analyzeConditionalAssociations {midasHLA} | R Documentation

## Stepwise conditional association analysis

### Description

`analyzeConditionalAssociations` perform stepwise conditional testing adding the previous top-associated variable as covariate, until there are no more significant variables based on a self-defined threshold.

### Usage

    analyzeConditionalAssociations(
      object,
      variables,
      placeholder = "term",
      correction = "bonferroni",
      n_correction = NULL,
      th,
      th_adj = TRUE,
      keep = FALSE,
      rss_th = 1e-07,
      exponentiate = FALSE
    )

### Arguments

`object` | An existing fit from a model function such as lm, glm and many others.
`variables` | Character vector specifying variables to use in association tests.
`placeholder` | String specifying term to substitute with value from `x`. Ignored if set to `NULL`.
`correction` | String specifying multiple testing correction method. See details for further information.
`n_correction` | Integer specifying number of comparisons to consider during multiple testing correction calculations. For Bonferroni correction it is possible to specify a number lower than the number of comparisons being made. This is useful in cases when knowledge about the biology or redundance of alleles reduces the need for correction. For other methods it must be at least equal to the number of comparisons being made; only set this (to non-default) when you know what you are doing!
`th` | Number specifying threshold for a variable to be considered significant.
`th_adj` | Logical flag indicating if adjusted p-value should be used as threshold criteria, otherwise unadjusted p-value is used.
`keep` | Logical flag indicating if the output should be a list of results resulting from each selection step. Default is to return only the final result.
`rss_th` | Number specifying residual sum of squares threshold at which function should stop adding additional variables. As the residual sum of squares approaches `0` the perfect fit is obtained making further attempts at variable selection nonsense. This behavior can be controlled using `rss_th`.
`exponentiate` | Logical flag indicating whether or not to exponentiate the coefficient estimates. Internally this is passed to `tidy`. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

### Value

Tibble with stepwise conditional testing results or a list of tibbles, see `keep` argument. The first column `"term"` hold the names of `variables`. Further columns depends on the used model and are determined by associated `tidy` function. Generally they will include `"estimate"`, `"std.error"`, `"statistic"`, `"p.value"`, `"conf.low"`, `"conf.high"`, `"p.adjusted"`.

### Examples

    midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                          colData = MiDAS_tut_pheno,
                          experiment = "hla_alleles")

    # analyzeConditionalAssociations expects model data to be a data.frame
    midas_data <- as.data.frame(midas)

    # define base model
    object <- lm(disease ~ term, data = midas_data)
    analyzeConditionalAssociations(object,
                                variables = c("B*14:02", "DRB1*11:01"),
                                th = 0.05)

[Package midasHLA version 1.2.0 Index]