waldTest: Run a Wald test

Description Usage Arguments Value See Also Examples

View source: R/LmWrapper.R

Description

Run a Wald tests on discrete and continuous components hypothesis can be one of a character giving complete factors or terms to be dropped from the model, CoefficientHypothesis giving names of coefficients to be dropped, Hypothesis giving contrasts using the symbolically, or a contrast matrix, with one row for each coefficient in the full model, and one column for each contrast being tested.

Usage

1
waldTest(object, hypothesis)

Arguments

object

LMlike or subclass

hypothesis

the hypothesis to be tested. See details.

Value

array giving test statistics

See Also

fit

lrTest

lht

Examples

1
2
#see ZlmFit-class for examples
example('ZlmFit-class')

Example output

Loading required package: SummarizedExperiment
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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    expand.grid

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: DelayedArray
Loading required package: matrixStats

Attaching package: 'matrixStats'

The following objects are masked from 'package:Biobase':

    anyMissing, rowMedians


Attaching package: 'DelayedArray'

The following objects are masked from 'package:matrixStats':

    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

The following object is masked from 'package:base':

    apply


Attaching package: 'MAST'

The following object is masked from 'package:stats':

    filter


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                       -7.04188443                        -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`'))
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`'))
, , 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

Warning messages:
1: In makeContrasts2(contrasts = h@.Data, levels = terms) :
  Some levels contain symbols.  Be careful to escape these names with backticks ('`') when specifying contrasts.
2: In makeContrasts2(contrasts = h@.Data, levels = terms) :
  Some levels contain symbols.  Be careful to escape these names with backticks ('`') when specifying contrasts.
Warning message:
system call failed: Cannot allocate memory 

MAST documentation built on May 10, 2018, 6 p.m.