Package 'flipscores'

anova.flipscores

Description

This is the anova method for flipscores object. Remark: it performs type III deviance decomposition as in car::Anova.

Usage

## S3 method for class 'flipscores'
anova(object, model1 = NULL, score_type = NULL, n_flips = 5000, id = NULL, ...)

Arguments

object	(the object) glm (or flipscores) object with the model under the null hypothesis (i.e. the covariates, the nuisance parameters).
model1	a glm (or flipscores) or a matrix (or vector). If it is a glm object, it has the model under the alternative hypothesis. The variables in model1 are the same variables in object plus one or more variables to be tested. Alternatively, if model1 is a matrix, it contains the tested variables column-wise.
score_type	The type of score that is computed. It can be "orthogonalized", "effective" or "basic". Default is "orthogonalized". "effective" and "orthogonalized" take into account the nuisance estimation. The default is NULL, in this case the value is taken from object.
n_flips	The number of random flips of the score contributions. When n_flips is equal or larger than the maximum number of possible flips (i.e. n^2), all possible flips are performed. Default is 5000.
id	a vector identifying the clustered observations. If NULL (default) observations are assumed to be independent. NOTE: if object is a flipscores and model$flip_param_call$id is not NULL, this is considered in the inference.
...	other parameters allowed in stats::anova.

Examples

set.seed(1)
dt=data.frame(X=scale(rnorm(50)),
   Z=factor(rep(LETTERS[1:3],length.out=50)))
dt$Y=rpois(n=nrow(dt),lambda=exp(dt$X*(dt$Z=="C")))
mod0=flipscores(Y~Z+X,data=dt,family="poisson")
summary(mod0)
anova(mod0)

mod1=flipscores(Y~Z*X,data=dt,family="poisson")
summary(mod1)
anova(mod0,model1 = mod1)

---

compute_scores

Description

Same usage as anova.glm. The parameter id is used too, if present in model0 (with priority) or in model1.

Usage

compute_scores(model0, model1, score_type = "standardized", ...)

Arguments

model0	a glm object with the model under the null hypothesis (i.e. the covariates, the nuisance parameters).
model1	a glm or a matrix (or vector). If it is a glm object, it has the model under the alternative hypothesis. The variables in model1 are the same variables in model0 plus one or more variables to be tested. Alternatively, if model1 is a matrix, it contains the tested variables column-wise.
score_type	The type of score that is computed. It is "orthogonalized", "effective" or "basic". "effective" and "orthogonalized" take into account the nuisance estimation.
...	other arguments.

Author(s)

Jesse Hemerik, Riccardo De Santis, Vittorio Giatti, Jelle Goeman and Livio Finos

Examples

set.seed(1)
Z=rnorm(20)
X=Z+rnorm(20)
Y=rpois(n=20,lambda=exp(Z+X))
mod0=glm(Y~Z,family="poisson")
X=data.frame(X=X)
scr0=compute_scores(model0 = mod0, model1 = X)
head(scr0)

---

Robust testing in GLMs, by sign-flipping score contributions

Description

Provides robust tests for testing in GLMs, by sign-flipping score contributions. The tests are often robust against overdispersion, heteroscedasticity and, in some cases, ignored nuisance variables.

Usage

flipscores(formula, family, data, score_type = "standardized",
n_flips = 5000, alternative = "two.sided", id = NULL,
seed = NULL, to_be_tested = NULL, flips = NULL,
precompute_flips=TRUE, ...)

Arguments

formula	see glm function. It can also be a model (usually generated by a call to glm); in this case, any other glm-related parameter (e.g. family, data, etc.) are discarded, the function will make use of the ones used to generate the model. (i.e. formula, family, data, etc) are not considered. It is NULL by default (i.e. not used).
family	see glm function.
data	see glm function.
score_type	The type of score that is computed. It can be "standardized" "orthogonalized", "effective" or "basic". Both "orthogonalized" and "effective" take into account the nuisance estimation and they provide the same test statistic. In case of small samples "effective score" might have a slight anti-conservative behaviour. "standardized effective score" gives a solution for this issue. "orthogonalized" has a similar intent, note however that in case of a big model matrix, it may be slow.
n_flips	The number of random flips of the score contributions. Overwritten with the nrow(flips) when flips is not NULL (see parameter flips for more details). When n_flips is equal or larger than the maximum number of possible flips (i.e. n^2), all possible flips are performed.
alternative	It can be "greater", "less" or "two.sided" (default)
id	a vector identifying the clustered observations. If NULL (default) observations are assumed to be independent. If id is not NULL, only score_type=="effective" is allowed, yet.
seed	NULL by default.
to_be_tested	vector of indices or names of coefficients of the glm model to be tested (it is faster than computing every scores and p-values of course).
flips	matrix fo +1 or -1, the matrix has n_flips rows and n (number of observations) columns
precompute_flips	TRUE by default. Overwritten if flips is not NULL. If FALSE the matrix of flips is not computed and the flips are made 'on-the-fly' before computing the test statistics; it may be usefull when flips is very large (see parameter flips for more details).
...	see glm function.

Details

flipscores borrows the same parameters from function glm (and glm.nb). See these helps for more details about parameters such as formula, data, family. Note: in order to use Negative Binomial family, family reference must have quotes (i.e. family="negbinom"). Furthermore, flipscores object contains two extra elements: scores – i.e. a matrix of n score contributions, one column for each tested coefficient – and Tspace – i.e. a matrix of size n_flips times ncol(scores). The fist row of Tspace contains column-wise the test statistics generated by randomly flipping the score contributions, each column refers to the same column of scores, the vector of observed test statistics (i.e. no flips) is in the first row of Tspace.

Value

an object of class flipscores. See also its methods (summary.flipscores, anova.flipscores, print.flipscores).

Author(s)

Livio Finos, Riccardo De Santis, Jesse Hemerik and Jelle Goeman

References

"Robust testing in generalized linear models by sign-flipping score contributions" by J.Hemerik, J.Goeman and L.Finos.

See Also

anova.flipscores, summary.flipscores, flip

Examples

set.seed(1)
dt=data.frame(X=rnorm(20),
   Z=factor(rep(LETTERS[1:3],length.out=20)))
dt$Y=rpois(n=20,lambda=exp(dt$Z=="C"))
mod=flipscores(Y~Z+X,data=dt,family="poisson",n_flips=1000)
summary(mod)

# Equivalent to:
model=glm(Y~Z+X,data=dt,family="poisson")
mod2=flipscores(model)
summary(mod2)

---

Methods for flipscores objects

Description

Methods for flipscores objects. The following are methods to extract and manipulate relevant information from a flipscores object.

Usage

## S3 method for class 'flipscores'
print(x, ...)

## S3 method for class 'flipscores'
summary(object, ...)

Arguments

x	a flipscores object
...	additional arguments to be passed
object	a flipscores object

---

It creates a n_flipsxn_obs matrix of random +1 and -1. The first row is made by ones (i.e. the observed test statistic is computed)

Description

It creates a n_flipsxn_obs matrix of random +1 and -1. The first row is made by ones (i.e. the observed test statistic is computed)

Usage

make_flips(n_obs, n_flips)

Arguments

n_obs	number of observations
n_flips	number of flips

Examples

# example code
make_flips(n_obs=10,n_flips=20)

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