Description Usage Arguments Value Note References Examples
A generic function to simulate surrogate residuals for cumulative link regression models using the latent method described in Liu and Zhang (2017).
It also support the signbased residuals (Li and Shepherd, 2010), generalized residuals (Franses and Paap, 2001), and deviance residuals for cumulative link regression models.
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73  ## S3 method for class 'clm'
residuals(
object,
type = c("surrogate", "sign", "general", "deviance"),
jitter = c("latent", "uniform"),
jitter.uniform.scale = c("probability", "response"),
nsim = 1L,
...
)
## S3 method for class 'lrm'
residuals(
object,
type = c("surrogate", "sign", "general", "deviance"),
jitter = c("latent", "uniform"),
jitter.uniform.scale = c("probability", "response"),
nsim = 1L,
...
)
## S3 method for class 'orm'
residuals(
object,
type = c("surrogate", "sign", "general", "deviance"),
jitter = c("latent", "uniform"),
jitter.uniform.scale = c("probability", "response"),
nsim = 1L,
...
)
## S3 method for class 'polr'
residuals(
object,
type = c("surrogate", "sign", "general", "deviance"),
jitter = c("latent", "uniform"),
jitter.uniform.scale = c("probability", "response"),
nsim = 1L,
...
)
## S4 method for signature 'vglm'
residuals(
object,
type = c("surrogate", "sign", "general", "deviance"),
jitter = c("latent", "uniform"),
jitter.uniform.scale = c("probability", "response"),
nsim = 1L,
...
)
## S4 method for signature 'vgam'
residuals(
object,
type = c("surrogate", "sign", "general", "deviance"),
jitter = c("latent", "uniform"),
jitter.uniform.scale = c("probability", "response"),
nsim = 1L,
...
)
## S3 method for class 'ord'
residuals(
object,
type = c("surrogate", "sign", "general", "deviance", "pearson", "working",
"response", "partial"),
jitter = c("latent", "uniform"),
jitter.uniform.scale = c("probability", "response"),
nsim = 1L,
...
)
## S3 method for class 'PAsso'
residuals(object, draw_id = 1, ...)

object 
An object of class 
type 
The type of residuals which should be returned. The alternatives are: "surrogate" (default), "sign", "general", and "deviance". Can be abbreviated.

jitter 
When the

jitter.uniform.scale 
When the 
nsim 
An integer specifying the number of replicates to use.
Default is 
... 
Additional optional arguments. 
draw_id 
A number refers to the ith draw of residuals. 
A numeric vector of class c("numeric", "resids")
containing
the simulated surrogate residuals. Additionally, if nsim
> 1,
then the result will contain the attributes:
draws
A matrix with nsim
columns, one for each
is a replicate of the surrogate residuals. Note, they correspond
to the original ordering of the data;
draws_id
A matrix with nsim
columns. Each column
contains the observation number each surrogate residuals corresponds to in
draws
. (This is used for plotting purposes.)
A matrix of class c("matrix", "resids")
containing
the simulated surrogate residuals used for the partial association
analysis in PAsso
. Additionally, if rep_num
> 1 in PAsso
,
then the result will contain the attributes:
draws
An array contains all draws of residuals.
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, D., Li, S., Yu, Y., & Moustaki, I. (2020). Assessing partial association between ordinal variables: quantification, visualization, and hypothesis testing. Journal of the American Statistical Association, 114. doi: 10.1080/01621459.2020.1796394
Liu, D., & Zhang, H. (2018). Residuals and diagnostics for ordinal regression models: A surrogate approach. Journal of the American Statistical Association, 113(522), 845854. doi: 10.1080/01621459.2017.1292915
Li, C., & Shepherd, B. E. (2010). Test of association between two ordinal variables while adjusting for covariates. Journal of the American Statistical Association, 105(490), 612620. doi: 10.1198/jasa.2010.tm09386
Franses, P. H., & Paap, R. (2001). Quantitative models in marketing research. Cambridge University Press. doi: 10.1017/CBO9780511753794
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  # 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)
# Misspecified mean structure
fm1 < glm(y ~ x, family = binomial(link = "probit"))
diagnostic.plot(fm1)
# Correctly specified mean structure
fm2 < glm(y ~ x + I(x ^ 2), family = binomial(link = "probit"))
diagnostic.plot(fm2)
# Load data
data("ANES2016")
PAsso_1 < PAsso(responses = c("PreVote.num", "PID"),
adjustments = c("income.num", "age", "edu.year"),
data = ANES2016)
# Compute residuals
res1 < residuals(PAsso_1)

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