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
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
coefD
vcovC
vcovD
LMlike
sca
SingleCellAssay
object useddeviance
loglik
df.null
df.resid
dispersion
dispersionNoShrink
priorDOF
priorVar
converged
hookOut
zlm
was called with onezlm 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 #>