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Pipe bias functions

Source: R/pipe.R
pipe.Rd

episensr also uses the pipe function, %>% to turn function composition into a series of imperative statements.

Arguments

lhs, rhs

Data or bias function and a function to apply to it

Examples

# Instead of
misclass(matrix(c(118, 832, 103, 884),
dimnames = list(c("BC+", "BC-"), c("AD+", "AD-")), nrow = 2, byrow = TRUE),
type = "exposure", bias_parms = c(.56, .58, .99, .97))
#> 
#> ── Observed data ───────────────────────────────────────────────────────────────
#> • Outcome: BC+
#> • Comparing: AD+ vs. AD-
#> 
#>     AD+ AD-
#> BC+ 118 832
#> BC- 103 884
#>                                        2.5%     97.5%
#> Observed Relative Risk: 1.1012443 0.9646019 1.2572431
#>    Observed Odds Ratio: 1.2172330 0.9192874 1.6117443
#> ── Bias-adjusted measures ──
#> 
#>                                                              2.5%    97.5%
#> Misclassification Bias Corrected Relative Risk: 1.272939                  
#>    Misclassification Bias Corrected Odds Ratio: 1.676452 1.150577 2.442679
# you can write
dat <- matrix(c(118, 832, 103, 884),
dimnames = list(c("BC+", "BC-"), c("AD+", "AD-")), nrow = 2, byrow = TRUE)
dat %>% misclass(., type = "exposure", bias_parms = c(.56, .58, .99, .97))
#> ── Observed data ───────────────────────────────────────────────────────────────
#> • Outcome: BC+
#> • Comparing: AD+ vs. AD-
#> 
#>     AD+ AD-
#> BC+ 118 832
#> BC- 103 884
#>                                        2.5%     97.5%
#> Observed Relative Risk: 1.1012443 0.9646019 1.2572431
#>    Observed Odds Ratio: 1.2172330 0.9192874 1.6117443
#> ── Bias-adjusted measures ──
#> 
#>                                                              2.5%    97.5%
#> Misclassification Bias Corrected Relative Risk: 1.272939                  
#>    Misclassification Bias Corrected Odds Ratio: 1.676452 1.150577 2.442679