Description Usage Arguments Value Note References Examples

Simulate surrogate response values for cumulative link regression models using the latent method described in Liu and Zhang (2017).

1 2 |

`object` |
An object of class |

`nsim` |
Integer specifying the number of bootstrap replicates to use.
Default is |

`method` |
Character string specifying which method to use to generate the
surrogate response values. Current options are |

`jitter.scale` |
Character string specifyint the scale on which to perform
the jittering whenever |

`...` |
Additional optional arguments. (Currently ignored.) |

A numeric vector of class `c("numeric", "surrogate")`

containing
the simulated surrogate response values. Additionally, if `nsim`

> 1,
then the result will contain the attributes:

`boot_reps`

A matrix with

`nsim`

columns, one for each bootstrap replicate of the surrogate values. Note, these are random and do not correspond to the original ordering of the data;`boot_id`

A matrix with

`nsim`

columns. Each column contains the observation number each surrogate value corresponds to in`boot_reps`

. (This is used for plotting purposes.)

Surrogate response values require sampling from a continuous distribution;
consequently, the result will be different with every call to
`surrogate`

. The internal functions used for sampling from truncated
distributions are based on modified versions of
`rtrunc`

and `qtrunc`

.

For `"glm"`

objects, only the `binomial()`

family is supported.

Liu, Dungang and Zhang, Heping. Residuals and Diagnostics for Ordinal
Regression Models: A Surrogate Approach.
*Journal of the American Statistical Association* (accepted). URL
http://www.tandfonline.com/doi/abs/10.1080/01621459.2017.1292915?journalCode=uasa20

Nadarajah, Saralees and Kotz, Samuel. R Programs for Truncated Distributions.
*Journal of Statistical Software, Code Snippet*, 16(2), 1-8, 2006. URL
https://www.jstatsoft.org/v016/c02.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
# Generate data from a quadratic probit model
set.seed(101)
n <- 2000
x <- runif(n, min = -3, max = 6)
z <- 10 + 3*x - 1*x^2 + rnorm(n)
y <- ifelse(z <= 0, yes = 0, no = 1)
# Scatterplot matrix
pairs(~ x + y + z)
# Setup for side-by-side plots
par(mfrow = c(1, 2))
# Misspecified mean structure
fm1 <- glm(y ~ x, family = binomial(link = "probit"))
s1 <- surrogate(fm1)
scatter.smooth(x, s1 - fm1$linear.predictors,
main = "Misspecified model",
ylab = "Surrogate residual",
lpars = list(lwd = 3, col = "red2"))
abline(h = 0, lty = 2, col = "blue2")
# Correctly specified mean structure
fm2 <- glm(y ~ x + I(x ^ 2), family = binomial(link = "probit"))
s2 <- surrogate(fm2)
scatter.smooth(x, s2 - fm2$linear.predictors,
main = "Correctly specified model",
ylab = "Surrogate residual",
lpars = list(lwd = 3, col = "red2"))
abline(h = 0, lty = 2, col = "blue2")
``` |

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