mvn | R Documentation |

Family for use with `gam`

implementing smooth multivariate Gaussian regression.
The means for each dimension are given by a separate linear predictor, which may contain smooth components. Extra linear predictors may also be specified giving terms which are shared between components (see `formula.gam`

). The Choleski factor of the response precision matrix is estimated as part of fitting.

```
mvn(d=2)
```

`d` |
The dimension of the response (>1). |

The response is `d`

dimensional multivariate normal, where the covariance matrix is estimated,
and the means for each dimension have sperate linear predictors. Model sepcification is via a list of gam like
formulae - one for each dimension. See example.

Currently the family ignores any prior weights, and is implemented using first derivative information sufficient for BFGS estimation of smoothing parameters. `"response"`

residuals give raw residuals, while `"deviance"`

residuals are standardized to be approximately independent standard normal if all is well.

An object of class `general.family`

.

Simon N. Wood simon.wood@r-project.org

Wood, S.N., N. Pya and B. Saefken (2016), Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association 111, 1548-1575 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2016.1180986")}

`gaussian`

```
library(mgcv)
## simulate some data...
V <- matrix(c(2,1,1,2),2,2)
f0 <- function(x) 2 * sin(pi * x)
f1 <- function(x) exp(2 * x)
f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
(10 * x)^3 * (1 - x)^10
n <- 300
x0 <- runif(n);x1 <- runif(n);
x2 <- runif(n);x3 <- runif(n)
y <- matrix(0,n,2)
for (i in 1:n) {
mu <- c(f0(x0[i])+f1(x1[i]),f2(x2[i]))
y[i,] <- rmvn(1,mu,V)
}
dat <- data.frame(y0=y[,1],y1=y[,2],x0=x0,x1=x1,x2=x2,x3=x3)
## fit model...
b <- gam(list(y0~s(x0)+s(x1),y1~s(x2)+s(x3)),family=mvn(d=2),data=dat)
b
summary(b)
plot(b,pages=1)
solve(crossprod(b$family$data$R)) ## estimated cov matrix
```

mgcv documentation built on July 26, 2023, 5:38 p.m.

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