Performs a t-test on each row/column of the input matrix.
row_t_equalvar(x, y, null = 0, alternative = "two.sided", conf.level = 0.95)col_t_equalvar(x, y, null = 0, alternative = "two.sided", conf.level = 0.95)
row_t_welch(x, y, null = 0, alternative = "two.sided", conf.level = 0.95)
col_t_welch(x, y, null = 0, alternative = "two.sided", conf.level = 0.95)
row_t_onesample(x, null = 0, alternative = "two.sided", conf.level = 0.95)
col_t_onesample(x, null = 0, alternative = "two.sided", conf.level = 0.95)
row_t_paired(x, y, null = 0, alternative = "two.sided", conf.level = 0.95)
col_t_paired(x, y, null = 0, alternative = "two.sided", conf.level = 0.95)
a data.frame where each row contains the results of a t.test
performed on the corresponding row/column of x.
The columns will vary depending on the type of test performed.
They will contain a subset of the following information:
1. obs.x - number of x observations
2. obs.y - number of y observations
3. obs.tot - total number of observations
4. obs.paired - number of paired observations (present in x and y)
5. mean.x - mean estiamte of x
6. mean.y - mean estiamte of y
7. mean.diff - mean estiamte of x-y difference
8. var.x - variance estiamte of x
9. var.y - variance estiamte of y
10. var.diff - variance estiamte of x-y difference
11. var.pooled - pooled variance estimate of x and y
12. stderr - standard error
13. df - degrees of freedom
14. statistic - t statistic
15. pvalue - p-value
16. conf.low - lower bound of the confidence interval
17. conf.high - higher bound of the confidence interval
18. mean.null - mean of the null hypothesis
19. alternative - chosen alternative hypothesis
20. conf.level - chosen confidence level
numeric matrix.
numeric matrix for the second group of observations.
true values of the means for the null hypothesis. A single number or numeric vector with values for each observation.
alternative hypothesis to use for each row/column of x. A single string or a vector with values for each observation. Values must be one of "two.sided" (default), "greater" or "less".
confidence levels used for the confidence intervals. A single number or a numeric vector with values for each observation. All values must be in the range of [0:1] or NA.
Karolis Koncevičius
Functions to perform one sample and two sample t-tests for rows/columns of matrices.
Main arguments and results were intentionally matched to the t.test()
function from default stats package. Other arguments were split into separate
functions:
row_t_onesample(x) - one sample t-test on rows.
col_t_onesample(x) - one sample t-test on columns.
Results should be the same as running t.test(x)
on every row (or column) of x.
row_t_equalvar(x, y) - two sample equal variance t-test on rows.
col_t_equalvar(x, y) - two sample equal variance t-test on columns.
Results should be the same as running t.test(x, y, var.equal=TRUE)
on every row (or column) of x and y.
row_t_welch(x, y) - two sample t-test with Welch correction on rows.
col_t_welch(x, y) - two sample t-test with Welch correction on columns.
Results should be the same as running t.test(x, y)
on every row (or column) of x and y.
row_t_paired(x, y) - two sample paired t-test on rows.
col_t_paired(x, y) - two sample paired t-test on columns.
Results should be the same as running t.test(x, y, paired=TRUE)
on every row (or column) of x and y.
X <- iris[iris$Species=="setosa",1:4]
Y <- iris[iris$Species=="virginica",1:4]
col_t_welch(X, Y)
# same row using different confidence levels
col_t_equalvar(X[,c(1,1,1)], Y[,c(1,1,1)], conf.level=c(0.9, 0.95, 0.99))
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