meta | R Documentation |
It conducts univariate and multivariate meta-analysis with maximum likelihood estimation method. Mixed-effects meta-analysis can be conducted by including study characteristics as predictors. Equality constraints on intercepts, regression coefficients, and variance components can be easily imposed by setting the same labels on the parameter estimates.
meta(y, v, x, data, intercept.constraints = NULL, coef.constraints = NULL,
RE.constraints = NULL, RE.startvalues=0.1, RE.lbound = 1e-10,
intervals.type = c("z", "LB"), I2="I2q", R2=TRUE,
model.name="Meta analysis with ML", suppressWarnings = TRUE,
silent = TRUE, run = TRUE, ...)
metaFIML(y, v, x, av, data, intercept.constraints=NULL,
coef.constraints=NULL, RE.constraints=NULL,
RE.startvalues=0.1, RE.lbound=1e-10,
intervals.type=c("z", "LB"), R2=TRUE,
model.name="Meta analysis with FIML",
suppressWarnings=TRUE, silent=TRUE, run=TRUE, ...)
y |
A vector of effect size for univariate meta-analysis or a |
v |
A vector of the sampling variance of the effect size for univariate
meta-analysis or a |
x |
A predictor or a |
av |
An auxiliary variable or a |
data |
An optional data frame containing the variables in the model. |
intercept.constraints |
A |
coef.constraints |
A |
RE.constraints |
A |
RE.startvalues |
A vector of |
RE.lbound |
A vector of |
intervals.type |
Either |
I2 |
Possible options are |
R2 |
Logical. If |
model.name |
A string for the model name in |
suppressWarnings |
Logical. If |
silent |
Logical. An argument to be passed to |
run |
Logical. If |
... |
Further arguments to be passed to |
An object of class meta
with a list of
call |
Object returned by |
data |
A data matrix of y, v and x |
no.y |
No. of effect sizes |
no.x |
No. of predictors |
miss.x |
A vector indicating whether the predictors are
missing. Studies will be removed before the analysis if they are
|
I2 |
Types of I2 calculated |
R2 |
Logical |
mx.fit |
A fitted object returned from
|
mx0.fit |
A fitted object without any predictor returned from
|
Missing values (NA) in y and their related elements in v
will be removed automatically. When there are missing values in v but
not in y, missing values will be replaced by 1e5. Effectively, these
effect sizes will have little impact on the
analysis. metaFIML()
uses FIML to handle missing covariates in
X. It is experimental. It may not be stable.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
Cheung, M. W.-L. (2008). A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. Psychological Methods, 13, 182-202.
Cheung, M. W.-L. (2009). Constructing approximate confidence intervals for parameters with structural equation models. Structural Equation Modeling, 16, 267-294.
Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20, 429-454.
Cheung, M. W.-L. (2015). Meta-analysis: A structural equation modeling approach. Chichester, West Sussex: John Wiley & Sons, Inc.
Hardy, R. J., & Thompson, S. G. (1996). A likelihood approach to meta-analysis with random effects. Statistics in Medicine, 15, 619-629.
Neale, M. C., & Miller, M. B. (1997). The use of likelihood-based confidence intervals in genetic models. Behavior Genetics, 27, 113-120.
Raudenbush, S. W. (2009). Analyzing effect sizes: random effects models. In H. M. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 295-315). New York: Russell Sage Foundation.
Xiong, C., Miller, J. P., & Morris, J. C. (2010). Measuring study-specific heterogeneity in meta-analysis: application to an antecedent biomarker study of Alzheimer's disease. Statistics in Biopharmaceutical Research, 2(3), 300-309. doi:10.1198/sbr.2009.0067
reml
, Hox02
,
Berkey98
, wvs94a
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