tnorm {msm} R Documentation

## Truncated Normal distribution

### Description

Density, distribution function, quantile function and random generation for the truncated Normal distribution with mean equal to `mean` and standard deviation equal to `sd` before truncation, and truncated on the interval `[lower, upper]`.

### Usage

```     dtnorm(x, mean=0, sd=1, lower=-Inf, upper=Inf, log = FALSE)
ptnorm(q, mean=0, sd=1, lower=-Inf, upper=Inf, lower.tail = TRUE, log.p = FALSE)
qtnorm(p, mean=0, sd=1, lower=-Inf, upper=Inf, lower.tail = TRUE, log.p = FALSE)
rtnorm(n, mean=0, sd=1, lower=-Inf, upper=Inf)
```

### Arguments

 `x,q` vector of quantiles. `p` vector of probabilities. `n` number of observations. If `length(n) > 1`, the length is taken to be the number required. `mean` vector of means. `sd` vector of standard deviations. `lower` lower truncation point. `upper` upper truncation point. `log, log.p` logical; if TRUE, probabilities p are given as log(p). `lower.tail` logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

### Details

The truncated normal distribution has density

f(x, mu, sigma) = phi(x, mu, sigma) / (Phi(upper, mu, sigma) - Phi(lower, mu, sigma))

for lower <= x <= upper, and 0 otherwise.

mean is the mean of the original Normal distribution before truncation,
sd is the corresponding standard deviation,
u is the upper truncation point,
l is the lower truncation point,
phi(x) is the density of the corresponding normal distribution, and
Phi(x) is the distribution function of the corresponding normal distribution.

If `mean` or `sd` are not specified they assume the default values of `0` and `1`, respectively.

If `lower` or `upper` are not specified they assume the default values of `-Inf` and `Inf`, respectively, corresponding to no lower or no upper truncation.

Therefore, for example, `dtnorm(x)`, with no other arguments, is simply equivalent to `dnorm(x)`.

Only `rtnorm` is used in the `msm` package, to simulate from hidden Markov models with truncated normal distributions. This uses the rejection sampling algorithms described by Robert (1995).

These functions are merely provided for completion, and are not optimized for numerical stability or speed. To fit a hidden Markov model with a truncated Normal response distribution, use a `hmmTNorm` constructor. See the `hmm-dists` help page for further details.

### Value

`dtnorm` gives the density, `ptnorm` gives the distribution function, `qtnorm` gives the quantile function, and `rtnorm` generates random deviates.

### Author(s)

C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk

### References

Robert, C. P. Simulation of truncated normal variables. Statistics and Computing (1995) 5, 121–125

`dnorm`
```x <- seq(50, 90, by=1)