Slopes of column-wise linear regressions of each column of a Filebacked Big Matrix, with some other associated statistics. Covariates can be added to correct for confounders.

big_univLinReg(
  X,
  y.train,
  ind.train = rows_along(X),
  ind.col = cols_along(X),
  covar.train = NULL,
  thr.eigval = 1e-04,
  ncores = 1
)

Arguments

X

An object of class FBM.

y.train

Vector of responses, corresponding to ind.train.

ind.train

An optional vector of the row indices that are used, for the training part. If not specified, all rows are used. Don't use negative indices.

ind.col

An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices.

covar.train

Matrix of covariables to be added in each model to correct for confounders (e.g. the scores of PCA), corresponding to ind.train. Default is NULL and corresponds to only adding an intercept to each model. You can use covar_from_df() to convert from a data frame.

thr.eigval

Threshold to remove "insignificant" singular vectors. Default is 1e-4.

ncores

Number of cores used. Default doesn't use parallelism. You may use nb_cores.

Value

A data.frame with 3 elements:

  1. the slopes of each regression,

  2. the standard errors of each slope,

  3. the t-scores associated with each slope. This is also an object of class mhtest. See methods(class = "mhtest").

See also

Examples

set.seed(1) X <- big_attachExtdata() n <- nrow(X) y <- rnorm(n) covar <- matrix(rnorm(n * 3), n) X1 <- X[, 1] # only first column of the Filebacked Big Matrix # Without covar test <- big_univLinReg(X, y) ## New class `mhtest` class(test)
#> [1] "mhtest" "data.frame"
attr(test, "transfo")
#> function (x) .Primitive("abs")
attr(test, "predict")
#> function (xtr) #> { #> lpval <- stats::pt(xtr, df = 515, lower.tail = FALSE, log.p = TRUE) #> (log(2) + lpval)/log(10) #> } #> <environment: base>
## plot results plot(test)
plot(test, type = "Volcano")
## To get p-values associated with the test test$p.value <- predict(test, log10 = FALSE) str(test)
#> Classes 'mhtest' and 'data.frame': 4542 obs. of 4 variables: #> $ estim : num -0.0311 0.0956 -0.0961 0.0327 -0.0246 ... #> $ std.err: num 0.0658 0.0783 0.071 0.08 0.062 ... #> $ score : num -0.473 1.221 -1.353 0.408 -0.397 ... #> $ p.value: num 0.636 0.223 0.177 0.683 0.692 ... #> - attr(*, "transfo")=function (x) #> - attr(*, "predict")=function (xtr)
summary(lm(y ~ X1))$coefficients[2, ]
#> Estimate Std. Error t value Pr(>|t|) #> -0.03111113 0.06575485 -0.47313823 0.63631503
# With all data str(big_univLinReg(X, y, covar = covar))
#> Classes 'mhtest' and 'data.frame': 4542 obs. of 3 variables: #> $ estim : num -0.0295 0.0994 -0.0939 0.035 -0.0217 ... #> $ std.err: num 0.066 0.0785 0.0714 0.0803 0.0625 ... #> $ score : num -0.447 1.266 -1.316 0.436 -0.347 ... #> - attr(*, "transfo")=function (x) #> - attr(*, "predict")=function (xtr)
summary(lm(y ~ X1 + covar))$coefficients[2, ]
#> Estimate Std. Error t value Pr(>|t|) #> -0.02947921 0.06597253 -0.44684070 0.65517903
# With only half of the data ind.train <- sort(sample(n, n/2)) str(big_univLinReg(X, y[ind.train], covar.train = covar[ind.train, ], ind.train = ind.train))
#> Classes 'mhtest' and 'data.frame': 4542 obs. of 3 variables: #> $ estim : num 0.00416 0.15009 0.03116 0.16321 -0.02771 ... #> $ std.err: num 0.0962 0.1131 0.1034 0.1102 0.0897 ... #> $ score : num 0.0432 1.3267 0.3012 1.4807 -0.3088 ... #> - attr(*, "transfo")=function (x) #> - attr(*, "predict")=function (xtr)
summary(lm(y ~ X1 + covar, subset = ind.train))$coefficients[2, ]
#> Estimate Std. Error t value Pr(>|t|) #> 0.004156134 0.096202616 0.043201885 0.965574678