Attachment-related mother-child interaction dataset

Description

During an attachment study, a mother and her child (middle childhood; age between eight and 12) were videotaped while working on a three-minutes stressful puzzle task. The interaction was coded in two-second intervals for the presence and absence of positive, negative, and task related behavior. The dataset contains seven variables and 90 time intervals.

Usage

AttachmentData

Format

A data frame with 90 rows and 7 variables:

Mpos

mother's positive behavior

Mneg

mother's negative behavior

MAlone

mother is working on the task alone

Togth

mother and child are working on the task together

Cpos

child's positive behavior

Cneg

child's negative behavior

CAlone

child is working on the task alone

Author(s)

Adinda Dujardin & Guy Bosmans guy.bosmans@kuleuven.be

References

Bodner, N., Bosmans, G., Sannen, J., Verhees, M., & Ceulemans, E. (2019). Unraveling middle childhood attachment-related behavior sequences using a micro-coding approach. PLOS ONE, 14(10), e0224372. https://doi.org/10.1371/journal.pone.0224372

Affective family interaction dataset

Description

These data was collected during a nine-minutes problem-solving family interaction between two parents and their adolescent son or daughter During this interaction, the presence and absence of expressions of ‘anger’, ‘dysphoric’ feelings and ‘happiness‘ were coded for each family member in an event-basis way (i.e., noting when a certain behavior starts and when it stops). The codes were subsequently restructured into second-to-second interval data, resulting in a 540 seconds by nine variables binary dataset.

Usage

FamilyData

Format

A data frame with 540 rows and 9 variables:

moanger

mother expressing anger

faanger

father expressing anger

adanger

adolescent expressing anger

modysph

mother expressing dysphoric feelings

fadysph

father expressing dysphoric feelings

addysph

adolescent expressing dysphoric feelings

mohappy

mother expressing happy feelings

fahappy

father expressing happy feelings

adhappy

adolescent expressing happy feelings

Author(s)

Lisa Sheeber lsheeber@ori.org

References

Sheeber, L. B., Kuppens, P., Shortt, J. W., Katz, L. F., Davis, B., & Allen, N. B. (2012). Depression is associated with the escalation of adolescents’ dysphoric behavior during interactions with parents. Emotion, 12(5), 913–918. https://doi.org/10.1037/a0025784

Allen, N. B., Kuppens, P., & Sheeber, L. B. (2012). Heart rate responses to parental behavior in depressed adolescents. Biological Psychology, 90(1), 80–87. https://doi.org/10.1016/j.biopsycho.2012.02.013

Bodner, N., Kuppens, P., Allen, N. B., Sheeber, L. B., & Ceulemans, E. (2018). Affective family interactions and their associations with adolescent depression: A dynamic network approach. Development and Psychopathology, 30(4), 1459–1473. https://doi.org/10.1017/S0954579417001699

Depression symptom dataset

Description

In this depression symptoms data, a patient reported for each week on the presence and absence of eight depression symptoms. The dataset contains the reports of the eight variables for 145 subsequent weeks.

Usage

SymptomData

Format

A data frame with 145 rows and 8 variables:

Core

core depression symptoms including depressed mood and/or diminished interest

Energy

lack of energy

Eat

eating problems and weight loss or gain

Sleep

sleeping problems including hyposomnia or hypersomnia

Motor

psychomotor problems

Guilt

feelings of guilt

Cogn

cognitive problems

Death

preoccupation with death

Author(s)

Bettina Hosenfeld & Peter de Jonge peter.de.jonge@rug.nl

References

Hosenfeld, B., Bos, E. H., Wardenaar, K. J., Conradi, H. J., van der Maas, H. L. J., Visser, I., & de Jonge, P. (2015). Major depressive disorder as a nonlinear dynamic system: Bimodality in the frequency distribution of depressive symptoms over time. BMC Psychiatry, 15(1), 222. https://doi.org/10.1186/s12888-015-0596-5

Bodner, N., Bringmann, L., Tuerlinckx, F., De Jonge, P., & Ceulemans, E. (in press). ConNEcT: A novel network approach for investigating the co-occurrence of binary psychopathological symptoms over time. Psychometrika. https://doi.org/10.1007/s11336-021-09765-2

Depict the relative frequencies (and conditional probabilities) of a binary time series in a barplot

Description

Depict the relative frequencies (and conditional probabilities) of a binary time series in a barplot

Usage

## S3 method for class 'conData'
barplot(height, plottype = "RelFreq", color = NULL, legend = TRUE, ...)

Arguments

Value

barplot

Examples

ExampleData <- cbind(rep(c(0,1),100),
                     rep(c(0,0,0,0,0,1,1,1,1,1),20),
                     c(rep(c(0,0,0,1,1),20),rep(c(0,1,1,1,1),20)),
                     ifelse(rnorm(200,0,1)<0.95,1,0),
                     c(ifelse(rnorm(100,0,1)<0.7,1,0),ifelse(rnorm(100,0,1)<0.7,0,1)),
                     ifelse(rnorm(200,0,1)<(-0.98),1,0))
 colnames(ExampleData) <- c('Var 1','Var 2','Var 3',
                            'Var 4','Var 5','Var 6')
 fancy.col <- c('purple','slateblue','royalblue','cyan4',
                'green3','olivedrab3')
 PersData <- conData(ExampleData)
 barplot(PersData, plottype='RelFreq', color=fancy.col)
 barplot(PersData, plottype='All', color=fancy.col)

 data(SymptomData)
 Sdata <- conData(SymptomData)
 FANCY= c('purple','slateblue', 'royalblue', 'cyan4', 'green3',
          'olivedrab3', 	'orange', 'orangered')
 barplot(Sdata,plottype='RelFreq', color = FANCY)

Explore and tidy raw data

Description

Removes not binary columns from multivariate time series data and calculates a table of relative frequency and auto-dependency for each binary variable

Usage

conData(data)

Arguments

Value

A conData-object including:

data Binary data in time points to variable format.

probs Table of relative frequency and auto-dependence for each variable.

varNames The names of all variables.

Examples

ExampleData <- cbind(rep(c(0,1),100),
                     rep(c(0,0,0,0,0,1,1,1,1,1),20),
                     c(
                       rep(c(0,0,0,1,1),20),
                       rep(c(0,1,1,1,1),20)
                     ),
                     ifelse(rnorm(200,0,1)<0.95,1,0),
                     c(
                       ifelse(rnorm(100,0,1)<0.7,1,0),
                       ifelse(rnorm(100,0,1)<0.7,0,1)
                     ),
                     ifelse(rnorm(200,0,1)<(-0.98),1,0))
colnames(ExampleData) <- c('Var 1','Var 2','Var 3',
                           'Var 4','Var 5','Var 6')
conData(ExampleData)

data(SymptomData)
Sdata <- conData(SymptomData)
Sdata$probs

Calculate contingency measure values of a (lagged) time series matrix

Description

Calculate contingency measure values of a (lagged) time series matrix

Usage

conMx(data, data2 = NULL, lag = 0, conFun)

Arguments

Value

list with two elements:

value Matrix of pairwise calculated contingency measures

para Parameter settings lag, funName and varNames

Examples

conMx(cbind(c(1,0,1,0,1,0,1),c(1,1,1,1,0,0,0)),lag=1,conFun=funCorrJacc)

Calculate the link strength between multiple behaviors and return them as a matrix (optionally discarting all non-significant links)

Description

Calculate the link strength between multiple behaviors and return them as a matrix (optionally discarting all non-significant links)

Usage

conNEcT(
  data,
  lag = 0,
  conFun,
  test = FALSE,
  typeOfTest = "permut",
  adCor = TRUE,
  nBlox = 10,
  nReps = 100,
  signLev = 0.05
)

Arguments

Value

A conNEcT-object including

allLinks Matrix of pairwise calculated contingency measures

signLinksMatrix of pairwise calculated contingency measures containing only significant links (others are set to 0)

pValue P-values for the one-sided upper significance test

para Parameter settings containing lag, test,typeOfTest, adCor, nBlox, nReps, funName, and varNames

probs Table of relative frequency and auto-dependency

Examples

netdata=cbind(rep(c(1,1,1,1,1,0,0,0,0,0),100),
rep(c(0,0,1,1,1,1,0,0,0,0),100))
conNEcT(netdata,lag=1,conFun=funKappa,test=TRUE,nBlox=5)

Compare different lags in a contingency profile

Description

Compare different lags in a contingency profile

Usage

conProf(data, maxlag, conFun)

Arguments

Value

A conProf-object consisting of

value Contingency matrices for different lags

para Parameters including maxlag, funName and varNames

Examples

 IntData <- cbind(rep(rep(c(0,0,1,0,1,0,1,0,0,0),each=5),times=5),
                            rep(rep(c(1,0,0,0), each=10), times=25))
           colnames(IntData) <- c('Var1','Var2')
           conProf(IntData,maxlag=10,conFun=funClassJacc)

Test significance

Description

Test significance

Usage

conTest(
  data,
  lag = 0,
  conFun,
  typeOfTest = "permut",
  adCor = TRUE,
  nBlox = 10,
  nReps = 100
)

Arguments

Value

A conTest-object including

allLinks Matrix of pairwise calculated contingency measures

percentile Matrix of raw percentiles, situating the observed value in the sample distribution

pValue Matrix of the p-values (upper one-sided significance test) calculated by subtracting the percentile from 1.

para: Saving the parameter settings for typeOfTest, adCor, nBlox, nReps, funName, lag, varNames

samples Saved generated replicates/samples for each variable combination under $NameVariable1$NameVariable2

Examples

 signdata=cbind(c(1,0,1,0,1,0,1,0),c(1,1,1,1,0,0,0,0),c(0,0,0,0,0,0,1,1))
           colnames(signdata) <-c ('momangry', 'momsad','adoangry')
           conTest(data=signdata,lag=1,conFun=funClassJacc,typeOfTest='model',
           adCor=FALSE)

Calculate the classic Jaccard index between two vectors

Description

Calculate the classic Jaccard index between two vectors

Usage

funClassJacc(vec1, vec2)

Arguments

Value

list with two elements

value of the classic Jaccard index and

funName name of the function

Examples

data1<-rep(c(1,0,1,1),25)
        data2<-ifelse(rnorm(100,0,1)<0.7,0,1)
        funClassJacc(data1,data2)

Calculate the corrected Jaccard index between two vectors

Description

Calculate the corrected Jaccard index between two vectors

Usage

funCorrJacc(vec1, vec2)

Arguments

Value

list with two elements

value of the corrected Jaccard index and

funName name of the function

Examples

data1<-rep(c(1,0,1,1),25)
        data2<-ifelse(rnorm(100,0,1)<0.7,0,1)
        funCorrJacc(data1,data2)

Calculate Cohen's kappa between two vectors

Description

Calculate Cohen's kappa between two vectors

Usage

funKappa(vec1, vec2)

Arguments

Value

list with two elements

value of the Cohen's kappa and

funName name of the function

Examples

data1<-rep(c(1,0,1,1),25)
        data2<-ifelse(rnorm(100,0,1)<0.7,0,1)
        funKappa(data1,data2)

Calculate the log odds ratio between two vectors

Description

Calculate the log odds ratio between two vectors

Usage

funLogOdds(vec1, vec2)

Arguments

Value

list with two elements

value of the log odds ratio and

funName name of the function

Examples

data1 <- rep(c(1,0,1,1),25)
        data2 <- ifelse(rnorm(100,0,1)<0.7,0,1)
        funLogOdds(data1,data2)

Calculate the odds ratio between two vectors

Description

Calculate the odds ratio between two vectors

Usage

funOdds(vec1, vec2)

Arguments

Value

list with two elements

value of the odds ratio and

funName name of the function

Examples

data1 <- rep(c(1,0,1,1),25)
        data2 <- ifelse(rnorm(100,0,1)<0.7,0,1)
        funOdds(data1,data2)

Calculate the phi correllation coefficient index between two vectors

Description

Calculate the phi correllation coefficient index between two vectors

Usage

funPhiCC(vec1, vec2)

Arguments

Value

list with two elements

value of the phi correlation coefficient and

funName name of the function

Examples

set.seed(1234)
          data1<-rep(c(1,0,1,1),25)
          data2<-ifelse(rnorm(100,0,1)<0.7,0,1)
          funPhiCC(data1,data2)

Calculate the proportion of agreement between two vectors

Description

Calculate the proportion of agreement between two vectors

Usage

funPropAgree(vec1, vec2)

Arguments

Value

list with two elements

value of the proportion of agreement and

funName name of the function

Examples

data1 <- rep(c(1,0,1,1),25)
        data2 <- ifelse(rnorm(100,0,1)<0.7,0,1)
        funPropAgree(data1,data2)

Retrieve (conditional) probabilities from binary time series

Description

Retrieve (conditional) probabilities from binary time series

Usage

getProb(ts)

Arguments

Value

List of three elements

p1 the prevalence p(X(t)=1) and

p1|1 and p1|0 the two auto-conditional probabilities p(X(t)=1|X(t-1)=1) & p(X(t)=1|X(t-1)=0)

Examples

getProb(c(1,0,1,0,1,0,1,1,1,1,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0))

Plot histogram matrix of the significance test

Description

Plot histogram matrix of the significance test

Usage

## S3 method for class 'conTest'
hist(x, signLev = 0.05, ...)

Arguments

Value

Histogram matrix with sample distribution and value from observed data for each variable combination

Examples

data(SymptomData)
          test.result <- conTest(SymptomData[,c(2,6,8)],conFun=funClassJacc,
                          typeOfTest='permut',nBlox=10,adCor=TRUE,nReps=100)
          hist(test.result)

Lag a matrix

Description

Lag a matrix

Usage

lagthemats(data, lag)

Arguments

Value

laggeddata Matrix in which all variables are lagged lag time points

Examples

lagthemats(cbind(c(1,0,1,0,1,0,1),c(1,1,1,1,0,0,0)),lag=2)

Generate data with model-based approach accounting for auto-dependence

Description

This function generates a new time serie that is similar to the original one in relative frequency and auto-dependence, by drawing samples time point per time point from a Bernoulli distribution with the different conditional probabilities as parameter, depending on the state of the previous time point.

Usage

modelAD(vec)

Arguments

Value

Time series vector that is similar to the original one in relative frequency and auto-dependence

Examples

ts=rep(c(1,1,1,1,1,0,0,0,0,0),15)
modelAD(ts)

Generate data with model-based approach ignoring auto-dependence

Description

This function generates a new time serie that is similar to the original one in relative frequency, but not in auto-dependence by drawing from a Bernoulli distribution with the relative frequency as parameter.

Usage

modelNO(vec)

Arguments

Value

Time series vector that is similar to the original one considering relative frequency

Examples

ts=rep(c(1,1,1,1,1,0,0,0,0,0),15)
modelNO(ts)

Generate data with permutation-based approach accounting for auto-dependence

Description

This function generates a new time serie with exactly the same relative frequency as the original one, and a similar auto-dependence by cutting the original variable in segments and shuffeling these segements.

Usage

permutAD(vec, nBloks = 10)

Arguments

Value

Time series vector with exactly the same relative frequency and a similar auto-dependence as the original vector

Examples

ts=rep(c(1,1,1,1,1,0,0,0),15)
permutAD(ts,nBloks=11)

Generate data with permutation-based approach ignoring auto-dependence

Description

This function generates a new time serie with exactly the same relative frequency as the original one, but with a lower auto-dependence by shuffeling all time points.

Usage

permutNO(vec)

Arguments

Value

Time series vector with exactly the same relative frequency as the original vector

Examples

ts=rep(c(1,1,1,1,1,0,0,0,0,0),15)
permutNO(ts)

Visualize the course of the variables over time

Description

Visualize the course of the variables over time

Usage

## S3 method for class 'conData'
plot(x, plottype = "interval", color = NULL, ...)

Arguments

Value

Plot visualizing the course of the variables over time

Examples

ExampleData <- cbind(rep(c(0,1),100),
                     rep(c(0,0,0,0,0,1,1,1,1,1),20),
                     c(rep(c(0,0,0,1,1),20),
                       rep(c(0,1,1,1,1),20)),
                     ifelse(rnorm(200,0,1)<0.95,1,0),
                     c(
                      ifelse(rnorm(100,0,1)<0.7,1,0),
                      ifelse(rnorm(100,0,1)<0.7,0,1)
                     ),
                     ifelse(rnorm(200,0,1)<(-0.98),1,0))
colnames(ExampleData) <- c('Var 1','Var 2','Var 3',
                           'Var 4','Var 5','Var 6')
PersData <- conData(ExampleData)
fancy.col=c('purple','slateblue', 'royalblue', 'cyan4',
            'green3', 'olivedrab3', 'orange', 'orangered')
plot(PersData,plottype='line',color=fancy.col)

data(SymptomData)
Sdata <- conData(SymptomData)
fancy.col=c('purple','slateblue', 'royalblue', 'cyan4',
            'green3', 'olivedrab3', 'orange', 'orangered')
plot(Sdata, plottype='interval',color=fancy.col)

Draw contingency profiles

Description

Draw contingency profiles

Usage

## S3 method for class 'conProf'
plot(x, ...)

Arguments

Value

Contingency profile matrix

Examples

 IntData <- cbind(rep(rep(c(0,0,1,0,1,0,1,0,0,0),each=5),times=5),
                            rep(rep(c(1,0,0,0), each=10), times=25))
           colnames(IntData) <- c('Var1','Var2')
           CP <- conProf(IntData,maxlag=10,conFun=funClassJacc)
           plot(CP)

Draws a Network figure

Description

Draws a Network figure

Usage

qgraph.conNEcT(x, signOnly = TRUE, ...)

Arguments

Value

A network plot

Examples

ADOangry=rep(c(1,1,1,1,1,0,0,0,0,0),100)
MAangry=rep(c(0,0,1,1,1,1,0,0,0,0),100)
MAsad=rep(c(0,0,1,1,1,1,0,0,0,0),100)
netdata <- cbind(ADOangry,MAangry,MAsad)
netnet <- conNEcT(netdata,lag=1,conFun=funKappa,
                           test=TRUE,nBlox=5)
qgraph.conNEcT(netnet,signOnly=FALSE)