Computes (log) odds ratios and their asymptotic variance covariance matrix for R x C (x strata) tables. Odds ratios are calculated for two array dimensions, separately for each level of all stratifying dimensions. See Friendly et al. (2011) for a sketch of a general theory.
loddsratio(x, …)
# S3 method for default
loddsratio(x, strata = NULL, log = TRUE,
ref = NULL, correct = any(x == 0L), …)# S3 method for formula
loddsratio(formula, data = NULL, …,
subset = NULL, na.action = NULL)
oddsratio(x, stratum = NULL, log = TRUE)
# S3 method for loddsratio
coef(object, log = object$log, …)
# S3 method for loddsratio
vcov(object, log = object$log, …)
# S3 method for loddsratio
print(x, log = x$log, …)
# S3 method for loddsratio
confint(object, parm, level = 0.95, log = object$log, …)
# S3 method for loddsratio
as.array(x, log=x$log, …)
# S3 method for loddsratio
t(x)
# S3 method for loddsratio
aperm(a, perm, …)
an object. For the default method a k-way matrix/table/array of frequencies. The number of margins has to be at least 2.
Numeric or character indicating the margins of a
$k$-way table x (with $k$ greater than 2) that should be employed
as strata. By default all dimensions except the first two are used.
numeric or character. Reference categories for the (non-stratum) row and column dimensions that should be employed for computing the odds ratios. By default, odds ratios for profile contrasts (or sequential contrasts, i.e., successive differences of adjacent categories) are used. See details below.
a formula specifying the variables used to create a
contingency table from data. A conditioning
formula can be specified; the conditioning variables will then be
used as strata variables.
either a data frame, or an object of class "table"
or "ftable".
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when
the data contain NAs. Ignored if data is a contingency table.
logical. Should the results be displayed on a log scale
or not? All internal computations are always on the log-scale but the
results are transformed by default if log = TRUE.
logical or numeric. Should a continuity correction
be applied before computing odds ratios?
If TRUE, 0.5 is added to all cells; if numeric (or an
array conforming to the data) that value is added to all cells. By default,
this not employed unless there are any zero cells in the table, but this
correction is often recommended to reduce bias when some frequencies are small
(Fleiss, 1981).
an object of class loddsratio as computed by
loddsratio.
numeric or character vector specifying a permutation of strata.
arguments passed to methods.
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
the confidence level required for the confint method.
An object of class loddsratio, with the following components:
A named vector, of length (R-1) x (C-1) x prod(dim(x)[strata])
containing the log odds ratios. Use the coef method to
extract these from the object, and the confint method for confidence intervals.
For a two-way table, the names for the log odds ratios are constructed in the form
Ri:Rj/Ci:Cj using the table names for rows and columns. For a stratified table,
the names are constructed in the form Ri:Rj/Ci:Cj|Lk.
Variance covariance matrix of the log odds ratios.
Dimension names for the log odds ratios, considered as a table of
size (R-1, C-1, dim(x)[strata]). Use the dim and dimnames methods
to extract these and manipulate the log odds ratios in relation to the original table.
Corresponding dimension vector.
A matrix C, such that C %*% as.vector(log(x)) gives the log odds
ratios. Each row corresponds to one log odds ratio, and is all zero, except for 4 elements
of c(1, -1, -1, 1) for a given 2 x 2 subtable.
A logical, indicating the value of log in the original call.
For an R x C table, (log) odds ratios are formed for the set of (R-1) x (C-1)
2 x 2 tables, corresponding to some set of contrasts among the row and column
variables. The ref argument allows these to be specified in a general
way.
ref = NULL (default) corresponds to “profile contrasts”
(or sequential contrasts or successive differences) for ordered categories,
i.e., R1--R2, R2--R3, R3--R4, etc., and similarly for the column categories.
These are sometimes called “local odds ratios”.
ref = 1 gives contrasts with the first category; ref = dim(x)
gives contrasts with the last category; ref = c(2, 4) or ref = list(2, 4)
corresponds to the reference being the second category in rows and
the fourth in columns.
Combinations like ref = list(NULL, 3) are also possible, as are character
vectors, e.g., ref = c("foo", "bar") also works ("foo" pertaining again to the
row reference and "bar" to column reference).
Note that all such parameterizations are equivalent, in that one can derive all other possible odds ratios from any non-redundant set, but the interpretation of these values depends on the parameterization.
Note also that these reference level parameterizations only have meaning when the primary (non-strata) table dimensions are larger than 2x2. In the 2x2 case, the odds ratios are defined by the order of levels of those variables in the table, so you can achieve a desired interpretation by manipulating the table.
See the help page of plot.loddsratio for visualization methods.
A. Agresti (2013), Categorical Data Analysis, 3rd Ed. New York: Wiley.
Fleiss, J. L. (1981). Statistical Methods for Rates and Proportions. 2nd Edition. New York: Wiley.
M. Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.
Friendly, M., Turner, H,, Firth, D., Zeileis, A. (2011). Advances in Visualizing Categorical Data Using the vcd, gnm and vcdExtra Packages in R. Correspondence Analysis and Related Methods (CARME 2011). http://www.datavis.ca/papers/adv-vcd-4up.pdf
plot.loddsratio for some plotting methods;
confint for confidence intervals;
coeftest for z-tests of significance
# NOT RUN {
## artificial example
set.seed(1)
x <- matrix(rpois(5 * 3, 7), ncol = 5, nrow = 3)
dimnames(x) <- list(Row = head(letters, 3), Col = tail(letters, 5))
x_lor <- loddsratio(x)
coef(x_lor)
x_lor
confint(x_lor)
summary(x_lor)
## 2 x 2 x k cases
#data(CoalMiners, package = "vcd")
lor_CM <- loddsratio(CoalMiners)
lor_CM
coef(lor_CM)
confint(lor_CM)
confint(lor_CM, log = FALSE)
## 2 x k x 2
lor_Emp <-loddsratio(Employment)
lor_Emp
confint(lor_Emp)
## 4 way tables
data(Punishment, package = "vcd")
lor_pun <- loddsratio(Freq ~ memory + attitude | age + education, data = Punishment)
lor_pun
confint(lor_pun)
summary(lor_pun)
# fit linear model using WLS
lor_pun_df <- as.data.frame(lor_pun)
pun_mod1 <- lm(LOR ~ as.numeric(age) * as.numeric(education),
data = lor_pun_df, weights = 1 / ASE^2)
anova(pun_mod1)
## illustrate ref levels
VA.fem <- xtabs(Freq ~ left + right, subset=gender=="female", data=VisualAcuity)
VA.fem
loddsratio(VA.fem) # profile contrasts
loddsratio(VA.fem, ref=1) # contrasts against level 1
loddsratio(VA.fem, ref=dim(VA.fem)) # contrasts against level 4
# }