only keeps non NA coefficients.

my_contrast_V2(m, linfct, confint = 0.95)

Arguments

m

linear model generated using lm

linfct

linear function

confint

confidence interval default 0.95

Examples

m <- sim_make_model_lm( "factors")
#> creating sampleName from fileName column
#> completing cases
#> Joining with `by = join_by(protein_Id)`
linfct <- linfct_from_model(m)$linfct_factors
my_contrast_V2(m, linfct, confint = 0.95)
#>                     lhs    sigma df estimate std.error statistic      p.value
#> BackgroundX BackgroundX 1.557675 13 18.63959 0.5507212  33.84577 4.612815e-14
#> BackgroundZ BackgroundZ 1.557675 13 18.19440 0.5507212  33.03740 6.294815e-14
#> TreatmentA   TreatmentA 1.557675 13 18.75377 0.5507212  34.05311 4.264271e-14
#> TreatmentB   TreatmentB 1.557675 13 18.08021 0.5507212  32.83007 6.825487e-14
#>             conf.low conf.high
#> BackgroundX 17.44983  19.82935
#> BackgroundZ 17.00464  19.38416
#> TreatmentA  17.56401  19.94353
#> TreatmentB  16.89045  19.26998
my_contrast_V2(m, linfct, confint = 0.99)
#>                     lhs    sigma df estimate std.error statistic      p.value
#> BackgroundX BackgroundX 1.557675 13 18.63959 0.5507212  33.84577 4.612815e-14
#> BackgroundZ BackgroundZ 1.557675 13 18.19440 0.5507212  33.03740 6.294815e-14
#> TreatmentA   TreatmentA 1.557675 13 18.75377 0.5507212  34.05311 4.264271e-14
#> TreatmentB   TreatmentB 1.557675 13 18.08021 0.5507212  32.83007 6.825487e-14
#>             conf.low conf.high
#> BackgroundX 16.98066  20.29851
#> BackgroundZ 16.53547  19.85332
#> TreatmentA  17.09485  20.41269
#> TreatmentB  16.42129  19.73914