# dmvnorm: Density of multivariate Gaussian distribution In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

## Description

Efficiently computes the density of observations for a generic multivariate Gaussian distribution.

## Usage

 `1` ```dmvnorm(data, mean, sigma, log = FALSE) ```

## Arguments

 `data` A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. `mean` A vector of means for each variable. `sigma` A positive definite covariance matrix. `log` A logical value indicating whether or not the logarithm of the densities should be returned.

## Value

A numeric vector whose ith element gives the density of the ith observation in `data` for the multivariate Gaussian distribution with parameters `mean` and `sigma`.

`dnorm`, `dens`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# univariate ngrid <- 101 x <- seq(-5, 5, length = ngrid) dens <- dmvnorm(x, mean = 1, sigma = 5) plot(x, dens, type = "l") # bivariate ngrid <- 101 x1 <- x2 <- seq(-5, 5, length = ngrid) mu <- c(1,0) sigma <- matrix(c(1,0.5,0.5,2), 2, 2) dens <- dmvnorm(as.matrix(expand.grid(x1, x2)), mu, sigma) dens <- matrix(dens, ngrid, ngrid) image(x1, x2, dens) contour(x1, x2, dens, add = TRUE) ```

### Example output

```Package 'mclust' version 5.4.7
Type 'citation("mclust")' for citing this R package in publications.
```

mclust documentation built on Nov. 5, 2021, 5:07 p.m.