Log-Linear Regression
Log-Linear Regression
Example usage
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
# ──────────────────────────────────────────────────────────────────
#
#
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 |
Returns
A results object containing:
| results$modelFit | a table |
| results$modelComp | a table |
| results$models | an array of groups |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$modelFit$asDF
as.data.frame(results$modelFit)
Elements in arrays can be accessed with [[n]]. For example:
results$models[[1]] # accesses the first element