Description Usage Arguments Details Value Author(s) Examples

Reproduces the "Resids vs. linear pred" plot from `gam.check`

but using randomised quantile residuals, a la Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking for "funnel"-type structures in the points, which is much easier with randomised quantile residuals than with deviance residuals, when your model uses a count distribution as the response.

1 | ```
rqgam.check(gam.obj, ...)
``` |

`gam.obj` |
a |

`...` |
arguments passed on to all plotting functions |

Note that this function only works with negative binomial and Tweedie response distributions.

Earlier versions of this function produced the full `gam.check`

output, but this was confusing as only one of the plots was really usedul. Checks of `k`

are not computed, these need to be done using `gam.check`

.

just plots!

Based on code provided by Natalie Kelly, bugs added by Dave Miller

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
library(Distance)
library(dsm)
library(tweedie)
# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)
# fit a detection function and look at the summary
hr.model <- ds(distdata, max(distdata$distance),
key = "hr", adjustment = NULL)
# fit a simple smooth of x and y with a Tweedie response with estimated
# p parameter
mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw())
rqgam.check(mod1)
``` |

dsm documentation built on July 1, 2018, 9:06 a.m.

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