CFA: Configural Frequencies Analysis Main Function In confreq: Configural Frequencies Analysis Using Log-Linear Modeling

Description

Calculates various coefficients for the Configural Frequencies Analysis (CFA) defining main- and (optionaly) interaction effects. The core principle is to use `glm` in package `stats` to calculate the expected counts considering a designmatrix, which is constructed based on an formular definition given in argument `form`.

Usage

 ```1 2 3``` ```CFA(patternfreq, alpha = 0.05, form = NULL, ccor = FALSE, family = poisson(), intercept = FALSE, method = "log", blank = NULL, cova = NULL, ...) ```

Arguments

 `patternfreq` an object of class `"Pfreq"`, which is data in pattern frequencies representation - see function `dat2fre`. `alpha` a numeric giving the alpha level for testing (default set to `alpha=.05`) `form` either a character expression which can be coerced into a model formula with the function `as.formula` in the package `stats`. If this argument is left empty (at default `form=NULL`) the (internal) function `design_cfg_cfa()` will return a designmatrix coding only main effects and no interactions – for a designmatrix refering to three variables (V1, V2, V3) for example, leaving the argument `form` empty will be equivalent to assigning the character `"~ V1 + V2 + V3"` to the argument (`form="~ V1 + V2 + V3"`). A special case is to define a null-model or rather a cfa model of order zero. In such a model no (main) effects are considered. This can be achieved bei passing the character expression `"null"` to the argument `form` – so: `form = "null"` – not to be confound with the default setting of this argument `form=NULL`. Another option is to define your own designmatrix and assign it to this argument (`form`) in this case the object assigned to `form` musst be of class `"matrix"` and must logicaly match to the argument `patternfreq`, which is currently not checked! - but simply assumed. `ccor` either a logical (TRUE / FALSE) determining wether to apply a continuity correction or not. When set to `ccor=TRUE` continuity correction is applied for expected values 5 =< expected =< 10. For `ccor=FALSE` no continuity correction is applied. Another option is to set `ccor=c(x,y)` where x is the lower and y the upper bound for expected values where continuity correction is applied. So `ccor=c(5,10)` is equivalent to `ccor=TRUE`. `family` argument passed to `glm.fit` with default set to `poisson()` `intercept` argument passed to `glm.fit` with default set to `FALSE` `method` charcter defining the estimation method for expected frequencies with default set to `method="log"` to estimate the expected frequencies using `glm`. An other option is to set this argument to `method="margins"` which will result in expected frequencies calculated based on the margins of the multidimensional contigency table. Only main effects models are posible in this case and thus the arguments `form`, `family` `cova` and `intercept` are ignored. `blank` can be used to indicate which pattern (configurations) are declared as structural cells (confgurations) for functional CFA. Should be either (1) a character vector defining the pattern (with spaces between variable categories), which will be ignored for calculation of expected frequencies; or (2) a numeric vector defining the (row) positions of the pattern in an object of class `"Pfreq"` (see. argument `patternfreq`), which will be ignored for calculation of expected frequencies. At default (`blank=NULL`) all possible pattern, as listed in object of class `"Pfreq"`, are included for calculation of expected frequencies. `cova` a matrix (possibly with one or more columns) holding the covariate (mean) values for each pattern (configurations) see function `dat2cov` . `...` additional parameters passed through to other functions.

Details

This is the main function of the package. It internaly calls several functions of the package `confreq` which are also available as single functions. For clasification of the observed patterns into 'Types' and 'Antitypes' according to Lienert (1971), a S3 summary method for the resulting object of class `"CFA"` can be applied - see `summary.CFA`.

Value

an object of class `CFA` with results.

References

Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift f<c3><bc>r Klinische Psychologie und Psychotherapie, 19(2), 99-115.

Gl<c3><bc>ck, J., & Von Eye, A. (2000). Including covariates in configural frequency analysis. Psychologische Beitrage, 42, 405<e2><80><93>417.

Victor, N. (1989). An Alternativ Approach to Configural Frequency Analysis. Methodika, 3, 61<e2><80><93>73.

Stemmler, M. (2014). Person-Centered Methods. Cham: Springer International Publishing.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```####################################### ######### some examples ######## data(LienertLSD) LienertLSD res1 <- CFA(LienertLSD) summary(res1) ## testing with (full) interactions res2 <- CFA(LienertLSD,form="~ C + T + A + C:T + C:A + T:A + C:T:A") summary(res2) #' ## testing the null model res3 <- CFA(LienertLSD,form="null") summary(res3) ####################### data(suicide) suicide # suicide data is in non tabulated data representation - so it must be tabulated ! res4 <- CFA(dat2fre(suicide)) summary(res4) ```

confreq documentation built on May 29, 2017, 5:46 p.m.