lavTables | R Documentation |

Frequency tables for categorical variables and related statistics.

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)

`object` |
Either a |

`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` |
If |

`categorical` |
Only used if |

`group` |
Only used if |

`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 |

`G2.min` |
Numeric. All cells with a G2 statistic larger than this number
are considered ‘large’, as reflected in the (optional) |

`X2.min` |
Numeric. All cells with a X2 statistic larger than this number
are considered ‘large’, as reflected in the (optional) |

`p.value` |
Logical. If |

`output` |
If |

`patternAsString` |
Logical. Only used for response patterns (dimension = 0L). If |

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.

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

`varTable`

.

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")

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