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, ...)
object |
|
---|---|
hypothesis | call to Hypothesis or CoefficientHypothesis or a matrix giving such contrasts. |
which | character vector, one of "C" (continuous) or "D" (discrete) specifying which component should be returned |
... | ignored |
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
.
coefC
matrix of continuous coefficients
coefD
matrix of discrete coefficients
vcovC
array of variance/covariance matrices for coefficients
vcovD
array of variance/covariance matrices for coefficients
LMlike
the LmWrapper object used
sca
the SingleCellAssay
object used
deviance
matrix of deviances
loglik
matrix of loglikelihoods
df.null
matrix of null (intercept only) degrees of freedom
df.resid
matrix of residual DOF
dispersion
matrix of dispersions (after shrinkage)
dispersionNoShrink
matrix of dispersion (before shrinkage)
priorDOF
shrinkage weight in terms of number of psuedo-obs
priorVar
shrinkage target
converged
output that may optionally be set by the underlying modeling function
hookOut
a list of length ngenes containing output from a hook function, if zlm
was called with one
zlm summary,ZlmFit-method
#> #>#Coefficients and standard errors coef(zlmVbeta, 'D')#> (Intercept) Stim.ConditionUnstim PopulationCD154-VbetaUnresponsive #> B3GAT1 -5.4302447 1.00685352 0.90199793 #> BAX -0.4990626 0.02145208 -0.89154721 #> BCL2 -3.9407287 -2.20065589 -0.37551513 #> CCL2 -3.3547797 0.42749817 -0.29365929 #> CCL3 -2.6590780 -1.92367996 -0.28467650 #> CCL4 -3.3030403 -0.99943502 -0.93080019 #> CCL5 -1.8947363 0.11842660 0.23701817 #> CCR2 -3.7005996 1.30851899 0.02582989 #> CCR4 -0.3695476 -0.21384188 -0.43287571 #> CCR5 -2.8721780 -1.19817513 0.14714636 #> PopulationCD154+VbetaResponsive PopulationCD154+VbetaUnresponsive #> B3GAT1 1.6063715 -0.5811225 #> BAX -0.4386521 -0.5869687 #> BCL2 -0.3862382 1.3492612 #> CCL2 -0.3052702 -0.2626161 #> CCL3 0.4259786 -0.2617315 #> CCL4 0.5515850 0.3325102 #> CCL5 0.6887183 0.1555835 #> CCR2 -0.5950754 1.1230267 #> CCR4 0.2698394 -0.5880348 #> CCR5 0.1345247 0.6467222 #> PopulationVbetaResponsive PopulationVbetaUnresponsive #> B3GAT1 -1.2153975 0.322296844 #> BAX 0.1308370 0.111068557 #> BCL2 2.1094602 1.928661427 #> CCL2 -1.1332949 -2.305617623 #> CCL3 -0.4638945 -2.238025106 #> CCL4 -1.9149361 -1.918902719 #> CCL5 -0.1764152 -0.305926046 #> CCR2 -0.7973931 -0.150296597 #> CCR4 0.1166578 0.240411574 #> CCR5 -0.3888459 -0.005858029coef(zlmVbeta, 'C')#> (Intercept) Stim.ConditionUnstim PopulationCD154-VbetaUnresponsive #> B3GAT1 18.19441 -1.7969063 -0.55003066 #> BAX 17.47359 -0.6534516 -0.08181114 #> BCL2 16.41403 1.3286670 -0.86358539 #> CCL2 16.90490 -0.2937144 3.11986916 #> CCL3 19.81370 NA -0.55622889 #> CCL4 22.31976 NA -5.32517963 #> CCL5 19.22605 0.4504418 -0.68280704 #> CCR2 18.46733 -1.2269270 0.82209897 #> CCR4 17.45862 -0.3702826 -0.18436839 #> CCR5 15.77781 0.9461661 -0.15253610 #> PopulationCD154+VbetaResponsive PopulationCD154+VbetaUnresponsive #> B3GAT1 NA NA #> BAX 0.19649762 -0.03911775 #> BCL2 2.32150932 0.94613777 #> CCL2 7.04188443 3.34328706 #> CCL3 0.04812137 3.32313947 #> CCL4 -2.77190815 -1.51398583 #> CCL5 0.84757938 0.64493103 #> CCR2 -3.19304135 0.98294260 #> CCR4 0.57583680 0.47465344 #> CCR5 0.51211945 1.66591584 #> PopulationVbetaResponsive PopulationVbetaUnresponsive #> B3GAT1 NA NA #> BAX 0.1543621 0.1850906 #> BCL2 0.7362327 NA #> CCL2 NA NA #> CCL3 0.5075641 NA #> CCL4 NA NA #> CCL5 -0.7668751 -0.4885039 #> CCR2 0.1209953 -1.1165337 #> CCR4 0.3141728 -0.1774801 #> CCR5 -0.7502419 -1.0153526#> #> X1 (Intercept) Stim.ConditionUnstim PopulationCD154-VbetaUnresponsive #> B3GAT1 1.1195139 1.9390550 1.9390550 #> BAX 0.2834781 0.2625570 0.3945849 #> BCL2 0.5300655 1.2983899 1.2983899 #> CCL2 2.5224874 4.3690763 3.5673358 #> CCL3 2.2877129 NA 3.0263597 #> CCL4 2.7577915 NA 4.7766350 #> CCL5 0.9651997 1.0038683 1.1357474 #> CCR2 1.1047933 1.2757054 1.3530899 #> CCR4 0.4658746 0.4372988 0.5966105 #> CCR5 1.0524207 2.1048414 1.2452410 #> #> X1 PopulationCD154+VbetaResponsive PopulationCD154+VbetaUnresponsive #> B3GAT1 NA NA #> BAX 0.3643741 0.4482183 #> BCL2 1.2983899 0.8655933 #> CCL2 3.5673358 4.3690763 #> CCL3 2.6825812 3.6171917 #> CCL4 3.2630629 3.9001061 #> CCL5 1.0847908 1.3068865 #> CCR2 1.5624136 1.2757054 #> CCR4 0.5532457 0.7298852 #> CCR5 1.2452410 1.2889469 #> #> X1 PopulationVbetaResponsive PopulationVbetaUnresponsive #> B3GAT1 NA NA #> BAX 0.3620264 0.3647365 #> BCL2 0.6940186 NA #> CCL2 NA NA #> CCL3 3.6171917 NA #> CCL4 NA NA #> CCL5 1.3267276 1.2838356 #> CCR2 1.8596442 1.5624136 #> CCR4 0.5968418 0.5927323 #> CCR5 1.8228462 1.3586693#Test for a Population effect by dropping the whole term (a 5 degree of freedom test) lrTest(zlmVbeta, 'Population')#>#> #>#> , , metric = lambda #> #> test.type #> primerid cont disc hurdle #> B3GAT1 0.000000 4.299060 4.299060 #> BAX 1.072472 10.720978 11.793450 #> BCL2 5.443797 19.740650 25.184447 #> CCL2 4.103765 4.300998 8.404763 #> CCL3 1.523584 7.074064 8.597648 #> CCL4 1.516648 8.541585 10.058232 #> CCL5 5.572644 5.419661 10.992304 #> CCR2 12.540757 4.686256 17.227013 #> CCR4 4.607460 8.733661 13.341121 #> CCR5 7.136405 1.520790 8.657194 #> #> , , 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 0.507209744 0.50720974 #> BAX 0.95651117 0.057201590 0.29911799 #> BCL2 0.24471407 0.001397782 0.00277378 #> CCL2 0.25047521 0.506941990 0.39496253 #> CCL3 0.82245576 0.215190133 0.47521191 #> CCL4 0.67843337 0.128806114 0.26096254 #> CCL5 0.35004597 0.366835821 0.35811788 #> CCR2 0.02808429 0.455356085 0.06948928 #> CCR4 0.46563569 0.120170837 0.20521972 #> CCR5 0.21069179 0.910658274 0.56491639 #> #> attr(,"test") #> [1] "Population"#Test only if the VbetaResponsive cells differ from the baseline group lrTest(zlmVbeta, CoefficientHypothesis('PopulationVbetaResponsive'))#>#> #>#> , , metric = lambda #> #> test.type #> primerid cont disc hurdle #> B3GAT1 0.000000000 0.49967559 0.4996756 #> BAX 0.187497834 0.05709950 0.2445973 #> BCL2 0.000000000 7.50139598 7.5013960 #> CCL2 0.000000000 1.51305236 1.5130524 #> CCL3 0.022203431 0.31780369 0.3400071 #> CCL4 0.000000000 2.44968166 2.4496817 #> CCL5 0.357112181 0.06828354 0.4253957 #> CCR2 0.006224572 0.58399389 0.5902185 #> CCR4 0.284055573 0.07376621 0.3578218 #> CCR5 0.210197743 0.11201241 0.3222102 #> #> , , metric = df #> #> test.type #> primerid cont disc hurdle #> B3GAT1 0 1 1 #> BAX 1 1 2 #> BCL2 0 1 1 #> 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 0.479642699 0.479642699 #> BAX 0.6650074 0.811140466 0.884884044 #> BCL2 1.0000000 0.006165119 0.006165119 #> CCL2 1.0000000 0.218673913 0.218673913 #> CCL3 0.8815472 0.572930548 0.843661814 #> CCL4 1.0000000 0.117548705 0.117548705 #> CCL5 0.5501144 0.793852536 0.808400350 #> CCR2 0.9371154 0.444751088 0.744450266 #> CCR4 0.5940553 0.785930097 0.836180408 #> CCR5 0.6466125 0.737864563 0.851202624 #> #> 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.#>#> #>#> , , metric = lambda #> #> test.type #> primerid cont disc hurdle #> B3GAT1 0.000000000 0.53957307 0.5395731 #> BAX 0.009603320 0.41964445 0.4292478 #> BCL2 2.004979393 2.89187997 4.8968594 #> CCL2 0.003581828 -0.15725169 -0.1536699 #> CCL3 1.389919527 -0.60229580 0.7876237 #> CCL4 0.695474388 1.22512019 1.9205946 #> CCL5 1.643474587 0.02224792 1.6657225 #> CCR2 0.037354159 1.49555138 1.5329055 #> CCR4 0.977322744 0.14461070 1.1219334 #> CCR5 3.776168456 0.50471809 4.2808865 #> #> , , 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 0.46260972 0.4626097 #> BAX 0.92193505 0.51711451 0.8068449 #> BCL2 0.15678342 0.08902699 0.0864292 #> CCL2 0.95227640 1.00000000 1.0000000 #> CCL3 0.23841869 1.00000000 0.6744809 #> CCL4 0.40430856 0.26835815 0.3827791 #> CCL5 0.19984941 0.88142947 0.4348034 #> CCR2 0.84674577 0.22135713 0.4646584 #> CCR4 0.32286067 0.70373971 0.5706571 #> CCR5 0.05198758 0.47743434 0.1176027 #> #> 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 0.4246764991 NA #> BAX 0.009305455 0.4499539395 0.459259394 #> BCL2 1.748463371 2.4975649351 4.246028306 #> CCL2 0.002614910 0.0007340968 0.003349006 #> CCL3 1.278016527 0.0007253470 1.278741874 #> CCL4 0.636616606 1.2610150758 1.897631682 #> CCL5 1.553679693 0.0235791990 1.577258892 #> CCR2 0.025434721 1.4461225520 1.471557273 #> CCR4 0.955370495 0.1340900313 1.089460527 #> CCR5 3.317281808 0.5214642628 3.838746071 #> #> , , 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 0.5146127 NA #> BAX 0.92315144 0.5023568 0.7948279 #> BCL2 0.18607002 0.1140225 0.1196704 #> CCL2 0.95921700 0.9783846 0.9983269 #> CCL3 0.25826815 0.9785138 0.5276242 #> CCL4 0.42493864 0.2614590 0.3871993 #> CCL5 0.21259304 0.8779605 0.4544672 #> CCR2 0.87328861 0.2291511 0.4791322 #> CCR4 0.32835604 0.7142285 0.5799982 #> CCR5 0.06855509 0.4702177 0.1466989 #>