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Meta-regression analysis for G agreement indices

Source: R/meta_model.R
meta.lm.agree.Rd

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a G-index of agreement. The estimates are OLS estimates with robust standard errors that accomodate residual heteroscedasticity.

For more details, see Section 3.4 of Bonett (2021, Volume 5).

Usage

meta.lm.agree(alpha, f11, f12, f21, f22, X)

Arguments

alpha

alpha level for 1-alpha confidence

f11

vector of frequency counts in cell 1,1

f12

vector of frequency counts in cell 1,2

f21

vector of frequency counts in cell 2,1

f22

vector of frequency counts in cell 2,2

X

matrix of predictor values

Value

Returns a matrix. The first row is for the intercept with one additional row per predictor. The matrix has the following columns:

  • Estimate - OLS estimate

  • SE - standard error

  • z - z-value

  • p - p-value

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

References

Bonett DG (2022). “Statistical inference for G-indices of agreement.” Journal of Educational and Behavioral Statistics, 47(4), 438–458. doi:10.3102/10769986221088561 .

Bonett DG (2021). Statistical Methods for Psychologists, Vol 1-5, https://dgbonett.sites.ucsc.edu/.

Examples

f11 <- c(40, 20, 25, 30)
f12 <- c(3, 2, 2, 1)
f21 <- c(7, 6, 8, 6)
f22 <- c(26, 25, 13, 25)
x1 <- c(1, 1, 4, 6)
x2 <- c(1, 1, 0, 0)
X <- matrix(cbind(x1, x2), 4, 2)
meta.lm.agree(.05, f11, f12, f21, f22, X)
#>    Estimate      SE     z     p      LL     UL
#> b0   0.1905 0.38773 0.491 0.623 -0.5695 0.9504
#> b1   0.0952 0.07142 1.334 0.182 -0.0447 0.2352
#> b2   0.4205 0.32384 1.299 0.194 -0.2142 1.0552

# Should return:
#    Estimate      SE     z     p      LL     UL
# b0   0.1905 0.38773 0.491 0.623 -0.5695 0.9504
# b1   0.0952 0.07142 1.334 0.182 -0.0447 0.2352
# b2   0.4205 0.32384 1.299 0.194 -0.2142 1.0552