dstat {quantchem} | R Documentation |
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)
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 |
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 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) |
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.
Lukasz Komsta
set.seed(1234) a = data.frame(x=rnorm(10),y=runif(10),z=rt(10,1)) dstat(a,0)