## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup, echo=FALSE--------------------------------------------------------
library(LikertMakeR)

## ----logo, fig.align='center', echo=FALSE, out.width = '25%'------------------
knitr::include_graphics("img/LikertMakeR_3.png")

## ----skew, fig.align='center', echo=FALSE, out.width = '85%', fig.cap="Off-centre means always give skewed distribution in bounded rating scales"----
knitr::include_graphics("img/skew_chart.png")

## ----lfastExample-------------------------------------------------------------
nItems <- 4
mean <- 2.5
sd <- 0.75

x1 <- lfast(
  n = 512,
  mean = mean,
  sd = sd,
  lowerbound = 1,
  upperbound = 5,
  items = nItems
)

## ----fig1, fig.height=4, fig.width=5, fig.align='center', echo = FALSE, crop = TRUE, fig.cap="Example: 4-item, 1-5 Likert scale"----
## distribution of x

hist(x1,
  cex.axis = 0.5, cex.main = 0.75,
  breaks = seq(from = (1 - (1 / 8)), to = (5 + (1 / 8)), by = (1 / 4)),
  col = "skyblue", xlab = NULL, ylab = NULL,
  main = paste0("mu=", round(mean(x1), 2), ", sd=", round(sd(x1), 2))
)

## ----lfastx2------------------------------------------------------------------
x2 <- lfast(256, 3, 2.5, 0, 10)

## ----fig2, fig.height=4, fig.width=5, fig.align='center', echo = FALSE, crop = TRUE, fig.cap="Example: likelihood-of-purchase scale"----
## generate histogram

hist(x2,
  cex.axis = 0.5, cex.main = 0.75,
  breaks = seq(from = -0.5, to = 10.5, by = 1),
  col = "skyblue", xlab = NULL, ylab = NULL,
  main = paste0("mu=", round(mean(x2), 2), ", sd=", round(sd(x2), 2))
)

## ----lcorExample--------------------------------------------------------------
## generate uncorrelated synthetic data
n <- 128
lowerbound <- 1
upperbound <- 5
items <- 5

mydat3 <- data.frame(
  x1 = lfast(n, 2.5, 0.75, lowerbound, upperbound, items),
  x2 = lfast(n, 3.0, 1.50, lowerbound, upperbound, items),
  x3 = lfast(n, 3.5, 1.00, lowerbound, upperbound, items)
)

## ----mydat3Head, echo = FALSE-------------------------------------------------
head(mydat3, 6)

## ----mydat3Moments, echo = FALSE----------------------------------------------
moments <- data.frame(
  mean = apply(mydat3, 2, mean) |> round(3),
  sd = apply(mydat3, 2, sd) |> round(3)
) |> t()

moments

## ----mydat3Cor, echo = FALSE--------------------------------------------------
cor(mydat3) |> round(2)

## ----tgt3---------------------------------------------------------------------
## describe a target correlation matrix
tgt3 <- matrix(
  c(
    1.00, 0.85, 0.75,
    0.85, 1.00, 0.65,
    0.75, 0.65, 1.00
  ),
  nrow = 3
)

## ----new3---------------------------------------------------------------------
## apply lcor() function
new3 <- lcor(data = mydat3, target = tgt3)

## ----new3Head, echo = FALSE---------------------------------------------------
head(new3, 10)

## ----new3Cor, echo = FALSE----------------------------------------------------
cor(new3) |> round(3)

## ----cor_matrix_4-------------------------------------------------------------
## define parameters
items <- 4
alpha <- 0.85
# variance <- 0.5 ## by default

## apply makeCorrAlpha() function
set.seed(42)

cor_matrix_4 <- makeCorrAlpha(items, alpha)

## ----cor_matrix_4Print, echo = FALSE------------------------------------------
cor_matrix_4 |> round(3)

## ----cor_matrix_4Alpha--------------------------------------------------------
## using helper function alpha()

alpha(cor_matrix_4)

## ----fig3, fig.height=4, fig.width=5, fig.align='center', echo = TRUE, crop = TRUE----
## using helper function eigenvalues()

eigenvalues(cor_matrix_4, 1)

## ----cor_matrix_12, message=FALSE---------------------------------------------
## define parameters
items <- 12
alpha <- 0.90
variance <- 1.0

## apply makeCorrAlpha() function
set.seed(42)

cor_matrix_12 <- makeCorrAlpha(items = items, alpha = alpha, variance = variance)

## ----cor_matrix_12Print, echo = FALSE-----------------------------------------
cor_matrix_12 |> round(2)

## ----fig4, fig.height=4, fig.width=5, fig.align='center', echo = TRUE, crop = TRUE----
## calculate Cronbach's Alpha
alpha(cor_matrix_12)

## calculate eigenvalues of the correlation matrix
eigenvalues(cor_matrix_12, 1) |> round(3)

## ----cor_matrix_d-------------------------------------------------------------
## apply makeCorrAlpha() with diagnostics
set.seed(42)

cor_matrix_5 <- makeCorrAlpha(
  items = 6,
  alpha = 0.90,
  diagnostics = TRUE
)

## ----cor_matrix_5_mat---------------------------------------------------------
## output
cor_matrix_5$R |> round(2)

cor_matrix_5$diagnostics

## ----parameters_makeCorrLoadings, echo=TRUE-----------------------------------
## Example loadings

factorLoadings <- matrix(
  c(
    0.05, 0.20, 0.70,
    0.10, 0.05, 0.80,
    0.05, 0.15, 0.85,
    0.20, 0.85, 0.15,
    0.05, 0.85, 0.10,
    0.10, 0.90, 0.05,
    0.90, 0.15, 0.05,
    0.80, 0.10, 0.10
  ),
  nrow = 8, ncol = 3, byrow = TRUE
)

## row and column names

rownames(factorLoadings) <- c("Q1", "Q2", "Q3", "Q4", "Q5", "Q6", "Q7", "Q8")
colnames(factorLoadings) <- c("Factor1", "Factor2", "Factor3")

## Factor correlation matrix**

factorCor <- matrix(
  c(
    1.0,  0.5, 0.4,
    0.5,  1.0, 0.3,
    0.4,  0.3, 1.0
  ),
  nrow = 3, byrow = TRUE
)

## ----apply_makeCorrLoadings, echo=TRUE----------------------------------------
## apply makeCorrLoadings() function
itemCorrelations <- makeCorrLoadings(factorLoadings, factorCor)

## derived correlation matrix to two decimal places
round(itemCorrelations, 2)

## ----test_makeCorrLoadings, echo=TRUE-----------------------------------------
## correlated factors mean that eigenvalues should suggest two or three factors
eigenvalues(cormatrix = itemCorrelations, scree = TRUE)

## ----orthogonal_makeCorrLoadings, echo=TRUE-----------------------------------
## orthogonal factors are assumed when factor correlation matrix is not included
orthogonalItemCors <- makeCorrLoadings(factorLoadings)

## derived correlation matrix to two decimal places
round(orthogonalItemCors, 2)

## ----test_orthogonal, echo=TRUE-----------------------------------------------
## eigenvalues should suggest exactly  three factors
eigenvalues(cormatrix = orthogonalItemCors, scree = TRUE)

## ----makeScalesExample1-------------------------------------------------------

## define parameters
n <- 128
dfMeans <- c(2.5, 3.0, 3.0, 3.5)
dfSds <- c(1.0, 1.0, 1.5, 0.75)
lowerbound <- rep(1, 4)
upperbound <- rep(5, 4)

corMat <- matrix(
  c(
    1.00, 0.25, 0.35, 0.45,
    0.25, 1.00, 0.70, 0.75,
    0.35, 0.70, 1.00, 0.85,
    0.45, 0.75, 0.85, 1.00
  ),
  nrow = 4, ncol = 4
)

var_names <- c("var1", "var2", "var3", "var4")
colnames(corMat) <- var_names
rownames(corMat) <- var_names

## apply makeScales() function
df <- makeScales(
  n = n,
  means = dfMeans,
  sds = dfSds,
  lowerbound = lowerbound,
  upperbound = upperbound,
  cormatrix = corMat
)

## ----str_makeScalesExample1---------------------------------------------------

## test the function
str(df)

## ----moments_makeScalesExample1-----------------------------------------------

### means should be correct to two decimal places
dfmoments <- data.frame(
  mean = apply(df, 2, mean) |> round(3),
  sd = apply(df, 2, sd) |> round(3)
) |> t()

dfmoments

## ----cors_makeScalesExample1--------------------------------------------------

### correlations should be correct to two decimal places
cor(df) |> round(3)

## ----makeScalesExample2-------------------------------------------------------
## define parameters
n <- 256
dfMeans <- c(3.9, 4.1, 3.6, 4.0)
dfSds <- c(0.6, 0.5, 0.8, 0.7)
lowerbound <- rep(1, 4)
upperbound <- rep(5, 4)
items <- c(4, 3, 4, 3)

corMat <- matrix(
  c(
    1.00, 0.75, 0.60, 0.70,
    0.75, 1.00, 0.65, 0.72,
    0.60, 0.65, 1.00, 0.68,
    0.70, 0.72, 0.68, 1.00
  ),
  nrow = 4, ncol = 4
)

scale_names <- c("BT", "BS", "BL", "BLY")
rownames(corMat) <- scale_names
colnames(corMat) <- scale_names

## apply makeScales() function
df <- makeScales(
  n = n,
  means = dfMeans,
  sds = dfSds,
  lowerbound = lowerbound,
  upperbound = upperbound,
  items = items,
  cormatrix = corMat
)

## test the function
head(df)
tail(df)

### means should be correct to two decimal places
dfmoments <- data.frame(
  mean = apply(df, 2, mean) |> round(3),
  sd = apply(df, 2, sd) |> round(3)
) |> t()

dfmoments

### correlations should be correct to two decimal places
cor(df) |> round(3)

## ----genCorrelation-----------------------------------------------------------
## define parameters
k <- 6
myAlpha <- 0.85

## generate correlation matrix
set.seed(42)
myCorr <- makeCorrAlpha(items = k, alpha = myAlpha)

## display correlation matrix
myCorr |> round(3)

### checking Cronbach's Alpha
alpha(cormatrix = myCorr)

## ----gendataframe-------------------------------------------------------------
## define parameters
n <- 256
myMeans <- c(2.75, 3.00, 3.00, 3.25, 3.50, 3.5)
mySds <- c(1.00, 0.75, 1.00, 1.00, 1.00, 1.5)
lowerbound <- rep(1, k)
upperbound <- rep(5, k)

## Generate Items
myItems <- makeScales(
  n = n, means = myMeans, sds = mySds,
  lowerbound = lowerbound, upperbound = upperbound,
  items = 1,
  cormatrix = myCorr
)

## resulting dataframe
head(myItems)
tail(myItems)

## means and standard deviations
myMoments <- data.frame(
  means = apply(myItems, 2, mean) |> round(3),
  sds = apply(myItems, 2, sd) |> round(3)
) |> t()
myMoments

## Cronbach's Alpha of dataframe
alpha(NULL, myItems)

## ----fig5, fig.height=5, fig.width=5, fig.align='center', echo=FALSE, warning=FALSE, crop = TRUE, fig.cap="Summary of dataframe from makeScales() function"----
# Correlation panel

panel.cor <- function(x, y) {
  usr <- par("usr")
  on.exit(par(usr))
  par(usr = c(0, 1, 0, 1))
  r <- round(cor(x, y), digits = 2)
  txt <- paste0(r)
  cex.cor <- 0.8 / strwidth(txt)
  text(0.5, 0.5, txt, cex = 1.25)
}

# Customize upper panel

upper.panel <- function(x, y) {
  points(x, y, pch = 19, col = "#0000ff11")
}

# diagonals

panel.hist <- function(x, ...) {
  usr <- par("usr")
  on.exit(par(usr))
  par(usr = c(usr[1:2], 0, 1.5))
  h <- hist(x, plot = FALSE)
  breaks <- h$breaks
  nB <- length(breaks)
  y <- h$counts
  y <- y / max(y)
  rect(breaks[-nB], 0, breaks[-1], y, col = "#87ceeb66")
}

# Create the plots

pairs(myItems,
  lower.panel = panel.cor,
  upper.panel = upper.panel,
  diag.panel = panel.hist
)

## ----generate_summated_scale--------------------------------------------------
## define parameters
n <- 256
mean <- 3.00
sd <- 0.85
lowerbound <- 1
upperbound <- 5
items <- 4

## apply lfast() function
meanScale <- lfast(
  n = n, mean = mean, sd = sd,
  lowerbound = lowerbound, upperbound = upperbound,
  items = items
)

## sum over all items
summatedScale <- meanScale * items

## ----summatedScale_histogram, echo=FALSE, fig.height=4, fig.width=5, fig.align='center', crop = TRUE, fig.cap="Summated scale distribution"----
## Histogram of summated scale

hist(summatedScale,
  cex.axis = 0.5, cex.main = 0.75,
  breaks = seq(
    from = ((lowerbound * items) - 0.5),
    to = ((upperbound * items) + 0.5), by = 1
  ),
  col = "skyblue", xlab = NULL, ylab = NULL,
  main = paste0(
    "mu=", round(mean * items, 2), ", sd=", round(sd * items, 2), ", range:",
    (lowerbound * items), ":", (upperbound * items)
  )
)

## ----makeItemsScale_example_1, fig.height=4, fig.width=5, fig.align='center', crop = TRUE----
## apply makeItemsScale() function

newItems_1 <- makeItemsScale(
  scale = summatedScale,
  lowerbound = lowerbound,
  upperbound = upperbound,
  items = items
)

### First 10 observations and summated scale
head(cbind(newItems_1, summatedScale), 10)

### correlation matrix
cor(newItems_1) |> round(2)

### default Cronbach's alpha = 0.80
alpha(data = newItems_1) |> round(4)

### calculate eigenvalues and print scree plot
eigenvalues(cor(newItems_1), 1) |> round(3)

## ----makeItemsScale_example_2, fig.height=4, fig.width=5, fig.align='center', crop = TRUE----
## apply makeItemsScale() function
newItems_2 <- makeItemsScale(
  scale = summatedScale,
  lowerbound = lowerbound,
  upperbound = upperbound,
  items = items,
  alpha = 0.9
)

### First 10 observations and summated scale
head(cbind(newItems_2, summatedScale), 10)

### correlation matrix
cor(newItems_2) |> round(2)

### requested Cronbach's alpha = 0.90
alpha(data = newItems_2) |> round(4)

### calculate eigenvalues and print scree plot
eigenvalues(cor(newItems_2), 1) |> round(3)

## ----makeItemsScale_example_3, fig.height=4, fig.width=5, fig.align='center', crop = TRUE----
## apply makeItemsScale() function
newItems_3 <- makeItemsScale(
  scale = summatedScale,
  lowerbound = lowerbound,
  upperbound = upperbound,
  items = items,
  alpha = 0.6,
  variance = 0.7
)

### First 10 observations and summated scale
head(cbind(newItems_3, summatedScale), 10)

### correlation matrix
cor(newItems_3) |> round(2)

### requested Cronbach's alpha = 0.70
alpha(data = newItems_3) |> round(4)

### calculate eigenvalues and print scree plot
eigenvalues(cor(newItems_3), 1) |> round(3)

## ----independent-samples_t-test, echo = TRUE----------------------------------
## define parameters
lower <- 1
upper <- 5
items <- 6

## generate two independent samples
x1 <- lfast(
  n = 20, mean = 2.5, sd = 0.75,
  lowerbound = lower, upperbound = upper, items = items
)
x2 <- lfast(
  n = 30, mean = 3.0, sd = 0.85,
  lowerbound = lower, upperbound = upper, items = items
)

## run independent-samples t-test
t.test(x1, x2)

## ----paired-sample_t-test_data, , echo = TRUE---------------------------------
## define parameters
n <- 20
means <- c(2.5, 3.0)
sds <- c(0.75, 0.85)
lower <- 1
upper <- 5
items <- 6
t <- -2.5

## run the function
pairedDat <- makePaired(
  n = n, means = means, sds = sds,
  t_value = t,
  lowerbound = lower, upperbound = upper, items = items
)

## ----makePaired_output_properties, echo=TRUE----------------------------------
## test function output
str(pairedDat)

cor(pairedDat) |> round(2)

pairedMoments <- data.frame(
  mean = apply(pairedDat, MARGIN = 2, FUN = mean) |> round(3),
  sd = apply(pairedDat, MARGIN = 2, FUN = sd) |> round(3)
) |> t()

pairedMoments

## ----paired-sample_t-test, echo=TRUE------------------------------------------
## run a paired-sample t-test

paired_t <- t.test(x = pairedDat$X1, y = pairedDat$X2, paired = TRUE)

# paired_t <- t.test(pairedDat$X1, pairedDat$X2, paired = TRUE)


paired_t

## ----makeRepeated-test, echo=TRUE---------------------------------------------
out1 <- makeRepeated(
  n = 128,
  k = 3,
  means = c(3.1, 3.5, 3.9),
  sds = c(1.0, 1.1, 1.0),
  items = 4,
  f_stat = 4.87,
  structure = "cs",
  diagnostics = FALSE
)

head(out1$data)
out1$correlation_matrix


out2 <- makeRepeated(
  n = 32, k = 4,
  means = c(2.75, 3.5, 4.0, 4.4),
  sds = c(0.8, 1.0, 1.2, 1.0),
  f_stat = 16,
  structure = "ar1",
  items = 5,
  lowerbound = 1, upperbound = 7,
  return_corr_only = FALSE,
  diagnostics = TRUE
)

print(out2)


out3 <- makeRepeated(
  n = 32, k = 4,
  means = c(2.0, 2.5, 3.0, 2.8),
  sds = c(0.8, 0.9, 1.0, 0.9),
  items = 4,
  f_stat = 24,
  structure = "toeplitz",
  diagnostics = TRUE
)

str(out3)

## ----correlateScales_dataframes, echo = TRUE----------------------------------
n <- 128
lower <- 1
upper <- 5

### attitude #1

#### generate a correlation matrix
cor_1 <- makeCorrAlpha(items = 4, alpha = 0.80)

#### specify moments as vectors
means_1 <- c(2.5, 2.5, 3.0, 3.5)
sds_1 <- c(0.75, 0.85, 0.85, 0.75)

#### apply makeScales() function
Att_1 <- makeScales(
  n = n, means = means_1, sds = sds_1,
  lowerbound = rep(lower, 4), upperbound = rep(upper, 4),
  items = 1,
  cormatrix = cor_1
)

### attitude #2

#### generate a correlation matrix
cor_2 <- makeCorrAlpha(items = 5, alpha = 0.85)

#### specify moments as vectors
means_2 <- c(2.5, 2.5, 3.0, 3.0, 3.5)
sds_2 <- c(0.75, 0.85, 0.75, 0.85, 0.75)

#### apply makeScales() function
Att_2 <- makeScales(
  n, means_2, sds_2,
  rep(lower, 5), rep(upper, 5),
  items = 1,
  cor_2
)

### attitude #3

#### generate a correlation matrix
cor_3 <- makeCorrAlpha(items = 6, alpha = 0.90)

#### specify moments as vectors
means_3 <- c(2.5, 2.5, 3.0, 3.0, 3.5, 3.5)
sds_3 <- c(0.75, 0.85, 0.85, 1.0, 0.75, 0.85)

#### apply makeScales() function
Att_3 <- makeScales(
  n, means_3, sds_3,
  rep(lower, 6), rep(upper, 6),
  items = 1,
  cor_3
)

### behavioural intention
intent <- lfast(n, mean = 4.0, sd = 3, lowerbound = 0, upperbound = 10) |>
  data.frame()
names(intent) <- "int"

## ----dataframe_properties-----------------------------------------------------
## Attitude #1
A1_moments <- data.frame(
  means = apply(Att_1, 2, mean) |> round(2),
  sds = apply(Att_1, 2, sd) |> round(2)
) |> t()

### Attitude #1 moments
A1_moments

### Attitude #1 correlations
cor(Att_1) |> round(2)

### Attitude #1 cronbach's alpha
alpha(cor(Att_1)) |> round(3)

## Attitude #2
A2_moments <- data.frame(
  means = apply(Att_2, 2, mean) |> round(2),
  sds = apply(Att_2, 2, sd) |> round(2)
) |> t()

### Attitude #2 moments
A2_moments

### Attitude #2 correlations
cor(Att_2) |> round(2)

### Attitude #2 cronbach's alpha
alpha(cor(Att_2)) |> round(3)

## Attitude #3
A3_moments <- data.frame(
  means = apply(Att_3, 2, mean) |> round(2),
  sds = apply(Att_3, 2, sd) |> round(2)
) |> t()

### Attitude #3 moments
A3_moments

### Attitude #3 correlations
cor(Att_3) |> round(2)

### Attitude #2 cronbach's alpha
alpha(cor(Att_3)) |> round(3)


## Behavioural Intention

intent_moments <- data.frame(
  mean = apply(intent, 2, mean) |> round(3),
  sd = apply(intent, 2, sd) |> round(3)
) |> t()

### Intention moments
intent_moments

## ----correlateScales_parameters-----------------------------------------------
### target scale correlation matrix
scale_cors <- matrix(
  c(
    1.0, 0.7, 0.6, 0.5,
    0.7, 1.0, 0.4, 0.3,
    0.6, 0.4, 1.0, 0.2,
    0.5, 0.3, 0.2, 1.0
  ),
  nrow = 4
)

### bring dataframes into a list
data_frames <- list("A1" = Att_1, "A2" = Att_2, "A3" = Att_3, "Int" = intent)

## ----my_correlated_scales-----------------------------------------------------
### apply correlateScales() function
my_correlated_scales <- correlateScales(
  dataframes = data_frames,
  scalecors = scale_cors
)

## ----fig6, fig.height=9, fig.width=9, fig.align='center', echo=FALSE, warning=FALSE,  crop = TRUE----
# Correlation panel
panel.cor <- function(x, y) {
  usr <- par("usr")
  on.exit(par(usr))
  par(usr = c(0, 1, 0, 1))
  r <- round(cor(x, y), digits = 2)
  txt <- paste0(r)
  cex.cor <- 0.8 / strwidth(txt)
  text(0.5, 0.5, txt, cex = 1.25)
}
# Customize upper panel
upper.panel <- function(x, y) {
  points(x, y, pch = 19, col = "#0000ff11")
}
# diagonals
panel.hist <- function(x, ...) {
  usr <- par("usr")
  on.exit(par(usr))
  par(usr = c(usr[1:2], 0, 1.5))
  h <- hist(x, plot = FALSE)
  breaks <- h$breaks
  nB <- length(breaks)
  y <- h$counts
  y <- y / max(y)
  rect(breaks[-nB], 0, breaks[-1], y, col = "#0000ff50")
}
# Create the plots
pairs(my_correlated_scales,
  lower.panel = panel.cor,
  upper.panel = upper.panel,
  diag.panel = panel.hist
)

## ----newdata_check------------------------------------------------------------
## data structure
str(my_correlated_scales)

## ----fig7, fig.height=4, fig.width=5, fig.align='center', echo = TRUE,  crop = TRUE----
## eigenvalues of dataframe correlations
Cor_Correlated_Scales <- cor(my_correlated_scales)
eigenvalues(cormatrix = Cor_Correlated_Scales, scree = TRUE) |> round(2)

## ----fig7a, fig.height=4, fig.width=5, fig.align='center', echo = TRUE,  crop = TRUE----
#### Eigenvalues of predictor variable items only
Cor_Attitude_items <- cor(my_correlated_scales[, -16])
eigenvalues(cormatrix = Cor_Attitude_items, scree = TRUE) |> round(2)

## ----alphaExample-------------------------------------------------------------
## define parameters
df <- data.frame(
  V1 = c(4, 2, 4, 3, 2, 2, 2, 1),
  V2 = c(3, 1, 3, 4, 4, 3, 2, 3),
  V3 = c(4, 1, 3, 5, 4, 1, 4, 2),
  V4 = c(4, 3, 4, 5, 3, 3, 3, 3)
)

corMat <- matrix(
  c(
    1.00, 0.35, 0.45, 0.75,
    0.35, 1.00, 0.65, 0.55,
    0.45, 0.65, 1.00, 0.65,
    0.75, 0.55, 0.65, 1.00
  ),
  nrow = 4, ncol = 4
)

## apply function examples
alpha(cormatrix = corMat)
alpha(data = df)
alpha(NULL, df)
alpha(corMat, df)

## ----eigenExample-------------------------------------------------------------
## define parameters
correlationMatrix <- matrix(
  c(
    1.00, 0.25, 0.35, 0.45,
    0.25, 1.00, 0.70, 0.75,
    0.35, 0.70, 1.00, 0.85,
    0.45, 0.75, 0.85, 1.00
  ),
  nrow = 4, ncol = 4
)

## apply function
evals <- eigenvalues(cormatrix = correlationMatrix)

print(evals)

## ----fig8, fig.height=4, fig.width=5, fig.align='center', echo = TRUE, crop = TRUE----
evals <- eigenvalues(correlationMatrix, 1)
print(evals)

## ----reliabilityExample, warning=FALSE, message=FALSE-------------------------

## create dataset
my_cor <- LikertMakeR::makeCorrAlpha(
  items = 4,
  alpha = 0.80
)

my_data <- LikertMakeR::makeScales(
  n = 64,
  means = c(2.75, 3.00, 3.25, 3.50),
  sds = c(1.25, 1.50, 1.30, 1.25),
  lowerbound = rep(1, 4),
  upperbound = rep(5, 4),
  cormatrix = my_cor
)

## run function
reliability(my_data)

reliability(
  my_data,
  include = c("lambda6", "polychoric")
)


## bootstrapped Confidence intervals can be slow!
reliability(
  my_data,
  include = "polychoric",
  ci = TRUE,
  n_boot = 64
)


## ----eval = FALSE-------------------------------------------------------------
# n <- 128
# sample(1:5, n,
#   replace = TRUE,
#   prob = c(0.1, 0.2, 0.4, 0.2, 0.1)
# )

