dstat {quantchem}R Documentation

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

x 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

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.

Value

A list containing following matrices (if data is a vector, only 5 first are returned):

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)

Note

This function calculates always *both* parametric and nonparametric tests, and choosing a test to take into account should be also decision of analyst, based on the other tests results.

Author(s)

Lukasz Komsta

See Also

vstat

Examples

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

[Package quantchem version 0.12-1 Index]