get_samples_fact_inputfile

<code>data.frame</code> or <code>list</code> containing at least two columns with column headings of data type <code>character</code>. Data must be column wise ordered.

dataframe

column name of dependent variable in dataframe, datatype <code>character</code>

varsample

column name of independent variable in dataframe, datatype <code>character</code>

varfactor

Selects columns defined by characters <code>varsample</code> and <code>varfactor</code> from <code>dataframe</code>, returns selected columns with their names.

Visualization of the most powerful statistical hypothesis test.
The function vistat() visualizes the statistical hypothesis testing between the
dependent variable (response) varsample and the independent variable (feature) varfactor.
The statistical hypothesis test (including the eventual corresponding post-hoc analysis) with
the highest statistical power fulfilling the assumptions of the corresponding test is chosen based on a decision tree.
A graph displaying the raw data accordingly to the chosen test is generated, the test statistics including eventual
post-hoc-analysis are returned. The automated workflow is especially suited for browser based interfaces to
server-based deployments of R. Implemented tests: lm(), t.test(), wilcox.test(), aov(), kruskal.test(), fisher.test(), chisqu.test().
Implemented tests to check the normal distribution of standardized residuals: shapiro.test() and ad.test().
Implemented post-hoc tests: TukeyHSD() for aov() and pairwise.wilcox.test() for kruskal.test().
For the comparison of averages, the following algorithm is implemented:
If the p-values of the standardized residuals of both shapiro.test() or ad.test() are smaller
than 1-conf.level, kruskal.test() resp. wilcox.test() are performed, otherwise the oneway.test()
and aov() resp. t.test() are performed and displayed. Exception:
If the sample size is bigger than 100, t.test() is always performed and wilcox.test() is never executed
(Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>).
For the test of independence of count data, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is implemented:
If more than 20 percent of all cells have a count smaller than 5, fisher.test() is performed and displayed,
otherwise chisqu.test(). In both cases case an additional mosaic plot is generated.

get_samples_fact_inputfile: Selects columns defined by characters varsample and varfactor from a data.frame

Description

Usage

Arguments

Value

Examples