Brandon McCormick
May 28, 2020, 1:19:05 PM5/28/20
to lavaan
I ran a cfa from 40 imputations (had the same result with just 5 imputations). And got an odd result of all my df = inf, as well as warning messages:
1: In if (attr(x, "information") == "observed") { :
Here are my full results:
the condition has length > 1 and only the first element will be used
2: In cbind(c1, c2, deparse.level = 0) :
number of rows of result is not a multiple of vector length (arg 1)
lavaan.mi object based on 40 imputed data sets.
See class?lavaan.mi help page for available methods.
Convergence information:
The model converged on 40 imputed data sets
Rubin's (1987) rules were used to pool point and SE estimates across 40 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.
Model Test User Model:
Test statistic 697.943
Degrees of freedom 77
P-value 0.000
Model Test Baseline Model:
Test statistic 7210.955
Degrees of freedom 91
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.913
Tucker-Lewis Index (TLI) 0.897
Root Mean Square Error of Approximation:
RMSEA 0.049
90 Percent confidence interval - lower 0.046
90 Percent confidence interval - upper 0.053
P-value RMSEA <= 0.05 0.636
Standardized Root Mean Square Residual:
SRMR 0.036
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Expected
Standard errors Structured
Information Structured
Latent Variables:
Estimate Std.Err t-value df P(>|t|) Std.lv Std.all
AD_W1 =~
AD1_W1 0.302 0.011 26.324 Inf 0.000 0.302 0.481
AD2_W1 0.229 0.009 25.789 Inf 0.000 0.229 0.472
AD3_W1 0.227 0.007 32.219 Inf 0.000 0.227 0.572
AD4_W1 0.258 0.011 23.682 Inf 0.000 0.258 0.438
AD5_W1 0.202 0.011 17.837 Inf 0.000 0.202 0.337
AD6_W1 0.210 0.009 24.049 Inf 0.000 0.210 0.444
AD7_W1 0.219 0.014 15.885 Inf 0.000 0.219 0.302
AD8_W1 0.334 0.010 32.996 Inf 0.000 0.334 0.584
AD9_W1 0.233 0.008 29.981 Inf 0.000 0.233 0.539
AD10_W1 0.207 0.006 32.528 Inf 0.000 0.207 0.577
AD11_W1 0.167 0.006 27.246 Inf 0.000 0.167 0.496
AD12_W1 0.305 0.013 22.755 Inf 0.000 0.305 0.422
AD13_W1 0.259 0.011 22.769 Inf 0.000 0.259 0.422
AD14_W1 0.251 0.009 26.567 Inf 0.000 0.251 0.485
Variances:
Estimate Std.Err t-value df P(>|t|) Std.lv Std.all
AD_W1 1.000 1.000 1.000
.AD1_W1 0.302 0.008 37.467 Inf 0.000 0.302 0.769
.AD2_W1 0.183 0.005 37.598 Inf 0.000 0.183 0.777
.AD3_W1 0.106 0.003 35.698 Inf 0.000 0.106 0.672
.AD4_W1 0.281 0.007 38.074 Inf 0.000 0.281 0.808
.AD5_W1 0.317 0.008 39.104 Inf 0.000 0.317 0.886
.AD6_W1 0.180 0.005 37.996 Inf 0.000 0.180 0.803
.AD7_W1 0.477 0.012 39.366 Inf 0.000 0.477 0.909
.AD8_W1 0.215 0.006 35.414 Inf 0.000 0.215 0.659
.AD9_W1 0.133 0.004 36.447 Inf 0.000 0.133 0.710
.AD10_W1 0.086 0.002 35.587 Inf 0.000 0.086 0.667
.AD11_W1 0.086 0.002 37.231 Inf 0.000 0.086 0.754
.AD12_W1 0.428 0.011 38.264 Inf 0.000 0.428 0.822
.AD13_W1 0.309 0.008 38.261 Inf 0.000 0.309 0.822
.AD14_W1 0.205 0.005 37.406 Inf 0.000 0.205 0.765
Warning messages:
1: In if (attr(x, "information") == "observed") { :
the condition has length > 1 and only the first element will be used
2: In cbind(c1, c2, deparse.level = 0) :
number of rows of result is not a multiple of vector length (arg 1)\
Here is my code:
library(lavaan)
library(semTools)
library(Amelia)
mTBI_SEM_Data <- read.delim("~/Desktop/mTBI_SEM_Data.txt", na.strings="-99")
View(mTBI_SEM_Data)
set.seed = 123456
mTBI_SEM_Data_amelia <- amelia(mTBI_SEM_Data, m = 40, noms = c("Sex", "Ethnicity", "HI_Bi_B21"))
imps <- mTBI_SEM_Data_amelia$imputations
AD_W1 <- 'AD_W1 =~ NA* AD1_W1 + AD2_W1 + AD3_W1 + AD4_W1 + AD5_W1 + AD6_W1 + AD7 _W1 + AD8_W1 + AD9 _W1 + AD10_W1 + AD11_W1 + AD12_W1 + AD13_W1 + AD14_W1
AD_W1 ~~ 1* AD_W1'
fitAD_W1 <- cfa.mi(AD_W1,data=imps)
summary(fitAD_W1,standardized=TRUE,fit.measures=TRUE)
Brandon McCormick
May 28, 2020, 4:22:14 PM5/28/20
to lavaan
I was able to replicate the error with the Holzinger Swineford data set (example from https://rdrr.io/cran/semTools/man/runMI.html). The only difference is some df values = inf rather than all.
Here are the results:
lavaan.mi object based on 3 imputed data sets.
See class?lavaan.mi help page for available methods.
Convergence information:
The model converged on 3 imputed data sets
Rubin's (1987) rules were used to pool point and SE estimates across 3 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.
Model Test User Model:
Test statistic 60.393
Degrees of freedom 24
P-value 0.000
Model Test Baseline Model:
Test statistic 656.799
Degrees of freedom 36
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.941
Tucker-Lewis Index (TLI) 0.912
Root Mean Square Error of Approximation:
RMSEA 0.071
90 Percent confidence interval - lower 0.049
90 Percent confidence interval - upper 0.094
P-value RMSEA <= 0.05 0.059
Standardized Root Mean Square Residual:
SRMR 0.063
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Expected
Standard errors Structured
Information Structured
Latent Variables:
Estimate Std.Err t-value df P(>|t|) Std.lv Std.all
visual =~
x1 1.000 0.895 0.768
x2 0.562 0.105 5.372 939.048 0.000 0.503 0.428
x3 0.735 0.115 6.419 870.405 0.000 0.658 0.583
textual =~
x4 1.000 0.952 0.819
x5 0.670 0.049 13.531 137.991 0.000 0.637 0.776
x6 0.991 0.068 14.528 233.415 0.000 0.943 0.862
speed =~
x7 1.000 0.631 0.580
x8 1.242 0.194 6.407 3229.343 0.000 0.784 0.775
x9 0.870 0.135 6.458 185.748 0.000 0.549 0.559
Covariances:
Estimate Std.Err t-value df P(>|t|) Std.lv Std.all
visual ~~
textual 0.420 0.076 5.546 210.207 0.000 0.493 0.493
speed 0.232 0.058 4.011 Inf 0.000 0.410 0.410
textual ~~
speed 0.159 0.051 3.152 2320.091 0.002 0.265 0.265
Variances:
Estimate Std.Err t-value df P(>|t|) Std.lv Std.all
.x1 0.557 0.118 4.731 575.366 0.000 0.557 0.410
.x2 1.129 0.106 10.686 Inf 0.000 1.129 0.817
.x3 0.842 0.094 8.919 3923.682 0.000 0.842 0.660
.x4 0.445 0.058 7.706 344.491 0.000 0.445 0.330
.x5 0.268 0.030 8.868 22.853 0.000 0.268 0.398
.x6 0.307 0.050 6.147 67.273 0.000 0.307 0.256
.x7 0.785 0.087 8.992 3667.386 0.000 0.785 0.663
.x8 0.408 0.090 4.525 8484.786 0.000 0.408 0.399
.x9 0.664 0.071 9.337 45.751 0.000 0.664 0.687
visual 0.801 0.150 5.324 1465.293 0.000 1.000 1.000
textual 0.905 0.117 7.769 5063.068 0.000 1.000 1.000
speed 0.398 0.094 4.241 4887.961 0.000 1.000 1.000
Warning messages:
1: In if (attr(x, "information") == "observed") { :
the condition has length > 1 and only the first element will be used
2: In cbind(c1, c2, deparse.level = 0) :
number of rows of result is not a multiple of vector length (arg 1)
Terrence Jorgensen
May 29, 2020, 4:18:57 AM5/29/20
to lavaan
all my df = inf
Only approximately. When df > 9999, I just print Inf to keep the output from being too messy. When a t statistic's df are that large, it is effectively a normal distribution.
Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam
Brandon McCormick
May 29, 2020, 12:03:35 PM5/29/20
to lav...@googlegroups.com
Thank you, that greatly helps my interpretation.
Do you have any insight into these warnings? I am coming up short in my investigation.
1: In if (attr(x, "information") == "observed") { :
the condition has length > 1 and only the first element will be used
2: In cbind(c1, c2, deparse.level = 0) :
number of rows of result is not a multiple of vector length (arg 1)
On May 29, 2020, at 3:19 AM, Terrence Jorgensen <tjorge...@gmail.com> wrote:
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Terrence Jorgensen
May 29, 2020, 5:09:24 PM5/29/20
to lavaan
Do you have any insight into these warnings?
1: In if (attr(x, "information") == "observed") { :the condition has length > 1 and only the first element will be used
No idea, and it seems strange because there should be only 1 value for the type of information used to derive SEs (i.e., length == 1).
2: In cbind(c1, c2, deparse.level = 0) :number of rows of result is not a multiple of vector length (arg 1)
I think this follows from the first warning, where the type of information is supposed to be printed, but there is more than one. Perhaps this is related to a change Yves made in version 0.6-6 ; is that the version you are using?
Brandon McCormick
May 29, 2020, 5:32:08 PM5/29/20
to lav...@googlegroups.com
Yes, I updated lavaan and semTools. The bug seems inconsequential fortunately.
On May 29, 2020, at 4:09 PM, Terrence Jorgensen <tjorge...@gmail.com> wrote:
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Yves Rosseel
May 31, 2020, 1:33:56 PM5/31/20
to lav...@googlegroups.com
On 5/29/20 11:09 PM, Terrence Jorgensen wrote:
> Do you have any insight into these warnings?
>
> 1: In if (attr(x, "information") == "observed") { :
> the condition has length > 1 and only the first element will be used
This will be due to a change in lavaan 0.6-6: the information= (and
> Do you have any insight into these warnings?
>
> 1: In if (attr(x, "information") == "observed") { :
> the condition has length > 1 and only the first element will be used
related options) can (optionally) be a vector of 2 elements, eg
information = c("observed", "expected"). The first one is used to
compute vcov (and hence the standard errors). The second one is only
used for the test statistic. Decoupling this turns out to be useful in
some settings.
> 2: In cbind(c1, c2, deparse.level = 0) :
> number of rows of result is not a multiple of vector length (arg 1)
> I think this follows from the first warning
Yves.
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