S4 class for storing null models for later usage with the
assocTest method
Objects
Objects of this class are created by calling nullModel.
Slots
The following slots are defined for NullModel objects:
type:
type of model
residuals:
residuals of linear model; for type
“bernoulli”, this is simply the trait vector (see
nullModel-methods for details)
model.matrix:
model matrix of the (generalized) linear
model trained for the covariates (if any)
inv.matrix:
pre-computed inverse of some matrix
needed for computing the null distribution; only used for
types “logistic” and “linear”
P0sqrt:
pre-computed square root of matrix P_0
(see Subsections 9.1 and 9.5 of the package vignette);
needed for computing the null distribution in case the small
sample correction is used for a logistic model; computed only
if nullModel is called with adjExact=TRUE.
coefficients:
coefficients of (generalized) linear
model trained for the covariates (if any)
na.omit:
indices of samples omitted from (generalized)
linear model because of missing values in target or covariates
n.cases:
for binary traits (types “logistic”
and “bernoulli”), the number of cases, i.e. the number of 1's
in the trait vector
variance:
for continuous traits (type
“linear”), this is a single numeric value with the variance
of residuals of the linear model; for logistic models with
binary traits (type “logistic”), this is a vector with
variances of the per-sample Bernoulli distributions; for later
use of the exact mixture-of-Bernoulli test (type “bernoulli”),
this is the variance of the Bernoulli distribution
prob:
for logistic models with
binary traits (type “logistic”), this is a vector with
probabilities of the per-sample Bernoulli distributions; for later
use of the exact mixture-of-Bernoulli test (type “bernoulli”),
this is the probability of the Bernoulli distribution
type.resampling:
which resampling algorithm was used
res.resampling:
matrix with residuals sampled under
the null hypothesis (if any)
res.resampling.adj:
matrix with residuals sampled under
the null hypothesis for the purpose of higher moment correction (if
any; only used for logistic models with small sample correction)
call:
the matched call with which the object was created
Details
This class serves as the general interface for storing the necessary
phenotype information for a later association test. Objects of this
class should only be created by the nullModel function.
Direct modification of object slots is strongly discouraged!
Methods
show
signature(object="NullModel"):
displays basic information about the null model, such as,
the type of the model and the numbers of covariates.
Accessors
residuals
signature(object="NullModel"):
returns the residuals slot.
names
signature(object="NullModel"):
returns the names of samples in the null model.
coefficients
signature(object="NullModel"):
returns the coefficients slot.
length
signature(x="NullModel"):
returns the number of samples that was used to train the null model.
Subsetting
For a NullModel object x and an index vector i
that is a permutation of 1:length(x),
x[i] returns a new NullModel object in which the samples
have been rearranged according to the permutation i. This is
meant for applications in which the order of the samples in a subsequent
association test is different from the order of the samples when the
null model was trained/created.
## read phenotype data from CSV file (continuous trait + covariates)
phenoFile <- system.file("examples/example1lin.csv", package="podkat")
pheno <-read.table(phenoFile, header=TRUE, sep=",")
## train null model with all covariates in data frame 'pheno'
model <- nullModel(y ~ ., pheno)
model
length(model)
residuals(model)
## read phenotype data from CSV file (binary trait + covariates)
phenoFile <- system.file("examples/example1log.csv", package="podkat")
pheno <-read.table(phenoFile, header=TRUE, sep=",")
## train null model with all covariates in data frame 'pheno'
model <- nullModel(y ~ ., pheno)
model
length(model)
residuals(model)
## "train" simple Bernoulli model on a subset of 100 samples
model <- nullModel(y ~ 0, pheno[1:100, ])
model
length(model)
residuals(model)
providing 
.