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.
## 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, ... )
x |
model object of class |
item |
a vector specifying which item(s) the plots are drawn for |
type |
types of the plot. It can be |
no.qvector |
the number of q vectors that need to be plotted when |
data.label |
logical; To show data label or not? |
eps |
the cutoff for PVAF. If not |
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 |
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.
## Not run: 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) ## End(Not run)
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