if (FALSE) {
# LM model for categorical responses without covariates
data("data_SRHS_long")
SRHS <- data_SRHS_long[1:2400,]
# Categories rescaled to vary from 0 (“poor”) to 4 (“excellent”)
SRHS$srhs <- 5 - SRHS$srhs
out <- lmest(responsesFormula = srhs ~ NULL,
index = c("id","t"),
data = SRHS,
k = 3,
modBasic = 1,
out_se = FALSE)
out.se <- se(out)
out1 <- lmest(responsesFormula = srhs ~ NULL,
index = c("id","t"),
data = SRHS,
k = 3,
modBasic = 1,
out_se = TRUE)
out1.se <- se(out1)
# LM model for categorical responses with covariates on the latent model
out2 <- lmest(responsesFormula = srhs ~ NULL,
latentFormula = ~
I(gender - 1) +
I( 0 + (race == 2) + (race == 3)) +
I(0 + (education == 4)) +
I(0 + (education == 5)) +
I(age - 50) + I((age-50)^2/100),
index = c("id","t"),
data = SRHS,
k = 2,
paramLatent = "multilogit",
start = 0)
out2.se <- se(out2)
# LM model for continous responses without covariates
data(data_long_cont)
out3 <- lmestCont(responsesFormula = Y1 + Y2 + Y3 ~ NULL,
index = c("id", "time"),
data = data_long_cont,
k =3,
modBasic = 1,
tol = 10^-5)
out3.se <- se(out3)
# LM model for continous responses with covariates
out4 <- lmestCont(responsesFormula = Y1 + Y2 + Y3 ~ NULL,
latentFormula = ~ X1 + X2 | X1 + X2,
index = c("id", "time"),
data = data_long_cont,
k = 3,
output = TRUE)
out4.se <- se(out4)
}
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