It summaries results for various class.
# S3 method for tssem1FEM
summary(object, ...)
# S3 method for tssem1FEM.cluster
summary(object, ...)
# S3 method for tssem1REM
summary(object, robust=FALSE, ...)
# S3 method for wls
summary(object, df.adjustment=0, ...)
# S3 method for wls.cluster
summary(object, df.adjustment=0, ...)
# S3 method for meta
summary(object, homoStat=TRUE, robust=FALSE, ...)
# S3 method for meta3LFIML
summary(object, allX=FALSE, robust=FALSE, ...)
# S3 method for reml
summary(object, ...)
# S3 method for mxRAMmodel
summary(object, robust=FALSE, ...)
# S3 method for CorPop
summary(object, ...)
# S3 method for Cor3L
summary(object, ...)
# S3 method for bootuniR2
summary(object, probs=c(0, 0.1, 0.5, 0.9, 1),
cutoff.chisq.pvalue=0.05, cutoff.CFI=0.9, cutoff.SRMR=0.1,
cutoff.RMSEA=0.05, ...)
# S3 method for osmasem
summary(object, fitIndices=FALSE, numObs, robust=FALSE, ...)
# S3 method for tssem1FEM
print.summary(x, ...)
# S3 method for wls
print.summary(x, ...)
# S3 method for meta
print.summary(x, ...)
# S3 method for meta3LFIML
print.summary(x, ...)
# S3 method for reml
print.summary(x, ...)
# S3 method for mxRAMmodel
print.summary(x, ...)
# S3 method for CorPop
print.summary(x, ...)
# S3 method for Cor3L
print.summary(x, ...)
# S3 method for bootuniR2
print.summary(x, ...)
An object returned from either class
tssem1FEM, class tssem1FEM.cluster,
class tssem1REM, class wls, class wls.cluster,
class meta, class meta3LFIML, class reml or class CorPop.
An object returned from either class summary.tssem1FEM,
class tssem1FEM.cluster, class summary.wls, class
summary.meta, class summary.meta3LFIML, class
summary.reml or class summary.CorPop.
Logical. Whether to conduct a homogeneity test on the effect sizes.
Logical. Whether to report the predictors and the auxiliary variables.
Logicial. Whether to use robust standard error from imxRobustSE.
Numeric. Adjust the degrees of freedom
manually. It may be necessary if the df calculated is incorrect when
diag.constraints=TRUE.
Quantiles for the parameter estimates.
Cutoff of the p-value for the chi-square statistic.
The cutoff of the CFI.
The cutoff of the SRMR.
The cutoff of the RMSEA.
Whether to calculate the chi-square statistic and various goodness-of-fit indices in osmasem. Note. It may take a while since statistics of the saturated and independence models are required.
The number of observations in calculating the fit statistics in osmasem. If it is missing, the total number of observations is used.
Further arguments to be passed to printCoefmat
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
tssem1, wls,
meta, reml,
rCor, bootuniR2,
osmasem