This holds output from a call to zlm. Many methods are defined to operate on it. See below.
# S4 method for ZlmFit,CoefficientHypothesis
lrTest(object, hypothesis, ...)
# S4 method for ZlmFit,Hypothesis
lrTest(object, hypothesis, ...)
# S4 method for ZlmFit,matrix
lrTest(object, hypothesis, ...)
# S4 method for ZlmFit,CoefficientHypothesis
waldTest(object, hypothesis)
# S4 method for ZlmFit,Hypothesis
waldTest(object, hypothesis)
# S4 method for ZlmFit
coef(object, which, ...)
# S4 method for ZlmFit
vcov(object, which, ...)
# S4 method for ZlmFit
se.coef(object, which, ...)ZlmFit
call to Hypothesis or CoefficientHypothesis or a matrix giving such contrasts.
ignored
character vector, one of "C" (continuous) or "D" (discrete) specifying which component should be returned
see "Methods (by generic)"
lrTest: Returns an array with likelihood-ratio tests on contrasts defined using CoefficientHypothesis().
lrTest: Returns an array with likelihood-ratio tests specified by Hypothesis, which is a Hypothesis.
lrTest: Returns an array with likelihood-ratio tests specified by Hypothesis, which is a contrast matrix.
waldTest: Returns an array with Wald Tests on contrasts defined using CoefficientHypothesis().
waldTest: Returns an array with Wald Tests on contrasts defined in Hypothesis()
coef: Returns the matrix of coefficients for component which.
vcov: Returns an array of variance/covariance matrices for component which.
se.coef: Returns a matrix of standard error estimates for coefficients on component which.
coefCmatrix of continuous coefficients
coefDmatrix of discrete coefficients
vcovCarray of variance/covariance matrices for coefficients
vcovDarray of variance/covariance matrices for coefficients
LMlikethe LmWrapper object used
scathe SingleCellAssay object used
deviancematrix of deviances
loglikmatrix of loglikelihoods
df.nullmatrix of null (intercept only) degrees of freedom
df.residmatrix of residual DOF
dispersionmatrix of dispersions (after shrinkage)
dispersionNoShrinkmatrix of dispersion (before shrinkage)
priorDOFshrinkage weight in terms of number of psuedo-obs
priorVarshrinkage target
convergedoutput that may optionally be set by the underlying modeling function
hookOuta list of length ngenes containing output from a hook function, if zlm was called with one
exprs_values`character` or `integer` with the `assay` used.
zlm summary,ZlmFit-method
data(vbetaFA)
zlmVbeta <- zlm(~ Stim.Condition+Population, subset(vbetaFA, ncells==1)[1:10,])
#>
#> Done!
#Coefficients and standard errors
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
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
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
#Test for a Population effect by dropping the whole term (a 5 degree of freedom test)
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"
#Test only if the VbetaResponsive cells differ from the baseline group
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"
# Test if there is a difference between CD154+/Unresponsive and CD154-/Unresponsive.
# Note that because we parse the expression
# the columns must be enclosed in backquotes
# to protect the \quote{+} and \quote{-} characters.
lrTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
`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"
waldTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
`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
#>