Skip to contents

betaMC: Example Using the PCorMC Function

Ivan Jacob Agaloos Pesigan

Source: vignettes/example-p-cor-mc.Rmd
example-p-cor-mc.Rmd

Confidence intervals for squared partial correlation coefficients are generated using the PCorMC() function from the betaMC package. In this example, we use the data set and the model used in betaMC: Example Using the BetaMC Function.

df <- betaMC::nas1982

Regression

Fit the regression model using the lm() function.

object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = df)

Monte Carlo Sampling Distribution of Parameters

Normal-Theory Approach 

mvn <- MC(object, type = "mvn")

Asymptotic distribution-free Approach 

adf <- MC(object, type = "adf")

Heteroskedasticity Consistent Approach (HC3) 

hc3 <- MC(object, type = "hc3")

Squared Partial Correlation Coefficients

Normal-Theory Approach 

mvn <- PCorMC(mvn)

Asymptotic distribution-free Approach 

adf <- PCorMC(adf)

Heteroskedasticity Consistent Approach (HC3) 

hc3 <- PCorMC(hc3)

Methods

summary

Summary of the results of PCorMC().

summary(mvn)
#> Call:
#> PCorMC(object = mvn)
#> 
#> Squared partial correlations
#> type = "mvn"
#>            est     se     R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
#> NARTIC  0.4874 0.1049 20000 0.1034 0.1848 0.2440 0.6505 0.7072 0.7681
#> PCTGRT  0.3757 0.1082 20000 0.0492 0.0985 0.1443 0.5657 0.6284 0.7117
#> PCTSUPP 0.2254 0.0991 20000 0.0023 0.0171 0.0459 0.4272 0.5040 0.6135
summary(adf)
#> Call:
#> PCorMC(object = adf)
#> 
#> Squared partial correlations
#> type = "adf"
#>            est     se     R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
#> NARTIC  0.4874 0.0982 20000 0.0497 0.1669 0.2446 0.6273 0.6845 0.7390
#> PCTGRT  0.3757 0.0999 20000 0.0448 0.0942 0.1504 0.5374 0.5903 0.6624
#> PCTSUPP 0.2254 0.1040 20000 0.0003 0.0108 0.0353 0.4361 0.5104 0.6065
summary(hc3)
#> Call:
#> PCorMC(object = hc3)
#> 
#> Squared partial correlations
#> type = "hc3"
#>            est     se     R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
#> NARTIC  0.4874 0.1197 20000 0.0090 0.0790 0.1751 0.6493 0.7096 0.7874
#> PCTGRT  0.3757 0.1155 20000 0.0074 0.0452 0.1043 0.5530 0.6153 0.6901
#> PCTSUPP 0.2254 0.1126 20000 0.0000 0.0047 0.0254 0.4569 0.5346 0.6370

coef

Return the vector of estimates.

coef(mvn)
#>    NARTIC    PCTGRT   PCTSUPP 
#> 0.4874382 0.3757383 0.2253739
coef(adf)
#>    NARTIC    PCTGRT   PCTSUPP 
#> 0.4874382 0.3757383 0.2253739
coef(hc3)
#>    NARTIC    PCTGRT   PCTSUPP 
#> 0.4874382 0.3757383 0.2253739

vcov

Return the sampling covariance matrix.

vcov(mvn)
#>               NARTIC       PCTGRT      PCTSUPP
#> NARTIC  0.0110123181 0.0006788020 0.0004072997
#> PCTGRT  0.0006788020 0.0116981645 0.0002504233
#> PCTSUPP 0.0004072997 0.0002504233 0.0098156649
vcov(adf)
#>              NARTIC       PCTGRT      PCTSUPP
#> NARTIC  0.009636431 0.0024215917 0.0019630480
#> PCTGRT  0.002421592 0.0099823353 0.0007595656
#> PCTSUPP 0.001963048 0.0007595656 0.0108131386
vcov(hc3)
#>              NARTIC      PCTGRT     PCTSUPP
#> NARTIC  0.014330661 0.004035119 0.002836184
#> PCTGRT  0.004035119 0.013344289 0.001024676
#> PCTSUPP 0.002836184 0.001024676 0.012672581

confint

Return confidence intervals.

confint(mvn, level = 0.95)
#>              2.5 %    97.5 %
#> NARTIC  0.24398002 0.6505411
#> PCTGRT  0.14431654 0.5657123
#> PCTSUPP 0.04590714 0.4271904
confint(adf, level = 0.95)
#>              2.5 %    97.5 %
#> NARTIC  0.24459215 0.6272720
#> PCTGRT  0.15036268 0.5374011
#> PCTSUPP 0.03525408 0.4360711
confint(hc3, level = 0.95)
#>              2.5 %    97.5 %
#> NARTIC  0.17513963 0.6492983
#> PCTGRT  0.10431428 0.5530302
#> PCTSUPP 0.02542185 0.4568997

References

Dudgeon, P. (2017). Some improvements in confidence intervals for standardized regression coefficients. Psychometrika, 82(4), 928–951. https://doi.org/10.1007/s11336-017-9563-z
Pesigan, I. J. A., & Cheung, S. F. (2023). Monte Carlo confidence intervals for the indirect effect with missing data. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02114-4