Package 'SSRA'

Title: Sakai Sequential Relation Analysis
Description: 'Takea Semantic Structure Analysis' (TSSA) and 'Sakai Sequential Relation Analysis' (SSRA) for polytomous items. Package includes functions for generating a sequential relation table and a treegram to visualize the sequential relations between pairs of items.
Authors: Takuya Yanagida [cre, aut], Keiko Sakai [aut]
Maintainer: Takuya Yanagida <[email protected]>
License: GPL-3
Version: 0.1-1
Built: 2024-11-17 06:54:10 UTC
Source: CRAN

Help Index


Example data based on Takeya (1991)

Description

A dataset containing 10 observations on 5 items.

Usage

exdat

Format

A data frame with 10 rows and 5 variables


Plot ssra

Description

Function for plotting the ssra object

Usage

## S3 method for class 'ssra'
plot(x, r.crt = NULL, r.sig = TRUE, d.sq = NULL,
  m.sig = TRUE, sig.col = TRUE, col = c("red2", "green4", "blue3",
  "black"), pch = c(1, 2, 0, 4), mar = c(3.5, 3.5, 1.5, 1), ...)

Arguments

x

requires the return object from the SSRA function

r.crt

minimal absolute correlation to be judged 'sequential'

r.sig

plot statistically significant correlations

d.sq

minimal effect size Cohen's d to be judged 'sequential'

m.sig

plot statistically significant mean difference

sig.col

significance in different colors

col

color code or name

pch

plotting character

mar

number of lines of margin to be specified on the four sides of the plot

...

further arguments passed to or from other methods

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

SSRA, treegram, scatterplot

Examples

## Not run: 
# Example data based on Takeya (1991)

# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
exdat.ssra <- SSRA(exdat, output = FALSE)
plot(exdat.ssra)

## End(Not run)

Sakai Sequential Relation Analysis Print

Description

print function for the ssra object

Usage

## S3 method for class 'ssra'
print(x, digits = 3, ...)

Arguments

x

requires the result object of hssr function

digits

integer indicating the number of decimal places to be used

...

further arguments passed to or from other methods

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

seqtable

Examples

# Example data based on Takeya (1991)

# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
exdat.ssra <- SSRA(exdat, output = FALSE)
print(exdat.ssra)

Semantric Structure Analysis Print

Description

print function for the tssa object

Usage

## S3 method for class 'tssa'
print(x, digits = 3, ...)

Arguments

x

requires the result object of hssr function

digits

integer indicating the number of decimal places to be used

...

further arguments passed to or from other methods

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

seqtable

Examples

# Example data based on Takeya (1991)

# Takea Semantic Structure Analysis
# ordering assesed according to the ordering coefficient
exdat.tssa <- TSSA(exdat, m = 5, output = FALSE)
print(exdat.tssa)

# Takea Semantic Structure Analysis including statistical testing
# ordering assesed according to the ordering coefficient and statistical significance
exdat.tssa <- TSSA(exdat, m = 5, sig = TRUE, output = FALSE)
print(exdat.tssa)

Scatterplot Matrices

Description

This function produces a scatterplot matrix

Usage

scatterplot(data, type = c("jitter", "size", "count", "sun"))

Arguments

data

a data frame

type

type of plot, i.e., 'jitter', 'size', 'count', and 'sun'

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

TSSA, SSRA

Examples

# Example data based on Takeya (1991)

# Scatterplot matrix: jitter
scatterplot(exdat)

# Scatterplot matrix: size
scatterplot(exdat, type = "size")

# Scatterplot matrix: count
scatterplot(exdat, type = "count")

# Scatterplot matrix: sun
scatterplot(exdat, type = "sun")

Sequential Relation Table

Description

This function builds a table for the tssa and ssra object used to create a treegram

Usage

seqtable(object, order = c("no", "decreasing", "increasing"), digits = 3,
  output = TRUE)

Arguments

object

requires the return object from the TSSA or SSRA function

order

sort by item mean of j?

digits

integer indicating the number of decimal places to be used

output

print result table?

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

TSSA, SSRA, treegram, summary.seqtable

Examples

# Example data based on Takeya (1991)

# Takea Semantic Structure Analysis
# ordering assesed according to the correlation coefficient and mean difference
exdat.tssa <- TSSA(exdat, m = 5, output = FALSE)
seqtable(exdat.tssa)

# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
exdat.ssra <- SSRA(exdat, output = FALSE)
seqtable(exdat.ssra)

Sakai Sequential Relation Analysis

Description

This function conducts the Sequential Relation Analysis based on Sakai 2016

Usage

SSRA(dat, r.crt = 0.3, mu.sq = 0, mu.eq = Inf, d.sq = 0.2, d.eq = 0.2,
  pairwise = TRUE, method = c("pearson", "kendall", "spearman"),
  alpha = 0.05, p.adjust.method = c("holm", "hochberg", "hommel",
  "bonferroni", "BH", "BY", "fdr", "none"), digits = 3, vnames = TRUE,
  order = c("no", "decreasing", "increasing"), exclude = TRUE,
  output = TRUE)

Arguments

dat

requires a data frame with polytomous data

r.crt

correlation coefficient criterion to be judged 'sequential' or 'equivalent

mu.sq

Absolute mean difference criterion to be judged 'sequential'

mu.eq

maximal absolute mean difference to be judged 'equivalent'

d.sq

effect size for mean difference criterion to be judged 'sequential'

d.eq

maximal effect size Cohen's d to be judged 'equivalent'

pairwise

pairwise deletion of missing data, if pairwise = FALSE listwise deletion is applied

method

character string indicating which correlation coefficient to be used, 'pearson' = Pearson's product moment correlation coefficien 'spearman' = Spearman's rho statistic 'kendall' = Kendall's tau (default)

alpha

significance level

p.adjust.method

p-value correction method for multiple comparisons, see: ?p.adjust (default = holm)

digits

integer indicating the number of decimal places to be used

vnames

use variable names for labeling?

order

sort by item mean of j and k?

exclude

exclude paths with no relationship?

output

print result table?

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Value

Returns an object of class ssra, to be used for the seqtable function. The object is a list with following entries: 'dat' (data frame), 'call" (function call), 'args' (specification of arguments), 'time' (time of analysis), 'R' (R version), 'package' (package version), and 'restab' (result table). The 'restab' entry has following entries:

j item j
k item k
n sample size
j.mean mean of item j
j.sd standard deviation of item j
k.mean mean of item k
k.sd standard deviation of item k
r correlation coefficient
r.t test statistic of the statistical significanc test for the correlation coefficient
r.p statistical significance value of the correlation
r.sig statistical significance of the correlation (0 = not significant / 1 = significant)
r.crt correlation criterion for judging 'sequential' or 'equal': 'r.p < alpha' and 'r > r.crt' (0 = no / 1 = yes)
m.diff mean difference
sd.diff standard deviation difference
m.diff.eff effect size Cohen's d for dependent samples
m.t test statistic of the statistical significanc test for mean difference
m.p statistical significance value of the mean difference
m.sig statistical significance of the mean difference (0 = not significant / 1 = significant)
m.crt.sq mean difference criteria for judging 'sequential': 'm.diff.p < alpha', 'm.diff > mu.sq' and 'm.diff.eff > d.sq' (0 = no / -1 = yes negative / 1 = yes postive)
m.crt.eq mean difference criteria for judging 'equivalence': statistical significant and 'm <= mu.eq' 'd <= d.sq' (0 = no 1 = yes)
seq sequential relation of item pairs ("+","-", "")
eq equivalence of item pairs ("=" or "")
order order structure of item pairs ("=", "+","-")

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

seqtable, TSSA, plot.ssra, scatterplot

Examples

# Example data based on Takeya (1991)

# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
SSRA(exdat)

Sequential Relationship Table Summary

Description

summary function for the seqtab object

Usage

## S3 method for class 'seqtable'
summary(object, exclude = TRUE, ...)

Arguments

object

requires the result object of seqtable function

exclude

exclude lower-order paths (i.e., paths included in higher order paths)?

...

additional arguments affecting the summary produced

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Value

rel relationship: sq = sequential / eq = equal
var variables involved in the sequential/equal paths

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

SSRA, TSSA

Examples

# Example data based on Takeya (1991)

# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
exdat.ssra <- SSRA(exdat, output = FALSE)
exdat.seqtab<- seqtable(exdat.ssra, output = FALSE)
summary(exdat.seqtab)

Treegram

Description

This function draws a treegram for the Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)

Usage

treegram(object, select = NULL, pos = NULL, col = NULL, mai = c(0.2, 0,
  0.2, 0.2), print.pos = TRUE, cex.text = 0.95, x.factor = 1.7,
  x.digits = 0, y.digits = 2, y.intersp = 1.45, cex.legend = 0.9)

Arguments

object

requires the result object of seqtab function

select

select items to be plotted

pos

position of items on the x-axis

col

color code or name for paths

mai

numeric vector of the form c(bottom, left, top, right) which gives the margin size specified in inches

print.pos

display x/y-position as legend

cex.text

text expansion factor relative to current par("cex")

x.factor

shift factor of legend position

x.digits

decimal places of x-position

y.digits

decimal places of y-position

y.intersp

legend character interspacing factor for vertical (y) line distances

cex.legend

legend character expansion factor relative to current par("cex)

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

seqtable

Examples

# Example data based on Takeya (1991)

# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
exdat.ssra <- SSRA(exdat, output = FALSE)
exdat.seqtab <- seqtable(exdat.ssra, output = FALSE)
treegram(exdat.seqtab)

# Select items to be plotted
exdat.ssra <- SSRA(exdat, output = FALSE)
exdat.seqtab <- seqtable(exdat.ssra, output = FALSE)
treegram(exdat.seqtab, select = c("Item2", "Item3", "Item4"))

# Define position for each item on the x-axis
exdat.ssra <- SSRA(exdat, output = FALSE)
exdat.seqtab <- seqtable(exdat.ssra, output = FALSE)
treegram(exdat.seqtab, pos = c(Item5 = 1, Item4 = 3,
                               Item3 = 5, Item2 = 2, Item1 = 4))

# Change colors for each path of an item
exdat.ssra <- SSRA(exdat, output = FALSE)
exdat.seqtab <- seqtable(exdat.ssra, output = FALSE)
treegram(exdat.seqtab,
         col = c(Item5 = "red3", Item4 = "blue3",
                 Item3 = "gray99", Item2 = "darkgreen", Item1 = "darkorange2"))

Takea Semantic Structure Analysis

Description

This function conducts the Semantic Structure Analysis for polytomous items based on Takeya 1991

Usage

TSSA(dat, m, crit = 0.93, pairwise = TRUE, sig = FALSE, exact = TRUE,
  alpha = 0.05, p.adjust.method = c("holm", "hochberg", "hommel",
  "bonferroni", "BH", "BY", "fdr", "none"), digits = 3, vnames = TRUE,
  order = c("no", "decreasing", "increasing"), exclude = TRUE,
  output = TRUE)

Arguments

dat

requires a data frame with polytomous data, all items need to have the same numbers of response categories

m

requires the number of item response categories

crit

criteria for ordering coefficient

pairwise

pairwise deletion of missing data, if pairwise = FALSE listwise deletion if applied

sig

if sig = TRUE, ordering will be assesed according to ordering coefficient and statistical significance

exact

if exact = TRUE, exact binomial test will be applied otherwise single-sample proportion test will be applied

alpha

significance level

p.adjust.method

p-value correction method for multiple comparisons, see: ?p.adjust (default = holm)

digits

integer indicating the number of decimal places to be used

vnames

use variable names for labeling?

order

sort by item mean of j and k?

exclude

exclude paths with no relationship?

output

print result table?

Details

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches

Value

Returns an object of class tssa, to be used for the seqtable function. The object is a list with following entries: 'dat' (data frame), 'call" (function call), 'args' (specification of arguments), 'time' (time of analysis), 'R' (R version), 'package' (package version), and 'restab' (result table). The 'restab' entry has following entries:

j item j
k item k
n sample size
j.mean mean of item j
j.sd standard devication of item j
k.mean mean of item k
k.sd standard devication of item k
c.jk ordering coefficient j -> k
p.jk p-value j -> k (available if sig = TRUE)
sig.jk statistical significane p-value j -> k (0 = no / 1 = yes; available if sig = TRUE)
c.kj ordering coefficient k -> j
p.kj p-value k -> j (0 = no / 1 = yes; available if sig = TRUE)
sig.kj statistical significane p-value k -> j (available if sig = TRUE)
crt.jk ordering j -> k
crt.kj ordering k -> j
order order structure of item pairs ("=", "+","-")

Author(s)

Takuya Yanagida Keiko Sakai

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

SSRA, seqtable, scatterplot

Examples

# Example data based on Takeya (1991)

# Takea Semantic Structure Analysis
# ordering assesed according to the ordering coefficient
TSSA(exdat, m = 5)

# Takea Semantic Structure Analysis including statistical testing
# ordering assesed according to the ordering coefficient and statistical significance
TSSA(exdat, m = 5, sig = TRUE)