Reputation: 21
I am running a CFA on my questionnaire using lavaan package in r. How can I get a correlation matrix of factors that also includes data on significance levels? (i.e. p-value)
When I use the line cov2cor(inspect(fit, what = "est")$psi)
I get the matrix but not the p-values.
Here's a sample code for the model:
CFA.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- lavaan(CFA.model, data = HolzingerSwineford1939,
auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE)
Upvotes: 0
Views: 1565
Reputation: 263301
I would not consider p-values to be estimates. They are more like random values. Does this deliver what you were looking for?
inspect(fit, what = "test")
$standard
$standard$test
[1] "standard"
$standard$stat
[1] 85.30552
$standard$stat.group
[1] 85.30552
$standard$df
[1] 24
$standard$refdistr
[1] "chisq"
$standard$pvalue
[1] 8.502553e-09
EDIT: You are working with covariances and they may not be Normally distributed. Furthermore, it's not clear what hypothesis should be tested. It appears that the authors of cov2cor
have not seen fit to deliver statistical tests on correlations derived from covariances. The authors of lavaan
and inspect.lavaan
have also not seen fit to construct a p-value matrix, so maybe these are not sensible tasks to carry out. Can you supply a reference that can be reviewed to back up this request as being statistically meaningful or uinterpretable? If you can do that, then the may be mechanisms to pull apart the S4 object that is the underlying structure of fit
. But unless I can get some theoretical guidance I don't feel qualified to just muck around in the code until I can find a standard errors matrix and compare ratios of correlations or covariance to such values.
It's possible that what you are expecting is delivered with summary.lavaan
:
summary(fit)
#-----------------------------------
lavaan 0.6-6 ended normally after 35 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 21
Number of observations 301
Model Test User Model:
Test statistic 85.306
Degrees of freedom 24
P-value (Chi-square) 0.000
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
visual =~
x1 1.000
x2 0.554 0.100 5.554 0.000
x3 0.729 0.109 6.685 0.000
textual =~
x4 1.000
x5 1.113 0.065 17.014 0.000
x6 0.926 0.055 16.703 0.000
speed =~
x7 1.000
x8 1.180 0.165 7.152 0.000
x9 1.082 0.151 7.155 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
visual ~~
textual 0.408 0.074 5.552 0.000
speed 0.262 0.056 4.660 0.000
textual ~~
speed 0.173 0.049 3.518 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.x1 0.549 0.114 4.833 0.000
.x2 1.134 0.102 11.146 0.000
.x3 0.844 0.091 9.317 0.000
.x4 0.371 0.048 7.779 0.000
.x5 0.446 0.058 7.642 0.000
.x6 0.356 0.043 8.277 0.000
.x7 0.799 0.081 9.823 0.000
.x8 0.488 0.074 6.573 0.000
.x9 0.566 0.071 8.003 0.000
visual 0.809 0.145 5.564 0.000
textual 0.979 0.112 8.737 0.000
speed 0.384 0.086 4.451 0.000
Upvotes: 1