Description Usage Arguments Details Value See Also Examples
Calculate the p-value for continous variables.
The decision which test to use is equal to m.cont
.
The p-value is calculated using one of the four tests:
Wilcoxon-Test, t-Test, Kruskal-Test, Anova.
1 2 3 4 5 6 7 8 9 10 11 | p.cont(
x,
group,
paired = F,
is.ordered = F,
nonparametric = F,
t.log = F,
var.equal = F,
index = c(),
create = "tex"
)
|
x |
Vector of the continous variable. |
group |
Vector of the grouping variable. |
paired |
Logical. Is the categorial Variable paired? |
is.ordered |
Logical. Is the categorial Variable ordered? |
nonparametric |
Logical. Should the continuous variable tested by using non-parametric methods. |
t.log |
Logical. Should be used the log of the original data. |
var.equal |
Logical. Should variances be assumed to be equal when applying t-tests? |
index |
Optional. Label for the footnote. The footnotes aren't produced in this function. |
create |
Which output document should be produced in the following step (one of "pdf", "tex", "knitr", or "word"). |
Wilcoxon Test: A nonparametric Test for a comparison of 2 dependent samples.
(see wilcox.test
).
Mann-Whitney-U Test: A nonparametric Test for a comparison of 2 independent samples. (
see wilcox.test
).
t-Test: A parametric Test for a comparison of 2 (in)dependent samples.
(see t.test
).
Friedman-Test: A nonparametric Test for a comparison of more than 2 dependent samples.
(see friedman.test
).
Anova Type III: A parametric Test for a comparison of more than 2 dependent samples.
(see Anova
with ).
Kruskal-Wallis-Test: A nonparametric Test for a comparison of more than 2 independent samples.
(see kruskal.test
).
Anova: A parametric Test for a comparison of more than 2 independent samples.
(see aov
).
The p-value with index which test is ussed is returned. author Lorenz Uhlmann, Csilla van Lunteren
lme
Anova
1 2 3 4 |
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