| Type: | Package |
| Title: | A Collection of Nonparametric Hypothesis Tests |
| Version: | 1.0.2 |
| Author: | D. Lukke Sweet |
| Maintainer: | D. Lukke Sweet <dlukkesweet@gmail.com> |
| Depends: | R (≥ 3.3.1) |
| Imports: | methods |
| Description: | Contains the following 5 nonparametric hypothesis tests: The Sign Test, The 2 Sample Median Test, Miller's Jackknife Procedure, Cochran's Q Test, & The Stuart-Maxwell Test. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2020-04-28 20:47:02 UTC; Lukke |
| Repository: | CRAN |
| Date/Publication: | 2020-04-29 08:20:02 UTC |
Cochran's Q Test
Description
This function will perform the Cochran's Q Test to test for identical treatment effects in a two-way randomized block design with k treatments.
Usage
cochrans.q(x, alpha=NULL)
Arguments
x |
A b x k matrix, where b is the number of blocking factors and k is the number of treatment factors. |
alpha |
The Significance level, defaults to 0.05. |
Value
Q |
This is the Cochran's Q Test Statistic. |
Degrees of Freedom |
The number of degrees of freedom used in calculating the p-value. |
Significance Level |
Returns the alpha value. |
P-value |
Returns the p-value from the Cochran's Q Test. |
Author(s)
D. Lukke Sweet
References
https://www.r-bloggers.com/cochran-q-test-for-k-related-samples-in-r/
http://rcompanion.org/handbook/H_07.html
Examples
## Run Cochran's Q Test on a matrix.
cochrans.q(matrix(c(1,1,1,1,1,1,
1,1,0,1,1,1,
0,0,0,1,0,0,
0,1,0,0,1,1), 6, 4))
## Cochran's Q Test works for any size matrix.
cochrans.q(matrix(c(0,1,0,0,1,0,0,0,1,0,0,0,0,0,
0,1,1,1,1,1,1,1,0,1,1,1,1,1,
0,1,0,0,0,0,0,0,1,0,0,0,0,0,
0,1,1,0,0,1,1,0,0,0,0,1,0,1), 14, 4), alpha=0.01)
2 Sample Median Test
Description
The 2 sample median test is for testing the medians of 2 samples to see if they are equal.
Usage
mediantest(x, y, alpha=NULL, exact=FALSE)
Arguments
x |
A vector containing data from the first sample. |
y |
A vector containing data from the second sample. |
alpha |
The Significance level, defaults to 0.05. |
exact |
Defaults to FALSE. Runs the exact test or a large sample approximation. |
Value
Z |
The test statistic for the large sample approximation. |
P-value |
Returns the p-value from the Median Test. |
Author(s)
D. Lukke Sweet
References
Higgins, J. J. (2005). An Introduction to modern nonparametric statistics. Belmont: Thomson Brooks/Cole.
Wiley Series in Probability and Statistics: Nonparametric Statistical Methods (3rd Edition). (2013). John Wiley & Sons.
Examples
## Run the Median Test on the 2 vectors.
mediantest(x = c(5.5, 5.8, 6.8, 6.9, 7.2, 7.3, 7.5, 7.6, 8.0),
y = c(5.3, 5.4, 5.6, 5.7, 6.2, 6.4, 6.6, 6.7, 8.2), exact=TRUE)
The Miller Jackknife Procedure
Description
This function will perform Miller's Jackknife Procedure to test differences in scale between 2 samples. It is best for large samples.
Usage
miller.jack(x, y, alpha = NULL,
alternative =c("two.sided", "greater", "less"), exact = FALSE)
Arguments
x |
A vector containing data from the first sample. |
y |
A vector containing data from the second sample. |
alpha |
The Significance level, defaults to 0.05. |
alternative |
Defaults to two.sided. Used to determine what type of test to run. |
exact |
Defaults to FALSE. Used to determine whether to run the exact procedure or a large sample approximation. |
Value
J |
The test statistic. |
Significance Level |
Returns the alpha value. |
P-value |
Returns the p-value from Miller's Jackknife Procedure. |
Author(s)
D. Lukke Sweet
References
Wiley Series in Probability and Statistics: Nonparametric Statistical Methods (3rd Edition). (2013). John Wiley & Sons.
Examples
## Run Miller's Jackknife Procedure on the 2 vectors.
miller.jack(x= c(6.2, 5.9, 8.9, 6.5, 8.6),
y = c(9.5, 9.8, 9.5, 9.6, 10.3), alpha=0.05, alternative="less")
The Sign Test
Description
A nonpametric test for center. The sign test compares the median to a value.
Usage
signtest(x, m = NULL, alpha = NULL,
alternative =c("two.sided", "greater", "less"), conf.level=NULL, exact = FALSE)
Arguments
x |
A vector of sample data. |
m |
The median to test. Defaults to 0. |
alpha |
The Significance level, defaults to 0.05. |
alternative |
Defaults to two.sided. Used to determine what type of test to run. |
conf.level |
Defaults to NULL. Used to construct a confidence interval. Input as a decimal. |
exact |
Defaults to FALSE. Used to determine whether to run the exact procedure or a large sample approximation. |
Value
B |
The Test Statistic |
Significance Level |
Returns the alpha value. |
P-value |
Returns the p-value from the Sign Test. |
Confidence Interval |
The confidence interval requested. |
Author(s)
D. Lukke Sweet
References
Higgins, J. J. (2005). An Introduction to modern nonparametric statistics. Belmont: Thomson Brooks/Cole.
Wiley Series in Probability and Statistics: Nonparametric Statistical Methods (3rd Edition). (2013). John Wiley & Sons.
Examples
## Run the Sign Test on the vector.
signtest(c(1.8, 3.3, 5.65, 2.25, 2.5, 3.5, 2.75, 3.25, 3.10, 2.70, 3, 4.75, 3.4), m=3.5)
The Stuart-Maxwell Test
Description
This function runs the Stuart-Maxwell Test, an extension of McNemar's for a 3x3 matrix.
Usage
stuart.maxwell(X, alpha = NULL)
Arguments
X |
A 3x3 matrix of frequencies. |
alpha |
The Significance level, defaults to 0.05. |
Value
Test Statistic |
The Test Statistic for the Stuart-Maxwell Test. |
Significance Level |
Returns the alpha value. |
P-value |
Returns the p-value from the Stuart-Maxwell Test. |
Author(s)
D. Lukke Sweet
Examples
## Run the Stuart-Maxwell Test on the 3x3 Matrix.
stuart.maxwell(matrix(c(12, 30, 13, 7, 70, 34, 3, 20, 32), 3,3))