Fit GLMM For Many Responses — glmmFitResponses,sparseMatrix-method • GenomicDataStreamRegression

Fit regression model Y[j,] ~ design for each feature j

Usage

# S4 method for class 'sparseMatrix'
glmmFitResponses(
  Y,
  design,
  family,
  Z,
  weights = NULL,
  offset = NULL,
  dcmpMethod = c("general", "categorical"),
  delta.range = c(-10, 10),
  tol = 1e-05,
  tol.eta = 1e-07,
  detail = 1,
  maxit = 100,
  lambda = 0,
  nthreads = 1,
  nReaders = 1,
  chunkSize = 1000,
  verbose = TRUE,
  ...
)

# S4 method for class 'ANY'
glmmFitResponses(
  Y,
  design,
  family,
  Z,
  weights = NULL,
  offset = NULL,
  dcmpMethod = c("general", "categorical"),
  delta.range = c(-10, 10),
  tol = 0.001,
  tol.eta = 0.001,
  detail = 1,
  maxit = 100,
  lambda = 0,
  nthreads = 1,
  nReaders = 1,
  chunkSize = NULL,
  verbose = TRUE,
  ...
)

# S4 method for class 'DelayedArray'
glmmFitResponses(
  Y,
  design,
  family,
  Z,
  weights = NULL,
  offset = NULL,
  dcmpMethod = c("general", "categorical"),
  delta.range = c(-10, 10),
  tol = 0.001,
  tol.eta = 0.001,
  detail = 1,
  maxit = 100,
  lambda = 0,
  nthreads = 1,
  nReaders = 1,
  chunkSize = NULL,
  verbose = TRUE,
  ...
)

Arguments

Y: matrix of responses as __rows__
design: design matrix
family: a description of the error distribution and link function to be used in the modelm just like for glm(). Also supports negative binomial as string "nb:theta".
Z: random effect design matrix
weights: sample-level weights
offset: sample-level offset values
dcmpMethod: use a "general" method (default) for SVD of Z. If Z is a categorical design matrix, used faster method "categorical"
delta.range: min and max values (in log space), of the search space for delta to fit the random effect
tol: convergence criterion for the 1D search of the delta space
tol.eta: convergence criterion eta in the PQL iteration
detail: level of model detail returned, with LEAST = 0, LOW = 1, MEDIUM = 2, HIGH = 3, MOST = 4, MAX = 5. LEAST (beta), LOW (beta, se, sigSq, rdf), MEDIUM (vcov), HIGH (residuals), MOST (hatvalues), MAX (deviance residuals)
maxit: max PQL iterations
lambda: ridge shrinkage parameter
nthreads: number of threads. Each model is fit in serial, analysis is parallelized across features
nReaders: number of threads used for reading data from disk
chunkSize: number of features to read per chunk
verbose: show progress
...: other args

Value

List of parameter estimates with entries coef, se, sigSq, rdf and other depending on detail

Details

Since the weights vary for each response, each model is computed separately without recycling precomputed values

Examples

library(fastglmm)
library(lme4)

set.seed(1)
sleepstudy$V <- rnorm(nrow(sleepstudy))

# lmer
fit <- lmer( Reaction ~ Days + V + (1 | Subject), sleepstudy)
fit
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Reaction ~ Days + V + (1 | Subject)
#>    Data: sleepstudy
#> REML criterion at convergence: 1782.453
#> Random effects:
#>  Groups   Name        Std.Dev.
#>  Subject  (Intercept) 37.12   
#>  Residual             31.06   
#> Number of obs: 180, groups:  Subject, 18
#> Fixed Effects:
#> (Intercept)         Days            V  
#>     251.457       10.472       -1.295  

# prepare response, design and random effect
Y <- matrix(sleepstudy$Reaction, nrow=1)
rownames(Y) <- "Reaction"
colnames(Y) <- rownames(sleepstudy)

design <- model.matrix(~ Days, sleepstudy)
Z <- preprocess_indicator(sleepstudy$Subject)

fit1 <- glmmFitResponses(Y, design, Z, family=gaussian())

fit1
#> 		 glmmFitResponses 
#> 
#> coefs(2): (Intercept), Days
#> responses(1): Reaction
#> family: gaussian/identity 
#> Estimated: se, dispersion, rdf, varFitted, sigSq_e, sigSq_g 
#> 

coef(fit1)
#>          (Intercept)     Days
#> Reaction    251.4051 10.46729