estimate_var_artifacts {psychmeta} R Documentation
Taylor series approximations for the variances of estimates artifact distributions.
Description
Taylor series approximations to estimate the variances of artifacts that have been estimated from other artifacts.
These functions are implemented internally in the create_ad
function and related functions, but are useful as general tools for manipulating artifact distributions.
Available functions include:
estimate_var_qxi
: Estimate the variance of a qxi distribution from a qxa distribution and a distribution of u ratios.
estimate_var_rxxi
: Estimate the variance of an rxxi distribution from an rxxa distribution and a distribution of u ratios.
estimate_var_qxa
: Estimate the variance of a qxa distribution from a qxi distribution and a distribution of u ratios.
estimate_var_rxxa
: Estimate the variance of an rxxa distribution from an rxxi distribution and a distribution of u ratios.
estimate_var_ut
: Estimate the variance of a true-score u ratio distribution from an observed-score u ratio distribution and a reliability distribution.
estimate_var_ux
: Estimate the variance of an observed-score u ratio distribution from a true-score u ratio distribution and a reliability distribution.
estimate_var_qyi
: Estimate the variance of a qyi distribution from the following distributions: qya, rxyi, and ux.
estimate_var_ryyi
: Estimate the variance of an ryyi distribution from the following distributions: ryya, rxyi, and ux.
estimate_var_qya
: Estimate the variance of a qya distribution from the following distributions: qyi, rxyi, and ux.
estimate_var_ryya
: Estimate the variance of an ryya distribution from the following distributions: ryyi, rxyi, and ux.
Usage
estimate_var_qxi(
qxa,
var_qxa = 0,
ux,
var_ux = 0,
cor_qxa_ux = 0,
ux_observed = TRUE,
indirect_rr = TRUE,
qxa_type = "alpha"
)
estimate_var_qxa(
qxi,
var_qxi = 0,
ux,
var_ux = 0,
cor_qxi_ux = 0,
ux_observed = TRUE,
indirect_rr = TRUE,
qxi_type = "alpha"
)
estimate_var_rxxi(
rxxa,
var_rxxa = 0,
ux,
var_ux = 0,
cor_rxxa_ux = 0,
ux_observed = TRUE,
indirect_rr = TRUE,
rxxa_type = "alpha"
)
estimate_var_rxxa(
rxxi,
var_rxxi = 0,
ux,
var_ux = 0,
cor_rxxi_ux = 0,
ux_observed = TRUE,
indirect_rr = TRUE,
rxxi_type = "alpha"
)
estimate_var_ut(
rxx,
var_rxx = 0,
ux,
var_ux = 0,
cor_rxx_ux = 0,
rxx_restricted = TRUE,
rxx_as_qx = FALSE
)
estimate_var_ux(
rxx,
var_rxx = 0,
ut,
var_ut = 0,
cor_rxx_ut = 0,
rxx_restricted = TRUE,
rxx_as_qx = FALSE
)
estimate_var_ryya(
ryyi,
var_ryyi = 0,
rxyi,
var_rxyi = 0,
ux,
var_ux = 0,
cor_ryyi_rxyi = 0,
cor_ryyi_ux = 0,
cor_rxyi_ux = 0
)
estimate_var_qya(
qyi,
var_qyi = 0,
rxyi,
var_rxyi = 0,
ux,
var_ux = 0,
cor_qyi_rxyi = 0,
cor_qyi_ux = 0,
cor_rxyi_ux = 0
)
estimate_var_qyi(
qya,
var_qya = 0,
rxyi,
var_rxyi = 0,
ux,
var_ux = 0,
cor_qya_rxyi = 0,
cor_qya_ux = 0,
cor_rxyi_ux = 0
)
estimate_var_ryyi(
ryya,
var_ryya = 0,
rxyi,
var_rxyi = 0,
ux,
var_ux = 0,
cor_ryya_rxyi = 0,
cor_ryya_ux = 0,
cor_rxyi_ux = 0
)
Arguments
qxa
Square-root of applicant reliability estimate.
var_qxa
Variance of square-root of applicant reliability estimate.
ux
Observed-score u ratio.
var_ux
Variance of observed-score u ratio.
cor_qxa_ux
Correlation between qxa and ux.
ux_observed
Logical vector determining whether u ratios are observed-score u ratios (TRUE
) or true-score u ratios (FALSE
).
indirect_rr
Logical vector determining whether reliability values are associated with indirect range restriction (TRUE
) or direct range restriction (FALSE
).
qxi
Square-root of incumbent reliability estimate.
var_qxi
Variance of square-root of incumbent reliability estimate.
cor_qxi_ux
Correlation between qxi and ux.
rxxa
Incumbent reliability value.
var_rxxa
Variance of incumbent reliability values.
cor_rxxa_ux
Correlation between rxxa and ux.
rxxi
Incumbent reliability value.
var_rxxi
Variance of incumbent reliability values.
cor_rxxi_ux
Correlation between rxxi and ux.
rxxi_type
, rxxa_type
, qxi_type
, qxa_type
String vector identifying the types of reliability estimates supplied (e.g., "alpha", "retest", "interrater_r", "splithalf"). See the documentation for ma_r
for a full list of acceptable reliability types.
rxx
Generic argument for a reliability estimate (whether this is a reliability or the square root of a reliability is clarified by the rxx_as_qx
argument).
var_rxx
Generic argument for the variance of reliability estimates (whether this pertains to reliabilities or the square roots of reliabilities is clarified by the rxx_as_qx
argument).
cor_rxx_ux
Correlation between rxx and ux.
rxx_restricted
Logical vector determining whether reliability estimates were incumbent reliabilities (TRUE
) or applicant reliabilities (FALSE
).
rxx_as_qx
Logical vector determining whether the reliability estimates were reliabilities (TRUE
) or square-roots of reliabilities (FALSE
).
ut
True-score u ratio.
var_ut
Variance of true-score u ratio.
cor_rxx_ut
Correlation between rxx and ut.
ryyi
Incumbent reliability value.
var_ryyi
Variance of incumbent reliability values.
rxyi
Incumbent correlation between X and Y.
var_rxyi
Variance of incumbent correlations.
cor_ryyi_rxyi
Correlation between ryyi and rxyi.
cor_ryyi_ux
Correlation between ryyi and ux.
cor_rxyi_ux
Correlation between rxyi and ux.
qyi
Square-root of incumbent reliability estimate.
var_qyi
Variance of square-root of incumbent reliability estimate.
cor_qyi_rxyi
Correlation between qyi and rxyi.
cor_qyi_ux
Correlation between qyi and ux.
qya
Square-root of applicant reliability estimate.
var_qya
Variance of square-root of applicant reliability estimate.
cor_qya_rxyi
Correlation between qya and rxyi.
cor_qya_ux
Correlation between qya and ux.
ryya
Applicant reliability value.
var_ryya
Variance of applicant reliability values.
cor_ryya_rxyi
Correlation between ryya and rxyi.
cor_ryya_ux
Correlation between ryya and ux.
Details
#### Partial derivatives to estimate the variance of qxa using ux ####
Indirect range restriction:
b u X = ( q X i 2 − 1 ) u X ( q X i 2 − 1 ) u X 2 + 1 b_{u_{X}}=\frac{(q_{X_{i}}^{2}-1)u_{X}}{\sqrt{(q_{X_{i}}^{2}-1)u_{X}^{2}+1}} b u X = ( q X i 2 − 1 ) u X 2 + 1 ( q X i 2 − 1 ) u X
b q X i = q X i 2 u X 2 ( q X i 2 − 1 ) u X 2 + 1 b_{q_{X_{i}}}=\frac{q_{X_{i}}^{2}u_{X}^{2}}{\sqrt{(q_{X_{i}}^{2}-1)u_{X}^{2}+1}} b q X i = ( q X i 2 − 1 ) u X 2 + 1 q X i 2 u X 2
Direct range restriction:
b u X = q X i 2 ( q X i 2 − 1 ) u X − q X i 2 q X i 2 ( u X 2 − 1 ) − u X 2 ( q X i 2 ( u X 2 − 1 ) − u X 2 ) 2 b_{u_{X}}=\frac{q_{X_{i}}^{2}(q_{X_{i}}^{2}-1)u_{X}}{\sqrt{-\frac{q_{X_{i}}^{2}}{q_{X_{i}}^{2}(u_{X}^{2}-1)-u_{X}^{2}}}(q_{X_{i}}^{2}(u_{X}^{2}-1)-u_{X}^{2})^{2}} b u X = − q X i 2 ( u X 2 − 1 ) − u X 2 q X i 2 ( q X i 2 ( u X 2 − 1 ) − u X 2 ) 2 q X i 2 ( q X i 2 − 1 ) u X
b q X i = q X i u X 2 − q X i 2 q X i 2 ( u X 2 − 1 ) − u X 2 ( q X i 2 ( u X 2 − 1 ) − u X 2 ) 2 b_{q_{X_{i}}}=\frac{q_{X_{i}}u_{X}^{2}}{\sqrt{-\frac{q_{X_{i}}^{2}}{q_{X_{i}}^{2}(u_{X}^{2}-1)-u_{X}^{2}}}(q_{X_{i}}^{2}(u_{X}^{2}-1)-u_{X}^{2})^{2}} b q X i = − q X i 2 ( u X 2 − 1 ) − u X 2 q X i 2 ( q X i 2 ( u X 2 − 1 ) − u X 2 ) 2 q X i u X 2
#### Partial derivatives to estimate the variance of rxxa using ux ####
Indirect range restriction:
b u X = 2 ( ρ X X i − 1 ) u X b_{u_{X}}=2\left(\rho_{XX_{i}}-1\right)u_{X} b u X = 2 ( ρ X X i − 1 ) u X
ρ X X i : b ρ X X i = u X 2 \rho_{XX_{i}}: b_{\rho_{XX_{i}}}=u_{X}^{2} ρ X X i : b ρ X X i = u X 2
Direct range restriction:
b u X = 2 ( ρ X X i − 1 ) ρ X X i u X ( − ρ X X i u X 2 + ρ X X i + u X 2 ) 2 b_{u_{X}}=\frac{2(\rho_{XX_{i}}-1)\rho_{XX_{i}}u_{X}}{(-\rho_{XX_{i}}u_{X}^{2}+\rho_{XX_{i}}+u_{X}^{2})^{2}} b u X = ( − ρ X X i u X 2 + ρ X X i + u X 2 ) 2 2 ( ρ X X i − 1 ) ρ X X i u X
b ρ X X i = u X 2 ( − ρ X X i u X 2 + ρ X X i + u X 2 ) 2 b_{\rho_{XX_{i}}}=\frac{u_{X}^{2}}{(-\rho_{XX_{i}}u_{X}^{2}+\rho_{XX_{i}}+u_{X}^{2})^{2}} b ρ X X i = ( − ρ X X i u X 2 + ρ X X i + u X 2 ) 2 u X 2
#### Partial derivatives to estimate the variance of rxxa using ut ####
b u T = 2 ( ρ X X i − 1 ) ∗ ρ X X i u T ( − ρ X X i u T 2 + ρ X X i + u T 2 ) 2 b_{u_{T}}=\frac{2(\rho_{XX_{i}}-1)*\rho_{XX_{i}}u_{T}}{(-\rho_{XX_{i}}u_{T}^{2}+\rho_{XX_{i}}+u_{T}^{2})^{2}} b u T = ( − ρ X X i u T 2 + ρ X X i + u T 2 ) 2 2 ( ρ X X i − 1 ) ∗ ρ X X i u T
b ρ X X i = u T 2 ( − ρ X X i u T 2 + ρ X X i + u T 2 ) 2 b_{\rho_{XX_{i}}}=\frac{u_{T}^{2}}{(-\rho_{XX_{i}}u_{T}^{2}+\rho_{XX_{i}}+u_{T}^{2})^{2}} b ρ X X i = ( − ρ X X i u T 2 + ρ X X i + u T 2 ) 2 u T 2
#### Partial derivatives to estimate the variance of qxa using ut ####
b u T = q X i 2 ( q X i 2 − 1 ) u T − q X i 2 q X i 2 ∗ ( u T 2 − 1 ) − u T 2 ( q X i 2 ( u T 2 − 1 ) − u T 2 ) 2 b_{u_{T}}=\frac{q_{X_{i}}^{2}(q_{X_{i}}^{2}-1)u_{T}}{\sqrt{\frac{-q_{X_{i}}^{2}}{q_{X_{i}}^{2}*(u_{T}^{2}-1)-u_{T}^{2}}}(q_{X_{i}}^{2}(u_{T}^{2}-1)-u_{T}^{2})^{2}} b u T = q X i 2 ∗ ( u T 2 − 1 ) − u T 2 − q X i 2 ( q X i 2 ( u T 2 − 1 ) − u T 2 ) 2 q X i 2 ( q X i 2 − 1 ) u T
b q X i = q X i u T 2 q X i 2 u T 2 − q X i 2 ( u T 2 − 1 ) ( u T 2 − q X i 2 ( u T 2 − 1 ) ) 2 b_{q_{X_{i}}}=\frac{q_{X_{i}}u_{T}^{2}}{\sqrt{\frac{q_{X_{i}}^{2}}{u_{T}^{2}-q_{X_{i}}^{2}(u_{T}^{2}-1)}}(u_{T}^{2}-q_{X_{i}}^{2}(u_{T}^{2}-1))^{2}} b q X i = u T 2 − q X i 2 ( u T 2 − 1 ) q X i 2 ( u T 2 − q X i 2 ( u T 2 − 1 ) ) 2 q X i u T 2
#### Partial derivatives to estimate the variance of qxi using ux ####
Indirect range restriction:
b u X = 1 − q x a 2 u X 3 q X a 2 + u X 2 − 1 u X 2 b_{u_{X}}=\frac{1-qxa^{2}}{u_{X}^{3}\sqrt{\frac{q_{X_{a}}^{2}+u_{X}^{2}-1}{u_{X}^{2}}}} b u X = u X 3 u X 2 q X a 2 + u X 2 − 1 1 − q x a 2
b q X a = q X a u X 2 q X a − 1 2 u X 2 + 1 b_{q_{X_{a}}}=\frac{q_{X_{a}}}{u_{X}^{2}\sqrt{\frac{q_{X_{a}-1}^{2}}{u_{X}^{2}}+1}} b q X a = u X 2 u X 2 q X a − 1 2 + 1 q X a
Direct range restriction:
b u X = − q X a 2 ( q X a 2 − 1 ) u X q X a 2 u X 2 q X a 2 ( u X 2 − 1 ) + 1 ( q X a 2 ( u X 2 − 1 ) + 1 ) 2 b_{u_{X}}=-\frac{q_{X_{a}}^{2}(q_{X_{a}}^{2}-1)u_{X}}{\sqrt{\frac{q_{X_{a}}^{2}u_{X}^{2}}{q_{X_{a}}^{2}(u_{X}^{2}-1)+1}}(q_{X_{a}}^{2}(u_{X}^{2}-1)+1)^{2}} b u X = − q X a 2 ( u X 2 − 1 ) + 1 q X a 2 u X 2 ( q X a 2 ( u X 2 − 1 ) + 1 ) 2 q X a 2 ( q X a 2 − 1 ) u X
b q X a = q X a u X 2 q X a 2 u X 2 q X a 2 ( u X 2 − 1 ) + 1 ( q X a 2 ( u X 2 − 1 ) + 1 ) 2 b_{q_{X_{a}}}=\frac{q_{X_{a}}u_{X}^{2}}{\sqrt{\frac{q_{X_{a}}^{2}u_{X}^{2}}{q_{X_{a}}^{2}(u_{X}^{2}-1)+1}}(q_{X_{a}}^{2}(u_{X}^{2}-1)+1)^{2}} b q X a = q X a 2 ( u X 2 − 1 ) + 1 q X a 2 u X 2 ( q X a 2 ( u X 2 − 1 ) + 1 ) 2 q X a u X 2
#### Partial derivatives to estimate the variance of rxxi using ux ####
Indirect range restriction:
b u X = 2 − 2 ρ X X a u X 3 b_{u_{X}}=\frac{2-2\rho_{XX_{a}}}{u_{X}^{3}} b u X = u X 3 2 − 2 ρ X X a
b ρ X X a = 1 u X 2 b_{\rho_{XX_{a}}}=\frac{1}{u_{X}^{2}} b ρ X X a = u X 2 1
Direct range restriction:
b u X = − 2 ( ρ X X a − 1 ) ρ X X a u X ( ρ X X a ( u X 2 − 1 ) + 1 ) 2 b_{u_{X}}=-\frac{2(\rho_{XX_{a}}-1)\rho_{XX_{a}}u_{X}}{(\rho_{XX_{a}}(u_{X}^{2}-1)+1)^{2}} b u X = − ( ρ X X a ( u X 2 − 1 ) + 1 ) 2 2 ( ρ X X a − 1 ) ρ X X a u X
b ρ X X a = u X 2 ( ρ X X a ( u X 2 − 1 ) + 1 ) 2 b_{\rho_{XX_{a}}}=\frac{u_{X}^{2}}{(\rho_{XX_{a}}(u_{X}^{2}-1)+1)^{2}} b ρ X X a = ( ρ X X a ( u X 2 − 1 ) + 1 ) 2 u X 2
#### Partial derivatives to estimate the variance of rxxi using ut ####
u T : b u T = − 2 ( ρ X X a − 1 ) ρ X X a u T ( ρ X X a ( u T 2 − 1 ) + 1 ) 2 u_{T}: b_{u_{T}}=-\frac{2(\rho_{XX_{a}}-1)\rho_{XX_{a}}u_{T}}{(\rho_{XX_{a}}(u_{T}^{2}-1)+1)^{2}} u T : b u T = − ( ρ X X a ( u T 2 − 1 ) + 1 ) 2 2 ( ρ X X a − 1 ) ρ X X a u T
b ρ X X a = u T 2 ( ρ X X a ( u T 2 − 1 ) + 1 ) 2 b_{\rho_{XX_{a}}}=\frac{u_{T}^{2}}{(\rho_{XX_{a}}(u_{T}^{2}-1)+1)^{2}} b ρ X X a = ( ρ X X a ( u T 2 − 1 ) + 1 ) 2 u T 2
#### Partial derivatives to estimate the variance of qxi using ut ####
b u T = − ( q X a − 1 ) q X a 2 ( q X a + 1 ) u T q X a 2 u T 2 q X a 2 u T 2 − q X a 2 + 1 ( q X a 2 u T 2 − q X a 2 + 1 ) 2 b_{u_{T}}=-\frac{(q_{X_{a}}-1)q_{X_{a}}^{2}(q_{X_{a}}+1)u_{T}}{\sqrt{\frac{q_{X_{a}}^{2}u_{T}^{2}}{q_{X_{a}}^{2}u_{T}^{2}-q_{X_{a}}^{2}+1}}(q_{X_{a}}^{2}u_{T}^{2}-q_{X_{a}}^{2}+1)^{2}} b u T = − q X a 2 u T 2 − q X a 2 + 1 q X a 2 u T 2 ( q X a 2 u T 2 − q X a 2 + 1 ) 2 ( q X a − 1 ) q X a 2 ( q X a + 1 ) u T
b q X a = q X a u T 2 q X a 2 u T 2 q X a 2 u T 2 − q X a 2 + 1 ( q X a 2 u T 2 − q X a 2 + 1 ) 2 b_{q_{X_{a}}}=\frac{q_{X_{a}}u_{T}^{2}}{\sqrt{\frac{q_{X_{a}}^{2}u_{T}^{2}}{q_{X_{a}}^{2}u_{T}^{2}-q_{X_{a}}^{2}+1}}(q_{X_{a}}^{2}u_{T}^{2}-q_{X_{a}}^{2}+1)^{2}} b q X a = q X a 2 u T 2 − q X a 2 + 1 q X a 2 u T 2 ( q X a 2 u T 2 − q X a 2 + 1 ) 2 q X a u T 2
#### Partial derivatives to estimate the variance of ut using qxi ####
b u X = q X i 2 u X q X i 2 u X 2 ( q X i 2 − 1 ) u X 2 + 1 ( ( q X i 2 − 1 ) u X 2 + 1 ) 2 b_{u_{X}}=\frac{q_{X_{i}}^{2}u_{X}}{\sqrt{\frac{q_{X_{i}}^{2}u_{X}^{2}}{(q_{X_{i}}^{2}-1)u_{X}^{2}+1}}((q_{X_{i}}^{2}-1)u_{X}^{2}+1)^{2}} b u X = ( q X i 2 − 1 ) u X 2 + 1 q X i 2 u X 2 (( q X i 2 − 1 ) u X 2 + 1 ) 2 q X i 2 u X
b q X i = − u X 2 ( u X 2 − 1 ) q X i 2 u X 2 ( q X i 2 − 1 ) u X 2 + 1 ( ( q X i 2 − 1 ) u X 2 + 1 ) 2 b_{q_{X_{i}}}=-\frac{u_{X}^{2}(u_{X}^{2}-1)}{\sqrt{\frac{q_{X_{i}}^{2}u_{X}^{2}}{(q_{X_{i}}^{2}-1)u_{X}^{2}+1}}((q_{X_{i}}^{2}-1)u_{X}^{2}+1)^{2}} b q X i = − ( q X i 2 − 1 ) u X 2 + 1 q X i 2 u X 2 (( q X i 2 − 1 ) u X 2 + 1 ) 2 u X 2 ( u X 2 − 1 )
#### Partial derivatives to estimate the variance of ut using rxxi ####
b u X = ρ X X i u X ρ X X i u X 2 ( ρ X X i − 1 ) u X 2 + 1 ( ( ρ X X i − 1 ) u X 2 + 1 ) 2 b_{u_{X}}=\frac{\rho_{XX_{i}}u_{X}}{\sqrt{\frac{\rho_{XX_{i}}u_{X}^{2}}{(\rho_{XX_{i}}-1)u_{X}^{2}+1}}((\rho_{XX_{i}}-1)u_{X}^{2}+1)^{2}} b u X = ( ρ X X i − 1 ) u X 2 + 1 ρ X X i u X 2 (( ρ X X i − 1 ) u X 2 + 1 ) 2 ρ X X i u X
b ρ X X i = − u X 2 ( u X 2 − 1 ) 2 ρ X X i u X 2 ( ρ X X i − 1 ) u X 2 + 1 ( ( ρ X X i − 1 ) u X 2 + 1 ) 2 b_{\rho_{XX_{i}}}=-\frac{u_{X}^{2}(u_{X}^{2}-1)}{2\sqrt{\frac{\rho_{XX_{i}}u_{X}^{2}}{(\rho_{XX_{i}}-1)u_{X}^{2}+1}}((\rho_{XX_{i}}-1)u_{X}^{2}+1)^{2}} b ρ X X i = − 2 ( ρ X X i − 1 ) u X 2 + 1 ρ X X i u X 2 (( ρ X X i − 1 ) u X 2 + 1 ) 2 u X 2 ( u X 2 − 1 )
#### Partial derivatives to estimate the variance of ut using qxa ####
b u X = u X q X a 2 q X a 2 + u X 2 − 1 q X a 2 b_{u_{X}}=\frac{u_{X}}{q_{X_{a}}^{2}\sqrt{\frac{q_{X_{a}}^{2}+u_{X}^{2}-1}{q_{X_{a}}^{2}}}} b u X = q X a 2 q X a 2 q X a 2 + u X 2 − 1 u X
b q X a = 1 − u X 2 q X a 3 q X a 2 + u X 2 − 1 q X a 2 b_{q_{X_{a}}}=\frac{1-u_{X}^{2}}{q_{X_{a}}^{3}\sqrt{\frac{q_{X_{a}}^{2}+u_{X}^{2}-1}{q_{X_{a}}^{2}}}} b q X a = q X a 3 q X a 2 q X a 2 + u X 2 − 1 1 − u X 2
#### Partial derivatives to estimate the variance of ut using rxxa ####
b u X = u X ρ X X a ρ X X a + u X 2 − 1 ρ X X a b_{u_{X}}=\frac{u_{X}}{\rho_{XX_{a}}\sqrt{\frac{\rho_{XX_{a}}+u_{X}^{2}-1}{\rho_{XX_{a}}}}} b u X = ρ X X a ρ X X a ρ X X a + u X 2 − 1 u X
b ρ X X a = 1 − u X 2 2 ρ X X a 2 ρ X X a + u X 2 − 1 ρ X X a b_{\rho_{XX_{a}}}=\frac{1-u_{X}^{2}}{2\rho_{XX_{a}}^{2}\sqrt{\frac{\rho_{XX_{a}}+u_{X}^{2}-1}{\rho_{XX_{a}}}}} b ρ X X a = 2 ρ X X a 2 ρ X X a ρ X X a + u X 2 − 1 1 − u X 2
#### Partial derivatives to estimate the variance of ux using qxi ####
b u T = q X i 2 u T u T 2 u T 2 − q X i 2 ( u T 2 − 1 ) ( u T 2 − q X i 2 ( u T 2 − 1 ) ) 2 b_{u_{T}}=\frac{q_{X_{i}}^{2}u_{T}}{\sqrt{\frac{u_{T}^{2}}{u_{T}^{2}-q_{X_{i}}^{2}(u_{T}^{2}-1)}}(u_{T}^{2}-q_{X_{i}}^{2}(u_{T}^{2}-1))^{2}} b u T = u T 2 − q X i 2 ( u T 2 − 1 ) u T 2 ( u T 2 − q X i 2 ( u T 2 − 1 ) ) 2 q X i 2 u T
b q X i = q X i ( u T 2 − 1 ) ( u T 2 u T 2 − q X i 2 ( u T 2 − 1 ) ) 1.5 u T 2 b_{q_{X_{i}}}=\frac{q_{X_{i}}(u_{T}^{2}-1)\left(\frac{u_{T}^{2}}{u_{T}^{2}-q_{X_{i}}^{2}(u_{T}^{2}-1)}\right)^{1.5}}{u_{T}^{2}} b q X i = u T 2 q X i ( u T 2 − 1 ) ( u T 2 − q X i 2 ( u T 2 − 1 ) u T 2 ) 1.5
#### Partial derivatives to estimate the variance of ux using rxxi ####
b u T = ρ X X i u T u T 2 − ρ X X i u T 2 + ρ X X i + u T 2 ( − ρ X X i u T 2 + ρ X X i + u T 2 ) 2 b_{u_{T}}=\frac{\rho_{XX_{i}}u_{T}}{\sqrt{\frac{u_{T}^{2}}{-\rho_{XX_{i}}u_{T}^{2}+\rho_{XX_{i}}+u_{T}^{2}}}(-\rho_{XX_{i}}u_{T}^{2}+\rho_{XX_{i}}+u_{T}^{2})^{2}} b u T = − ρ X X i u T 2 + ρ X X i + u T 2 u T 2 ( − ρ X X i u T 2 + ρ X X i + u T 2 ) 2 ρ X X i u T
b ρ X X i = ( u T 2 − 1 ) ( u T 2 − ρ X X i u T 2 + ρ X X i + u T 2 ) 1.5 2 u T 2 b_{\rho_{XX_{i}}}=\frac{(u_{T}^{2}-1)\left(\frac{u_{T}^{2}}{-\rho_{XX_{i}}u_{T}^{2}+\rho_{XX_{i}}+u_{T}^{2}}\right)^{1.5}}{2u_{T}^{2}} b ρ X X i = 2 u T 2 ( u T 2 − 1 ) ( − ρ X X i u T 2 + ρ X X i + u T 2 u T 2 ) 1.5
#### Partial derivatives to estimate the variance of ux using qxa ####
b u T = q X a 2 u T q X a 2 ( u T 2 − 1 ) + 1 b_{u_{T}}=\frac{q_{X_{a}}^{2}u_{T}}{\sqrt{q_{X_{a}}^{2}(u_{T}^{2}-1)+1}} b u T = q X a 2 ( u T 2 − 1 ) + 1 q X a 2 u T
b q X a = q X a ( u T − 1 ) q X a 2 ( u T 2 − 1 ) + 1 b_{q_{X_{a}}}=\frac{q_{X_{a}}(u_{T}-1)}{\sqrt{q_{X_{a}}^{2}(u_{T}^{2}-1)+1}} b q X a = q X a 2 ( u T 2 − 1 ) + 1 q X a ( u T − 1 )
#### Partial derivatives to estimate the variance of ux using rxxa ####
b u T = ρ X X a u T ρ X X a ( u T 2 − 1 ) + 1 b_{u_{T}}=\frac{\rho_{XX_{a}}u_{T}}{\sqrt{\rho_{XX_{a}}(u_{T}^{2}-1)+1}} b u T = ρ X X a ( u T 2 − 1 ) + 1 ρ X X a u T
b ρ X X a = u T 2 − 1 2 ρ X X a ( u T 2 − 1 ) + 1 b_{\rho_{XX_{a}}}=\frac{u_{T}^{2}-1}{2\sqrt{\rho_{XX_{a}}(u_{T}^{2}-1)+1}} b ρ X X a = 2 ρ X X a ( u T 2 − 1 ) + 1 u T 2 − 1
#### Partial derivatives to estimate the variance of ryya ####
b ρ Y Y i = 1 ρ X Y i 2 ( 1 u X 2 − 1 ) + 1 b_{\rho_{YY_{i}}}=\frac{1}{\rho_{XY_{i}}^{2}\left(\frac{1}{u_{X}^{2}}-1\right)+1} b ρ Y Y i = ρ X Y i 2 ( u X 2 1 − 1 ) + 1 1
b u X = 2 ( ρ Y Y i − 1 ) ρ X Y i 2 u X ( u X 2 − ρ X Y i 2 ( u X 2 − 1 ) ) 2 b_{u_{X}}=\frac{2(\rho_{YY_{i}}-1)\rho_{XY_{i}}^{2}u_{X}}{(u_{X}^{2}-\rho_{XY_{i}}^{2}(u_{X}^{2}-1))^{2}} b u X = ( u X 2 − ρ X Y i 2 ( u X 2 − 1 ) ) 2 2 ( ρ Y Y i − 1 ) ρ X Y i 2 u X
b ρ X Y i = 2 ( ρ Y Y i − 1 ) ρ X Y i u X 2 ( u X 2 − 1 ) ( u X 2 − ρ X Y i 2 ( u X 2 − 1 ) ) 2 b_{\rho_{XY_{i}}}=\frac{2(\rho_{YY_{i}}-1)\rho_{XY_{i}}u_{X}^{2}(u_{X}^{2}-1)}{(u_{X}^{2}-\rho_{XY_{i}}^{2}(u_{X}^{2}-1))^{2}} b ρ X Y i = ( u X 2 − ρ X Y i 2 ( u X 2 − 1 ) ) 2 2 ( ρ Y Y i − 1 ) ρ X Y i u X 2 ( u X 2 − 1 )
#### Partial derivatives to estimate the variance of qya ####
b q Y i = q Y i [ 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) ] 1 − 1 − q Y i 2 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) b_{q_{Y_{i}}}=\frac{q_{Y_{i}}}{\left[1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)\right]\sqrt{1-\frac{1-q_{Y_{i}}^{2}}{1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)}}} b q Y i = [ 1 − ρ X Y i 2 ( 1 − u X 2 1 ) ] 1 − 1 − ρ X Y i 2 ( 1 − u X 2 1 ) 1 − q Y i 2 q Y i
b u X = − ( 1 − q Y i 2 ) ρ X Y i 2 u X 3 [ 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) ] 1 − 1 − q Y i 2 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) b_{u_{X}}=-\frac{(1-q_{Y_{i}}^{2})\rho_{XY_{i}}^{2}}{u_{X}^{3}\left[1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)\right]\sqrt{1-\frac{1-q_{Y_{i}}^{2}}{1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)}}} b u X = − u X 3 [ 1 − ρ X Y i 2 ( 1 − u X 2 1 ) ] 1 − 1 − ρ X Y i 2 ( 1 − u X 2 1 ) 1 − q Y i 2 ( 1 − q Y i 2 ) ρ X Y i 2
b ρ X Y i = − ( 1 − q Y i 2 ) ρ X Y i ( 1 − 1 u X 2 ) [ 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) ] 1 − 1 − q Y i 2 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) b_{\rho_{XY_{i}}}=-\frac{(1-q_{Y_{i}}^{2})\rho_{XY_{i}}\left(1-\frac{1}{u_{X}^{2}}\right)}{\left[1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)\right]\sqrt{1-\frac{1-q_{Y_{i}}^{2}}{1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)}}} b ρ X Y i = − [ 1 − ρ X Y i 2 ( 1 − u X 2 1 ) ] 1 − 1 − ρ X Y i 2 ( 1 − u X 2 1 ) 1 − q Y i 2 ( 1 − q Y i 2 ) ρ X Y i ( 1 − u X 2 1 )
#### Partial derivatives to estimate the variance of ryyi ####
ρ Y Y a : b ρ Y Y a = ρ X Y i 2 ( 1 u X 2 − 1 ) + 1 \rho_{YY_{a}}: b_{\rho_{YY_{a}}}=\rho_{XY_{i}}^{2}\left(\frac{1}{u_{X}^{2}}-1\right)+1 ρ Y Y a : b ρ Y Y a = ρ X Y i 2 ( u X 2 1 − 1 ) + 1
b u X = − 2 ( ρ Y Y a − 1 ) ρ X Y i 2 u X 3 b_{u_{X}}=-\frac{2(\rho_{YY_{a}}-1)\rho_{XY_{i}}^{2}}{u_{X}^{3}} b u X = − u X 3 2 ( ρ Y Y a − 1 ) ρ X Y i 2
b ρ X Y i = − 2 ( ρ Y Y a − 1 ) ρ X Y i ( u X 2 − 1 ) u X 2 b_{\rho_{XY_{i}}}=-\frac{2(\rho_{YY_{a}}-1)\rho_{XY_{i}}(u_{X}^{2}-1)}{u_{X}^{2}} b ρ X Y i = − u X 2 2 ( ρ Y Y a − 1 ) ρ X Y i ( u X 2 − 1 )
#### Partial derivatives to estimate the variance of qyi ####
b q Y a = q Y a [ 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) ] 1 − ( 1 − q Y a ) [ 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) ] b_{q_{Y_{a}}}=\frac{q_{Y_{a}}\left[1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)\right]}{\sqrt{1-\left(1-q_{Y_{a}}\right)\left[1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)\right]}} b q Y a = 1 − ( 1 − q Y a ) [ 1 − ρ X Y i 2 ( 1 − u X 2 1 ) ] q Y a [ 1 − ρ X Y i 2 ( 1 − u X 2 1 ) ]
b u X = ( 1 − q Y a 2 ) ρ X Y i ( 1 − 1 u X 2 ) 1 − ( 1 − q Y a ) [ 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) ] b_{u_{X}}=\frac{(1-q_{Y_{a}}^{2})\rho_{XY_{i}}\left(1-\frac{1}{u_{X}^{2}}\right)}{\sqrt{1-\left(1-q_{Y_{a}}\right)\left[1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)\right]}} b u X = 1 − ( 1 − q Y a ) [ 1 − ρ X Y i 2 ( 1 − u X 2 1 ) ] ( 1 − q Y a 2 ) ρ X Y i ( 1 − u X 2 1 )
b ρ X Y i = ( 1 − q Y a 2 ) ρ X Y i 2 u X 3 1 − ( 1 − q Y a ) [ 1 − ρ X Y i 2 ( 1 − 1 u X 2 ) ] b_{\rho_{XY_{i}}}=\frac{(1-q_{Y_{a}}^{2})\rho_{XY_{i}}^{2}}{u_{X}^{3}\sqrt{1-\left(1-q_{Y_{a}}\right)\left[1-\rho_{XY_{i}}^{2}\left(1-\frac{1}{u_{X}^{2}}\right)\right]}} b ρ X Y i = u X 3 1 − ( 1 − q Y a ) [ 1 − ρ X Y i 2 ( 1 − u X 2 1 ) ] ( 1 − q Y a 2 ) ρ X Y i 2
Examples
estimate_var_qxi(qxa = c(.8, .85, .9, .95), var_qxa = c(.02, .03, .04, .05),
ux = .8, var_ux = 0,
ux_observed = c(TRUE, TRUE, FALSE, FALSE),
indirect_rr = c(TRUE, FALSE, TRUE, FALSE))
estimate_var_qxa(qxi = c(.8, .85, .9, .95), var_qxi = c(.02, .03, .04, .05),
ux = .8, var_ux = 0,
ux_observed = c(TRUE, TRUE, FALSE, FALSE),
indirect_rr = c(TRUE, FALSE, TRUE, FALSE))
estimate_var_rxxi(rxxa = c(.8, .85, .9, .95),
var_rxxa = c(.02, .03, .04, .05), ux = .8, var_ux = 0,
ux_observed = c(TRUE, TRUE, FALSE, FALSE),
indirect_rr = c(TRUE, FALSE, TRUE, FALSE))
estimate_var_rxxa(rxxi = c(.8, .85, .9, .95), var_rxxi = c(.02, .03, .04, .05),
ux = .8, var_ux = 0,
ux_observed = c(TRUE, TRUE, FALSE, FALSE),
indirect_rr = c(TRUE, FALSE, TRUE, FALSE))
estimate_var_ut(rxx = c(.8, .85, .9, .95), var_rxx = 0,
ux = c(.8, .8, .9, .9), var_ux = c(.02, .03, .04, .05),
rxx_restricted = c(TRUE, TRUE, FALSE, FALSE),
rxx_as_qx = c(TRUE, FALSE, TRUE, FALSE))
estimate_var_ux(rxx = c(.8, .85, .9, .95), var_rxx = 0,
ut = c(.8, .8, .9, .9), var_ut = c(.02, .03, .04, .05),
rxx_restricted = c(TRUE, TRUE, FALSE, FALSE),
rxx_as_qx = c(TRUE, FALSE, TRUE, FALSE))
estimate_var_ryya(ryyi = .9, var_ryyi = .04, rxyi = .4, var_rxyi = 0, ux = .8, var_ux = 0)
estimate_var_ryya(ryyi = .9, var_ryyi = .04, rxyi = .4, var_rxyi = 0, ux = .8, var_ux = 0)
estimate_var_qyi(qya = .9, var_qya = .04, rxyi = .4, var_rxyi = 0, ux = .8, var_ux = 0)
estimate_var_ryyi(ryya = .9, var_ryya = .04, rxyi = .4, var_rxyi = 0, ux = .8, var_ux = 0)
[Package
psychmeta version 2.7.0
Index ]