Package 'confreq' reference manual

Title:	Configural Frequencies Analysis Using Log-Linear Modeling
Description:	Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
Authors:	Joerg-Henrik Heine, R.W. Alexandrowicz (function stirling_cfa() ) and some package testing by Mark Stemmler
Maintainer:	Joerg-Henrik Heine <[email protected]>
License:	GPL-3
Version:	1.6.1-1
Built:	2025-02-14 04:26:00 UTC
Source:	https://github.com/cran/confreq

Configural Frequencies Analysis Using Log-linear Modeling

Description

The package confreq offers some functions for Configural Frequencies Analysis (CFA) proposed by G.A. Lienert as an analysis of types and antitypes of persons or objects grouped according to their characteristic (response) pattern. The core principle in the package confreq is to use the function glm to compute the expected counts based on a model (design) matrix. The main functions are CFA and S2CFA (see details).

Details

The simplest entry to the package confreq is to use the main function CFA, which will compute several coefficients of Configural Frequencies Analysis at once.

More sophisticated control can be achieved by using the several single functions like expected_cfa, design_cfg_cfa, chi_local_test_cfa , stirling_cfa , etc. ...

Two-Sample-CFA, to detect discriminating pattern between two (sub-) samples, can be performed with the function S2CFA

For further description see description of the respective functions.

A good introduction into the theory and applications of Configural Frequencies Analysis is given in the Textbook 'Person-Centered Methods' by Mark Stemmler (see references).

Additional Information: Some users running R on 'Linux like' OS distributions (like e.g. Ubuntu – and in rare cases MAC OS) might report trouble during installation of confreq due to the package dependency gmp, which is used in confreq to perform the exact binomial test. This (miss-)behavior can usually traced back to a missing of 'the GNU Multiple Precision Arithmetic Library' in the respective OS installation. To fix this, users might consider to run the following Ubuntu Linux command in a terminal to install the latest GMP Library:

'sudo apt-get install libgmp3-dev'.

For additional information see also http://www.mathemagix.org/www/mmdoc/doc/html/external/gmp.en.html and https://gmplib.org/.

Annotation: The foundations for this R-Package were established and discussed in Rothenberge (2011) and (finally) in Klagenfurt at FGME 2013 with Rainer Alexandrowicz and Mark Stemmler ...

Author(s)

Joerg-Henrik Heine <[email protected]>
R.W. Alexandrowicz (function stirling_cfa())

References

von Eye, A. (2002). Configural Frequency Analysis. Methods, Models, and Applications. Mahwah, NJ, LEA.

Krauth, J., & Lienert, G. A. (1973). Die Konfigurationsfrequenzanalyse (KFA) und ihre Anwendung in Psychologie und Medizin: ein multivariates nichtparametrisches Verfahren zur Aufdeckung von Typen und Syndromen; mit 70 Tabellen. Freiburg; München: Alber Karl.

Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.

Lienert, G. A. (1978). Verteilungsfreie Methoden in der Biostatistik (Band II) [Non-parametrical methods in the field of biometrics (Vol. II)]. Meisenheim am Glan, Germany: Hain.

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

Stemmler, M. (2020). Person-Centered Methods – Configural Frequency Analysis (CFA) and Other Methods for the Analysis of Contingency Tables. Cham Heidelberg New York Dordrecht London: Springer.

Stemmler, M., & Hammond, S. (1997). Configural frequency analysis of dependent samples for intra-patient treatment comparisons. Studia Psychologica, 39, 167–175.

Examples

#######################################
######### some examples ########
data(LienertLSD)
LienertLSD
CFA(LienertLSD)
## testing with (full) interactions
CFA(LienertLSD,form="~ C + T + A + C:T + C:A + T:A + C:T:A")
## testing the null model
CFA(LienertLSD,form="null")
#######################
data(suicide)
suicide
# suicide data is in non tabulated data representation 
# so it must be tabulated !
CFA(dat2fre(suicide))
#######################################
######### some examples ########
data(LienertLSD)
LienertLSD
CFA(LienertLSD)
## testing with (full) interactions
CFA(LienertLSD,form="~ C + T + A + C:T + C:A + T:A + C:T:A")
## testing the null model
CFA(LienertLSD,form="null")
#######################
data(suicide)
suicide
# suicide data is in non tabulated data representation 
# so it must be tabulated !
CFA(dat2fre(suicide))

Binomial Test

Description

Calculates the (exact) binomial test based on obseved, expected frequencies an the total number of observations.

Usage

binomial_test_cfa(observed, expected, ntotal = sum(observed))
binomial_test_cfa(observed, expected, ntotal = sum(observed))

Arguments

`observed`	a vector giving the observed frequencies.
`expected`	a vector giving the expected frequencies.
`ntotal`	optional a numeric giving the total number of observations. By default ntotal is calculated as `ntotal=sum(observed)`.

Details

No details

Value

a numeric giving the p-value.

References

No references in the moment

Examples

#######################################
# first calculate expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected<-expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
 binomial_test_cfa(observed,expected)
####################################### 
#######################################
# first calculate expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected<-expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
 binomial_test_cfa(observed,expected)
#######################################

Configural Frequencies Analysis Main Function

Description

Calculates various coefficients for the Configural Frequencies Analysis (CFA) defining main- and (optional) 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 formula definition given in argument form.

Usage

CFA(
  patternfreq,
  alpha = 0.05,
  form = NULL,
  ccor = FALSE,
  family = poisson(),
  intercept = FALSE,
  method = "log",
  blank = NULL,
  cova = NULL,
  bintest = TRUE,
  ...
)
CFA(
  patternfreq,
  alpha = 0.05,
  form = NULL,
  ccor = FALSE,
  family = poisson(),
  intercept = FALSE,
  method = "log",
  blank = NULL,
  cova = NULL,
  bintest = TRUE,
  ...
)

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 referring 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` must be of class `"matrix"` and must logical match to the argument `patternfreq`, which is currently not checked! - but simply assumed.
`ccor`	either a logical (TRUE / FALSE) determining whether to apply a continuity correction or not for the Binomial Approximation to the z-Test. 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`	character 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 contingency table. Only main effects models are possible 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 (configurations) 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`.
`bintest`	a logical with default set to `bintest=TRUE`; if set to `bintest=FALSE` no calculations for the exact binomial test are performed, which can reduce processing time in some cases dramatically.
`...`	additional parameters passed through to other functions.

Details

This is the main function of the package. It internal calls several functions of the package confreq-package which are also available as single functions. For classification 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. An S3 plot method is useful for visualization of the contingency table and the 'Types' and 'Antitypes' – see plot.CFA. Since version 1.6.0-1 of confreq survey weights are supported when tabluating a data set with function dat2fre. In case that for the resulting tabulated data in the object of class c("data.frame","Pfreq") survey weights were used the function CFA will take into account those weigts for estimation of the expected counts – currently only when method="log".

Value

an object of class CFA with results.

References

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

Glück, J., & Von Eye, A. (2000). Including covariates in Configural Frequency Analysis. Psychologische Beitrage, 42, 405–417.

Victor, N. (1989). An Alternativ Approach to Configural Frequency Analysis. Methodika, 3, 61–73.

Stemmler, M. (2020). Person-Centered Methods. Cham: Springer International Publishing.

Examples

#######################################
######### 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)
#######################################
######### 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)

Local Chi-Square Test

Description

Calculates the local chi-square test based on obseved and expected frequencies.

Usage

chi_local_test_cfa(observed, expected)
chi_local_test_cfa(observed, expected)

Arguments

`observed`	a vector giving the observed frequencies.
`expected`	a vector giving the expected frequencies.

Details

No details in the moment.

Value

a list with chi-square statistic and corresponding degrees of freedom an p-value.

References

No references in the moment

Examples

#######################################
# first calculate expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected<-expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
 chi_local_test_cfa(observed,expected)
####################################### 
#######################################
# first calculate expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected<-expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
 chi_local_test_cfa(observed,expected)
#######################################

S3 coefficients for CFA

Description

S3 coefficients method for object of class"CFA".

Usage

## S3 method for class 'CFA'
coef(object, ...)
## S3 method for class 'CFA'
coef(object, ...)

Arguments

`object`	object of class `"CFA"`.
`...`	other parameters passed trough.

Value

Coefficients extracted from the model object of class"CFA".

conversion of a covariate dataset into summary covariate values

Description

Given a dataset x, this function returns summary values for some (numeric) covariate variables in x for each pattern (configuration) defined by a set of factor variables in x.

Usage

dat2cov(
  x,
  FUN = "mean",
  ...,
  notobs = 0,
  katorder = FALSE,
  caseorder = TRUE,
  wgt = NULL
)
dat2cov(
  x,
  FUN = "mean",
  ...,
  notobs = 0,
  katorder = FALSE,
  caseorder = TRUE,
  wgt = NULL
)

Arguments

`x`	an object of class "data.frame" with at least 2 `factor` variables representing the pattern (configurations) and at least 1 `numeric` variable representing the covariate(s).
`FUN`	a function to compute the summary statistics which can be applied to all covariate variables in `x`. See function `aggregate`.
`...`	further arguments passed to or used by methods in `FUN`.
`notobs`	a numeric vector possibly with length equal to the number of `numeric` variables in `x`, defining the summary value for the respective covariate variable to use for unobserved pattern (configurations) defined by the `factor` variables in `x`. By default it is assumed that this value is 0. `notobs` is recycled if only one value is given.
`katorder`	see `dat2fre`
`caseorder`	see `dat2fre`
`wgt`	a numerical vector of survey weights to weight the cases (rows) in `x`

Details

No further details

Value

An object of class c("data.frame", "Pcov") holding the summary statistics for the covariate variables corresponding to the pattern (configurations) of the given dataset in the argument x.

dataset to pattern frequency conversion

Description

Given a dataset this function returns a (response) pattern frequencies table representation of it.

Usage

dat2fre(
  x,
  katorder = FALSE,
  caseorder = TRUE,
  kat = NULL,
  codes = NULL,
  wgt = NULL,
  ...
)
dat2fre(
  x,
  katorder = FALSE,
  caseorder = TRUE,
  kat = NULL,
  codes = NULL,
  wgt = NULL,
  ...
)

Arguments

`x`	an object of class "matrix" or "data.frame". If `x` is a "data.frame" each variable (column) must be an `integer` or a `factor`. If `x` is a "matrix" it is assumed that the categories for each variable in `x` start with `1` – there is no check for that !!!
`katorder`	logical with default set to `katorder==FALSE`. When set to `katorder==TRUE` variables are ordered according to their number of categories (variable with most categories is the rightmost variable) in the resulting object.
`caseorder`	logical with default set to `caseorder==TRUE`. When set to `caseorder==FALSE` configurations are only ordered according to the categories of the rightmost variable in the resulting object.
`kat`	ignored when `x` is a `data.frame`! If `x` is a `"matrix"` the optional argument `kat` must be an integer vector defining the number of categories for every variable in `x` (in the respective order). If left empty the (max) number of categories is estimated from the data given in `x`.
`codes`	a list with character vectors containing coding for integers in matrix (if `x` is a numeric `matrix`). If `codes` is not empty (and the argument `x` is an object of class "matrix") the return object will be pattern frequencies table as `data.frame`.
`wgt`	a numerical vector of survey weights to weight the cases (rows) in `x`
`...`	other parameters passed through to `table` (in case of x being a `data.frame`) or to `tabulate` (in case of x being a matrix).

Details

To use survey weights a vector of positive numeric values with length matching the number of rows in x must be assigned to the argument wgt. The individual case weights are then aggregated (respective sum of weights) for each pattern observed in the data (assigned to argument x).

Value

An object of class c("data.frame","Pfreq") containing the (response) pattern frequencies table representation of the given dataset in the argument x.

References

No references in the moment

Examples

#######################################
data(suicide)# loading data in data frame (702 cases) representation 
dat2fre(suicide) # converting it into a pattern frequencies table

########### 
#######################################
data(LienertLSD)# loading example pattern frequencies table ..
test<-fre2dat(LienertLSD)# and coverting it into a simple (data) matrix
test<-test[sample(c(1:65),65),] # making a messy order 
############
dat2fre(test) # making a proper ordered pattern frequencies table again
##### try it with a data.frame too!
#######################################
#######################################
data(suicide)# loading data in data frame (702 cases) representation 
dat2fre(suicide) # converting it into a pattern frequencies table

########### 
#######################################
data(LienertLSD)# loading example pattern frequencies table ..
test<-fre2dat(LienertLSD)# and coverting it into a simple (data) matrix
test<-test[sample(c(1:65),65),] # making a messy order 
############
dat2fre(test) # making a proper ordered pattern frequencies table again
##### try it with a data.frame too!
#######################################

Designmatrix for log linear CFA models

Description

Calculates the designmatrix corresponding to a dataset with length(kat) columns (variables).

Usage

design_cfg_cfa(
  kat,
  form = paste("~", paste(paste("V", 1:length(kat), sep = ""), collapse = " + ")),
  ...
)
design_cfg_cfa(
  kat,
  form = paste("~", paste(paste("V", 1:length(kat), sep = ""), collapse = " + ")),
  ...
)

Arguments

kat

a numerical vector containing kardinal numbers, giving the number of categories for each variable of a dataset (in the respective order of the variables in such a dataset) which corresponds to the requested designmatrix. So the length of this numerical vector represents the number of variables.

form

a character string which can be coerced into a model formulae with the function as.formula in the package stats. If this argument is left empty the function design_cfg_cfa() will return a designmatrix coding only main effects and no interactions – for a designmatrix refering to three variables 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"

...

additional parameters passed through to function model.matrix in package stats.

Details

This function internaly calls the function pos_cfg_cfa.

For further information on designmatrices see decription on function model.matrix in the package stats.

Value

A designmatrix - an object of class c("matrix","design_cfg_cfa") - for the formula therm given in argumentform.

References

No references in the moment

Examples

#######################################
# designmatrix with three main effects.
# three variables with two categories each.
design_cfg_cfa(kat=c(2,2,2))
# two variables with two categories each and one variable
# with 7 categories (Lienert LSD example).
design_cfg_cfa(kat=c(2,2,7))
###########
# designmatrix with three main effects an three interactions.
# three variables with two categories each.
design_cfg_cfa(kat=c(2,2,2),form="~ V1 + V2 + V3 + V1:V2 + V1:V3 + V2:V3")
# two variables with two categories each and one variable
# with 7 categories (e.g. The Krauth & Lienert suicide Data).
design_cfg_cfa(kat=c(2,2,7),form="~ V1 + V2 + V3 + V1:V2 + V1:V3 + V2:V3")
#######################################
#######################################
# designmatrix with three main effects.
# three variables with two categories each.
design_cfg_cfa(kat=c(2,2,2))
# two variables with two categories each and one variable
# with 7 categories (Lienert LSD example).
design_cfg_cfa(kat=c(2,2,7))
###########
# designmatrix with three main effects an three interactions.
# three variables with two categories each.
design_cfg_cfa(kat=c(2,2,2),form="~ V1 + V2 + V3 + V1:V2 + V1:V3 + V2:V3")
# two variables with two categories each and one variable
# with 7 categories (e.g. The Krauth & Lienert suicide Data).
design_cfg_cfa(kat=c(2,2,7),form="~ V1 + V2 + V3 + V1:V2 + V1:V3 + V2:V3")
#######################################

Degrees of freedom

Description

Calculates the degrees of freedom based on an designmatrix for a (log liniear) CFA model.

Usage

df_des_cfa(des)
df_des_cfa(des)

Arguments

des

a designmatrix (object of class "matrix") as returned by function design_cfg_cfa.

Details

No details

Value

An object of class "integer" giving the degrees of freedom for the designmatrix defined in argument des.

References

No references in the moment

Examples

#######################################
# degrees of freedom for designmatrix with three main effects.
# three variables with two categories each.
df_des_cfa(design_cfg_cfa(kat=c(2,2,2)))
# two variables with two categories each and one variable
# with 7 categories (e.g. The Krauth & Lienert suicide Data).
df_des_cfa(design_cfg_cfa(kat=c(2,2,7)))
###########
# degrees of freedom for designmatrix with three main effects
# and three 'two by two'interactions.
# and tripple interaction --> saturated model --> df=0
# three variables with two categories each.
df_des_cfa(design_cfg_cfa(kat=c(2,2,2),form="~ V1 + V2 + V3 + V1:V2 + V1:V3 + V2:V3 + V1:V2:V3"))
####################################### 
#######################################
# degrees of freedom for designmatrix with three main effects.
# three variables with two categories each.
df_des_cfa(design_cfg_cfa(kat=c(2,2,2)))
# two variables with two categories each and one variable
# with 7 categories (e.g. The Krauth & Lienert suicide Data).
df_des_cfa(design_cfg_cfa(kat=c(2,2,7)))
###########
# degrees of freedom for designmatrix with three main effects
# and three 'two by two'interactions.
# and tripple interaction --> saturated model --> df=0
# three variables with two categories each.
df_des_cfa(design_cfg_cfa(kat=c(2,2,2),form="~ V1 + V2 + V3 + V1:V2 + V1:V3 + V2:V3 + V1:V2:V3"))
#######################################

Expected frequencies with glm

Description

Calculates the expected frequencies of counts using log liniear model.

Usage

expected_cfa(des, observed, family = poisson(), intercept = FALSE, ...)
expected_cfa(des, observed, family = poisson(), intercept = FALSE, ...)

Arguments

`des`	a designmatrix (object of class `"matrix"`) as returned by function `design_cfg_cfa`.
`observed`	a integer vector with `lenght(observed) == dim(des)[1]`. WARNING: The observed frequencies counts must be in an order corresponding to the coding sheme in designmatix (see argument `des`).
`family`	argument passed to `glm.fit` with default set to `poisson()`
`intercept`	argument passed to `glm.fit` with default set to `FALSE`
`...`	aditional arguments optional passed to `glm.fit`

Details

No details

Value

An vector object giving the expected counts.

References

No references in the moment

Examples

#######################################
# expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
####################################### 
#######################################
# expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
#######################################

Expected frequencies using margins

Description

Calculates the expected frequencies of counts based on the margins of the k-dimensional contingency table.

Usage

expected_margin_cfa(Pfreq, blank = NULL)
expected_margin_cfa(Pfreq, blank = NULL)

Arguments

Pfreq

Object of class "Pfreq" (see. function dat2fre).

blank

Either (1) 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 position(s) of the pattern in object of class "Pfreq", 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.

Details

only main effects are considered.

Value

An vector object giving the expected counts.

References

No references in the moment

Examples

#######################################
# expected counts for LienertLSD data example.
data(LienertLSD) # load example data
expected_margin_cfa(Pfreq = LienertLSD) # calculation of expected counts (only main effects).
####################################### 
#######################################
# expected counts for LienertLSD data example.
data(LienertLSD) # load example data
expected_margin_cfa(Pfreq = LienertLSD) # calculation of expected counts (only main effects).
#######################################

pattern frequency to dataset conversion

Description

Given a (response) pattern frequencies table this function returns a dataset representation of it.

Usage

fre2dat(x, fact = FALSE, ...)
fre2dat(x, fact = FALSE, ...)

Arguments

x

an object of class "matrix" which is a (response) pattern frequencies table. It is assumed, that the last column of the object x represents the frequencies of the (respose) patern represented by the other columns in x.

fact

logical, default is (fact=FALSE). If this argument is set to (fact=TRUE) the result is coerced to a data.frame with factor variables.

...

additional parameters passed trough. This is an option to assign factor labels to the resulting data.frame (when setting argument fact=TRUE) –> see factor in the base package and examples.

WARNING using this option will only work correct when all 'pattern' columns (variables) in the frequencies table share the same number of categories

Details

No details

Value

An object of class "matrix" or "data.frame" (depending on the argument fact) containing the dataset representation of the (response) pattern frequencies table give in the argument x.

References

No references in the moment

Examples

#######################################
data(LienertLSD)# loading example pattern frequencies table
fre2dat(LienertLSD)# coverting it into a (data) matrix
# for a matrix without colnames
colnames(LienertLSD)<-NULL # first removing the colnames
fre2dat(LienertLSD) # conversion with automatic new colnames
# requesting a data.frame using factor levels 
fre2dat(LienertLSD,fact=TRUE,labels=c("yes","no"))
#######################################
data(LienertLSD)# loading example pattern frequencies table
fre2dat(LienertLSD)# coverting it into a (data) matrix
# for a matrix without colnames
colnames(LienertLSD)<-NULL # first removing the colnames
fre2dat(LienertLSD) # conversion with automatic new colnames
# requesting a data.frame using factor levels 
fre2dat(LienertLSD,fact=TRUE,labels=c("yes","no"))

pattern frequency to table conversion

Description

Given data as pattern frequencies (object of class class c("data.frame","Pfreq", see function dat2fre) this function returns a typical array representation (class "table" , see table) of it.

Usage

fre2tab(patternfreq, form = NULL)
fre2tab(patternfreq, form = NULL)

Arguments

`patternfreq`	an object of class c("data.frame","Pfreq")
`form`	a formula object with possibly both left and right hand sides specifying the order of the variables in the resulting table. At default (`(formula=NULL)`) all variables in `(x)` are used in their respective order.

Details

This function was introduced in order to connect the typical confreq data representation in the objects of the class c("data.frame","Pfreq"), see function dat2fre, to the R-typical array representation as it exists in objects of the "table" class, see table. This array representation of multi-dimensional contingency tables is used more universally in R – e.g. also in the R package vcd, see the examples section below.

It is assumed, that the last column of the object patternfreq represents the frequencies of the (response) pattern represented by the other columns in patternfreq.

Value

An object of class "table" see table.

References

No references at the moment

Examples

#######################################
data(LienertLSD)# loading example pattern frequencies table
fre2tab(LienertLSD)# coverting it into a table

### examples using functions from package vcd
data(Lienert1978)# loading example pattern frequencies table
fre2tab(Lienert1978)# coverting it into a table
strucplot(fre2tab(Lienert1978))# plotting data with 'vcd'
structable(fre2tab(Lienert1978),direction = "v")# flatten table (vertical) with 'vcd'

# changing the vertical grouping when flattening the table by unsing a 'formula':
structable(fre2tab(Lienert1978, form=~Group + Student + Teacher),direction = "v")# flatten table
#######################################
data(LienertLSD)# loading example pattern frequencies table
fre2tab(LienertLSD)# coverting it into a table

### examples using functions from package vcd
data(Lienert1978)# loading example pattern frequencies table
fre2tab(Lienert1978)# coverting it into a table
strucplot(fre2tab(Lienert1978))# plotting data with 'vcd'
structable(fre2tab(Lienert1978),direction = "v")# flatten table (vertical) with 'vcd'

# changing the vertical grouping when flattening the table by unsing a 'formula':
structable(fre2tab(Lienert1978, form=~Group + Student + Teacher),direction = "v")# flatten table

Tabulating Answer Categories in Data

Description

Function tabulating (answer) categories in X.

Usage

ftab(X, catgories = NULL, na.omit = FALSE)
ftab(X, catgories = NULL, na.omit = FALSE)

Arguments

`X`	Data as a `"matrix"`, a `"data.frame"` or even a `"vector"` or `"factor"`. `"vector"` or `"factor"` are coerced to a `"data.frame"` with one column.
`catgories`	optional a vector (`"numeric"` or `"character"`) containig the categories to tabulate. At default (`catgories=NULL`) the fuction looks for unique categories in `X`.
`na.omit`	logical (default: `na.omit=FALSE` ) wether to return frequencies for missing values, `NA`s.

Details

X can either be a ("numeric" or "character") "matrix" containing response vectors of persons (rows) or a "data.frame" containing "numeric", "character" or "factor" variables (columns).

Value

a "matrix" with category frequencies

Examples

########
data(suicide)
ftab(suicide)
########
data(suicide)
ftab(suicide)

The Data Example from Lazarsfeld and Henry

Description

data example by Lazarsfeld and Henry (1968) where n = 1000 subjects need to solve questions or problems (i.e., A,B, and C). They either ‘1’ = solved or ‘2’ = did not solve the problems. The data is in pattern frequencies table representation (object of class c("data.frame", "Pfreq" )). This data example is used in the textbook by Mark Stemmler (2020, Table 6.6, p. 81).

Usage

  data(lazar)
data(lazar)

Format

A matrix with 4 columns and 8 rows. The last column gives the frequencies for the (response) pattern in column 1:3.

Details

No detail in the moment

References

Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.

Stemmler, M. (2020). Person-Centered Methods – Configural Frequency Analysis (CFA) and Other Methods for the Analysis of Contingency Tables. Cham Heidelberg New York Dordrecht London: Springer.

Examples

##############################################################
data(lazar)
dim(lazar)
##############################################################
##############################################################
data(lazar)
dim(lazar)
##############################################################

The Lienert (1978) Data

Description

Data used as an example for two-sample CFA in the textbook by Mark Stemmler (2020, Table 7.7, p.97) taken from Lienert (1978, p. 978). The data is in pattern frequencies table representation (object of class c("data.frame", "Pfreq" )).

Usage

  data(Lienert1978)
data(Lienert1978)

Format

A data frame (object of class c("data.frame", "Pfreq") ) with 4 columns and 12 rows. The last column gives the frequencies for the (response) pattern in column 1:2 of the respective 'Group' given in column 3.

Details

no details at the moment ...

References

Lienert, G. A. (1978). Verteilungsfreie Methoden in der Biostatistik (Band II) [Non-parametrical 168 methods in the field of biometrics (Vol. II)]. Meisenheim am Glan, Germany: Hain.

Stemmler, M. (2020). Person-Centered Methods – Configural Frequency Analysis (CFA) and Other Methods for the Analysis of Contingency Tables. Cham Heidelberg New York Dordrecht London: Springer.

Examples

data(Lienert1978)
dim(Lienert1978)
##############################################################
colnames(Lienert1978) # show all variable names of Lienert1978
data(Lienert1978)
dim(Lienert1978)
##############################################################
colnames(Lienert1978) # show all variable names of Lienert1978

The Lienert LSD Data

Description

Data from the classical Lienert LSD trial as an example for CFA (see Lienert, 1971, p. 103, ’Tabelle 1’). The data is in pattern frequencies table representation (object of class c("data.frame", "Pfreq" )).

Usage

  data(LienertLSD)
data(LienertLSD)

Format

A data frame (object of class c("data.frame", "Pfreq") ) with 4 columns and 8 rows. The last column gives the frequencies for the observed pattern of the psychotoxic basic syndrome (in column 1:3) due to the intake of lysergic acid diethylamide (LSD).

Details

The first three columns are named C, T and A which are abbreviations for the observed symptoms after taking LSD:

C = narrowed consciousness

T = thought disturbance

A = affective disturbance

The coding of the observations is for all symptoms: present='+' and absent='-'

References

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

Examples

data(LienertLSD)
dim(LienertLSD)
##############################################################
colnames(LienertLSD) # show all variable names of matrix LienertLSD
data(LienertLSD)
dim(LienertLSD)
##############################################################
colnames(LienertLSD) # show all variable names of matrix LienertLSD

Likelihood Ratio Chi-square (LR)

Description

Calculates the likelihod ratio chi-square statistic based on observed and expected counts.

Usage

lr(observed, expected)
lr(observed, expected)

Arguments

`observed`	a vector giving the observed frequencies.
`expected`	a vector giving the expected frequencies.

Details

No details in the moment.

Value

numeric giving the likelihood ratio chi-square statistic.

References

Stemmler, M. (2014). Person-Centered Methods – Configural Frequency Analysis (CFA) and Other Methods for the Analysis of Contingency Tables. Cham Heidelberg New York Dordrecht London: Springer.

Examples

#######################################
######### some examples ########
data(newborns)
newborns
designmatrix <- design_cfg_cfa(kat=c(2,2)) # generate an designmatrix (only main effects)
observed <- newborns[,3] # extract observed counts
expected <- expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
lr(observed,expected) # calculation of the likelihood ratio chi-square statistic

#######################################
######### some examples ########
data(newborns)
newborns
designmatrix <- design_cfg_cfa(kat=c(2,2)) # generate an designmatrix (only main effects)
observed <- newborns[,3] # extract observed counts
expected <- expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
lr(observed,expected) # calculation of the likelihood ratio chi-square statistic

The Data Example from Stemmler 2020

Description

data example by Stemmler (2020, table 4.1, p. 33) where n = 56 newborns 'with seizures' = 1 or 'without seizures' = 2 (coded in the in first column named 'A') were tested with an intelligence test while they attended kindergarten. Children’s intelligence was divided into 'average or above' = 1 and 'below average' = 2 (coded in the in second column named 'B'). The third column gives the frequencies of the respective pattern.

Usage

  data(newborns)
data(newborns)

Format

A data.frame with 3 columns and 4 rows. The last column gives the frequencies for the observed pattern in column 1:2. The data is in pattern frequencies table representation (object of class c("data.frame", "Pfreq"))

Details

No detail in the moment

References

Stemmler, M. (2020). Person-Centered Methods – Configural Frequency Analysis (CFA) and Other Methods for the Analysis of Contingency Tables. Cham Heidelberg New York Dordrecht London: Springer.

Examples

##############################################################
data(newborns)
dim(newborns)
newborns
##############################################################
##############################################################
data(newborns)
dim(newborns)
newborns
##############################################################

S3 plot for CFA

Description

S3 plot method for object of class"CFA"

Usage

## S3 method for class 'CFA'
plot(
  x,
  type = "z.pChi",
  fill = c("red", "blue", "grey"),
  adjalpha = "bonferroni",
  ...
)
## S3 method for class 'CFA'
plot(
  x,
  type = "z.pChi",
  fill = c("red", "blue", "grey"),
  adjalpha = "bonferroni",
  ...
)

Arguments

`x`	object of class `"CFA"`
`type`	character indicating which test to use for visualizing whether the observed pattern are 'Types', 'Antitypes' or not significant at all. Possible options for `type` are `"pChi"`, `"ex.bin.test"`, `"z.pChi"`, `"z.pBin"` and `"p.stir"`.
`fill`	a vector of (three) colors defining the coloring of significant 'Types' (default "red"), 'Antitypes' (default "blue") or not significant cells (default "grey") in the plot.
`adjalpha`	character with default `adjalpha = "bonferroni"`. Selector for the type of alpha adjustment for multiple testing. Possible options are: `adjalpha = "none"`, for no adjustment; `adjalpha = "bonferroni"`, for bonferroni adjustment (default); `adjalpha = "holm"`, for alpha adjustment according to Holm (1979); other options to come ... .
`...`	other parameters passed trough

Value

a plot visualizing the results.

References

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

Bonferroni, C. E. (1935). Il calcolo delle assicurazioni su gruppi di teste. In S.O. Carboni (Ed.), Studi in Onore del Professore Salvatore Ortu Carboni (S. 13–60). Roma, Tipografia del Senato: Bardi.

S3 plot for S2CFA

Description

S3 plot method for object of class"S2CFA"

Usage

## S3 method for class 'S2CFA'
plot(
  x,
  type = "ex.fisher.test",
  fill = c("red", "grey"),
  adjalpha = "bonferroni",
  ...
)
## S3 method for class 'S2CFA'
plot(
  x,
  type = "ex.fisher.test",
  fill = c("red", "grey"),
  adjalpha = "bonferroni",
  ...
)

Arguments

`x`	object of class`"S2CFA"`
`type`	character with default `type="ex.fisher.test"`, to return wether the observed pattern are 'discriminating Types' or not significant at all based on the respective p-value. Another option for `type` is `type="pChi"`.
`fill`	a vector of (two) colors defining the coloring of discriminating 'Types' (default "red"), or not discriminating cells (default "grey") in the plot.
`adjalpha`	character with default `adjalpha = "bonferroni"`. Selector for the type of alpha adjustment for multiple testing. Possible options are: `adjalpha = "none"`, for no adjustment; `adjalpha = "bonferroni"`, for bonferroni adjustment (default); `adjalpha = "holm"`, for alpha adjustment according to Holm (1979); other options to come ... .
`...`	other parameters passed trough.

Value

a plot visualizing the results.

References

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

Possible configurations

Description

Calculates all possible configuartions for some variables with different numbers of categories.

Usage

pos_cfg_cfa(kat, fact = FALSE)
pos_cfg_cfa(kat, fact = FALSE)

Arguments

`kat`	a numerical vector containing kardinal numbers, giving the number of categories for each variable. So the length of this numerical vector represents the number of variables.
`fact`	logical, default is `(fact=FALSE)`. If this argument is set to `(fact=TRUE)` the result is coerced to a data.frame with factor variables.

Details

No details

Value

An object of class "matrix" or "data.frame" (depending on the argument fact) containing all possible configurations for lenght(kat) variables with the respective number of categories given as kardinal numbers in the vector kat.

References

No references in the moment

Examples

#######################################
# possible configurations for ...
# three variables with two categories each (Lienert LSD example).
pos_cfg_cfa(kat=c(2,2,2))
#######################################
#######################################
# possible configurations for ...
# three variables with two categories each (Lienert LSD example).
pos_cfg_cfa(kat=c(2,2,2))
#######################################

S3 print for Pfreq

Description

S3 print method for object of class"Pfreq"

Usage

## S3 method for class 'Pfreq'
print(
  x,
  ...,
  digits = NULL,
  quote = FALSE,
  right = TRUE,
  row.names = TRUE,
  max = NULL
)
## S3 method for class 'Pfreq'
print(
  x,
  ...,
  digits = NULL,
  quote = FALSE,
  right = TRUE,
  row.names = TRUE,
  max = NULL
)

Arguments

`x`	object of class `"Pfreq"`
`...`	further arguments passed to or from other methods.
`digits`	minimal number of significant digits, see `print.default`.
`quote`	logical, indicating whether or not strings should be printed with surrounding quotes.
`right`	logical, indicating whether or not strings should be right aligned.
`row.names`	logical (or character vector), indicating whether (or what) row names should be printed.
`max`	numeric or `NULL`, specifying the maximal number of entries to be printed. By default, when `NULL`, `getOption("max.print")` used.

Value

output printed to the console

Configural Frequencies Analysis for two Samples.

Description

Calculates coefficients for the two-sample CFA. Instead of differentiating between 'Types' and 'Antitypes', two-sample CFA looks for discrimination types, that is configurations with significant differences in frequencies between two sub samples.

Usage

S2CFA(patternfreq, alpha = 0.05, ccor = FALSE, ...)
S2CFA(patternfreq, alpha = 0.05, ccor = FALSE, ...)

Arguments

`patternfreq`	an object of class `"Pfreq"`, which is data in pattern frequencies representation - see function `dat2fre`. The variable defining the two sub samples (a variable with max. two categories) must be located in the last but one column of the object of class `"Pfreq"`
`alpha`	a numeric giving the alpha level for testing (default set to `alpha=.05`)
`ccor`	a logical (TRUE / FALSE) determining whether to apply a continuity correction or not. When set to `ccor=TRUE` continuity correction is applied. For `ccor=FALSE` no continuity correction is applied.
`...`	additional parameters passed through to other functions.

Details

no details at the moment ...

Value

an object of class S2CFA with results.

References

Stemmler, M. (2020). Person-Centered Methods – Configural Frequency Analysis (CFA) and Other Methods for the Analysis of Contingency Tables. Cham Heidelberg New York Dordrecht London: Springer.

Stemmler, M., & Hammond, S. (1997). Configural frequency analysis of dependent samples for intra-patient treatment comparisons. Studia Psychologica, 39, 167–175.

Examples

#######################################
############### some examples #########
######### example from Marks Textbook
data(Lienert1978)
res1 <- S2CFA(Lienert1978)
summary(res1)
res2 <- S2CFA(Lienert1978, ccor=TRUE) # with continuity correction
summary(res2)
######### example with biger numbers
data(suicide)
ftab(suicide) # 'Epoche' may divide the sample into 2 subsamples  
suicide_2s <- suicide[, c(1,3,2) ] # reorder data that 'Epoche' is the last column
ftab(suicide_2s) # check reordering
suicide_2s_fre <- dat2fre(suicide_2s)
res3 <- S2CFA(suicide_2s_fre)
summary(res3)
res4 <- S2CFA(suicide_2s_fre, ccor=TRUE) # with continuity correction
summary(res4)
#######################################
############### some examples #########
######### example from Marks Textbook
data(Lienert1978)
res1 <- S2CFA(Lienert1978)
summary(res1)
res2 <- S2CFA(Lienert1978, ccor=TRUE) # with continuity correction
summary(res2)
######### example with biger numbers
data(suicide)
ftab(suicide) # 'Epoche' may divide the sample into 2 subsamples  
suicide_2s <- suicide[, c(1,3,2) ] # reorder data that 'Epoche' is the last column
ftab(suicide_2s) # check reordering
suicide_2s_fre <- dat2fre(suicide_2s)
res3 <- S2CFA(suicide_2s_fre)
summary(res3)
res4 <- S2CFA(suicide_2s_fre, ccor=TRUE) # with continuity correction
summary(res4)

Approximation to the binomial using Stirling's Formula

Description

Calculates the binomial aproximation using stirling's formula (Version of function: V 1.0 - November 2013)

Usage

stirling_cfa(
  observed,
  expected = NULL,
  n = sum(observed),
  p = NULL,
  cum = T,
  verb = T
)
stirling_cfa(
  observed,
  expected = NULL,
  n = sum(observed),
  p = NULL,
  cum = T,
  verb = T
)

Arguments

`observed`	a integer vector with observed freqencies
`expected`	a vector giving the expected frequencies. expected can be set to `expected=NULL` if an vector of cell probabilities is given in argument `p`.
`n`	number of trials (scalar) default is `n = sum(observed)` .
`p`	a vector of cell probabilities. If p is not NULL the argument `expected` is ignored and this vector p of cell probabilities is used for calculatio instead of expected counts
`cum`	a logical - computation of cumulative density. If `cum=TRUE` (default) computes tail probability. If `cum=FALSE` computes prob. only for one cell (i.e. execute stircore only).
`verb`	logical - verbose results: If `verb=TRUE` (default) builds a results table. If `verb=FALSE` returns vector of cell p-values only.

Details

Vector p must be of same length as observed _or_ p may be a scalar (e.g. in case of the zero-order CFA).
The routine autoselects the upper or lower tail:
- if obs > exp then sum obs:n
- else sum 0:obs
The stirling approximation cannot be evaluated if the observed frequency is 0 or n. Therefore, the proposal of A. von Eye (20xx) is adopted, taking the sum up to 1 or n-1, respectively.

Author(s)

R.W. Alexandrowicz

References

von Eye, A. (2002). Configural Frequency Analysis. Methods, Models, and Applications. Mahwah, NJ, LEA.

The Krauth & Lienert suicide Data

Description

Data from the Krauth & Lienert suicide example for CFA (see Tables 39a and 39b; Krauth & Lienert, 1973). The data describe suicide patterns in pre- and post-WWII Germany – see von Eye, A., (2002); p. 385.

Usage

  data(suicide)
data(suicide)

Format

A data.frame with 3 columns (as factors). The data is in data list representation – each row represents one case.

Details

The three columns are named 'Geschlecht', 'Epoche' and 'Suizidart' which is 'gender', 'epoch' and 'type od suicide'. each of the variables are factors with the following levels:

Geschlecht: 'm' = 1 (male); 'w' = 2 (female)

Epoche: '44' = 1 (the epoch 1944); '52' = 2 (the epoch 1952)

Suizidart: 'Eh' = 1(hang); 'Es' = 2 (shoot); 'Et' = 3(drown); 'G' = 4(gas); 'H' = 5(crashing down); 'P' = 6(open vein); 'S' = 7(barbiturate);

References

von Eye, A. (2002). Configural Frequency Analysis: Methods, models, and applications. Mahwah, N.J.: Lawrence Erlbaum Associates.

Examples

##############################################################
data(suicide) # to load the data.frame included in the package
class(suicide)
dim(suicide)
str(suicide)
##############################################################
data(suicide) # to load the data.frame included in the package
class(suicide)
dim(suicide)
str(suicide)

S3 Summary for CFA

Description

S3 summary method for object of class"CFA"

Usage

## S3 method for class 'CFA'
summary(
  object,
  digits = 3,
  type = "z.pChi",
  sorton = NULL,
  decreasing = FALSE,
  showall = TRUE,
  holm = FALSE,
  wide = FALSE,
  adjalpha = "bonferroni",
  ...
)
## S3 method for class 'CFA'
summary(
  object,
  digits = 3,
  type = "z.pChi",
  sorton = NULL,
  decreasing = FALSE,
  showall = TRUE,
  holm = FALSE,
  wide = FALSE,
  adjalpha = "bonferroni",
  ...
)

Arguments

`object`	object of class`"CFA"`
`digits`	integer rounds the values to the specified number of decimal places, default is `digits=3`.
`type`	character indicating which test to use for inference whether the observed pattern are 'Types', 'Antitypes' or not significant at all. Possible options for `type` are `"pChi"`, `"ex.bin.test"`, `"z.pChi"`, `"z.pBin"` and `"p.stir"`.
`sorton`	sort results of local test by any column. By default the output is not sorted. Other options may be `"pat."`, `"obs."`, `"exp."`, `"Type"`, `"Chi"`, etc. ... So all column names that can potentially appear in the result.
`decreasing`	logical. Should the sort be increasing or decreasing? see `order`
`showall`	logical with default `showall = TRUE`. To return only significant pattern ('Types' / 'Antitypes') set it to `showall = FALSE`.
`holm`	logical with default `holm = FALSE`. If set to `holm = TRUE`, significance testing is based on the holm procedure – see references. This argument is deprecated (since version 1.5.6) and kept only for downward compatibility. Use argument `adjalpha` for any type of alpha adjustment.
`wide`	logical with default `wide = FALSE`. If set to `wide = TRUE`, results for all significance tests are returned.
`adjalpha`	character with default `adjalpha = "bonferroni"`. Selector for the type of alpha adjustment for multiple testing. Possible options are: `adjalpha = "none"`, for no adjustment; `adjalpha = "bonferroni"`, for bonferroni adjustment (default); `adjalpha = "holm"`, for alpha adjustment according to Holm (1979); other options to come ... .
`...`	other parameters passed trough.

Value

a summary of the results printed on the console.

References

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

S3 Summary for S2CFA

Description

S3 summary method for object of class"S2CFA"

Usage

## S3 method for class 'S2CFA'
summary(
  object,
  digits = 3,
  type = "ex.fisher.test",
  sorton = NULL,
  decreasing = FALSE,
  showall = TRUE,
  adjalpha = "bonferroni",
  ...
)
## S3 method for class 'S2CFA'
summary(
  object,
  digits = 3,
  type = "ex.fisher.test",
  sorton = NULL,
  decreasing = FALSE,
  showall = TRUE,
  adjalpha = "bonferroni",
  ...
)

Arguments

`object`	object of class`"S2CFA"`
`digits`	integer rounds the values to the specified number of decimal places, default is `digits=3`.
`type`	character with default `type="ex.fisher.test"`, to return wether the observed pattern are 'discriminating Types' or not significant at all based on the respective p-value. Another option for `type` is `type="pChi"`.
`sorton`	sort results of local test by any column. By default the output is not sorted. Other options may be `"pat."`, `"disc.Type"`, etc. ... So all column names that can potentially appear in the result.
`decreasing`	logical. Should the sort be increasing or decreasing? see `order`
`showall`	logical with default `showall = TRUE`. To return only significant pattern (discriminating types) set it to `showall = FALSE`.
`adjalpha`	character with default `adjalpha = "bonferroni"`. Selector for the type of alpha adjustment for multiple testing. Possible options are: `adjalpha = "none"`, for no adjustment; `adjalpha = "bonferroni"`, for bonferroni adjustment (default); `adjalpha = "holm"`, for alpha adjustment according to Holm (1979); other options to come ... .
`...`	other parameters passed trough.

Value

a summary of the results printed on the console.

References

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

Two z-Approximation Tests

Description

Calculates the Chi-square approximation to the z-test and the binomial approximation to the z-test.

Usage

z_tests_cfa(observed, expected, ccor = FALSE, ntotal = sum(observed))
z_tests_cfa(observed, expected, ccor = FALSE, ntotal = sum(observed))

Arguments

`observed`	a vector giving the observed frequencies.
`expected`	a vector giving the expected frequencies.
`ccor`	either a logical (TRUE / FALSE) determining wether to apply a continuity correction or not to the Binomial Approximation of the z-Test. 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`.
`ntotal`	optional a numeric giving the total number of observations. By default ntotal is calculated as `ntotal=sum(observed)`.

Details

An continuity correction can be applied to the binomial approximation – see argument ccor.

Value

a list with z and p-values.

References

No references in the moment

Examples

#######################################
# expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected<-expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
z_tests_cfa(observed,expected)
####################################### 
#######################################
# expected counts for LienertLSD data example.
designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects)
data(LienertLSD) # load example data
observed<-LienertLSD[,4] # extract observed counts
expected<-expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts
z_tests_cfa(observed,expected)
#######################################

Package 'confreq'

Help Index

Configural Frequencies Analysis Using Log-linear Modeling

Description

Details

Author(s)

References

Examples

Binomial Test

Description

Usage

Arguments

Details

Value

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Examples

Configural Frequencies Analysis Main Function

Description

Usage

Arguments

Details

Value

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Examples

Local Chi-Square Test

Description

Usage

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Details

Value

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Examples

S3 coefficients for CFA

Description

Usage

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Value

conversion of a covariate dataset into summary covariate values

Description

Usage

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Value

dataset to pattern frequency conversion

Description

Usage

Arguments

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Value

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Examples

Designmatrix for log linear CFA models

Description

Usage

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Details

Value

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Examples

Degrees of freedom

Description

Usage

Arguments

Details

Value

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Examples

Expected frequencies with glm

Description

Usage

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Details

Value

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Examples

Expected frequencies using margins

Description

Usage

Arguments

Details