counts_to_cases

a <code>data.frame</code> of counts generated from a contingency table.

character string, name of the column of x containing the counts. Default name of the column is "Freq".

countcol

Convert data frame of counts to data frame of cases.
data frame must contain a column with frequencies (counts) as generated by as.data.frame from a contingency table

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.

Sabine Schilling

visStatistics

Automated Visualization of Statistical Tests

Peter Kauf

counts_to_cases function

<dl><dt>x</dt>
<dd>a <code>data.frame</code> of counts generated from a contingency table.</dd>
<dt>countcol</dt>
<dd>character string, name of the column of x containing the counts. Default name of the column is "Freq".</dd></dl>

Arguments

Convert data frame of counts to data frame of cases.
data frame must contain a column with frequencies (counts) as generated by as.data.frame from a contingency table — counts_to_cases

<dl>

<dt>x</dt>
<dd>a <code>data.frame</code> of counts generated from a contingency table.</dd>


<dt>countcol</dt>
<dd>character string, name of the column of x containing the counts. Default name of the column is "Freq".</dd>

</dl>

Convert data frame of counts to data frame of cases.
data frame must contain a column with frequencies (counts) as generated by as.data.frame from a contingency table

counts_to_cases: Convert data frame of counts to data frame of cases. data frame must contain a column with frequencies (counts) as generated by as.data.frame from a contingency table

Description

Usage

Value

Arguments

Examples