Nothing
library(mvord)
data(data_mvord_toy)
tolerance <- 1e-6
# convert data_mvord_toy into long format
df <- cbind.data.frame("i" = rep(1:100,2), "j" = rep(1:2,each = 100),
"Y" = c(data_mvord_toy$Y1,data_mvord_toy$Y2),
"X1" = rep(data_mvord_toy$X1,2),
"X2" = rep(data_mvord_toy$X2,2),
"f1" = factor(sample(rep(data_mvord_toy$Y2,2)), ordered =F),
"f2" = factor(rep(data_mvord_toy$Y1,2), ordered=F))
df_NA <- df[-c(1,90:110),]
res <- mvord:::mvord(formula = MMO(Y, i, j) ~ 0 + X1 + X2,
data = df_NA,
#index = c("i", "j"),
link = mvprobit(),
control = mvord.control(solver = "newuoa"),
#se = T,
error.structure = cor_general(~1),
threshold.constraints = c(1,2),
coef.constraints = c(1,1))
print(res)
summary(res)
print(summary(res))
coef(res)
thresholds(res)
AIC(res)
BIC(res)
logLik(res)
nobs(res)
vcov(res)
terms(res)
model.matrix(res)
fitted(res)
constraints(res)
names_constraints(Y ~ 0 + X1 + X2, df_NA)
#predict functions
marginal_predict(res, type = "prob", subjectID = c(2,5,32,88))
marginal_predict(res, type = "class", subjectID = c(2,5,32,88)+2)
marginal_predict(res, type = "class")
marginal_predict(res, type = "cum.prob", subjectID = c(2,5,32,88)+8)
predict(res, type = "prob", subjectID = c(3,6,33,55,90))
predict(res, type = "class", subjectID = c(3,6,33,55,90)+1)
predict(res, type = "cum.prob", subjectID = c(3,6,33,55,90)+2)
joint_probabilities(res, response.cat = c(1,2))
joint_probabilities(res, response.cat = res$rho$y)
joint_probabilities(res, type = "cum.prob", response.cat = c(1,2))
error_structure(res)
newdata <- df[1:20,]
newdata[,"i"] <- rep(1:10,2)
newdata[,"j"] <- rep(1:2, each=10)
#newdata[,"Y"] <- NA
marginal_predict(res, type = "prob", newdata = newdata)#subjectID = c(2,5,32,88))
marginal_predict(res, type = "class", newdata = newdata)#subjectID = c(2,5,32,88)+2)
marginal_predict(res, type = "linpred", newdata = newdata)
marginal_predict(res, type = "cum.prob", newdata = newdata)
marginal_predict(res, type = "all.prob", newdata = newdata)
predict(res, type = "prob", newdata=newdata)
predict(res, type = "class", newdata=newdata, subjectID = c(1:5))
predict(res, type = "cum.prob", newdata=newdata)
joint_probabilities(res, response.cat = c(1,2), newdata=newdata)
mat <- matrix(sample(1:3, 20, replace=T),ncol=2)
joint_probabilities(res, response.cat = mat,
newdata=newdata)
joint_probabilities(res, response.cat = mat,
type = "cum.prob", newdata=newdata)
joint_probabilities(res, response.cat = mat,
type = "cum.prob", newdata=newdata, subjectID = c(1,3))
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