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

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

## Arguments

table Table or (matrix/data.frame with two columns) Switch reference If TRUE exact binomial proportions CI/test will be used Optional null hypothesis (test prevalenc, sensitivity, ...) 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.

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)
#>                         Estimate Std.Err   2.5%  97.5%  P-value
#> Prevalence                0.3419         0.2567 0.4353
#> Test                      0.5385         0.4439 0.6310
#> Sensitivity               0.7000         0.5347 0.8344
#> Specificity               0.5455         0.4279 0.6594
#> PositivePredictiveValue   0.4444         0.3192 0.5751
#> NegativePredictiveValue   0.7778         0.6440 0.8796
#> Accuracy                  0.5983         0.5036 0.6878
#> Homogeneity               0.7447         0.5965 0.8606 0.001089
#> attr(,"names")
#>  [1] "Prevalence"              "Test"
#>  [3] "Sensitivity"             "Specificity"
#>  [5] "PositivePredictiveValue" "NegativePredictiveValue"
#>  [7] "Accuracy"                "Homogeneity"
#>  [9]
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