dstat: Descriptive statistics of quantitative analysis results
Performs comprehensive statistical evaluation of quantitative analysis results.
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)
a vector of results, of a dataframe with results to compare
expected reference value
if TRUE, the matrices are sorted by means, variances or p-values.
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
logical: should NA values be removed?
level for calculate confidence intervals
alternative for all tests performed.
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
A list containing following matrices (if data is a vector, only 5 first are returned):
- mean, its confidence interval and t-test for expected value
- median, its confidence interval and Wilcoxon test for expected value
- variance, standard deviation, standard error and Dixon test for outlier
- relative standard deviation, its confidence interval and Grubbs test for outlier
- minimum and maximum value, range, IQR, MAD and Shapiro-Wilk test for normality
- ratios of variances, their confidence intervals and F test with p-value
- differences between means, their confidence intervals and t test with p-value
- nonparametric differences in scale, their confidence intervals
and Ansari-Bradley test with p-value
- nonparametric differences in location, their confidence intervals
and Wilcoxon test with p-value
- ANOVA between all data
- Kruskal-Wallis test (nonparametric equivalent for ANOVA)
- Bartlett test for homogeneity of all variances
- Fligner-Killeen test for equal variances (nonparametric alternative to Bartlett)
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.
a = data.frame(x=rnorm(10),y=runif(10),z=rt(10,1))