EjemploDario.R
In dariopadula/triNOPAR: What the Package Does (One Line, Title Case)
#' Script de ejemplo de uso de las funciones
#'
#'
library(devtools)
library(parallel)
library(data.table)
library(tidyverse)
library(np)
#'libary(here)
load_all()
#'library(dplyr)
#'library(mirt)
#'library(psych)
#'library(MASS)
#******************************************************************
#Primero generamos data frame vacio donde iran los diferentes
#items que componen nuestro banco
#Esto se debe inicializar una sola vez a menos que se quiera cambiar
#el banco completo
bancoITEMS<- genBancoDF(nparam=15)
#Generamos un banco de 300 items
set.seed(12345)
m1pl=matrix(c(1,1,-2.5,1.5),byrow=T,ncol=2,nrow=2)
bancoITEMS=genitmodelo(1000,"1PL",m1pl,bancoITEMS)
m2pl=matrix(c(0.5,2,-2.5,1.5),byrow=T,ncol=2,nrow=2)
bancoITEMS=genitmodelo(1000,"2PL",m2pl,bancoITEMS)
m3pl=matrix(c(0.5,2,-2.5,1.5,0.2,0.4),byrow=T,ncol=2,nrow=3)
bancoITEMS=genitmodelo(1000,"3PL",m3pl,bancoITEMS)
#diseno=matrix(c(100,"2PL",100,"1PL",100,"3PL"),ncol=2,byrow = T)
diseno=matrix(c(200,"1PL"),ncol=2,byrow = T)
bancTeor = bancoTAI(bancoITEMS,diseno)
thetas=creathetas("Normal",n=10000,mean=0,sd=1)
respu=GenResp(banco = bancTeor,thetas)
# Estimacion paramétrica de los parametros en 1 dimension de bancTeor
modelo=1
#tipit<-c(rep("2PL",100),rep("Rasch",100),rep("3PL",100))
tipit<-c(rep("Rasch",200))
paramsEst = estpar(respu,modelo,tipit)
#Estimados
aest=paramsEst$parametros$items[,"a"]
best=paramsEst$parametros$items[,"b"]
cest=paramsEst$parametros$items[,"g"]
#' Calcula la correlacion y diferencia entre estimado y real en el parametro b
cor(best,bancTeor[,4])
difepar=best-bancTeor[,4]
# Habilidades
thest=paramsEst$habilidades
mean(thest)
sd(thest)
difeth=thest-thetas
# Estimacion no parametrica Caso 1 dimension
itdim1=colnames(respu)[1:200]
itemsgs=list(dim1=itdim1)
pesos=list(dim1=rep(1,200))
respG=estFunG_Simple(respu,itemsgs,pesos)
thetaest = estthetaNP(scores = respG,Dth = qnorm)
cor(thetaest[,"dtg1"],thetas)
mean(thetaest[,"dtg1"])
sd(thetaest[,"dtg1"])
difeth2=thetaest[,"dtg1"]-thetas
unido=cbind(thetas,thetaest[,"dtg1"],difeth2)
#Estimacion NP con la ventana elegida de forma optima
## Lo hago de forma paralela
# puntosNP = seq(-3,3,0.01)
# puntosNP = seq(0,1,0.001)
puntosNP = seq(round(pnorm(-3),3),round(pnorm(3),3),0.001)
th_use = 'pcg'
funEstNoPar = function(ii) {
hT=ventana1D(items = ii,th_use = th_use,test = thetaest,nucleodes="gaussian",muestra=2000)
iccnp=estRegNoPar(items = ii,h=last(hT),th_use = th_use,
test=thetaest,puntos=puntosNP,
nucleo=normal,sigma=1)
return(as.vector(iccnp[["NPICC"]]))
}
trials = 1:200
cl <- makeCluster(detectCores())
clusterExport(cl, c("thetaest","puntosNP","th_use","ventana1D","estRegNoPar","normal"))
clusterEvalQ(cl, {
library(tidyverse)
library(np)})
tini = Sys.time()
system.time({
results <- parallel::parLapply(cl,trials,funEstNoPar)
})
stopCluster(cl)
tfin = Sys.time()
difftime(tfin,tini,units = 'secs')
#### data frame con curvas ICC estimadas con regresion no parametrica
iccnp_mat = do.call(cbind,results)
colnames(iccnp_mat) = colnames(thetaest)[trials]
#############################################
####### Guardo resultados haste el momento
# save.image(file = 'Resultados/ResHasta_iccnp_mat.RData')
# load('Resultados/ResHasta_iccnp_mat.RData')
nIt = 100
ICC = iccFun(a = bancTeor[nIt,'P1'],b = bancTeor[nIt,'P2'],c = 0,grilla = qnorm(puntosNP))
plot(qnorm(puntosNP),ICC)
lines(qnorm(puntosNP),iccnp_mat[,nIt],col = 'red')
############################################
############################################
############################################
##### Estima curvas np isotonicas de forma paralela
puntosicc = puntosNP
if(sum(abs(puntosNP) > 1) > 0) puntosicc = pnorm(puntosNP)
thetaiso = puntosNP
funEstNoParIso = function(ii) {
iccnp = iccnp_mat[,ii]
hdi= 0.9*length(iccnp)^(-1/5)*min(sd(iccnp),(quantile(iccnp,prob=0.75)-quantile(iccnp,prob=0.25))/1.364)
nopariso=icciso(icc1=iccnp,hd=hdi,
thetaiso = thetaiso,nt=200,
puntosicc = puntosicc,nucleod=normal)
return(nopariso$resfin)
}
cl <- makeCluster(detectCores())
clusterExport(cl, c("iccnp_mat","puntosicc","icciso","normal","epa","thetaiso"))
clusterEvalQ(cl, {
library(tidyverse)
library(np)})
tini = Sys.time()
system.time({
resultsISO <- parallel::parLapply(cl,trials,funEstNoParIso)
})
stopCluster(cl)
tfin = Sys.time()
difftime(tfin,tini,units = 'secs')
#### data frame con curvas ICC estimadas de forma isotonica
icciso_mat = do.call(cbind,resultsISO)
colnames(icciso_mat) = colnames(iccnp_mat)
nIt = 15
ICC = iccFun(a = bancTeor[nIt,'P1'],b = bancTeor[nIt,'P2'],c = 0,grilla = qnorm(puntosNP))
plot(qnorm(puntosNP),ICC)
lines(qnorm(puntosNP),icciso_mat[,nIt],col = 'red')
lines(qnorm(puntosNP),iccnp_mat[,nIt],col = 'green')
############################################
############################################
############################################
####### Estima la funcion de informacion para las curvas parametricas y las isotonicas
#### Items parametricos
parEst = paramsEst[[1]]$items
colnames(parEst) = c('a','b','c','d')
infoFunPar = do.call(cbind,
sapply(trials, function(xx) {
a = parEst[xx,'a']
b = parEst[xx,'b']
c = parEst[xx,'c']
info = parInfoFun(a,b,c,
grillaUni = puntosicc,
trnfFun = qnorm)
list(info)
}))
colnames(infoFunPar) = rownames(parEst)[trials]
### CURVAS ISOTONICAS
infoFunIso = do.call(cbind,
sapply(trials, function(xx) {
iccis = icciso_mat[,xx]
info = derISO_Info(iccis,
puntuni = thetaiso,
nucleo = normal,
hd = NULL,
shiftFun = qnorm)
list(info$informEntorno)
}))
colnames(infoFunIso) = colnames(icciso_mat)[trials]
nIt = 1
plot(qnorm(puntosNP),infoFunPar[,nIt])
plot(qnorm(puntosNP),infoFunIso[,nIt])
#################################################
#################################################
#################################################
##### Calcula la funcion KL para los items parametris los NP y lo NPiso
## Parametrico
KLFunPar = kl_mat_par(paramsMIRT = parEst[trials,],
grillaEval = thetaiso,
distTrans = qnorm)
colnames(KLFunPar) = rownames(parEst)[trials]
##############################
### ICC no par
KLFunNoPar = kl_mat_NOpar(iccNP_mat = iccnp_mat[,trials],
sepGrilla = 0.001,
entorno = 0.1,
puntuni = puntuni,
shiftFun = qnorm)
##############################
### ICC no par isotonica
KLFunNoParIso = kl_mat_NOpar(iccNP_mat = icciso_mat[,trials],
sepGrilla = 0.001,
entorno = 0.1,
puntuni = puntuni,
shiftFun = qnorm)
nIt = 20
plot(qnorm(puntuni),KLFunPar[,nIt])
plot(qnorm(puntuni),KLFunNoPar[,nIt])
plot(qnorm(puntuni),KLFunNoParIso[,nIt])
####################################################################
####### INSUMOS PARA EL TAI (prueba)
## Parametros verdaderos (adecuo el df para que se interprete bien en la funcion)
parametros = bancTeor[,c('NombreIt','Modelo',paste0('P',1:3))]
rownames(parametros) = apply(parametros[,c('NombreIt','Modelo')],1,function(xx) paste(xx,collapse = ''))
parametros$P3 = ifelse(is.na(parametros$P3),0,parametros$P3)
####################################################
############ GUARDO HASTA ACA
# save.image(file = 'Resultados/ResHasta_iccnp_mat.RData')
# load('Resultados/ResHasta_iccnp_mat.RData')
#############################################################
sujtai = rnorm(100)
epsilon = 0.01
minit = 0
maxit = 20
curvaNOPAR = NULL
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('InfoFun'),
matrizSelect = infoFunPar,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
plot(res[,c(1,4)])
cor(res[,c(1,4)])
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR = NULL,
parametros,
parEst,
itemsSelec = c('Random'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
plot(res[,c(1,4)])
cor(res[,c(1,4)])
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
###Caso Ceci
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('InfoFun'),
matrizSelect = infoFunIso,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
plot(res[,c(1,4)])
cor(res[,c(1,4)])
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('KL'),
matrizSelect = KLFunNoParIso,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('Random'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('ESH'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = iccnp_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('KL'),
matrizSelect = KLFunNoPar,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = iccnp_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('ESH'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
#### Cambio esto para mario
### Cambio para Dario
dariopadula/triNOPAR documentation built on April 25, 2022, 7:46 a.m.
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#' Script de ejemplo de uso de las funciones
#'
#'
library(devtools)
library(parallel)
library(data.table)
library(tidyverse)
library(np)
#'libary(here)
load_all()
#'library(dplyr)
#'library(mirt)
#'library(psych)
#'library(MASS)
#******************************************************************
#Primero generamos data frame vacio donde iran los diferentes
#items que componen nuestro banco
#Esto se debe inicializar una sola vez a menos que se quiera cambiar
#el banco completo
bancoITEMS<- genBancoDF(nparam=15)
#Generamos un banco de 300 items
set.seed(12345)
m1pl=matrix(c(1,1,-2.5,1.5),byrow=T,ncol=2,nrow=2)
bancoITEMS=genitmodelo(1000,"1PL",m1pl,bancoITEMS)
m2pl=matrix(c(0.5,2,-2.5,1.5),byrow=T,ncol=2,nrow=2)
bancoITEMS=genitmodelo(1000,"2PL",m2pl,bancoITEMS)
m3pl=matrix(c(0.5,2,-2.5,1.5,0.2,0.4),byrow=T,ncol=2,nrow=3)
bancoITEMS=genitmodelo(1000,"3PL",m3pl,bancoITEMS)
#diseno=matrix(c(100,"2PL",100,"1PL",100,"3PL"),ncol=2,byrow = T)
diseno=matrix(c(200,"1PL"),ncol=2,byrow = T)
bancTeor = bancoTAI(bancoITEMS,diseno)
thetas=creathetas("Normal",n=10000,mean=0,sd=1)
respu=GenResp(banco = bancTeor,thetas)
# Estimacion paramétrica de los parametros en 1 dimension de bancTeor
modelo=1
#tipit<-c(rep("2PL",100),rep("Rasch",100),rep("3PL",100))
tipit<-c(rep("Rasch",200))
paramsEst = estpar(respu,modelo,tipit)
#Estimados
aest=paramsEst$parametros$items[,"a"]
best=paramsEst$parametros$items[,"b"]
cest=paramsEst$parametros$items[,"g"]
#' Calcula la correlacion y diferencia entre estimado y real en el parametro b
cor(best,bancTeor[,4])
difepar=best-bancTeor[,4]
# Habilidades
thest=paramsEst$habilidades
mean(thest)
sd(thest)
difeth=thest-thetas
# Estimacion no parametrica Caso 1 dimension
itdim1=colnames(respu)[1:200]
itemsgs=list(dim1=itdim1)
pesos=list(dim1=rep(1,200))
respG=estFunG_Simple(respu,itemsgs,pesos)
thetaest = estthetaNP(scores = respG,Dth = qnorm)
cor(thetaest[,"dtg1"],thetas)
mean(thetaest[,"dtg1"])
sd(thetaest[,"dtg1"])
difeth2=thetaest[,"dtg1"]-thetas
unido=cbind(thetas,thetaest[,"dtg1"],difeth2)
#Estimacion NP con la ventana elegida de forma optima
## Lo hago de forma paralela
# puntosNP = seq(-3,3,0.01)
# puntosNP = seq(0,1,0.001)
puntosNP = seq(round(pnorm(-3),3),round(pnorm(3),3),0.001)
th_use = 'pcg'
funEstNoPar = function(ii) {
hT=ventana1D(items = ii,th_use = th_use,test = thetaest,nucleodes="gaussian",muestra=2000)
iccnp=estRegNoPar(items = ii,h=last(hT),th_use = th_use,
test=thetaest,puntos=puntosNP,
nucleo=normal,sigma=1)
return(as.vector(iccnp[["NPICC"]]))
}
trials = 1:200
cl <- makeCluster(detectCores())
clusterExport(cl, c("thetaest","puntosNP","th_use","ventana1D","estRegNoPar","normal"))
clusterEvalQ(cl, {
library(tidyverse)
library(np)})
tini = Sys.time()
system.time({
results <- parallel::parLapply(cl,trials,funEstNoPar)
})
stopCluster(cl)
tfin = Sys.time()
difftime(tfin,tini,units = 'secs')
#### data frame con curvas ICC estimadas con regresion no parametrica
iccnp_mat = do.call(cbind,results)
colnames(iccnp_mat) = colnames(thetaest)[trials]
#############################################
####### Guardo resultados haste el momento
# save.image(file = 'Resultados/ResHasta_iccnp_mat.RData')
# load('Resultados/ResHasta_iccnp_mat.RData')
nIt = 100
ICC = iccFun(a = bancTeor[nIt,'P1'],b = bancTeor[nIt,'P2'],c = 0,grilla = qnorm(puntosNP))
plot(qnorm(puntosNP),ICC)
lines(qnorm(puntosNP),iccnp_mat[,nIt],col = 'red')
############################################
############################################
############################################
##### Estima curvas np isotonicas de forma paralela
puntosicc = puntosNP
if(sum(abs(puntosNP) > 1) > 0) puntosicc = pnorm(puntosNP)
thetaiso = puntosNP
funEstNoParIso = function(ii) {
iccnp = iccnp_mat[,ii]
hdi= 0.9*length(iccnp)^(-1/5)*min(sd(iccnp),(quantile(iccnp,prob=0.75)-quantile(iccnp,prob=0.25))/1.364)
nopariso=icciso(icc1=iccnp,hd=hdi,
thetaiso = thetaiso,nt=200,
puntosicc = puntosicc,nucleod=normal)
return(nopariso$resfin)
}
cl <- makeCluster(detectCores())
clusterExport(cl, c("iccnp_mat","puntosicc","icciso","normal","epa","thetaiso"))
clusterEvalQ(cl, {
library(tidyverse)
library(np)})
tini = Sys.time()
system.time({
resultsISO <- parallel::parLapply(cl,trials,funEstNoParIso)
})
stopCluster(cl)
tfin = Sys.time()
difftime(tfin,tini,units = 'secs')
#### data frame con curvas ICC estimadas de forma isotonica
icciso_mat = do.call(cbind,resultsISO)
colnames(icciso_mat) = colnames(iccnp_mat)
nIt = 15
ICC = iccFun(a = bancTeor[nIt,'P1'],b = bancTeor[nIt,'P2'],c = 0,grilla = qnorm(puntosNP))
plot(qnorm(puntosNP),ICC)
lines(qnorm(puntosNP),icciso_mat[,nIt],col = 'red')
lines(qnorm(puntosNP),iccnp_mat[,nIt],col = 'green')
############################################
############################################
############################################
####### Estima la funcion de informacion para las curvas parametricas y las isotonicas
#### Items parametricos
parEst = paramsEst[[1]]$items
colnames(parEst) = c('a','b','c','d')
infoFunPar = do.call(cbind,
sapply(trials, function(xx) {
a = parEst[xx,'a']
b = parEst[xx,'b']
c = parEst[xx,'c']
info = parInfoFun(a,b,c,
grillaUni = puntosicc,
trnfFun = qnorm)
list(info)
}))
colnames(infoFunPar) = rownames(parEst)[trials]
### CURVAS ISOTONICAS
infoFunIso = do.call(cbind,
sapply(trials, function(xx) {
iccis = icciso_mat[,xx]
info = derISO_Info(iccis,
puntuni = thetaiso,
nucleo = normal,
hd = NULL,
shiftFun = qnorm)
list(info$informEntorno)
}))
colnames(infoFunIso) = colnames(icciso_mat)[trials]
nIt = 1
plot(qnorm(puntosNP),infoFunPar[,nIt])
plot(qnorm(puntosNP),infoFunIso[,nIt])
#################################################
#################################################
#################################################
##### Calcula la funcion KL para los items parametris los NP y lo NPiso
## Parametrico
KLFunPar = kl_mat_par(paramsMIRT = parEst[trials,],
grillaEval = thetaiso,
distTrans = qnorm)
colnames(KLFunPar) = rownames(parEst)[trials]
##############################
### ICC no par
KLFunNoPar = kl_mat_NOpar(iccNP_mat = iccnp_mat[,trials],
sepGrilla = 0.001,
entorno = 0.1,
puntuni = puntuni,
shiftFun = qnorm)
##############################
### ICC no par isotonica
KLFunNoParIso = kl_mat_NOpar(iccNP_mat = icciso_mat[,trials],
sepGrilla = 0.001,
entorno = 0.1,
puntuni = puntuni,
shiftFun = qnorm)
nIt = 20
plot(qnorm(puntuni),KLFunPar[,nIt])
plot(qnorm(puntuni),KLFunNoPar[,nIt])
plot(qnorm(puntuni),KLFunNoParIso[,nIt])
####################################################################
####### INSUMOS PARA EL TAI (prueba)
## Parametros verdaderos (adecuo el df para que se interprete bien en la funcion)
parametros = bancTeor[,c('NombreIt','Modelo',paste0('P',1:3))]
rownames(parametros) = apply(parametros[,c('NombreIt','Modelo')],1,function(xx) paste(xx,collapse = ''))
parametros$P3 = ifelse(is.na(parametros$P3),0,parametros$P3)
####################################################
############ GUARDO HASTA ACA
# save.image(file = 'Resultados/ResHasta_iccnp_mat.RData')
# load('Resultados/ResHasta_iccnp_mat.RData')
#############################################################
sujtai = rnorm(100)
epsilon = 0.01
minit = 0
maxit = 20
curvaNOPAR = NULL
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('InfoFun'),
matrizSelect = infoFunPar,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
plot(res[,c(1,4)])
cor(res[,c(1,4)])
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR = NULL,
parametros,
parEst,
itemsSelec = c('Random'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
plot(res[,c(1,4)])
cor(res[,c(1,4)])
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
###Caso Ceci
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('InfoFun'),
matrizSelect = infoFunIso,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
plot(res[,c(1,4)])
cor(res[,c(1,4)])
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('KL'),
matrizSelect = KLFunNoParIso,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('Random'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = icciso_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('ESH'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = iccnp_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('KL'),
matrizSelect = KLFunNoPar,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
######################
curvaNOPAR = iccnp_mat
res = TAIgeneric(sujtai,
epsilon,
minit,
maxit,
curvaNOPAR,
parametros,
parEst,
itemsSelec = c('ESH'),
matrizSelect = NULL,
seqTheta = puntosNP)
###################################################
#### Calculo de los errores
errYses = ERRYSES(simData = res,grilla = seq(1:100)/100)
errYses[[1]]
#### Cambio esto para mario
### Cambio para Dario
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