gelmansim {eeptools} R Documentation

## Generate prediction intervals for model functions

### Description

Generate prediction intervals from R models following Gelman and Hill

### Usage

gelmansim(mod, newdata, n.sims, na.omit = TRUE)


### Arguments

 mod Name of a model object such as lm, glm, or merMod newdata Sets of new data to generate predictions for n.sims Number of simulations per case na.omit Logical indicating whether to remove NAs from newdata

### Details

Currently gelmansim does not work for lm objects because of the way sim in the arm package handles variable names for these objects. It is recommended users use glm in these cases.

### Value

A dataframe with newdata and prediction intervals

### References

Modified from Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

### Examples

 #Examples of "sim"
set.seed (1)
J <- 15
n <- J*(J+1)/2
group <- rep (1:J, 1:J)
mu.a <- 5
sigma.a <- 2
a <- rnorm (J, mu.a, sigma.a)
b <- -3
x <- rnorm (n, 2, 1)
sigma.y <- 6
y <- rnorm (n, a[group] + b*x, sigma.y)
u <- runif (J, 0, 3)
y123.dat <- cbind (y, x, group)
# Linear regression
x1 <- y123.dat[,2]
y1 <- y123.dat[,1]
M1 <- glm (y1 ~ x1)

cases <- data.frame(x1 = seq(-2, 2, by=0.1))
sim.results <- gelmansim(M1, newdata=cases, n.sims=200, na.omit=TRUE)
## Not run:

dat <- as.data.frame(y123.dat)
M2 <- glm (y1 ~ x1 + group, data=dat)

cases <- expand.grid(x1 = seq(-2, 2, by=0.1),
group=seq(1, 14, by=2))

sim.results <- gelmansim(M2, newdata=cases, n.sims=200, na.omit=TRUE)

## End(Not run)


[Package eeptools version 1.2.4 Index]