Analysis functions for manuscript

Functions for the palasso manuscript.

.prepare(X, filter = 1, cutoff = "zero", scale = TRUE)

.simulate(x, effects)

.predict(
  y,
  X,
  nfolds.ext = 5,
  nfolds.int = 5,
  adaptive = TRUE,
  standard = TRUE,
  elastic = TRUE,
  shrink = TRUE,
  family = "binomial",
  ...
)

.select(y, X, index, nfolds = 5, standard = TRUE, adaptive = TRUE, ...)

Arguments

X

covariates: matrix with \(n\) rows and \(p\) columns

filter

numeric, multiplying the sample size

cutoff

character "zero", "knee", or "half"

scale

logical

x

covariates: list of length \(k\), including matrices with \(n\) rows and \(p\) columns

effects

number of causal covariates: vector of length \(k\)

y

response: vector of length \(n\)

nfolds.ext

number of external folds

...

arguments for palasso

index

indices of causal covariates: list of length \(k\), including vectors

Details

.prepare: pre-processes sequencing data by removing features with a low total abundance, and adjusting for different library sizes; obtains two transformations of the same data by (1) binarising the counts with some cutoff and (2) taking the Anscombe transform; scales all covariates to mean zero and unit variance.

.simulate: simulates the response by exploiting two experimental covariate matrices; allows for different numbers of non-zero coefficients for X and Z.

.predict: estimates the predictive performance of different lasso models (standard X and/or Z, adaptive X and/or Z, paired X and Z); minimises the loss function "deviance", but also returns other loss functions; supports logistic and Cox regression.

.select: estimates the selective performance of different lasso models (standard X and/or Z, adaptive X and/or Z, paired X and Z); limits the number of covariates to \(10\); returns the number of selected covariates, and the number of correctly selected covariates.

See also

Use palasso to fit the paired lasso.

Examples

if (FALSE) set.seed(1)
n <- 30; p <- 40
X <- matrix(rpois(n*p,lambda=3),nrow=n,ncol=p)
x <- palasso:::.prepare(X)
y <- palasso:::.simulate(x,effects=c(1,2))
predict <- palasso:::.predict(y,x)
#>                         lambda       nzero length
#> standard_x   [0.3,(0.1),0.003]  [0,(8),17]    100
#> standard_z  [0.3,(0.07),0.003]  [0,(5),17]    100
#> standard_xz [0.3,(0.05),0.003] [0,(12),15]    100
#> adaptive_x  [0.1,(0.05),0.001]  [0,(8),17]    100
#> adaptive_z  [0.1,(0.03),0.001]  [0,(5),17]    100
#> adaptive_xz [0.3,(0.05),0.003] [0,(12),15]    100
#> between_xz  [0.3,(0.07),0.003]  [0,(9),15]    100
#> within_xz   [0.3,(0.05),0.003] [0,(12),15]    100
#> elastic95   [0.3,(0.05),0.003] [0,(12),19]    200
#>                         lambda       nzero length
#> standard_x  [0.3,(0.03),0.003] [0,(14),16]    100
#> standard_z  [0.2,(0.08),0.002]   [0,(6),9]    100
#> standard_xz [0.3,(0.01),0.003] [0,(15),16]    100
#> adaptive_x     [7,(0.07),0.07]   [0,(7),8]    100
#> adaptive_z  [0.1,(0.04),0.001]   [0,(6),9]    100
#> adaptive_xz     [50,(0.6),0.5]   [0,(7),8]    100
#> between_xz     [5,(0.05),0.05] [0,(15),16]    100
#> within_xz       [1000,(10),10]   [0,(7),8]    100
#> elastic95   [0.3,(0.02),0.003] [0,(16),19]    200
#>                         lambda       nzero length
#> standard_x  [0.3,(0.02),0.003] [0,(12),15]    100
#> standard_z   [0.2,(0.2),0.002]  [0,(2),16]    100
#> standard_xz [0.3,(0.03),0.003] [0,(12),16]    100
#> adaptive_x       [20,(20),0.2]   [0,(0),9]    100
#> adaptive_z  [0.1,(0.09),0.001]  [0,(2),16]    100
#> adaptive_xz      [30,(30),0.3]   [0,(0),9]    100
#> between_xz     [2,(0.02),0.02] [0,(12),13]    100
#> within_xz        [300,(300),3]   [0,(0),9]    100
#> elastic95   [0.3,(0.02),0.003] [0,(14),16]    200
#>                          lambda       nzero length
#> standard_x    [0.3,(0.1),0.003]  [0,(5),13]    100
#> standard_z    [0.2,(0.2),0.002]  [0,(0),22]    100
#> standard_xz   [0.3,(0.3),0.003]  [0,(0),18]    100
#> adaptive_x         [9,(9),0.09]   [0,(0),6]    100
#> adaptive_z  [0.09,(0.09),9e-04]  [0,(0),22]    100
#> adaptive_xz       [30,(30),0.3]   [0,(0),6]    100
#> between_xz         [2,(2),0.02]  [0,(0),13]    100
#> within_xz         [300,(300),3]   [0,(0),5]    100
#> elastic95    [0.3,(0.06),0.003] [0,(14),19]    200
#>                           lambda       nzero length
#> standard_x    [0.3,(0.03),0.003]  [0,(9),11]    100
#> standard_z    [0.2,(0.02),0.002] [0,(19),20]    100
#> standard_xz   [0.3,(0.04),0.003] [0,(11),13]    100
#> adaptive_x          [30,(2),0.3]   [0,(3),4]    100
#> adaptive_z  [0.09,(0.009),9e-04] [0,(19),20]    100
#> adaptive_xz         [90,(2),0.9]   [0,(4),4]    100
#> between_xz        [4,(0.1),0.04]   [0,(9),9]    100
#> within_xz        [2000,(200),20]   [0,(3),4]    100
#> elastic95     [0.3,(0.04),0.003] [0,(11),14]    200
#> Setting levels: control = 0, case = 1
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#> Setting levels: control = 0, case = 1
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#> Setting levels: control = 0, case = 1
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select <- palasso:::.select(y,x,attributes(y)) # \dontrun{}