tam.se.Rd
Standard error computation for objects of the classes tam
and tam.mml
.
tam.se(tamobj, item_pars=TRUE, ...) tam_mml_se_quick(tamobj, numdiff.parm=0.001, item_pars=TRUE ) tam_latreg_se_quick(tamobj, numdiff.parm=0.001 )
tamobj | An object generated by |
---|---|
item_pars | Logical indicating whether standard errors should also be computed for item parameters |
numdiff.parm | Step width parameter for numerical differentiation |
... | Further arguments to be passed |
Covariances between parameters estimates are ignored in this standard error calculation. The standard error is obtained by numerical differentiation.
A list with following entries:
Data frame with \(\xi\) parameters (est
)
and their corresponding standard errors (se
)
Data frame with \(\beta\) regression parameters and their standard error estimates
Data frame with loading parameters and their corresponding standard errors
Standard error estimation for variances and covariances is not yet
implemented.
Standard error estimation for loading parameters in case of
irtmodel='GPCM.design'
is highly experimental.
############################################################################# # EXAMPLE 1: 1PL model, data.sim.rasch ############################################################################# data(data.sim.rasch) # estimate Rasch model mod1 <- TAM::tam.mml(resp=data.sim.rasch[1:500,1:10]) # standard error estimation se1 <- TAM::tam.se( mod1 ) # proportion of standard errors estimated by 'tam.se' and 'tam.mml' prop1 <- se1$xsi$se / mod1$xsi$se ## > summary( prop1 ) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1.030 1.034 1.035 1.036 1.039 1.042 ##=> standard errors estimated by tam.se are a bit larger if (FALSE) { ############################################################################# # EXAMPLE 2: Standard errors differential item functioning ############################################################################# data(data.ex08) formulaA <- ~ item*female resp <- data.ex08[["resp"]] facets <- as.data.frame( data.ex08[["facets"]] ) # investigate DIF mod <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA ) summary(mod) # estimate standard errors semod <- TAM::tam.se(mod) prop1 <- semod$xsi$se / mod$xsi$se summary(prop1) # plot differences in standard errors plot( mod$xsi$se, semod$xsi$se, pch=16, xlim=c(0,.15), ylim=c(0,.15), xlab="Standard error 'tam.mml'", ylab="Standard error 'tam.se'" ) lines( c(-6,6), c(-6,6), col="gray") round( cbind( mod$xsi, semod$xsi[,-1] ), 3 ) ## xsi se.xsi N est se ## I0001 -1.956 0.092 500 -1.956 0.095 ## I0002 -1.669 0.085 500 -1.669 0.088 ## [...] ## I0010 2.515 0.108 500 2.515 0.110 ## female1 -0.091 0.025 500 -0.091 0.041 ## I0001:female1 -0.051 0.070 500 -0.051 0.071 ## I0002:female1 0.085 0.067 500 0.085 0.068 ## [...] ## I0009:female1 -0.019 0.068 500 -0.019 0.068 ## #=> The largest discrepancy in standard errors is observed for the # main female effect (.041 in 'tam.se' instead of .025 in 'tam.mml') }