentialMobility
Recipro~d*ResidentialMobility
ResidentialMobility~h*Affect
ResidentialMobility~i*Author
ResidentialMobility~j*Recipro
Affect~~Author+Recipro+ResidentialMobility
Author~~Recipro+ResidentialMobility
Recipro~~ResidentialMobility
Coun~SavingMotherPercentage
ab:=a*b
ac:=a*c
ad:=a*d
be:=b*e
cf:=c*f
dg:=d*g
'
fit <- cfa(model01, estimator = "MLR", data = data01, missing = "FIML")
summary(fit, standardized = TRUE, fit.measures = TRUE)
输出：
lavaan (0.5-21) 93次迭代后正常收敛
Used Total
Number of observations 502 506
Number of missing patterns 4
Estimator ML Robust
Minimum Function Test Statistic NA NA
Degrees of freedom -2 -2
Minimum Function Value 0.0005232772506
Scaling correction factor
for the Yuan-Bentler correction
User model versus baseline model:
Comparative Fit Index (CFI) NA NA
Tucker-Lewis Index (TLI) NA NA
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -5057.346 -5057.346
Loglikelihood unrestricted model (H1) -5057.084 -5057.084
Number of free parameters 29 29
Akaike (AIC) 10172.693 10172.693
Bayesian (BIC) 10295.032 10295.032
Sample-size adjusted Bayesian (BIC) 10202.984 10202.984
Root Mean Square Error of Approximation:
RMSEA NA NA
90 Percent Confidence Interval NA NA NA NA
P-value RMSEA <= 0.05 NA NA
Standardized Root Mean Square Residual:
SRMR 0.006 0.006
Parameter Estimates:
Information Observed
Standard Errors Robust.huber.white
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ResidentialMobility ~
Coun (a) -1.543 0.255 -6.052 0.000 -1.543 -0.540
SavingMotherPercentage ~
Affect (e) 3.093 1.684 1.837 0.066 3.093 0.122
Author (f) 2.618 0.923 2.835 0.005 2.618 0.145
Recipro (g) 0.061 1.344 0.046 0.964 0.061 0.003
Affect ~
RsdntlMblt (b) -0.311 0.075 -4.125 0.000 -0.311 -0.570
Author ~
RsdntlMblt (c) -0.901 0.119 -7.567 0.000 -0.901 -1.180
Recipro ~
RsdntlMblt (d) -0.313 0.082 -3.841 0.000 -0.313 -0.512
ResidentialMobility ~
Affect (h) -0.209 0.193 -1.082 0.279 -0.209 -0.114
Author (i) 0.475 0.192 2.474 0.013 0.475 0.363
Recipro (j) 0.178 0.346 0.514 0.607 0.178 0.109
Coun ~
SvngMthrPr 0.003 0.001 2.225 0.026 0.003 0.108
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Affect ~~
.Author 0.667 0.171 3.893 0.000 0.667 0.534
.Recipro 0.669 0.119 5.623 0.000 0.669 0.773
.ResidentialMobility ~~
.Affect 0.624 0.144 4.347 0.000 0.624 0.474
.Author ~~
.Recipro 0.565 0.173 3.267 0.001 0.565 0.416
.ResidentialMobility ~~
.Author 1.029 0.288 3.572 0.000 1.029 0.499
.Recipro 0.564 0.304 1.851 0.064 0.564 0.395
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ResidentlMblty 1.813 NA 1.813 1.270
.SvngMthrPrcntg 29.591 7.347 4.027 0.000 29.591 1.499
.Affect 5.701 0.169 33.797 0.000 5.701 7.320
.Author 5.569 0.275 20.259 0.000 5.569 5.109
.Recipro 5.149 0.186 27.642 0.000 5.149 5.889
.Coun 0.367 0.069 5.336 0.000 0.367 0.735
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ResidentlMblty 2.169 0.259 8.378 0.000 2.169 1.064
.SvngMthrPrcntg 363.792 23.428 15.528 0.000 363.792 0.934
.Affect 0.797 0.129 6.153 0.000 0.797 1.314
.Author 1.957 0.343 5.713 0.000 1.957 1.647
.Recipro 0.941 0.126 7.439 0.000 0.941 1.231
.Coun 0.242 0.004 54.431 0.000 0.242 0.969
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ab 0.480 0.120 3.991 0.000 0.480 0.308
ac 1.390 0.261 5.328 0.000 1.390 0.637
ad 0.483 0.133 3.640 0.000 0.483 0.276
be -0.962 0.548 -1.757 0.079 -0.962 -0.070
cf -2.359 0.851 -2.771 0.006 -2.359 -0.171
dg -0.019 0.421 -0.046 0.964 -0.019 -0.001
为什么你得到 NA 我认为是因为你指定了一个自由度为 -2 的模型。您应该以不同方式指定模型，以便获得正数的自由度。
我是 运行 Lavaan 的非递归模型。但是，发生了两件事，我不太明白。首先，拟合优度指数和一些标准误差为 "NA"。第二，不同方向的两个变量之间的两个系数不一致（非递归部分：ResidentialMobility--作者）：一个为正，一个为负（至少应该是同向的，否则如何解释？）。有人可以帮我吗？如果您想让我进一步澄清，请告诉我。谢谢！
model01<-'ResidentialMobility~a*Coun
SavingMotherPercentage~e*Affect
SavingMotherPercentage~f*Author
SavingMotherPercentage~g*Recipro
Affect~b*ResidentialMobility
Author~c*ResidentialMobility
Recipro~d*ResidentialMobility
ResidentialMobility~h*Affect
ResidentialMobility~i*Author
ResidentialMobility~j*Recipro
Affect~~Author+Recipro+ResidentialMobility
Author~~Recipro+ResidentialMobility
Recipro~~ResidentialMobility
Coun~SavingMotherPercentage
ab:=a*b
ac:=a*c
ad:=a*d
be:=b*e
cf:=c*f
dg:=d*g
'
fit <- cfa(model01, estimator = "MLR", data = data01, missing = "FIML")
summary(fit, standardized = TRUE, fit.measures = TRUE)
输出：
lavaan (0.5-21) 93次迭代后正常收敛
Used Total
Number of observations 502 506
Number of missing patterns 4
Estimator ML Robust
Minimum Function Test Statistic NA NA
Degrees of freedom -2 -2
Minimum Function Value 0.0005232772506
Scaling correction factor
for the Yuan-Bentler correction
User model versus baseline model:
Comparative Fit Index (CFI) NA NA
Tucker-Lewis Index (TLI) NA NA
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -5057.346 -5057.346
Loglikelihood unrestricted model (H1) -5057.084 -5057.084
Number of free parameters 29 29
Akaike (AIC) 10172.693 10172.693
Bayesian (BIC) 10295.032 10295.032
Sample-size adjusted Bayesian (BIC) 10202.984 10202.984
Root Mean Square Error of Approximation:
RMSEA NA NA
90 Percent Confidence Interval NA NA NA NA
P-value RMSEA <= 0.05 NA NA
Standardized Root Mean Square Residual:
SRMR 0.006 0.006
Parameter Estimates:
Information Observed
Standard Errors Robust.huber.white
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ResidentialMobility ~
Coun (a) -1.543 0.255 -6.052 0.000 -1.543 -0.540
SavingMotherPercentage ~
Affect (e) 3.093 1.684 1.837 0.066 3.093 0.122
Author (f) 2.618 0.923 2.835 0.005 2.618 0.145
Recipro (g) 0.061 1.344 0.046 0.964 0.061 0.003
Affect ~
RsdntlMblt (b) -0.311 0.075 -4.125 0.000 -0.311 -0.570
Author ~
RsdntlMblt (c) -0.901 0.119 -7.567 0.000 -0.901 -1.180
Recipro ~
RsdntlMblt (d) -0.313 0.082 -3.841 0.000 -0.313 -0.512
ResidentialMobility ~
Affect (h) -0.209 0.193 -1.082 0.279 -0.209 -0.114
Author (i) 0.475 0.192 2.474 0.013 0.475 0.363
Recipro (j) 0.178 0.346 0.514 0.607 0.178 0.109
Coun ~
SvngMthrPr 0.003 0.001 2.225 0.026 0.003 0.108
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Affect ~~
.Author 0.667 0.171 3.893 0.000 0.667 0.534
.Recipro 0.669 0.119 5.623 0.000 0.669 0.773
.ResidentialMobility ~~
.Affect 0.624 0.144 4.347 0.000 0.624 0.474
.Author ~~
.Recipro 0.565 0.173 3.267 0.001 0.565 0.416
.ResidentialMobility ~~
.Author 1.029 0.288 3.572 0.000 1.029 0.499
.Recipro 0.564 0.304 1.851 0.064 0.564 0.395
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ResidentlMblty 1.813 NA 1.813 1.270
.SvngMthrPrcntg 29.591 7.347 4.027 0.000 29.591 1.499
.Affect 5.701 0.169 33.797 0.000 5.701 7.320
.Author 5.569 0.275 20.259 0.000 5.569 5.109
.Recipro 5.149 0.186 27.642 0.000 5.149 5.889
.Coun 0.367 0.069 5.336 0.000 0.367 0.735
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ResidentlMblty 2.169 0.259 8.378 0.000 2.169 1.064
.SvngMthrPrcntg 363.792 23.428 15.528 0.000 363.792 0.934
.Affect 0.797 0.129 6.153 0.000 0.797 1.314
.Author 1.957 0.343 5.713 0.000 1.957 1.647
.Recipro 0.941 0.126 7.439 0.000 0.941 1.231
.Coun 0.242 0.004 54.431 0.000 0.242 0.969
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ab 0.480 0.120 3.991 0.000 0.480 0.308
ac 1.390 0.261 5.328 0.000 1.390 0.637
ad 0.483 0.133 3.640 0.000 0.483 0.276
be -0.962 0.548 -1.757 0.079 -0.962 -0.070
cf -2.359 0.851 -2.771 0.006 -2.359 -0.171
dg -0.019 0.421 -0.046 0.964 -0.019 -0.001
为什么你得到 NA 我认为是因为你指定了一个自由度为 -2 的模型。您应该以不同方式指定模型，以便获得正数的自由度。