Mesa plot for Q-matrix validation

The mesa plot was first proposed by de la Torre and Ma (2016) for graphically illustrating the best q-vector(s) for each item. The q-vector on the edge of the mesa is likely to be the best q-vector.

Usage

# S3 method for class 'Qval'
plot(
  x,
  item,
  type = "best",
  no.qvector = 10,
  data.label = TRUE,
  eps = "auto",
  original.q.label = FALSE,
  auto.ylim = TRUE,
  ...
)

Arguments

x

model object of class Qvalidation

item

a vector specifying which item(s) the plots are drawn for

type

types of the plot. It can be "best" or "all". If "best", for all q-vectors requiring the same number of attributes, only the one with the largest PVAF is plotted, which means \(K_j\) q-vectors are plotted; If "all", all q-vectors will be plotted.

no.qvector

the number of q vectors that need to be plotted when type="all". The default is 10, which means the 10 q vectors with the largest PVAFs are plotted.

data.label

logical; To show data label or not?

eps

the cutoff for PVAF. If not NULL, it must be a value between 0 and 1. A horizontal line will be drawn accordingly.

original.q.label

logical; print the label showing the original q-vector or not?

auto.ylim

logical; create y range automatically or not?

...

additional arguments passed to plot function

References

de la Torre, J., & Ma, W. (2016, August). Cognitive diagnosis modeling: A general framework approach and its implementation in R. A Short Course at the Fourth Conference on Statistical Methods in Psychometrics, Columbia University, New York.

See also

Qval, autoGDINA

Examples

if (FALSE) { # \dontrun{
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
Q[1,] <- c(0,1,0)
mod1 <- GDINA(dat = dat, Q = Q, model = "GDINA")
out <- Qval(mod1,eps = 0.9)
item <- c(1,2,10)
plot(out,item=item,data.label=FALSE,type="all")
plot(out,item=10,type="best",eps=0.95)
plot(out,item=10,type="all",no.qvector=6)
} # }