Calculate diagnostic tests for 2x2 table

Calculate prevalence, sensitivity, specificity, and positive and negative predictive values

Usage

diagtest(
  table,
  positive = 2,
  exact = FALSE,
  p0 = NA,
  confint = c("logit", "arcsin", "pseudoscore", "exact"),
  ...
)

Arguments

table: Table or (matrix/data.frame with two columns)
positive: Switch reference
exact: If TRUE exact binomial proportions CI/test will be used
p0: Optional null hypothesis (test prevalenc, sensitivity, ...)
confint: Type of confidence limits
...: Additional arguments to lower level functions

Details

Table should be in the format with outcome in columns and test in rows. Data.frame should be with test in the first column and outcome in the second column.

Author

Klaus Holst

Examples

M <- as.table(matrix(c(42,12,
                       35,28),ncol=2,byrow=TRUE,
                     dimnames=list(rater=c("no","yes"),gold=c("no","yes"))))
diagtest(M,exact=TRUE)
#> Call: diagtest(table = M, exact = TRUE)
#> Confidence limits: exact
#> ────────────────────────────────────────────────────────────────────────────────
#>      gold
#> rater no yes        no   yes
#>   no  42  12     0.359 0.103
#>   yes 35  28     0.299 0.239
#> 
#> Positive outcome: 'yes'
#> ────────────────────────────────────────────────────────────────────────────────
#>                         Estimate    2.5%   97.5% P-value
#> Prevalence               0.34188 0.25670 0.43528        
#> Test                     0.53846 0.44389 0.63104        
#> Sensitivity              0.70000 0.53468 0.83437        
#> Specificity              0.54545 0.42790 0.65940        
#> PositivePredictiveValue  0.44444 0.31917 0.57511        
#> NegativePredictiveValue  0.77778 0.64400 0.87956        
#> Accuracy                 0.59829 0.50363 0.68785        
#> Homogeneity              0.74468 0.59650 0.86055  0.0011
#> ────────────────────────────────────────────────────────────────────────────────
#> 
#> Prevalence:				Prob( outcome+ )
#> Test:					Prob( test+ )
#> Sensitivity (True positive rate):	Prob( test+ | outcome+ )
#> Specificity (True negative rate):	Prob( test- | outcome- )
#> Positive predictive value (Precision):	Prob( outcome+ | test+ )
#> Negative predictive value:		Prob( outcome- | test- )
#> Accuracy:				Prob( correct classification )
#> Homogeneity/Symmetry:			Prob( outcome+, test- | discordant ), H0: p=0.5 
#>