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lavTables

lavaan frequency tables

Description

Frequency tables for categorical variables and related statistics.

Usage

lavTables(object, dimension = 2L, type = "cells", categorical = NULL,
    group = NULL, statistic = "default", G2.min = 3, X2.min = 3,
    p.value = FALSE, output = "data.frame", patternAsString = TRUE)

Arguments

object

Either a data.frame, or an object of class lavaan.
dimension

Integer. If 0L, display all response patterns. If 1L, display one-dimensional (one-way) tables; if 2L, display two-dimensional (two-way or pairwise) tables. For the latter, we can change the information per row: if type = "cells", each row is a cell in a pairwise table; if type = "table", each row is a table.
type

If "cells", display information for each cell in the (one-way or two-way) table. If "table", display information per table. If "pattern", display response patterns (implying "dimension = 0L").
categorical

Only used if object is a data.frame. Specify variables that need to be treated as categorical.
group

Only used if object is a data.frame. Specify a grouping variable.
statistic

Either a character string, or a vector of character strings requesting one or more statistics for each cell, pattern or table. Always available are X2 and G2 for the Pearson and LRT based goodness-of-fit statistics. A distinction is made between the unrestricted and restricted model. The statistics based on the former have an extension *.un, as in X2.un and G2.un. If object is a data.frame, the unrestricted versions of the statistics are the only ones available. For one-way tables, additional statistics are the thresholds (th.un and th). For two-way tables and type = "table", the following statistics are available: X2, G2, cor (polychoric correlation), RMSEA and the corresponding unrestricted versions (X2.un etc). Additional statistics are G2.average, G2.nlarge and G2.plarge statistics based on the cell values G2: G2.average is the average of the G2 values in each cell of the two-way table; G2.nlarge is the number of cells with a G2 value larger than G2.min, and G2.plarge is the proportion of cells with a G2 value larger than G2.min. A similar set of statistics based on X2 is also available. If "default", the selection of statistics (if any) depends on the dim and type arguments, and if the object is a data.frame or a fitted lavaan object.
G2.min

Numeric. All cells with a G2 statistic larger than this number are considered ‘large’, as reflected in the (optional) "G2.plarge" and "G2.nlarge" columns.
X2.min

Numeric. All cells with a X2 statistic larger than this number are considered ‘large’, as reflected in the (optional) "X2.plarge" and "X2.nlarge" columns.
p.value

Logical. If "TRUE", p-values are computed for requested statistics (eg G2 or X2) if possible.
output

If "data.frame", the output is presented as a data.frame where each row is either a cell, a table, or a response pattern, depending on the "type" argument. If "table", the output is presented as a table (or matrix) or a list of tables. Only a single statistic can be shown in this case, and if the statistic is empty, the observed frequencies are shown.
patternAsString

Logical. Only used for response patterns (dimension = 0L). If "TRUE", response patterns are displayed as a compact string. If "FALSE", as many columns as observed variables are displayed.

Value

If output = "data.frame", the output is presented as a data.frame where each row is either a cell, a table, or a response pattern, depending on the "type" argument. If output = "table" (only for two-way tables), a list of tables (if type = "cells") where each list element corresponds to a pairwise table, or if type = "table", a single table (per group). In both cases, the table entries are determined by the (single) statistic argument.

References

Joreskog, K.G. & Moustaki, I. (2001). Factor analysis of ordinal variables: A comparison of three approaches. Multivariate Behavioral Research, 36, 347-387.

See Also

Examples

HS9 <- HolzingerSwineford1939[,c("x1","x2","x3","x4","x5",
                                 "x6","x7","x8","x9")]
HSbinary <- as.data.frame( lapply(HS9, cut, 2, labels=FALSE) )

# using the data only
lavTables(HSbinary, dim = 0L, categorical = names(HSbinary))
lavTables(HSbinary, dim = 1L, categorical = names(HSbinary), stat=c("th.un"))
lavTables(HSbinary, dim = 2L, categorical = names(HSbinary), type = "table")

# fit a model
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- cfa(HS.model, data=HSbinary, ordered=names(HSbinary))


lavTables(fit, 1L)
lavTables(fit, 2L, type="cells")
lavTables(fit, 2L, type="table", stat=c("cor.un", "G2", "cor"))
lavTables(fit, 2L, type="table", output="table", stat="X2")
lavaan

Latent Variable Analysis

v0.6-10
GPL (>= 2)
Authors
Yves Rosseel [aut, cre] (<https://orcid.org/0000-0002-4129-4477>), Terrence D. Jorgensen [aut] (<https://orcid.org/0000-0001-5111-6773>), Nicholas Rockwood [aut] (<https://orcid.org/0000-0001-5931-183X>), Daniel Oberski [ctb], Jarrett Byrnes [ctb], Leonard Vanbrabant [ctb], Victoria Savalei [ctb], Ed Merkle [ctb], Michael Hallquist [ctb], Mijke Rhemtulla [ctb], Myrsini Katsikatsou [ctb], Mariska Barendse [ctb], Florian Scharf [ctb], Han Du [ctb]
Initial release

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