# addreg_hhsmm_predict: predicting the response values for the regime switching model In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

## predicting the response values for the regime switching model

### Description

This function computes the predictions of the response variable for the Gaussian linear (Markov-switching) regression model for different states for any observation matrix of the covariates

### Usage

```addreg_hhsmm_predict(object, x, K)
```

### Arguments

 `object` a fitted model of class `"hhsmm"` estimated by `hhsmmfit` `x` the observation matrix of the covariates `K` the degrees of freedom for the B-spline

### Value

list of predictions of the response variable

### Author(s)

Morteza Amini, morteza.amini@ut.ac.ir

### Examples

```J <- 3
initial <- c(1, 0, 0)
semi <- rep(FALSE, 3)
P <- matrix(c(0.5, 0.2, 0.3, 0.2, 0.5, 0.3, 0.1, 0.4, 0.5), nrow = J,
byrow = TRUE)
par <- list(intercept = list(3, list(-10, -1), 14),
coefficient = list(-1, list(1, 5), -7),
csigma = list(1.2, list(2.3, 3.4), 1.1),
mix.p = list(1, c(0.4, 0.6), 1))
model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
dens.emis = dmixlm, semi = semi)
train <- simulate(model, nsim = c(20, 30, 42, 50), seed = 1234,
remission = rmixlm, covar = list(mean = 0, cov = 1))
clus = initial_cluster(train = train, nstate = 3, nmix = NULL,
ltr = FALSE, final.absorb = FALSE, verbose = TRUE, regress = TRUE)
initmodel = initialize_model(clus = clus ,mstep = additive_reg_mstep,
dens.emission = dnorm_additive_reg, sojourn = NULL, semi = rep(FALSE, 3),
M = max(train\$N),verbose = TRUE)
fit1 = hhsmmfit(x = train, model = initmodel, mstep = additive_reg_mstep,
M = max(train\$N))
plot(train\$x[, 1] ~ train\$x[, 2], col = train\$s, pch = fit1\$yhat,
xlab = "x", ylab = "y")
text(0,30, "colors are real states",col="red")
text(0,28, "characters are predicted states")
pred <- addreg_hhsmm_predict(fit1, train\$x[, 2], 5)
yhat1 <- pred[[1]]
yhat2 <- pred[[2]]
yhat3 <- pred[[3]]

lines(yhat1[order(train\$x[, 2])]~sort(train\$x[, 2]),col = 2)
lines(yhat2[order(train\$x[, 2])]~sort(train\$x[, 2]),col = 1)
lines(yhat3[order(train\$x[, 2])]~sort(train\$x[, 2]),col = 3)

```

hhsmm documentation built on Aug. 5, 2022, 5:10 p.m.