R: Log-Linear Regression

logLinear {jmv} | R Documentation

## Log-Linear Regression

### Description

Log-Linear Regression

### Usage

    logLinear(data, factors = NULL, counts = NULL, blocks = list(list()),
      refLevels = NULL, modelTest = FALSE, dev = TRUE, aic = TRUE,
      bic = FALSE, pseudoR2 = list("r2mf"), omni = FALSE, ci = FALSE,
      ciWidth = 95, RR = FALSE, ciRR = FALSE, ciWidthRR = 95,
      emMeans = list(list()), ciEmm = TRUE, ciWidthEmm = 95,
      emmPlots = TRUE, emmTables = FALSE, emmWeights = TRUE)

### Arguments

`data` | the data as a data frame
`factors` | a vector of strings naming the factors from `data`
`counts` | a string naming a variable in `data` containing counts, or NULL if each row represents a single observation
`blocks` | a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list
`refLevels` | a list of lists specifying reference levels of the dependent variable and all the factors
`modelTest` | `TRUE` or `FALSE` (default), provide the model comparison between the models and the NULL model
`dev` | `TRUE` (default) or `FALSE`, provide the deviance (or -2LogLikelihood) for the models
`aic` | `TRUE` (default) or `FALSE`, provide Aikaike's Information Criterion (AIC) for the models
`bic` | `TRUE` or `FALSE` (default), provide Bayesian Information Criterion (BIC) for the models
`pseudoR2` | one or more of `'r2mf'`, `'r2cs'`, or `'r2n'`; use McFadden's, Cox & Snell, and Nagelkerke pseudo-R², respectively
`omni` | `TRUE` or `FALSE` (default), provide the omnibus likelihood ratio tests for the predictors
`ci` | `TRUE` or `FALSE` (default), provide a confidence interval for the model coefficient estimates
`ciWidth` | a number between 50 and 99.9 (default: 95) specifying the confidence interval width
`RR` | `TRUE` or `FALSE` (default), provide the exponential of the log-rate ratio estimate, or the rate ratio estimate
`ciRR` | `TRUE` or `FALSE` (default), provide a confidence interval for the model coefficient rate ratio estimates
`ciWidthRR` | a number between 50 and 99.9 (default: 95) specifying the confidence interval width
`emMeans` | a list of lists specifying the variables for which the estimated marginal means need to be calculate. Supports up to three variables per term.
`ciEmm` | `TRUE` (default) or `FALSE`, provide a confidence interval for the estimated marginal means
`ciWidthEmm` | a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
`emmPlots` | `TRUE` (default) or `FALSE`, provide estimated marginal means plots
`emmTables` | `TRUE` or `FALSE` (default), provide estimated marginal means tables
`emmWeights` | `TRUE` (default) or `FALSE`, weigh each cell equally or weigh them according to the cell frequency

### Value

A results object containing:

`results$modelFit` |  |  |  |  | a table
`results$modelComp` |  |  |  |  | a table
`results$models` |  |  |  |  | an array of model specific results

Tables can be converted to data frames with `asDF` or `as.data.frame`. For example:

`results$modelFit$asDF`

`as.data.frame(results$modelFit)`

### Examples

    data('mtcars')

    tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
    dat <- as.data.frame(tab)

    logLinear(data = dat, factors = c("gear", "cyl"),  counts = "Freq",
              blocks = list(list("gear", "cyl", c("gear", "cyl"))),
              refLevels = list(
                  list(var="gear", ref="3"),
                  list(var="cyl", ref="4")))

    #
    #  LOG-LINEAR REGRESSION
    #
    #  Model Fit Measures
    #  ---------------------------------------
    #    Model    Deviance    AIC     R²-McF
    #  ---------------------------------------
    #        1    4.12e-10    41.4     1.000
    #  ---------------------------------------
    #
    #
    #  MODEL SPECIFIC RESULTS
    #
    #  MODEL 1
    #
    #  Model Coefficients
    #  ------------------------------------------------------------------
    #    Predictor          Estimate     SE          Z            p
    #  ------------------------------------------------------------------
    #    Intercept          -4.71e-16        1.00    -4.71e-16    1.000
    #    gear:
    #    4 – 3                  2.079        1.06        1.961    0.050
    #    5 – 3                  0.693        1.22        0.566    0.571
    #    cyl:
    #    6 – 4                  0.693        1.22        0.566    0.571
    #    8 – 4                  2.485        1.04        2.387    0.017
    #    gear:cyl:
    #    (4 – 3):(6 – 4)       -1.386        1.37       -1.012    0.311
    #    (5 – 3):(6 – 4)       -1.386        1.73       -0.800    0.423
    #    (4 – 3):(8 – 4)      -26.867    42247.17    -6.36e -4    0.999
    #    (5 – 3):(8 – 4)       -2.485        1.44       -1.722    0.085
    #  ------------------------------------------------------------------
    #
    #

[Package jmv version 0.8.6.2 Index]