# General SEM Discussions

## How can I use SEM to predict variable scores

If I know four of the five variables, can I use the model to predict the 5th variable? How can I do this?

## Problems with model fit

2: In eval(expr, envir, enclos) :

Could not compute QR decomposition of Hessian.

Optimization probably did not converge.

> model.dhp <- specifyModel ()

1: EA_response -> EA_survival,NA,1

2: EA_response -> EA_progeny,gam12

3: EA_response -> EA_progeny_wet_mass,gam13

4: EA_response -> EA_progeny_dry_mass,gam14

5: FC_response -> FC_survival,NA,1

6: FC_response -> FC_progeny,gam22

7: BA_response -> Ba_emergence,NA,1

8: BA_response -> Ba_shoot_length,gam32

9: BA_response -> Ba_root_length,gam33

- Read more about Problems with model fit
- 1 comment
- Log in or register to post comments

## Factor analysis with weights

The code is below and the data set is in the attachment. I don't know if the weights are calculated with the data in the analysis.

dsetA <- read.table("dsetA.txt",sep="")

# Possible values of the weighting variable

valm <- seq(21,40,by=1/10)

- Read more about Factor analysis with weights
- 4 comments
- Log in or register to post comments

## random variance parameters, or approximations of such?

## "Observed" correlation matrix with missing data--is it computed?

model$data

It returns the raw dataset (with missing values still missing).

Any ideas?

## AIC/BIC is NA, but other FIs are computed

I've run a model and am a bit puzzled that it won't give me the information criteria (AIC and BIC). Everything else is computed, including chi, k, and df, which are the sufficient statistics. Am I missing something? I've included a screenshot. It seems the AIC should be

1557.625 + ncol(d)*(ncol(d)-1)-2*453 = 1707.625,

where ncol(d) = 33

- Read more about AIC/BIC is NA, but other FIs are computed
- 8 comments
- Log in or register to post comments

## ML analysis

- Read more about ML analysis
- 2 comments
- Log in or register to post comments

## Latent Variable Indicators

Thanks for any help

- Read more about Latent Variable Indicators
- 1 comment
- Log in or register to post comments

## Convergence problems

## Automatically computing residual variances for a specified RAM matrix

#### create a RAM model for testing

RAM = data.frame(matrix(c(

"F1", "A1", 1, .4,

"F1", "A2", 1, .4,

"F1", "A3", 1, .4,

"F2", "A4", 1, .4,

"F2", "A5", 1, .4,

"F2", "A6", 1, .4,

"F3", "A7", 1, .4,

"F3", "A8", 1, .4,

"F3", "A9", 1, .4,

"A10", "A9", 2, .5), ncol=4, byrow=T

))

names(RAM) = c("From", "To", "Arrows", "Values")

####

observed = c("A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10")

#### Pagination

- Previous page
- Page 5
- Next page