modelShowClassFunctions {irtProb} | R Documentation |
A set of functions to manipulate modelShow class objects.
is.modelShow
is used to verify if the object correspond to a modelShow
class object.
Round.modelShow
is used to control the number of decimals of the report.
Finally, summary.modelShow
produces different summary reports according th the report
value.
## S3 method for class 'modelShow': is(x) Round(x, digits) ## S3 method for class 'modelShow': Round(x, digits = 6) ## S3 method for class 'modelShow': summary(object, ..., report="means", statistics=c("LL","BIC","T"), criteria="LL", digits=6, tol=0.20, color="grey")
x |
modelShow: modelShow object. |
object |
modelShow: modelShow object. |
digits |
numeric: number of report decimal digits. |
report |
character: summary type to be reported (means, choose, table or add). |
statistics |
character: a vector of variables for which means of statistics
from m4plModelShow will be computed. |
criteria |
character: criteria used to choose between models (LL, AIC or BIC). |
tol |
numeric: tolerance around the choose criteria, so that more models can be considered. |
color |
character: color of the bars if a histogram is choosen for the report. |
... |
generic: to be able to pass parameters from the generic summary function. |
Generic functions for the modelShow class:
is.modelShow |
logical: is the object of the class modelShow? |
Round.modelShow |
data.frame: return the modelShow object results rounded to digits decimals. |
report="means" |
data.frame: means of the statistics choosen for each models. |
report="choosen" |
list: which model is choosen for each subject. |
report="table" |
table: table of frequencies of each model chhosen. |
report="histogram" |
histogram: histogram of frequencies of each model choosen. |
report="add" |
data.frame: the choosen model is added to the essai modelShow object. |
Gilles Raiche, Universite du Quebec a Montreal (UQAM),
Departement d'education et pedagogie
Raiche.Gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/
## GENERATION OF VECTORS OF RESPONSE # NOTE THE USUAL PARAMETRIZATION OF THE ITEM DISCRIMINATION, # THE VALUE OF THE PERSONNAL FLUCTUATION FIXED AT 0, # AND THE VALUE OF THE PERSONNAL PSEUDO-GUESSING FIXED AT 0.30. # IT COULD BE TYPICAL OF PLAGIARISM BEHAVIOR. nItems <- 40 a <- rep(1.702,nItems); b <- seq(-5,5,length=nItems) c <- rep(0,nItems); d <- rep(1,nItems) nSubjects <- 1; rep <- 100 theta <- seq(-2,-2,length=nSubjects) S <- runif(n=nSubjects,min=0.0,max=0.0) C <- runif(n=nSubjects,min=0.3,max=0.3) D <- runif(n=nSubjects,min=0,max=0) set.seed(seed = 100) X <- ggrm4pl(n=nItems, rep=rep, theta=theta, S=S, C=C, D=D, s=1/a, b=b,c=c,d=d) ## Results for each subjects for each models essai <- m4plModelShow(X, b=b, s=1/a, c=c, d=d, m=0, prior="uniform") ## Is essai of class modelShow? is.modelShow(essai) ## Rounding to 2 decimals the first 5 results of essai Round(essai[1:5,], 2) ## Means for each models rounded to 3 decimals summary(essai, report="means", statistics=c("LL","AIC","BIC","T","SeT"), digits=3) ## Model choosen for each of the first 5 subjects ## and the frequency of these choices with the BIC criteria summary(essai[which(essai$ID == (1:5)),], report="choose", criteria="BIC") ## Frequency of the models choosen for all the subjects ## with the LL, AIC and BIC criteria ## Generally, BIC chooses the less models AIC the more. summary(essai, report="table", criteria="LL") summary(essai, report="table", criteria="AIC") summary(essai, report="table", criteria="BIC") ## Frequency of the models choosen for all the subjects ## with the BIC criteria, but with a histogram summary(essai, report="histogram", criteria="BIC", color="blue") ## The choosen model is added to the essai modelShow object for all the subjects ## with the LL, AIC and BIC criteria and statistics about theta are computed ## Recall thet rhe generating theta was fixed at -2.00 ## The LL criteria seems the best one her according to bias and standard error resultLL <- summary(essai, report="add", criteria="LL") resultAIC <- summary(essai, report="add", criteria="AIC") resultBIC <- summary(essai, report="add", criteria="BIC") # LL summary(resultLL[which(resultLL$critLL == TRUE),]$T) sd(resultLL[which(resultLL$critLL == TRUE),]$T, na.rm=TRUE) # AIC summary(resultAIC[which(resultAIC$critAIC == TRUE),]$T) sd(resultAIC[which(resultAIC$critAIC == TRUE),]$T, na.rm=TRUE) # BIC summary(resultBIC[which(resultBIC$critBIC == TRUE),]$T) sd(resultBIC[which(resultBIC$critBIC == TRUE),]$T, na.rm=TRUE)