pd_row_t_test {proDA} | R Documentation

## Row-wise tests of difference using the probabilistic dropout model

### Description

This is a helper function that combines the call of `proDA()` and `test_diff()`. If you need more flexibility use those functions.

### Usage

    pd_row_t_test(
      X,
      Y,
      moderate_location = TRUE,
      moderate_variance = TRUE,
      alternative = c("two.sided", "greater", "less"),
      pval_adjust_method = "BH",
      location_prior_df = 3,
      max_iter = 20,
      epsilon = 0.001,
      return_fit = FALSE,
      verbose = FALSE
    )

    pd_row_f_test(
      X,
      ...,
      groups = NULL,
      moderate_location = TRUE,
      moderate_variance = TRUE,
      pval_adjust_method = "BH",
      location_prior_df = 3,
      max_iter = 20,
      epsilon = 0.001,
      return_fit = FALSE,
      verbose = FALSE
    )

### Arguments

`X, Y, ...` | the matrices for condition 1, 2 and so on. They must have the same number of rows.
`moderate_location` | boolean values to indicate if the location and the variances are moderated. Default: `TRUE`
`moderate_variance` | boolean values to indicate if the location and the variances are moderated. Default: `TRUE`
`alternative` | a string that decides how the hypothesis test is done. This parameter is only relevant for the Wald-test specified using the 'contrast' argument. Default: `"two.sided"`
`pval_adjust_method` | a string the indicates the method that is used to adjust the p-value for the multiple testing. It must match the options in `p.adjust`. Default: `"BH"`
`location_prior_df` | the number of degrees of freedom used for the location prior. A large number (> 30) means that the prior is approximately Normal. Default: `3`
`max_iter` | the maximum of iterations `proDA()` tries to converge to the hyper-parameter estimates. Default: `20`
`epsilon` | if the remaining error is smaller than `epsilon` the model has converged. Default: `1e-3`
`return_fit` | boolean that signals that in addition to the data.frame with the hypothesis test results, the fit from `proDA()` is returned. Default: `FALSE`
`verbose` | boolean that signals if the method prints messages during the fitting. Default: `FALSE`
`groups` | a factor or character vector with that assignes the columns of `X` to different conditions. This parameter is only applicable for the F-test and must be specified if only a single matrix is provided.

### Details

The `pd_row_t_test` is not actually doing a t-test, but rather a Wald test. But, as the two are closely related and term t-test is more widely understood, we choose to use that name.

### Value

If `return_fit == FALSE` a data.frame is returned with the content that is described in `test_diff`.

If `return_fit == TRUE` a list is returned with two elements: `fit` with a reference to the object returned from `proDA()` and a `test_result()` with the data.frame returned from `test_diff()`.

### See Also

`proDA` and `test_diff` for more flexible versions. The function was inspired by the `rowFtests` function in the genefilter package.

### Examples

      data1 <- matrix(rnorm(10 * 3), nrow=10)
      data2 <- matrix(rnorm(10 * 4), nrow=10)
      data3 <- matrix(rnorm(10 * 2), nrow=10)

      # Comparing two datasets
      pd_row_t_test(data1, data2)

      # Comparing multiple datasets
      pd_row_f_test(data1, data2, data3)

      # Alternative
      data_comb <- cbind(data1, data2, data3)
      pd_row_f_test(data_comb,
         groups = c(rep("A",3), rep("B", 4), rep("C", 2)))

      # t.test, lm, pd_row_t_test, and pd_row_f_test are
      # approximately equivalent on fully observed data
      set.seed(1)
      x <- rnorm(5)
      y <- rnorm(5, mean=0.3)

      t.test(x, y)
      summary(lm(c(x, y) ~ cond,
                 data = data.frame(cond = c(rep("x", 5),
                                            rep("y", 5)))))$coefficients[2,]
      pd_row_t_test(matrix(x, nrow=1), matrix(y, nrow=1),
                    moderate_location = FALSE,
                    moderate_variance = FALSE)
      pd_row_f_test(matrix(x, nrow=1), matrix(y, nrow=1),
                    moderate_location = FALSE,
                    moderate_variance = FALSE)

[Package proDA version 1.4.0 Index]