# R/int_to_prob.R In mHMMbayes: Multilevel Hidden Markov Models Using Bayesian Estimation

#### Defines functions int_to_probprob_to_int

```#' @keywords internal
# computes probabilities from intercepts
int_to_prob <- function(int1) {
if(is.matrix(int1)){
prob1 <- matrix(nrow = nrow(int1), ncol = ncol(int1) + 1)
for(r in 1:nrow(int1)){
exp_int1 	<- matrix(exp(c(0, int1[r,])), nrow  = 1)
prob1[r,] <- exp_int1 / as.vector(exp_int1 %*% c(rep(1, (dim(exp_int1)))))
}
} else {
exp_int1 	<- matrix(exp(c(0, int1)), nrow  = 1)
prob1 		<- exp_int1 / as.vector(exp_int1 %*% c(rep(1, (dim(exp_int1)))))
}
return(round(prob1,4))
}

# computes intercepts from probabilities, per row of input matrix
# first catagory is reference catagory
prob_to_int <- function(prob1){
prob1 <- prob1 + 0.00001
b0 <- matrix(NA, nrow(prob1), ncol(prob1)-1)
sum_exp <- numeric(nrow(prob1))
for(r in 1:nrow(prob1)){
sum_exp[r] <- (1/prob1[r,1]) - 1
for(cr in 2:ncol(prob1)){
#for every b0 except the first collumn (e.g. b012 <- log(y12/y11-y12))
b0[r,(cr-1)] <- log(prob1[r,cr]*(1+sum_exp[r]))
}
}
return(round(b0,4))
}
```

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mHMMbayes documentation built on Oct. 30, 2019, 5:05 p.m.