dstat: Descriptive statistics of quantitative analysis results

Description

Performs comprehensive statistical evaluation of quantitative analysis results.

Usage

dstat(x, expected = median(unlist(x)), sort = TRUE, inverse.f = TRUE, na.rm = FALSE, conf.level = 0.95, alternative = c("two.sided", "less", "greater"), ansari = FALSE)

Arguments

a vector of results, of a dataframe with results to compare

expected

expected reference value

sort

if TRUE, the matrices are sorted by means, variances or p-values.

inverse.f

if F value in variance comparison is below 1, the inverse is taken (has no effect on p-value, but there are sometimes need to have such F

na.rm

logical: should NA values be removed?

conf.level

level for calculate confidence intervals

alternative

alternative for all tests performed.

ansari

due to reports of errors on some datasets, the ansari.test() on data is turned off by default since 0.12. you can turn it on by setting this variable to TRUE

Value

mean: mean, its confidence interval and t-test for expected value
median: median, its confidence interval and Wilcoxon test for expected value
var: variance, standard deviation, standard error and Dixon test for outlier
rsd: relative standard deviation, its confidence interval and Grubbs test for outlier
range: minimum and maximum value, range, IQR, MAD and Shapiro-Wilk test for normality
vartest: ratios of variances, their confidence intervals and F test with p-value
ttest: differences between means, their confidence intervals and t test with p-value
atest: nonparametric differences in scale, their confidence intervals and Ansari-Bradley test with p-value
atest: nonparametric differences in location, their confidence intervals and Wilcoxon test with p-value
anova: ANOVA between all data
kruskal: Kruskal-Wallis test (nonparametric equivalent for ANOVA)
bartlett: Bartlett test for homogeneity of all variances
fligner: Fligner-Killeen test for equal variances (nonparametric alternative to Bartlett)

Details

If argument is vector, several one-row matrices are produced (see below). If argument is a data.frame, there are also additional matrices with pairwise comparisons. The comparison of all groups by appropriate test are also calculated. This function prints its results with significance stars and returns a list invisibly.

Examples

Run this code

set.seed(1234)
a = data.frame(x=rnorm(10),y=runif(10),z=rt(10,1))
dstat(a,0)