Get a parameter estimate table from a lavaan.mi object?
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Shu Fai Cheung
Apr 16, 2023, 10:27:10 AM4/16/23
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Dear All,
I would like to ask a quick question on runMI() and lavaan.mi class objects.
parameterEstimates() can be used to get the parameter estimates of the output of lavaan() and its wrappers in the form of a lavaan.data.frame object.
I found that
parameterEstimates()
does not work on a lavaan.mi object. Only lhs, op, and rhs is returned, as shown in the example below.
However, if I call summary() but output is not set to "text", the default, then, the output is a lavaan.data.frame, as shown below. I also compared the results with those from summary() with output = "text" and confirmed that results are the same.
Is this an intended behavior of the summary-method of lavaan.mi? Can I trust this way to use summary() as
the lavaan.mi version of parameterEstimates()?
Regards,
Shu Fai Cheung (張樹輝)
The long example appended below:
# Adapted from the example of runMI()
library(semTools)
#> Loading required package: lavaan
#> This is lavaan 0.6-15
#> lavaan is FREE software! Please report any bugs.
#>
#> ###############################################################################
#> This is semTools 0.5-6
#> All users of R (or SEM) are invited to submit functions or ideas for functions.
#> ###############################################################################
HSMiss <- HolzingerSwineford1939[ , c(paste("x", 1:9, sep = ""),
"ageyr","agemo","school")]
set.seed(12345)
HSMiss$x5 <- ifelse(HSMiss$x5 <= quantile(HSMiss$x5, .3), NA, HSMiss$x5)
age <- HSMiss$ageyr + HSMiss$agemo/12
HSMiss$x9 <- ifelse(age <= quantile(age, .3), NA, HSMiss$x9)
HS.model <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
'
out1 <- cfa.mi(HS.model, data = HSMiss, m = 20, seed = 12345,
miArgs = list(noms = "school"))
#> Loading required namespace: Amelia
#> ##
#> ## Amelia II: Multiple Imputation
#> ## (Version 1.8.1, built: 2022-11-18)
#> ## Copyright (C) 2005-2023 James Honaker, Gary King and Matthew Blackwell
#> ## Refer to http://gking.harvard.edu/amelia/ for more information
#> ##
# `parameterEstimates()` does not work
parameterEstimates(out1)
#> lhs op rhs
#> 1 visual =~ x1
#> 2 visual =~ x2
#> 3 visual =~ x3
#> 4 textual =~ x4
#> 5 textual =~ x5
#> 6 textual =~ x6
#> 7 speed =~ x7
#> 8 speed =~ x8
#> 9 speed =~ x9
#> 10 x1 ~~ x1
#> 11 x2 ~~ x2
#> 12 x3 ~~ x3
#> 13 x4 ~~ x4
#> 14 x5 ~~ x5
#> 15 x6 ~~ x6
#> 16 x7 ~~ x7
#> 17 x8 ~~ x8
#> 18 x9 ~~ x9
#> 19 visual ~~ visual
#> 20 textual ~~ textual
#> 21 speed ~~ speed
#> 22 visual ~~ textual
#> 23 visual ~~ speed
#> 24 textual ~~ speed
# Set output = "" to get a lavaan.data.frame
summary(out1, output = "")
#> lhs op rhs est se t df pvalue
#> 1 visual =~ x1 1.000 0.000 NA NA NA
#> 2 visual =~ x2 0.556 0.108 5.153 Inf 0.000
#> 3 visual =~ x3 0.728 0.118 6.180 Inf 0.000
#> 4 textual =~ x4 1.000 0.000 NA NA NA
#> 5 textual =~ x5 0.661 0.052 12.792 116.901 0.000
#> 6 textual =~ x6 0.993 0.072 13.760 463.762 0.000
#> 7 speed =~ x7 1.000 0.000 NA NA NA
#> 8 speed =~ x8 1.176 0.185 6.348 1374.016 0.000
#> 9 speed =~ x9 0.901 0.142 6.366 321.076 0.000
#> 10 x1 ~~ x1 0.549 0.123 4.475 Inf 0.000
#> 11 x2 ~~ x2 1.132 0.110 10.297 Inf 0.000
#> 12 x3 ~~ x3 0.846 0.098 8.634 Inf 0.000
#> 13 x4 ~~ x4 0.450 0.061 7.374 491.475 0.000
#> 14 x5 ~~ x5 0.272 0.032 8.588 150.445 0.000
#> 15 x6 ~~ x6 0.309 0.053 5.844 201.536 0.000
#> 16 x7 ~~ x7 0.768 0.090 8.525 5997.168 0.000
#> 17 x8 ~~ x8 0.449 0.088 5.104 675.850 0.000
#> 18 x9 ~~ x9 0.634 0.074 8.569 264.311 0.000
#> 19 visual ~~ visual 0.809 0.157 5.147 Inf 0.000
#> 20 textual ~~ textual 0.901 0.121 7.422 5617.667 0.000
#> 21 speed ~~ speed 0.415 0.099 4.189 8611.644 0.000
#> 22 visual ~~ textual 0.428 0.079 5.406 6361.340 0.000
#> 23 visual ~~ speed 0.242 0.061 3.951 Inf 0.000
#> 24 textual ~~ speed 0.177 0.054 3.264 3695.871 0.001
# Compare the estimates with default options
summary(out1)
#> lavaan.mi object based on 20 imputed data sets.
#> See class?lavaan.mi help page for available methods.
#>
#> Convergence information:
#> The model converged on 20 imputed data sets
#>
#> Rubin's (1987) rules were used to pool point and SE estimates across 20 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Latent Variables:
#> Estimate Std.Err t-value df P(>|t|)
#> visual =~
#> x1 1.000
#> x2 0.556 0.108 5.153 Inf 0.000
#> x3 0.728 0.118 6.180 Inf 0.000
#> textual =~
#> x4 1.000
#> x5 0.661 0.052 12.792 116.901 0.000
#> x6 0.993 0.072 13.760 463.762 0.000
#> speed =~
#> x7 1.000
#> x8 1.176 0.185 6.348 1374.016 0.000
#> x9 0.901 0.142 6.366 321.076 0.000
#>
#> Covariances:
#> Estimate Std.Err t-value df P(>|t|)
#> visual ~~
#> textual 0.428 0.079 5.406 6361.340 0.000
#> speed 0.242 0.061 3.951 Inf 0.000
#> textual ~~
#> speed 0.177 0.054 3.264 3695.871 0.001
#>
#> Variances:
#> Estimate Std.Err t-value df P(>|t|)
#> .x1 0.549 0.123 4.475 Inf 0.000
#> .x2 1.132 0.110 10.297 Inf 0.000
#> .x3 0.846 0.098 8.634 Inf 0.000
#> .x4 0.450 0.061 7.374 491.475 0.000
#> .x5 0.272 0.032 8.588 150.445 0.000
#> .x6 0.309 0.053 5.844 201.536 0.000
#> .x7 0.768 0.090 8.525 5997.168 0.000
#> .x8 0.449 0.088 5.104 675.850 0.000
#> .x9 0.634 0.074 8.569 264.311 0.000
#> visual 0.809 0.157 5.147 Inf 0.000
#> textual 0.901 0.121 7.422 5617.667 0.000
#> speed 0.415 0.099 4.189 8611.644 0.000
Terrence Jorgensen
Apr 18, 2023, 8:45:50 AM4/18/23
to lavaan
parameterEstimates() can be used to get the parameter estimates of the output of lavaan() and its wrappers in the form of a lavaan.data.frame object.
The latter is its class (inherits from data.frame), not a wrapper function. The special class is just so lavaan can define a nicer print method than the default one for data.frame objects.
parameterEstimates() does not work on a lavaan.mi object.
No, that is not a generic function, so I can't write a method for it to operate on lavaan.mi objects. lavaan will allow parameterEstimates() to be applied to a class?lavaanList object, which is what lavaan.mi inherits from, but it is not informative.
Is this an intended behavior of the summary-method of lavaan.mi? Can I trust this way to use summary() as the lavaan.mi version of parameterEstimates()?
Yes, the lavaan.mi analog is just the summary() method (which is a generic function). To make it return a familiar data.frame like parameterEstimates() does, just set the argument output = "data.frame" (see class?lavaan.mi).
lavaan's summary() method also just calls parameterEstimates(..., output = "text"), but will return it in a list with anything else you request (e.g., fitMeasures() or modindices(), by setting the corresponding arguments TRUE).
Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam
Shu Fai Cheung
Apr 18, 2023, 9:39:36 AM4/18/23
to lav...@googlegroups.com
Thanks a lot for the detailed explanation and sorry for my oversight. I was studying the help page of lavaan.mi class when I received your post. I should have read it first before posting.
It would be nice if commonly used lavaan functions can be used directly on lavaan.mi objects, or raise an error when a function should not be used on lavaan.mi objects, but I understand this may not be desirable. Changes may need to be made in lavaan, as in the case of parameterEstimates(). For personal use, I am compiling a list of functions that I usually use on lavaan objects (I do not use lavaanList objects) and see whether and/or how these functions work on lavaan.mi objects.
Regards,
Shu Fai Cheung (張樹輝)
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