![[Experimental]](figures/lifecycle-experimental.svg)
A show method that displays essential information of objects.
Usage
# S4 method for class 'SampleSize'
show(object)
# S4 method for class 'MCTab'
show(object)
# S4 method for class 'BAsummary'
show(object)
# S4 method for class 'RefInt'
show(object)
# S4 method for class 'tpROC'
show(object)
# S4 method for class 'Desc'
show(object)Examples
# Sample zie calculation
size_one_prop(p1 = 0.95, p0 = 0.9, alpha = 0.05, power = 0.8)
#>
#> Sample size determination for one Proportion
#>
#> Call: size_one_prop(p1 = 0.95, p0 = 0.9, alpha = 0.05, power = 0.8)
#>
#> optimal sample size: n = 239
#>
#> p1:0.95 p0:0.9 alpha:0.05 power:0.8 alternative:two.sided
size_ci_corr(r = 0.9, lr = 0.85, alpha = 0.025, alternative = "greater")
#>
#> Sample size determination for a Given Lower Confidence Interval of Pearson's Correlation
#>
#> Call: size_ci_corr(r = 0.9, lr = 0.85, alpha = 0.025, alternative = "greater")
#>
#> optimal sample size: n = 86
#>
#> r:0.9 lr:0.85 alpha:0.025 interval:c(10, 1e+05) tol:1e-05 alternative:greater
# Get 2x2 Contingency Table
qualData %>% diagTab(formula = ~ CandidateN + ComparativeN)
#> Contingency Table:
#>
#> levels: 0 1
#> ComparativeN
#> CandidateN 0 1
#> 0 54 16
#> 1 8 122
# Bland-Altman analysis
data("platelet")
blandAltman(x = platelet$Comparative, y = platelet$Candidate)
#> Call: blandAltman(x = platelet$Comparative, y = platelet$Candidate)
#>
#> Absolute difference type: Y-X
#> Relative difference type: (Y-X)/(0.5*(X+Y))
#>
#> Absolute.difference Relative.difference
#> N 120 120
#> Mean (SD) 7.330 (15.990) 0.064 ( 0.145)
#> Median 6.350 0.055
#> Q1, Q3 ( 0.150, 15.750) ( 0.001, 0.118)
#> Min, Max (-47.800, 42.100) (-0.412, 0.667)
#> Limit of Agreement (-24.011, 38.671) (-0.220, 0.347)
#> Confidence Interval of Mean ( 4.469, 10.191) ( 0.038, 0.089)
# Reference Interval
data("calcium")
refInterval(x = calcium$Value, RI_method = "nonparametric", CI_method = "nonparametric")
#>
#> Reference Interval Method: nonparametric, Confidence Interval Method: nonparametric
#>
#> Call: refInterval(x = calcium$Value, RI_method = "nonparametric", CI_method = "nonparametric")
#>
#> N = 240
#> Outliers: NULL
#> Reference Interval: 9.10, 10.30
#> RefLower Confidence Interval: 8.9000, 9.2000
#> Refupper Confidence Interval: 10.3000, 10.4000
# Comparing the Paired ROC when Non-inferiority margin <= -0.1
data("ldlroc")
aucTest(
x = ldlroc$LDL, y = ldlroc$OxLDL, response = ldlroc$Diagnosis,
method = "non-inferiority", h0 = -0.1
)
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
#>
#> The hypothesis for testing non-inferiority based on Paired ROC curve
#>
#> Test assay:
#> Area under the curve: 0.7995
#> Standard Error(SE): 0.0620
#> 95% Confidence Interval(CI): 0.6781-0.9210 (DeLong)
#>
#> Reference/standard assay:
#> Area under the curve: 0.5617
#> Standard Error(SE): 0.0836
#> 95% Confidence Interval(CI): 0.3979-0.7255 (DeLong)
#>
#> Comparison of Paired AUC:
#> Alternative hypothesis: the difference in AUC is non-inferiority to -0.1
#> Difference of AUC: 0.2378
#> Standard Error(SE): 0.0790
#> 95% Confidence Interval(CI): 0.0829-0.3927 (standardized differenec method)
#> Z: 4.2739
#> Pvalue: 9.606e-06
data(adsl_sub)
# Count multiple variables by treatment
adsl_sub %>%
descfreq(
var = c("AGEGR1", "SEX", "RACE"),
bygroup = "TRTP",
format = "xx (xx.x%)",
addtot = TRUE,
na_str = "0"
)
#> Error in as.list.default(X): no method for coercing this S4 class to a vector
# Summarize multiple variables by treatment
adsl_sub %>%
descvar(
var = c("AGE", "BMIBL", "HEIGHTBL"),
bygroup = "TRTP",
stats = c("N", "MEANSD", "MEDIAN", "RANGE", "IQR"),
autodecimal = TRUE,
addtot = TRUE
)
#> Error in as.list.default(X): no method for coercing this S4 class to a vector