lrt {edge} | R Documentation

## Performs F-test (likelihood ratio test using Normal likelihood)

### Description

`lrt` performs a generalized likelihood ratio test using the full and null models.

### Usage

    lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL,
      bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...)

    ## S4 method for signature 'deSet,missing'
    lrt(object, de.fit, nullDistn = c("normal",
      "bootstrap"), weights = NULL, bs.its = 100, seed = NULL,
      verbose = TRUE, mod.F = FALSE, ...)

    ## S4 method for signature 'deSet,deFit'
    lrt(object, de.fit, nullDistn = c("normal",
      "bootstrap"), weights = NULL, bs.its = 100, seed = NULL,
      verbose = TRUE, mod.F = FALSE, ...)

### Arguments

`object` | `S4 object`: `deSet`.
`de.fit` | `S4 object`: `deFit`. Optional.
`nullDistn` | `character`: either "normal" or "bootstrap", If "normal" then the p-values are calculated using the F distribution. If "bootstrap" then a bootstrap algorithm is implemented to simulate statistics from the null distribution. In the "bootstrap" case, empirical p-values are calculated using the observed and null statistics (see `empPvals`). Default is "normal".
`weights` | `matrix`: weights for each observation. Default is NULL.
`bs.its` | `integer`: number of null statistics generated (only applicable for "bootstrap" method). Default is 100.
`seed` | `integer`: set the seed value. Default is NULL.
`verbose` | `boolean`: print iterations for bootstrap method. Default is TRUE.
`mod.F` | `boolean`: Moderated F-test, recommended for experiments with a small sample size. Default is FALSE.
`...` | Additional arguments for `apply_qvalue` and `empPvals` function.

### Details

`lrt` fits the full and null models to each gene using the function `fit_models` and then performs a likelihood ratio test. The user has the option to calculate p-values a Normal distribution assumption or through a bootstrap algorithm. If `nullDistn` is "bootstrap" then empirical p-values will be determined from the `qvalue` package (see `empPvals`).

### Value

`deSet` object

### Author(s)

John Storey, Andrew Bass

### References

Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences, 102: 12837-12842.

http://en.wikipedia.org/wiki/Likelihood-ratio_test

### See Also

`deSet`, `build_models`, `odp`

### Examples

    # import data
    library(splines)
    data(kidney)
    age <- kidney$age
    sex <- kidney$sex
    kidexpr <- kidney$kidexpr
    cov <- data.frame(sex = sex, age = age)

    # create models
    null_model <- ~sex
    full_model <- ~sex + ns(age, df = 4)

    # create deSet object from data
    de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model,
    full.model = full_model)

    # lrt method
    de_lrt <- lrt(de_obj, nullDistn = "normal")

    # to generate p-values from bootstrap
    de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 30)

    # input a deFit object directly
    de_fit <- fit_models(de_obj, stat.type = "lrt")
    de_lrt <- lrt(de_obj, de.fit = de_fit)

    # summarize object
    summary(de_lrt)

[Package edge version 2.19.0 Index]