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jmv

logLinear

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 = vars(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
#  ------------------------------------------------------------------
#
#

jmv

The 'jamovi' Analyses

v1.2.23

GPL (>= 2)

Authors

Ravi Selker, Jonathon Love, Damian Dropmann

Initial release

2020-06-26