Dear all,
According to CHANGES IN R 3.0.0:
o diag() as used to generate a diagonal matrix has been re-written
in C for speed and less memory usage. It now forces the result
to be numeric in the case diag(x) since it is said to have 'zero
off-diagonal entries'.
diag(x) does
0 0
#[2,] 0 b 0 0 0
#[3,] 0 0 c 0 0
#[4,] 0 0 0 d 0
#[5,] 0 0 0 0 e
A.K.
- Original Message -
From: Mike Cheung mikewlche...@gmail.com
To: r-help r-help@r-project.org
Cc:
Sent: Tuesday, April 9, 2013 3:15 AM
Subject: [R] Behaviors of diag() with character
Hi, Emilie.
For your second question. You may check Gleser and Olkin (2009). They gave
several formulas to estimate the sampling covariance for dependent effect
sizes. One of them can be applied in your case.
Gleser, L. J., Olkin, I. (2009). Stochastically dependent effect sizes. In
H. Cooper,
Dear Alain,
There were 16 variables with 10 cases with missing values. The sample
covariance matrix is not positive definite. It has nothing to do with
lavaan. You need more cases before you can fit a CFA with 16 variables.
Regards,
Mike
--
Dear Alain,
You may speed up the analysis by using the sample covariance matrix based on
a listwise deletion:
cov.cfa - cov(your.raw.data, use=complete.obs)
Since you have 36671 cases, the results should be similar to those based on
the raw data unless you have lots of missing data and/or the
Dear luri,
The metaSEM package
(http://courses.nus.edu.sg/course/psycwlm/Internet/metaSEM/) may be
used to fit structural equation models on the pooled
correlation/covariance matrices with weighted least squares as the
estimation method. You may refer to the examples in tssem1() and
tssem2().
Dear Andrew,
The reported df in lavaan is 0 which is correct. It is because this
path model is saturated. 28 is not the df, it is the no. of pieces
of information. The no. of parameter estimates is also 28. Thus, the
df is 0.
However, you are correct that there are only 13, not 28, free
Hi Steph,
You may try the metafor package
http://cran.r-project.org/web/packages/metafor/index.html
Regards,
Mike
--
-
Mike W.L. Cheung Phone: (65) 6516-3702
Department of Psychology Fax: (65) 6773-1843
Phone: (65) 6516-3702
Department of Psychology Fax: (65) 6773-1843
National University of Singapore
http://courses.nus.edu.sg/course/psycwlm/internet/
-
On Wed, Jun 16, 2010 at 4:47 PM, Mike Cheung mikewlche...@gmail.com
Dear Gerrit,
If the correlations of the dependent effect sizes are unknown, one
approach is to conduct the meta-analysis by assuming that the effect
sizes are independent. A robust standard error is then calculated to
adjust for the dependence. You may refer to Hedges et. al., (2010) for
more
Dear Tal,
There are several approaches in doing it (see Steiger, 2003). It
should not be difficult to implement them in R.
Steiger, J.H. (2003). Comparing correlations. In A. Maydeu-Olivares
(Ed.) Psychometrics. A festschrift to Roderick P. McDonald. Mahwah,
NJ: Lawrence Erlbaum Associates.
Dear Gang,
Here are just some general thoughts. Wolfgang Viechtbauer will be a
better position to answer questions related to metafor.
For multivariate effect sizes, we first have to estimate the
asymptotic sampling covariance matrix among the effect sizes. Formulas
for some common effect sizes
(weighted average?) before the meta analysis?
Your suggestions are highly appreciated.
Best wishes,
Gang
On Sun, Feb 7, 2010 at 10:39 AM, Mike Cheung mikewlche...@gmail.com wrote:
Dear Gang,
Here are just some general thoughts. Wolfgang Viechtbauer will be a
better position to answer
Dear Sebastian,
Many researchers may transform the Pearson coefficients into Fisher's
z scores first by using
z - 0.5*log((1+r)/(1-r)).
The standard errors of the Fisher's z scores are z.SE - 1/sqrt(n-3)
where n are the sample sizes (see
http://en.wikipedia.org/wiki/Fisher_transformation).
Dear Carlos,
One approach is to use structural equation modeling (SEM). Some SEM
packages, such as LISREL, Mplus and Mx, allow inequality and nonlinear
constraints. Phantom variables (Rindskopf, 1984) may be used to impose
inequality constraints. Your model is basically:
y = b0 + b1*b1*x1 +
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