Set up model. library(lavaan) ## This is lavaan 0.6-1.1131 ## lavaan is BETA software! Please report any bugs. HS.model <- ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' Say I fit a model with ML. Looks ok. fit <- lavaan(HS.model, data=HolzingerSwineford1939, auto.var=TRUE, auto.fix.first=TRUE, auto.cov.lv.x=TRUE, mimic="EQS",estimator="ML") summary(fit) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##

lavaan (0.6-1.1131) converged normally after Number of observations

35 iterations 301

Estimator Model Fit Test Statistic Degrees of freedom P-value (Chi-square)

ML 85.022 24 0.000

Parameter Estimates: Information Standard Errors Latent Variables: visual =~ x1 x2 x3 textual =~ x4 x5 x6 speed =~ x7 x8 x9 Covariances: visual ~~ textual speed textual ~~ speed

Expected Standard Estimate

Std.Err

z-value

P(>|z|)

1.000 0.554 0.729

0.100 0.109

5.544 6.674

0.000 0.000

1.000 1.113 0.926

0.066 0.056

16.986 16.675

0.000 0.000

1.000 1.180 1.082

0.165 0.151

7.140 7.143

0.000 0.000

Estimate

Std.Err

z-value

P(>|z|)

0.410 0.263

0.074 0.057

5.543 4.652

0.000 0.000

0.174

0.050

3.512

0.000

1

## Variances: ## ## .x1 ## .x2 ## .x3 ## .x4 ## .x5 ## .x6 ## .x7 ## .x8 ## .x9 ## visual ## textual ## speed

Estimate 0.551 1.138 0.847 0.372 0.448 0.357 0.802 0.489 0.568 0.812 0.983 0.385

Std.Err 0.114 0.102 0.091 0.048 0.059 0.043 0.082 0.075 0.071 0.146 0.113 0.087

z-value 4.825 11.128 9.301 7.766 7.630 8.263 9.806 6.562 7.990 5.555 8.723 4.444

P(>|z|) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Then, I fit a model using MLM but don’t change any other options. Meanstructure is not requested, but from the output we can see that it is added and all intercepts are fixed to 0, which results in really bad fit. fit <- lavaan(HS.model, data=HolzingerSwineford1939, auto.var=TRUE, auto.fix.first=TRUE, auto.cov.lv.x=TRUE, mimic="EQS",estimator="MLM") summary(fit) ## lavaan (0.6-1.1131) converged normally after 79 iterations ## ## Number of observations 301 ## ## Estimator ML Robust ## Model Fit Test Statistic 1400.440 1379.242 ## Degrees of freedom 33 33 ## P-value (Chi-square) 0.000 0.000 ## Scaling correction factor 1.015 ## for the Satorra-Bentler correction ## ## Parameter Estimates: ## ## Information Expected ## Standard Errors Robust.sem ## ## Latent Variables: ## Estimate Std.Err z-value P(>|z|) ## visual =~ ## x1 1.000 ## x2 1.220 0.003 403.175 0.000 ## x3 0.463 0.002 306.143 0.000 ## textual =~ ## x4 1.000 ## x5 1.402 0.005 260.270 0.000 ## x6 0.730 0.004 199.707 0.000 ## speed =~ ## x7 1.000 ## x8 1.319 0.003 510.779 0.000 ## x9 1.284 0.002 521.853 0.000 ##

2

## Covariances: ## ## visual ~~ ## textual ## speed ## textual ~~ ## speed ## ## Intercepts: ## ## .x1 ## .x2 ## .x3 ## .x4 ## .x5 ## .x6 ## .x7 ## .x8 ## .x9 ## visual ## textual ## speed ## ## Variances: ## ## .x1 ## .x2 ## .x3 ## .x4 ## .x5 ## .x6 ## .x7 ## .x8 ## .x9 ## visual ## textual ## speed

Estimate

Std.Err

z-value

P(>|z|)

15.524 20.940

0.085 0.058

183.314 359.362

0.000 0.000

13.029

0.065

201.793

0.000

Estimate 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Std.Err

z-value

P(>|z|)

Estimate 0.709 1.211 0.988 0.447 0.302 0.506 0.872 0.563 0.474 25.016 10.278 17.837

Std.Err 0.092 0.136 0.067 0.046 0.060 0.058 0.076 0.072 0.065 0.099 0.099 0.069

z-value 7.670 8.931 14.698 9.790 5.075 8.686 11.538 7.863 7.248 251.582 103.579 259.452

P(>|z|) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Explicitly setting observed variable intercepts free with int.ov.free=TRUE. Works fine. fit <- lavaan(HS.model, data=HolzingerSwineford1939, auto.var=TRUE, auto.fix.first=TRUE, auto.cov.lv.x=TRUE, mimic="EQS",estimator="MLM", int.ov.free=TRUE) summary(fit) ## lavaan (0.6-1.1131) converged normally after ## ## Number of observations ## ## Estimator ## Model Fit Test Statistic ## Degrees of freedom ## P-value (Chi-square)

3

35 iterations 301 ML 85.022 24 0.000

Robust 81.141 24 0.000

## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##

Scaling correction factor for the Satorra-Bentler correction

1.048

Parameter Estimates: Information Standard Errors Latent Variables: visual =~ x1 x2 x3 textual =~ x4 x5 x6 speed =~ x7 x8 x9 Covariances: visual ~~ textual speed textual ~~ speed Intercepts: .x1 .x2 .x3 .x4 .x5 .x6 .x7 .x8 .x9 visual textual speed Variances: .x1 .x2 .x3 .x4 .x5 .x6

Expected Robust.sem Estimate

Std.Err

z-value

P(>|z|)

1.000 0.554 0.729

0.103 0.115

5.359 6.367

0.000 0.000

1.000 1.113 0.926

0.066 0.060

16.762 15.497

0.000 0.000

1.000 1.180 1.082

0.152 0.132

7.758 8.169

0.000 0.000

Estimate

Std.Err

z-value

P(>|z|)

0.410 0.263

0.082 0.055

4.966 4.762

0.000 0.000

0.174

0.055

3.139

0.002

Estimate 4.936 6.088 2.250 3.061 4.341 2.186 4.186 5.527 5.374 0.000 0.000 0.000

Std.Err 0.067 0.068 0.065 0.067 0.075 0.063 0.063 0.058 0.058

z-value 73.229 89.556 34.464 45.542 58.258 34.552 66.544 94.539 92.238

P(>|z|) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Estimate 0.551 1.138 0.847 0.372 0.448 0.357

Std.Err 0.139 0.108 0.085 0.050 0.058 0.046

z-value 3.968 10.554 9.985 7.423 7.688 7.700

P(>|z|) 0.000 0.000 0.000 0.000 0.000 0.000

4

## ## ## ## ## ##

.x7 .x8 .x9 visual textual speed

0.802 0.489 0.568 0.812 0.983 0.385

0.079 0.075 0.068 0.168 0.121 0.083

10.168 6.567 8.332 4.837 8.109 4.635

0.000 0.000 0.000 0.000 0.000 0.000

Use cfa() instead with MLM. Also no problem. fit <- cfa(HS.model, data=HolzingerSwineford1939, mimic="EQS",estimator="MLM") summary(fit) ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##

lavaan (0.6-1.1131) converged normally after Number of observations

35 iterations 301

Estimator Model Fit Test Statistic Degrees of freedom P-value (Chi-square) Scaling correction factor for the Satorra-Bentler correction

ML 85.022 24 0.000

Parameter Estimates: Information Standard Errors Latent Variables: visual =~ x1 x2 x3 textual =~ x4 x5 x6 speed =~ x7 x8 x9 Covariances: visual ~~ textual speed textual ~~ speed Intercepts: .x1

Expected Robust.sem Estimate

Std.Err

z-value

P(>|z|)

1.000 0.554 0.729

0.103 0.115

5.359 6.367

0.000 0.000

1.000 1.113 0.926

0.066 0.060

16.762 15.497

0.000 0.000

1.000 1.180 1.082

0.152 0.132

7.758 8.169

0.000 0.000

Estimate

Std.Err

z-value

P(>|z|)

0.410 0.263

0.082 0.055

4.966 4.762

0.000 0.000

0.174

0.055

3.139

0.002

Estimate 4.936

Std.Err 0.067

z-value 73.229

P(>|z|) 0.000

5

Robust 81.141 24 0.000 1.048

## .x2 ## .x3 ## .x4 ## .x5 ## .x6 ## .x7 ## .x8 ## .x9 ## visual ## textual ## speed ## ## Variances: ## ## .x1 ## .x2 ## .x3 ## .x4 ## .x5 ## .x6 ## .x7 ## .x8 ## .x9 ## visual ## textual ## speed

6.088 2.250 3.061 4.341 2.186 4.186 5.527 5.374 0.000 0.000 0.000

0.068 0.065 0.067 0.075 0.063 0.063 0.058 0.058

89.556 34.464 45.542 58.258 34.552 66.544 94.539 92.238

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Estimate 0.551 1.138 0.847 0.372 0.448 0.357 0.802 0.489 0.568 0.812 0.983 0.385

Std.Err 0.139 0.108 0.085 0.050 0.058 0.046 0.079 0.075 0.068 0.168 0.121 0.083

z-value 3.968 10.554 9.985 7.423 7.688 7.700 10.168 6.567 8.332 4.837 8.109 4.635

P(>|z|) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

6

This is lavaan 0.6-1.1131 ## lavaan is BETA software! -

Then, I fit a model using MLM but don't change any other options. Meanstructure is not requested, but from the output we can see that it is added and all ...
Download PDF
90KB Sizes 5 Downloads 126 Views
Report

Recommend Documents

Beta Reduction is Invariant, Indeed
Jul 18, 2014 - tional Semantics.; F.4.1 [Mathematical Logic and Formal Lan- guages]: Mathematical Logic — Lambda Calculus and Related. Systems.
Beta Reduction is Invariant, Indeed
contexts are not made out of applications only; for instance tλx.(〈·〉u) is an application context.) Then: Definition V.1 (Useful/Useless Steps and Derivations). An.
this is us 2013 1080p.pdf
Connect more apps... Try one of the apps below to open or edit this item. this is us 2013 1080p.pdf. this is us 2013 1080p.pdf. Open. Extract. Open with. Sign In.
this is earl nightingale pdf
Loading… Whoops! There was a problem loading more pages. Whoops! There was a problem previewing this document. Retrying... Download. Connect more apps... Try one of the apps below to open or edit this item. this is earl nightingale pdf. this is ear
This is 40 My Draft.pdf
Sign in. Loading… Page 1. Whoops! There was a problem loading more pages. Retrying... This is 40 My Draft.pdf. This is 40 My Draft.pdf. Open. Extract. Open with. Sign In. Main menu. Displaying This is 40 My Draft.pdf.
this is earl nightingale pdf
There was a problem previewing this document. Retrying... Download. Connect more apps ... this is earl nightingale pdf. this is earl nightingale pdf. Open. Extract.
This is Not an Article
Ranking of IS Journals http://www.bus.ucf.edu./csaunders/newjournal.htm. Ranking of IS Journals http://www.isworld.org/csaunders/rankings.htm. IS Journals http://www.cis.gsu.edu/~dstraub/Research/TargetJournalList/Target%20Journals.htm. IS Journals h
Know what this is about.pdf
Footballers. will know what this isabout zwinglius redivivus. Solved anybody. know what this is?. Football posts on twitter retweet if you know. what this is.
this love is kill me.pdf
There was a problem previewing this document. Retrying... Download. Connect more apps... Try one of the apps below to open or edit this item. this love is kill ...
This is the first page -
The Controller and Player can then proceed to the next section (SIGN OUT or. SEND COMMAND). If no RESPONSE ACKNOWLEDGE message is received, the ...
This is my desire.pdf
I live for You alone. G D/F#. Every breath that I take. Am7. Every moment I'm awa__ke. C/D D C D/C Bm Em C D/C Bm C/D D..... Lord, have Your way in me.
armatmuhendislik.com - This website is for sale! - armatmuhendislik ...
Click here to buy this domain. Sedo Logo This webpage was generated by the domain owner using Sedo Domain Parking. Disclaimer: Sedo maintains no ...
one direction: this is us.pdf
Page 1. Whoops! There was a problem loading more pages. Retrying... one direction: this is us.pdf. one direction: this is us.pdf. Open. Extract. Open with. Sign In.
What Child Is This - Svigel.pdf
Angeles: Western Front, 1999); Amy Fryckholm Johnson, Rapture Culture: Left. Behind in Evangelical America (New York: Oxford University Press, 2004). Page 1 of 27 ... It did not lead to a clearly pre- seven-year chronology. ... “seems to have had i
What Child Is This
King. Chris - an - of tian, gels kings fear: greet sal - for with................... va - sin - an - tion ners thems brings; here sweet,. Let. The. While.............. lov - si - shep - ing lent herds hearts. Word watch en - is are...
This is the first page - PDFKUL.COM
REPEAT. Repeat play. PIP. PIP (93/95 only). Show/hide Picture-in-Picture. HDM. RESOLUTION. Switch output resolution. SUH. SUBTITLE (hold). Press and hold the SUBTITLE key. This activates the subtitle shift feature. NFX. NETFLIX (103/105 only). Netfli
This is england 90
object ofmass 2 g. Which this isengland 90 by an ... Lord oftherings battlefor middleearth 2 the witch. Heavy object ... Visual.net pdf.Last. week s02e30 720p.
This is page one -
May 16, 2018 - file:///C:/suriya/myworkspace/psms-client/report/tmp/temp.html. 1/3. This is page one. Page 2. 5/16/2018. Title.
Download-This-Talent-Is-Overr.pdf
straightforward within the eyes, some have got a ton of ... 100 THINGS YOU DONT WANNA KNOW ABOUT TALENT IS OVERRATED. Study Online and Download Ebook 100 Things You Dont Wanna Know About Talent Is Overrated. Download Lucas Capper. ebook file totally
THIS IS YOUR LIFE..pdf
THIS IS YOUR LIFE..pdf. THIS IS YOUR LIFE..pdf. Open. Extract. Open with. Sign In. Details. Comments. General Info. Type. Dimensions. Size. Duration.
Buy this domain - hyderabadspider.com - This website is for sale ...
Sponsored listings. Play this free online game! It's addictive. http://LosAngelesPost.com ... Buy this domain · Click here to buy this domain. Related Searches.
Open Position: Software Engineer MAQ Software is a ... -
Our solutions use advanced Business Intelligence features of SQL. Server 2012, the latest ... Display web site traffic data for ten million users per day. In order to ...
Throughout this note, K is an algebraically closed field and Λ is a basic ...
Throughout this note, K is an algebraically closed field and Λ is a basic finite dimen- sional K-algebra. ... top Sj and the Loewy length ℓ(M) = t. Namely, M has a ...