An object returned by polygenic polygenic mixed model analysis
routine. The sub-objects used are measuredIDs, residualY, and InvSigma. One can supply
mmscore with a fake h2object, containing these list elements.
data
An object of gwaa.data-class.
ALWAYS PASS THE SAME OBJECT WHICH WAS USED FOR ipolygenic ANALYSIS,
NO SUB-SETTING IN IDs (USE IDSUBSET ARGUMENT FOR SUB-SETTING)!!!
snpsubset
Index, character or logical vector with subset of SNPs to run analysis on.
If missing, all SNPs from data are used for analysis.
idsubset
Index, character or logical vector with subset of IDs to run analysis on.
If missing, all people from data/cc are used for analysis.
strata
Stratification variable. If provieded, scores are computed within strata and
then added up.
times
If more then one, the number of replicas to be used in derivation of
empirical genome-wide significance. NOTE: The structure of the data
is not exchangable, therefore do not use times > 1
unless you are really sure you understand what you are doing!
quiet
do not print warning messages
bcast
If the argument times > 1, progress is reported once in bcast replicas
clambda
If inflation facot Lambda is estimated as lower then one, this parameter
controls if the original P1df (clambda=TRUE) to be reported in Pc1df,
or the original 1df statistics is to be multiplied onto this "deflation"
factor (clambda=FALSE).
If a numeric value is provided, it is used as a correction factor.
propPs
proportion of non-corrected P-values used to estimate the inflation factor Lambda,
passed directly to the estlambda
where G is the vector of genotypes (coded 0, 1, 2) and E[G] is
a vector of (strata-specific) mean genotypic values; V^{-1} is the
InvSigma and residualY are residuals from the trait analysis
with polygenic procedure.
This test is similar to that implemented by Abecasis et al. (see reference).
Value
Object of class scan.gwaa-class; only 1 d.f. test is
implemented currently.
Author(s)
Yurii Aulchenko
References
Chen WM, Abecasis GR. Family-based association tests for genome-wide association
scans. Am J Hum Genet. 2007 Nov;81(5):913-26.
# ge03d2 is rather bad data set to demonstrate,
# because this is a population-based study
require(GenABEL.data)
data(ge03d2.clean)
#take half for speed
ge03d2.clean <- ge03d2.clean[1:100,]
gkin <- ibs(ge03d2.clean,w="freq")
h2ht <- polygenic(height ~ sex + age,kin=gkin,ge03d2.clean)
h2ht$est
mm <- mmscore(h2ht,data=ge03d2.clean)
# compute grammar
gr <- qtscore(h2ht$pgres,data=ge03d2.clean,clam=FALSE)
#compute GC
gc <- qtscore(height ~ sex + age,data=ge03d2.clean)
#compare
plot(mm,df="Pc1df",cex=0.5)
add.plot(gc,df="Pc1df",col="red")
add.plot(gr,df="Pc1df",col="lightgreen",cex=1.1)
# can see that mmscore and grammar are quite the same... in contrast to GC
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(GenABEL)
Loading required package: MASS
Loading required package: GenABEL.data
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GenABEL/mmscore.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mmscore
> ### Title: Score test for association in related people
> ### Aliases: mmscore
> ### Keywords: htest
>
> ### ** Examples
>
> # ge03d2 is rather bad data set to demonstrate,
> # because this is a population-based study
> require(GenABEL.data)
> data(ge03d2.clean)
> #take half for speed
> ge03d2.clean <- ge03d2.clean[1:100,]
> gkin <- ibs(ge03d2.clean,w="freq")
> h2ht <- polygenic(height ~ sex + age,kin=gkin,ge03d2.clean)
LM estimates of fixed parameters:
desmat(Intercept) desmatsex desmatage
0.007548131 1.147885901 -0.013061168
iteration = 0
Step:
[1] 0 0 0 0 0
Parameter:
[1] 0.007548131 1.147885901 -0.013061168 0.300000000 0.648103376
Function Value
[1] 55.66681
Gradient:
[1] -0.7970343 -0.9002341 -9.4983581 2.6684209 1.9871480
iteration = 1
Step:
[1] 7.486420e-07 8.455760e-07 8.921661e-06 -2.506406e-06 -1.866497e-06
Parameter:
[1] 0.00754888 1.14788675 -0.01305225 0.29999749 0.64810151
Function Value
[1] 55.66676
Gradient:
[1] -0.5938191 -0.7822453 1.4375897 2.6679804 1.9867986
iteration = 2
Step:
[1] 6.411025e-07 8.260613e-07 -1.420975e-07 -2.767827e-06 -2.061155e-06
Parameter:
[1] 0.007549521 1.147887573 -0.013052388 0.299994726 0.648099449
Function Value
[1] 55.66674
Gradient:
[1] -0.5965473 -0.7837615 1.2894201 2.6679508 1.9863418
iteration = 3
Step:
[1] 0.0008347448 0.0010744650 -0.0001030451 -0.0035973360 -0.0026786085
Parameter:
[1] 0.008384266 1.148962038 -0.013155433 0.296397390 0.645420840
Function Value
[1] 55.65488
Gradient:
[1] -2.285251 -1.676400 -91.361968 2.630593 1.381695
iteration = 4
Step:
[1] 1.322176e-03 1.700846e-03 -9.103557e-05 -5.692290e-03 -4.237774e-03
Parameter:
[1] 0.009706441 1.150662883 -0.013246469 0.290705100 0.641183066
Function Value
[1] 55.64038
Gradient:
[1] -3.3182495 -2.1363395 -149.8012931 2.5667793 0.4018102
iteration = 5
Step:
[1] 0.002656949 0.003416602 -0.000101741 -0.011432701 -0.008509260
Parameter:
[1] 0.01236339 1.15407949 -0.01334821 0.27927240 0.63267381
Function Value
[1] 55.61714
Gradient:
[1] -3.551523 -1.986481 -168.151619 2.415280 -1.638642
iteration = 6
Step:
[1] 1.790707e-03 2.301377e-03 -2.328430e-07 -7.700486e-03 -5.727560e-03
Parameter:
[1] 0.01415410 1.15638086 -0.01334844 0.27157191 0.62694625
Function Value
[1] 55.60394
Gradient:
[1] -2.1668395 -0.9869862 -97.4901478 2.2932996 -3.0656585
iteration = 7
Step:
[1] 3.200400e-04 4.099593e-04 5.321959e-05 -1.372948e-03 -1.015213e-03
Parameter:
[1] 0.01447414 1.15679082 -0.01329522 0.27019896 0.62593103
Function Value
[1] 55.6002
Gradient:
[1] -0.7185306 -0.1113437 -20.1306596 2.2667329 -3.3206498
iteration = 8
Step:
[1] -1.718455e-04 -2.218168e-04 2.187892e-05 7.397491e-04 5.564166e-04
Parameter:
[1] 0.01430229 1.15656900 -0.01327334 0.27093871 0.62648745
Function Value
[1] 55.59994
Gradient:
[1] -0.35805604 0.07873817 -0.32076029 2.27692770 -3.17700825
iteration = 9
Step:
[1] -4.370588e-05 -5.678488e-05 2.534222e-06 1.873169e-04 1.446266e-04
Parameter:
[1] 0.01425859 1.15651222 -0.01327081 0.27112603 0.62663208
Function Value
[1] 55.59991
Gradient:
[1] -0.33482824 0.08733883 1.02753378 2.27960854 -3.13980566
iteration = 10
Step:
[1] -8.239766e-05 -1.084886e-04 4.557311e-06 3.510772e-04 2.842011e-04
Parameter:
[1] 0.01417619 1.15640373 -0.01326625 0.27147711 0.62691628
Function Value
[1] 55.59986
Gradient:
[1] -0.2963599 0.1003585 3.2826041 2.2845664 -3.0667127
iteration = 11
Step:
[1] -1.125355e-04 -1.519011e-04 6.387450e-06 4.741790e-04 4.181547e-04
Parameter:
[1] 0.01406365 1.15625183 -0.01325987 0.27195129 0.62733443
Function Value
[1] 55.59972
Gradient:
[1] -0.2410545 0.1194671 6.5118265 2.2910730 -2.9591862
iteration = 12
Step:
[1] -1.694592e-04 -2.394344e-04 1.018578e-05 6.988233e-04 7.156662e-04
Parameter:
[1] 0.01389419 1.15601240 -0.01324968 0.27265011 0.62805010
Function Value
[1] 55.59936
Gradient:
[1] -0.1476295 0.1533271 11.9219898 2.3001185 -2.7752158
iteration = 13
Step:
[1] -2.161573e-04 -3.363417e-04 1.464905e-05 8.474314e-04 1.161466e-03
Parameter:
[1] 0.01367804 1.15567605 -0.01323503 0.27349754 0.62921156
Function Value
[1] 55.59845
Gradient:
[1] 0.001112525 0.211353210 20.419478588 2.309517384 -2.476852963
iteration = 14
Step:
[1] -2.088932e-04 -4.252463e-04 1.953791e-05 6.764871e-04 1.927817e-03
Parameter:
[1] 0.01346914 1.15525081 -0.01321549 0.27417403 0.63113938
Function Value
[1] 55.5961
Gradient:
[1] 0.2404949 0.3153473 33.7968188 2.3118226 -1.9823418
iteration = 15
Step:
[1] 3.369320e-05 -3.592887e-04 1.987934e-05 -7.174277e-04 3.127761e-03
Parameter:
[1] 0.01350284 1.15489152 -0.01319561 0.27345660 0.63426714
Function Value
[1] 55.59023
Gradient:
[1] 0.6095540 0.5025267 53.6679644 2.2831435 -1.1823413
iteration = 16
Step:
[1] 9.517544e-04 2.484443e-04 2.110645e-06 -5.483510e-03 4.790299e-03
Parameter:
[1] 0.01445459 1.15513996 -0.01319350 0.26797309 0.63905744
Function Value
[1] 55.57644
Gradient:
[1] 1.12710151 0.83201800 79.66543023 2.16314911 0.03686396
iteration = 17
Step:
[1] 3.276494e-03 2.152327e-03 -6.180638e-05 -1.703340e-02 6.065806e-03
Parameter:
[1] 0.01773109 1.15729229 -0.01325531 0.25093970 0.64512325
Function Value
[1] 55.54862
Gradient:
[1] 1.680746 1.348176 102.947673 1.841034 1.573250
iteration = 18
Step:
[1] 0.0067836249 0.0054898848 -0.0001817005 -0.0337960096 0.0042469859
Parameter:
[1] 0.02451471 1.16278218 -0.01343701 0.21714369 0.64937023
Function Value
[1] 55.50832
Gradient:
[1] 1.903909 1.949906 101.515816 1.243282 2.667115
iteration = 19
Step:
[1] 0.0074591765 0.0068599559 -0.0002434808 -0.0359910803 -0.0019551959
Parameter:
[1] 0.03197389 1.16964213 -0.01368049 0.18115261 0.64741504
Function Value
[1] 55.47725
Gradient:
[1] 1.4808322 2.2839733 63.6782878 0.6107531 2.2529366
iteration = 20
Step:
[1] 0.0028906311 0.0032049533 -0.0001284513 -0.0131711019 -0.0051624914
Parameter:
[1] 0.03486452 1.17284708 -0.01380894 0.16798150 0.64225254
Function Value
[1] 55.46738
Gradient:
[1] 0.7967716 2.1623817 20.4515607 0.3685095 1.0282382
iteration = 21
Step:
[1] -2.784687e-04 7.698114e-05 -1.399988e-05 1.816476e-03 -2.620403e-03
Parameter:
[1] 0.03458605 1.17292407 -0.01382294 0.16979798 0.63963214
Function Value
[1] 55.46605
Gradient:
[1] 0.4379634 1.9641385 1.3430342 0.3986591 0.3972915
iteration = 22
Step:
[1] -3.270487e-04 -2.478647e-04 6.965654e-06 1.653009e-03 -3.538324e-04
Parameter:
[1] 0.03425900 1.17267620 -0.01381598 0.17145099 0.63927831
Function Value
[1] 55.46599
Gradient:
[1] 0.3932840 1.9176691 -0.4484869 0.4286262 0.3127178
iteration = 23
Step:
[1] -4.784154e-05 -4.278799e-05 1.572678e-06 2.324088e-04 -1.091677e-05
Parameter:
[1] 0.03421116 1.17263341 -0.01381440 0.17168340 0.63926739
Function Value
[1] 55.46598
Gradient:
[1] 0.3960712 1.9156881 -0.2012950 0.4328866 0.3102883
iteration = 24
Step:
[1] -1.406280e-04 -1.321151e-04 4.869164e-06 6.735730e-04 -2.933699e-05
Parameter:
[1] 0.03407053 1.17250130 -0.01380953 0.17235697 0.63923806
Function Value
[1] 55.46596
Gradient:
[1] 0.4076998 1.9113822 0.7141208 0.4452626 0.3038117
iteration = 25
Step:
[1] -1.815502e-04 -1.840432e-04 6.479883e-06 8.490928e-04 -3.921039e-05
Parameter:
[1] 0.03388898 1.17231725 -0.01380305 0.17320606 0.63919885
Function Value
[1] 55.46591
Gradient:
[1] 0.4236036 1.9053465 1.9495903 0.4609189 0.2951103
iteration = 26
Step:
[1] -3.225724e-04 -3.642832e-04 1.191613e-05 1.451996e-03 -7.162312e-05
Parameter:
[1] 0.03356641 1.17195297 -0.01379114 0.17465806 0.63912722
Function Value
[1] 55.46578
Gradient:
[1] 0.4517307 1.8917950 4.1526486 0.4878505 0.2791380
iteration = 27
Step:
[1] -5.038257e-04 -6.636901e-04 1.958384e-05 2.123968e-03 -1.157060e-04
Parameter:
[1] 0.03306258 1.17128928 -0.01377155 0.17678203 0.63901152
Function Value
[1] 55.46544
Gradient:
[1] 0.4943046 1.8628452 7.5581252 0.5276730 0.2531842
iteration = 28
Step:
[1] -8.114979e-04 -1.314960e-03 3.404966e-05 3.047294e-03 -1.959990e-04
Parameter:
[1] 0.03225108 1.16997432 -0.01373750 0.17982932 0.63881552
Function Value
[1] 55.46459
Gradient:
[1] 0.5588949 1.7957354 12.9264734 0.5860258 0.2089228
iteration = 29
Step:
[1] -1.217903e-03 -2.598437e-03 5.747896e-05 3.623871e-03 -3.192590e-04
Parameter:
[1] 0.03103318 1.16737588 -0.01368003 0.18345319 0.63849626
Function Value
[1] 55.46253
Gradient:
[1] 0.6454153 1.6428071 20.6705524 0.6589930 0.1363910
iteration = 30
Step:
[1] -1.577059e-03 -4.933319e-03 9.050066e-05 2.309069e-03 -4.794397e-04
Parameter:
[1] 0.02945612 1.16244256 -0.01358952 0.18576226 0.63801682
Function Value
[1] 55.45794
Gradient:
[1] 0.73091182 1.31355035 29.89133067 0.71690170 0.02728171
iteration = 31
Step:
[1] -0.0012317086 -0.0077383644 0.0001106893 -0.0041005588 -0.0005461381
Parameter:
[1] 0.02822441 1.15470420 -0.01347884 0.18166170 0.63747068
Function Value
[1] 55.44984
Gradient:
[1] 0.7329958 0.7322085 35.1445579 0.6705916 -0.0968014
iteration = 32
Step:
[1] 5.698246e-04 -7.445346e-03 6.254423e-05 -1.485682e-02 -2.392241e-04
Parameter:
[1] 0.02879424 1.14725885 -0.01341629 0.16680488 0.63723146
Function Value
[1] 55.44095
Gradient:
[1] 0.55641476 0.08627035 27.60368874 0.42841983 -0.15516890
iteration = 33
Step:
[1] 2.159427e-03 -1.776124e-03 -3.622643e-05 -1.612603e-02 3.043117e-04
Parameter:
[1] 0.03095367 1.14548273 -0.01345252 0.15067885 0.63753577
Function Value
[1] 55.43664
Gradient:
[1] 0.30839267 -0.16767320 11.28184429 0.14077064 -0.09577137
iteration = 34
Step:
[1] 1.329160e-03 1.693991e-03 -5.186773e-05 -5.690937e-03 3.438878e-04
Parameter:
[1] 0.03228283 1.14717672 -0.01350439 0.14498791 0.63787966
Function Value
[1] 55.43592
Gradient:
[1] 0.18565922 -0.10103476 1.74546967 0.03155258 -0.01987259
iteration = 35
Step:
[1] 2.406545e-04 8.303668e-04 -1.505216e-05 -2.357550e-04 8.198596e-05
Parameter:
[1] 0.03252348 1.14800709 -0.01351944 0.14475216 0.63796164
Function Value
[1] 55.43588
Gradient:
[1] 0.1663142655 -0.0486182523 -0.0347759084 0.0247638710 -0.0001023537
iteration = 36
Step:
[1] 2.176142e-06 9.103011e-05 -1.007218e-06 1.235949e-04 -6.864185e-07
Parameter:
[1] 0.03252566 1.14809812 -0.01352044 0.14487575 0.63796096
Function Value
[1] 55.43588
Gradient:
[1] 1.671713e-01 -4.137044e-02 -2.856068e-02 2.674682e-02 -4.598633e-05
iteration = 37
Step:
[1] -1.453431e-06 5.199930e-06 -6.797254e-09 1.576377e-05 -1.088460e-06
Parameter:
[1] 0.03252420 1.14810332 -0.01352045 0.14489152 0.63795987
Function Value
[1] 55.43588
Gradient:
[1] 0.1675834653 -0.0407588935 -0.0060229155 0.0270183520 -0.0002847074
iteration = 38
Step:
[1] -7.017383e-06 2.131608e-05 5.815876e-09 6.556917e-05 -4.916925e-06
Parameter:
[1] 0.03251719 1.14812463 -0.01352045 0.14495709 0.63795495
Function Value
[1] 55.43588
Gradient:
[1] 0.169519694 -0.038110365 0.100884193 0.028149714 -0.001373358
iteration = 39
Step:
[1] -1.025427e-05 2.604552e-05 4.083262e-08 7.849710e-05 -6.020150e-06
Parameter:
[1] 0.03250693 1.14815068 -0.01352040 0.14503558 0.63794893
Function Value
[1] 55.43588
Gradient:
[1] 0.171911054 -0.034856843 0.234368841 0.029506126 -0.002709427
iteration = 40
Step:
[1] -2.379391e-05 4.831506e-05 1.612984e-07 1.416594e-04 -1.099259e-05
Parameter:
[1] 0.03248314 1.14819899 -0.01352024 0.14517724 0.63793794
Function Value
[1] 55.43588
Gradient:
[1] 0.176224276 -0.028886242 0.479592657 0.031960603 -0.005151911
iteration = 41
Step:
[1] -4.974928e-05 7.663727e-05 4.670611e-07 2.152449e-04 -1.693183e-05
Parameter:
[1] 0.03243339 1.14827563 -0.01351978 0.14539249 0.63792101
Function Value
[1] 55.43588
Gradient:
[1] 0.182679116 -0.019624663 0.858370356 0.035707750 -0.008918818
iteration = 42
Step:
[1] -1.155456e-04 1.304279e-04 1.336242e-06 3.422482e-04 -2.749375e-05
Parameter:
[1] 0.03231784 1.14840606 -0.01351844 0.14573474 0.63789351
Function Value
[1] 55.43587
Gradient:
[1] 0.192634715 -0.004420488 1.474916985 0.041713598 -0.015046943
iteration = 43
Step:
[1] -2.700211e-04 2.180644e-04 3.580531e-06 5.124003e-04 -4.266691e-05
Parameter:
[1] 0.03204782 1.14862412 -0.01351486 0.14624714 0.63785084
Function Value
[1] 55.43585
Gradient:
[1] 0.20669982 0.01960128 2.43492991 0.05083293 -0.02458527
iteration = 44
Step:
[1] -6.374940e-04 3.638183e-04 9.250493e-06 7.093342e-04 -6.316136e-05
Parameter:
[1] 0.03141033 1.14898794 -0.01350561 0.14695647 0.63778768
Function Value
[1] 55.43579
Gradient:
[1] 0.22387329 0.05626885 3.86359905 0.06380574 -0.03877417
iteration = 45
Step:
[1] -1.427181e-03 5.665772e-04 2.201987e-05 7.663047e-04 -7.976909e-05
Parameter:
[1] 0.02998315 1.14955452 -0.01348359 0.14772278 0.63770791
Function Value
[1] 55.43566
Gradient:
[1] 0.23597286 0.10543612 5.68528371 0.07880336 -0.05685445
iteration = 46
Step:
[1] -2.735094e-03 7.120950e-04 4.418140e-05 2.290971e-04 -6.016065e-05
Parameter:
[1] 0.02724805 1.15026661 -0.01343941 0.14795187 0.63764775
Function Value
[1] 55.43541
Gradient:
[1] 0.21944838 0.15003425 7.10112407 0.08637638 -0.07086943
iteration = 47
Step:
[1] -3.608467e-03 4.540611e-04 6.087715e-05 -1.309658e-03 4.059928e-05
Parameter:
[1] 0.02363958 1.15072068 -0.01337853 0.14664221 0.63768835
Function Value
[1] 55.43509
Gradient:
[1] 0.14641586 0.14458115 6.29014409 0.06883794 -0.06266090
iteration = 48
Step:
[1] -2.169531e-03 -2.345980e-04 3.930676e-05 -2.473572e-03 1.497231e-04
Parameter:
[1] 0.02147005 1.15048608 -0.01333922 0.14416864 0.63783808
Function Value
[1] 55.43489
Gradient:
[1] 0.04943418 0.07432343 3.02547571 0.02944034 -0.02997969
iteration = 49
Step:
[1] 5.538011e-05 -4.539854e-04 1.419474e-06 -1.461388e-03 1.104231e-04
Parameter:
[1] 0.02152543 1.15003209 -0.01333780 0.14270726 0.63794850
Function Value
[1] 55.43484
Gradient:
[1] 0.003354728 0.015157962 0.568033897 0.004343484 -0.005495274
iteration = 50
Step:
[1] 3.837095e-04 -1.617866e-04 -5.892266e-06 -2.344168e-04 2.408204e-05
Parameter:
[1] 0.02190914 1.14987031 -0.01334370 0.14247284 0.63797258
Function Value
[1] 55.43484
Gradient:
[1] -0.0012287558 0.0004852009 0.0080586062 -0.0001852385 -0.0000210747
iteration = 51
Step:
[1] -4.061941e-04 -1.328228e-05 8.100305e-06 -1.627047e-05 1.346592e-07
Parameter:
[1] 0.02150295 1.14985702 -0.01333560 0.14245657 0.63797272
Function Value
[1] 55.43484
Gradient:
[1] 0.0000212097 0.0002483116 -0.0203513687 0.0003547828 -0.0001413246
iteration = 52
Step:
[1] -2.231080e-05 -3.599462e-06 4.674186e-07 -2.107252e-05 7.435516e-07
Parameter:
[1] 0.02148064 1.14985342 -0.01333513 0.14243550 0.63797346
Function Value
[1] 55.43484
Gradient:
[1] -3.952003e-05 -2.805228e-05 -1.970186e-03 2.897856e-05 1.443574e-06
iteration = 53
Step:
[1] -7.629347e-08 2.420720e-08 2.613255e-09 -1.534385e-06 1.327946e-08
Parameter:
[1] 0.02148056 1.14985345 -0.01333513 0.14243396 0.63797347
Function Value
[1] 55.43484
Gradient:
[1] -9.625509e-06 -6.581824e-06 -4.536232e-04 1.316671e-06 1.798028e-06
iteration = 54
Step:
[1] 5.075833e-08 1.172702e-08 -6.729833e-10 -6.725046e-08 -5.737290e-09
Parameter:
[1] 0.02148061 1.14985346 -0.01333513 0.14243389 0.63797347
Function Value
[1] 55.43484
Gradient:
[1] -1.126210e-06 -6.621866e-07 -5.630632e-05 -4.813927e-08 2.609468e-07
iteration = 55
Parameter:
[1] 0.02148062 1.14985346 -0.01333513 0.14243390 0.63797347
Function Value
[1] 55.43484
Gradient:
[1] -3.851142e-08 -1.464523e-08 -2.151523e-06 -1.104894e-08 1.197265e-08
Successive iterates within tolerance.
Current iterate is probably solution.
difFGLS:
[1] 3.707954e-12 1.010592e-10 3.253092e-12
******************************************
*** GOOD convergence indicated by FGLS ***
******************************************
Warning message:
In polygenic(height ~ sex + age, kin = gkin, ge03d2.clean) :
some eigenvalues close/less than 1e-8, setting them to 1e-8
you can also try option llfun='polylik' instead
> h2ht$est
[1] 0.1424339
> mm <- mmscore(h2ht,data=ge03d2.clean)
Warning message:
In mmscore(h2ht, data = ge03d2.clean) : Lambda estimated < 1, set to 1
> # compute grammar
> gr <- qtscore(h2ht$pgres,data=ge03d2.clean,clam=FALSE)
> #compute GC
> gc <- qtscore(height ~ sex + age,data=ge03d2.clean)
> #compare
> plot(mm,df="Pc1df",cex=0.5)
> add.plot(gc,df="Pc1df",col="red")
> add.plot(gr,df="Pc1df",col="lightgreen",cex=1.1)
> # can see that mmscore and grammar are quite the same... in contrast to GC
>
>
>
>
>
> dev.off()
null device
1
>