Given a fitted model, return the standard errors of the coefficient
se.coef(object, ...)a model implementing vcov
passed to methods
vector or matrix
ZlmFit-class
#see ZlmFit-class for examples
example('ZlmFit-class')
#>
#> ZlmFt-> data(vbetaFA)
#>
#> ZlmFt-> zlmVbeta <- zlm(~ Stim.Condition+Population, subset(vbetaFA, ncells==1)[1:10,])
#>
#> Done!
#>
#> ZlmFt-> #Coefficients and standard errors
#> ZlmFt-> coef(zlmVbeta, 'D')
#> (Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
#> B3GAT1 -4.1795641 0.63282096 -1.162182250
#> BAX -0.9330077 0.02794714 -0.162052054
#> BCL2 -3.9226304 -2.20112377 1.332228054
#> CCL2 -3.8309742 0.54760417 0.147212758
#> CCL3 -2.3287704 -1.94930881 -0.567152079
#> CCL4 -2.9386008 -1.06755978 -0.003368973
#> CCL5 -1.2350667 0.09455937 -0.484094746
#> CCR2 -4.1050742 1.37877860 1.504033462
#> CCR4 -0.1126440 -0.21758890 -0.839917265
#> CCR5 -2.7417205 -1.20694083 0.521925468
#> PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
#> B3GAT1 -1.14875333 -0.16659777
#> BAX 0.45518519 -0.46291603
#> BCL2 -1.29400053 -0.39116499
#> CCL2 0.84204812 0.14680392
#> CCL3 -0.14648827 -0.60189062
#> CCL4 0.02118041 -1.25132594
#> CCL5 -0.65755684 -0.41313077
#> CCR2 0.40289360 0.39925195
#> CCR4 -0.24604817 -0.68742757
#> CCR5 -0.16103855 0.02099718
#> PopulationVbetaResponsive PopulationVbetaUnresponsive
#> B3GAT1 -1.8211123 -0.53512412
#> BAX 0.5582425 0.53846239
#> BCL2 2.0919143 1.91118512
#> CCL2 -0.7797822 -2.02874891
#> CCL3 -0.7669146 -2.46844725
#> CCL4 -2.1491640 -2.15309456
#> CCL5 -0.8123545 -0.94068157
#> CCR2 -0.4731219 0.18547502
#> CCR4 -0.1364576 -0.01272681
#> CCR5 -0.5077805 -0.12742084
#>
#> ZlmFt-> coef(zlmVbeta, 'C')
#> (Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
#> B3GAT1 18.19441 -1.7969063 NA
#> BAX 17.67008 -0.6534516 -0.2356154
#> BCL2 18.73554 1.3286670 -1.3753715
#> CCL2 23.94679 -7.3355989 -3.6985974
#> CCL3 19.86182 NA 3.2750181
#> CCL4 19.54785 NA 1.2579223
#> CCL5 20.07363 0.4504418 -0.2026483
#> CCR2 15.27429 -1.2269270 4.1759840
#> CCR4 18.03446 -0.3702826 -0.1011834
#> CCR5 16.28993 0.9461661 1.1537964
#> PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
#> B3GAT1 NA -0.5500307
#> BAX -0.19649762 -0.2783088
#> BCL2 NA -3.1850947
#> CCL2 -7.04188444 -3.9220153
#> CCL3 -0.04812137 -0.6043503
#> CCL4 2.77190815 -2.5532715
#> CCL5 -0.84757938 -1.5303864
#> CCR2 3.19304135 4.0151403
#> CCR4 -0.57583680 -0.7602052
#> CCR5 -0.51211945 -0.6646556
#> PopulationVbetaResponsive PopulationVbetaUnresponsive
#> B3GAT1 NA NA
#> BAX -0.04213554 -0.01140701
#> BCL2 -1.58527659 -2.32150932
#> CCL2 NA NA
#> CCL3 0.45944273 NA
#> CCL4 NA NA
#> CCL5 -1.61445447 -1.33608326
#> CCR2 3.31403666 2.07650762
#> CCR4 -0.26166401 -0.75331686
#> CCR5 -1.26236131 -1.52747203
#>
#> ZlmFt-> se.coef(zlmVbeta, 'C')
#>
#> X1 (Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
#> B3GAT1 1.1195139 1.9390550 NA
#> BAX 0.2289293 0.2625570 0.4158706
#> BCL2 1.1852624 1.2983899 1.3686231
#> CCL2 2.5224874 4.3690763 4.3690763
#> CCL3 1.4009323 NA 3.1325799
#> CCL4 1.7441805 NA 3.2630629
#> CCL5 0.4951370 1.0038683 1.0106941
#> CCR2 1.1047933 1.2757054 1.2757054
#> CCR4 0.2983985 0.4372988 0.6361878
#> CCR5 0.6656093 2.1048414 0.9984140
#>
#> X1 PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
#> B3GAT1 NA 1.9390550
#> BAX 0.3643741 0.3574157
#> BCL2 NA 1.6762142
#> CCL2 3.5673358 3.5673358
#> CCL3 2.6825812 2.4264860
#> CCL4 3.2630629 4.2723522
#> CCL5 1.0847908 0.7768348
#> CCR2 1.5624136 1.3530899
#> CCR4 0.5532457 0.4774376
#> CCR5 1.2452410 0.9413137
#>
#> X1 PopulationVbetaResponsive PopulationVbetaUnresponsive
#> B3GAT1 NA NA
#> BAX 0.3211103 0.3241627
#> BCL2 1.2670988 1.2983899
#> CCL2 NA NA
#> CCL3 3.1325799 NA
#> CCL4 NA NA
#> CCL5 1.0362222 0.9807059
#> CCR2 1.8596442 1.5624136
#> CCR4 0.4777267 0.4725825
#> CCR5 1.6304032 1.0869354
#>
#> ZlmFt-> #Test for a Population effect by dropping the whole term (a 5 degree of freedom test)
#> ZlmFt-> lrTest(zlmVbeta, 'Population')
#> Refitting on reduced model...
#>
#> Done!
#> , , metric = lambda
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 0.000000 3.293892 3.293892
#> BAX 1.072472 10.728081 11.800553
#> BCL2 5.443797 20.567934 26.011731
#> CCL2 4.103765 4.775242 8.879007
#> CCL3 1.523584 7.006491 8.530076
#> CCL4 1.516648 8.444875 9.961523
#> CCL5 5.572644 5.389674 10.962318
#> CCR2 12.540757 4.640800 17.181557
#> CCR4 4.607460 8.728194 13.335653
#> CCR5 7.136405 1.526627 8.663031
#>
#> , , metric = df
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 0 5 5
#> BAX 5 5 10
#> BCL2 4 5 9
#> CCL2 3 5 8
#> CCL3 4 5 9
#> CCL4 3 5 8
#> CCL5 5 5 10
#> CCR2 5 5 10
#> CCR4 5 5 10
#> CCR5 5 5 10
#>
#> , , metric = Pr(>Chisq)
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 1.00000000 0.6547769674 0.65477697
#> BAX 0.95651117 0.0570459966 0.29862654
#> BCL2 0.24471407 0.0009773063 0.00203398
#> CCL2 0.25047521 0.4439211826 0.35260548
#> CCL3 0.82245576 0.2201579879 0.48173135
#> CCL4 0.67843337 0.1333623010 0.26773693
#> CCL5 0.35004597 0.3701952302 0.36046135
#> CCR2 0.02808429 0.4612686366 0.07044188
#> CCR4 0.46563569 0.1204092082 0.20550576
#> CCR5 0.21069179 0.9099767975 0.56435354
#>
#> attr(,"test")
#> [1] "Population"
#>
#> ZlmFt-> #Test only if the VbetaResponsive cells differ from the baseline group
#> ZlmFt-> lrTest(zlmVbeta, CoefficientHypothesis('PopulationVbetaResponsive'))
#> Refitting on reduced model...
#>
#> Done!
#> , , metric = lambda
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 0.00000000 2.2974635 2.2974635
#> BAX 0.01776885 2.5081546 2.5259234
#> BCL2 1.80508046 9.4881861 11.2932665
#> CCL2 0.00000000 0.6678630 0.6678630
#> CCL3 0.02425584 1.1530015 1.1772574
#> CCL4 0.00000000 3.8914009 3.8914009
#> CCL5 2.54893181 3.3702326 5.9191644
#> CCR2 3.99053248 0.1406114 4.1311439
#> CCR4 0.30752656 0.1884803 0.4960069
#> CCR5 0.73461509 0.3702349 1.1048500
#>
#> , , metric = df
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 0 1 1
#> BAX 1 1 2
#> BCL2 1 1 2
#> CCL2 0 1 1
#> CCL3 1 1 2
#> CCL4 0 1 1
#> CCL5 1 1 2
#> CCR2 1 1 2
#> CCR4 1 1 2
#> CCR5 1 1 2
#>
#> , , metric = Pr(>Chisq)
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 1.0000000 0.129585466 0.129585466
#> BAX 0.8939563 0.113258489 0.282815165
#> BCL2 0.1790995 0.002067992 0.003529379
#> CCL2 1.0000000 0.413797670 0.413797670
#> CCL3 0.8762357 0.282921705 0.555087964
#> CCL4 1.0000000 0.048533925 0.048533925
#> CCL5 0.1103689 0.066384383 0.051840572
#> CCR2 0.0457566 0.707673921 0.126745777
#> CCR4 0.5792020 0.664184477 0.780357269
#> CCR5 0.3913913 0.542876237 0.575552395
#>
#> attr(,"test")
#> [1] "PopulationVbetaResponsive"
#>
#> ZlmFt-> # Test if there is a difference between CD154+/Unresponsive and CD154-/Unresponsive.
#> ZlmFt-> # Note that because we parse the expression
#> ZlmFt-> # the columns must be enclosed in backquotes
#> ZlmFt-> # to protect the \quote{+} and \quote{-} characters.
#> ZlmFt-> lrTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
#> ZlmFt-+ `PopulationCD154-VbetaUnresponsive`'))
#> Warning: Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
#> Refitting on reduced model...
#>
#> Done!
#> , , metric = lambda
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 0.000000000 0.19720610 0.1972061
#> BAX 0.009603320 0.43127916 0.4408825
#> BCL2 2.004979393 2.88482572 4.8898051
#> CCL2 0.003581828 -0.10852740 -0.1049456
#> CCL3 1.389919527 -0.59684167 0.7930779
#> CCL4 0.695474388 1.22446841 1.9199428
#> CCL5 1.643474587 0.01090422 1.6543788
#> CCR2 0.037354159 1.44946742 1.4868216
#> CCR4 0.977322744 0.13931993 1.1166427
#> CCR5 3.776168456 0.50849269 4.2846611
#>
#> , , metric = df
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 0 1 1
#> BAX 1 1 2
#> BCL2 1 1 2
#> CCL2 1 1 2
#> CCL3 1 1 2
#> CCL4 1 1 2
#> CCL5 1 1 2
#> CCR2 1 1 2
#> CCR4 1 1 2
#> CCR5 1 1 2
#>
#> , , metric = Pr(>Chisq)
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 1.00000000 0.65698552 0.65698552
#> BAX 0.92193505 0.51136195 0.80216477
#> BCL2 0.15678342 0.08941768 0.08673459
#> CCL2 0.95227640 1.00000000 1.00000000
#> CCL3 0.23841869 1.00000000 0.67264409
#> CCL4 0.40430856 0.26848550 0.38290384
#> CCL5 0.19984941 0.91683346 0.43727657
#> CCR2 0.84674577 0.22861343 0.47548935
#> CCR4 0.32286067 0.70895803 0.57216874
#> CCR5 0.05198758 0.47579209 0.11738096
#>
#> attr(,"test")
#> [1] "Contrast Matrix"
#>
#> ZlmFt-> waldTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
#> ZlmFt-+ `PopulationCD154-VbetaUnresponsive`'))
#> Warning: Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
#> , , metric = lambda
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 NA 2.632691e-01 NA
#> BAX 0.009305455 4.373274e-01 0.446632837
#> BCL2 1.748463371 2.500540e+00 4.249003565
#> CCL2 0.002614910 1.215898e-07 0.002615031
#> CCL3 1.278016527 1.692421e-03 1.279708948
#> CCL4 0.636616606 1.271218e+00 1.907834528
#> CCL5 1.553679693 1.810976e-02 1.571789452
#> CCR2 0.025434721 1.431864e+00 1.457299039
#> CCR4 0.955370495 1.297495e-01 1.085120034
#> CCR5 3.317281808 5.264420e-01 3.843723848
#>
#> , , metric = df
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 1 1 2
#> BAX 1 1 2
#> BCL2 1 1 2
#> CCL2 1 1 2
#> CCL3 1 1 2
#> CCL4 1 1 2
#> CCL5 1 1 2
#> CCR2 1 1 2
#> CCR4 1 1 2
#> CCR5 1 1 2
#>
#> , , metric = Pr(>Chisq)
#>
#> test.type
#> primerid cont disc hurdle
#> B3GAT1 NA 0.6078831 NA
#> BAX 0.92315144 0.5084152 0.7998617
#> BCL2 0.18607002 0.1138073 0.1194925
#> CCL2 0.95921700 0.9997218 0.9986933
#> CCL3 0.25826815 0.9671850 0.5273692
#> CCL4 0.42493864 0.2595383 0.3852290
#> CCL5 0.21259304 0.8929499 0.4557118
#> CCR2 0.87328861 0.2314604 0.4825602
#> CCR4 0.32835604 0.7186919 0.5812583
#> CCR5 0.06855509 0.4681066 0.1463342
#>