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The forum for discussion of VSN International software and general statistical issuesenCopyright 2000-2010 VSN International Ltd.forumadmin@vsni.co.ukforumadmin@vsni.co.ukhttp://blogs.law.harvard.edu/tech/rss60Thu, 29 Oct 2020 19:26:37 GMTThu, 29 Oct 2020 19:26:37 GMThttp://www.vsni.co.uk/common/images/asreml145.gifForum | VSN International
http://forum.vsni.co.uk//
ASReml - Factor Analytic structure in random regression coefficients
http://forum.vsni.co.uk/viewtopic.php?p=5600#5600
Hi Everyone,
<br />
<br />
I have a question regarding fitting random regression coefficients with factor analytic order 1 structure. I fitted the model with <div class="code">Code:<div class="inside_code">str(~Cultivar + covariate:Cultivar + covariate_square:Cultivar, ~fa(3):id(18))</div></div>. I have 24 cultivars.
<br />
However, it seems that this code is not the correct one.
<br />
<br />
My question is how to fit random regression coefficients with ASReml-R 4?
<br />
<br />
I thank you for your inputs.
<br />
<br />
Best regards,
<br />
HarimurtiASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1929Harimurti BuntaranMon, 17 Aug 2020 19:40:16 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5600#5600ASReml - RE: Spatial bivariate animal model
http://forum.vsni.co.uk/viewtopic.php?p=5599#5599
<div class="quote">srevans wrote:<div class="inside_quote">Hi, wondering if anyone knows how to specify trait specific spatial autocorrelation structures in a bivariate model, as a route to dealing with this? Something along the lines of the below (which doesn't work).
<br />
<br />
+ at(trait, 1):ar1v(col):ar1(row)
<br />
+ at(trait, 2):ar1v(col):ar1(row)</div></div>
<br />
<br />
you can try 'diag(trait):ar1v(col):ar1(row)'ASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1903yzhlinscauWed, 29 Jul 2020 02:32:39 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5599#5599Announcements - Gblup does not provide solutions for some IDs in the pedigre
http://forum.vsni.co.uk/viewtopic.php?p=5598#5598
Hello,
<br />
<br />
I am trying to fit a Gblup model in ASReml standalone. There are 2065 entries in the pedigree of which 2023 are genotypes and 42 are parents and grandparents. I have SNP marker data for 2023 genotypes and 20 parents. I do not have SNP data for 22 other parents and grandparents. I calculated the genomic relationship matrix for 2043 IDs and converted that into a GIV file. I fitted a simple additive genetic effect to check whether the model works fine. The model converges and things look fine in the asr file. But when I look at the SLN and PVS files, the model does not provide solutions and or predictions for 22 IDs. I checked the order of genotypes and matched it with the pedigree. The GIV file is missing 22 parents that were not genotyped but I sorted the genomic relationship matrix as it appears in the pedigree. I am not sure why the model is not providing the solutions for 22 genotypes. I know it has something to do with the 22 non-genotyped parents but I am not sure how to fix it to get solutions and predictions for all 2023 genotypes and 42 parents.
<br />
Below is the asr output. I also attached SLN and PVS files. Any help and suggestion would be appreciated.
<br />
Thanks,
<br />
Nasir
<br />
<br />
<br />
<br />
ASReml 4.2 [01 Jan 2016] Title: ACE Gblup analysis.
<br />
Build mv [18 Dec 2019] 64 bit Windows x64
<br />
09 Jun 2020 12:23:37.893 16026 Mbyte ktop\ACE1\asreml2\out/ACE9_VOL
<br />
Licensed to: North Carolina State University 30-nov-2020
<br />
*****************************************************************
<br />
* Contact <a href="mailto:support@asreml.co.uk">support@asreml.co.uk</a> for licensing and support *
<br />
*********************************************************** ARG *
<br />
Folder: C:\Users\nasir\Desktop\ACE1\asreml2
<br />
CLONE !P
<br />
FSCLONE !A
<br />
FAMILY !A
<br />
TYPE !A
<br />
ACE_ped.csv !SKIP 1 !ALPHA !SORT !GIV !DIAG
<br />
Notice: Sorted pedigree written to: ACE_ped.csv.SRT
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First 42 rows are parents.
<br />
Reading pedigree file ACE_ped.csv.SRT: skipping 1 lines
<br />
Pedigree Header Line: ID,Female,Male
<br />
Pedigree check: Male N07056 previously occurred as a Fema Now at line 42: N111062 N11010 N07056
<br />
Pedigree check: Fema N11010 previously occurred as a Male Now at line 42: N111062 N11010 N07056
<br />
Pedigree check: Male N11210 previously occurred as a Fema Now at line 131: ACE_04X01 N21209 N11210
<br />
Pedigree check: Male N11249 previously occurred as a Fema Now at line 245: ACE_08X01 N11281 N11249
<br />
Pedigree check: Male N11266 previously occurred as a Fema Now at line 281: ACE_09X02 N11281 N11266
<br />
Pedigree check: Fema N111069 previously occurred as a Male Now at line 523: ACE_21X02 N111069 N071029
<br />
Pedigree check: Male N111066 previously occurred as a Fema Now at line 1116: ACE_48X01 N111111 N111066
<br />
Pedigree check: Male N111111 previously occurred as a Fema Now at line 1323: ACE_55X13 N111128 N111111
<br />
Pedigree check: Male N111128 previously occurred as a Fema Now at line 1448: ACE_63X02 N11220 N111128
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Pedigree check: Male N11225 previously occurred as a Fema Now at line 1577: ACE_66X01 N11220 N11225
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15 individuals appear as both male and female parent
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2065 identities in the pedigree over 3+ generations.
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For first parent labelled Fema, second labelled Male
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Fema Fema_of_Fema Male_of_Fema Male Fema_of_Male Male_of_Male
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32 7 6 25 6 6
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Using an adapted version of Meuwissen & Luo GSE 1992 305-313:
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PEDIGREE [ACE_ped.csv.SRT ] has 2065 identities, 6126 Non zero elements
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<br />
Reading C:\Users\nasir\Desktop\ACE1\asreml2\G.giv skipping 0
<br />
header lines
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Inverse G structure of 2043 rows having 2087946 non zero cells read from C:\Users\nasir\Desktop\ACE1\asreml2\G.giv
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GIV0 NRM 2065 7 -1382.81
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GIV1 C:\Users\nas 2043 7 -0.00
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QUALIFIERS: !SKIP 1
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QUALIFIERS: !MAXIT 100 !CONTINUE
<br />
QUALIFIER: !DOPART 9 is active
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Reading C:\Users\nasir\Desktop\ACE1\asreml2\ACE_data.csv FREE FORMAT skipping 1 lines
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<br />
Univariate analysis of VOL
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Summary of 11516 records retained of 11519 read
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<br />
Model term Size #miss #zero MinNon0 Mean MaxNon0 StndDevn
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1 CLONE !P 2065 0 0 43 1040 2065
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2 FSCLONE 1954 299 0 1 945.6995 1954
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3 TEST 8 0 0 1 4.0854 8
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4 ROW 146 0 0 1 27.0572 146
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5 COL 84 0 0 1 29.6149 84
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6 STRT 353 0 1.000 3.408 6.000 1.239
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7 RUST 0 10869 1.000 0.5618E-01 1.000 0.2303
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8 VOL Variate 0 0 5.947 50.93 183.5 27.07
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9 FAMILY 58 0 0 1 26.6803 58
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10 TYPE 2 0 0 1 1.0260 2
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11 STDVOL 0 0 97.07 100.0 105.0 0.9997
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12 mu 1
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Warning: GRM matrix is too SMALL
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13 giv1(CLONE) 2043
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14 idh(TEST) 8
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15 idh(TEST).ROW 1168 14 idh(TEST) : 8 4 ROW : 146
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16 idh(TEST).COL 672 14 idh(TEST) : 8 5 COL : 84
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idh(TEST) in idh(TEST).COL has size 8, parameters: 17 24
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idh(TEST).COL [17:24] initialized.
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idh(TEST) in idh(TEST).ROW has size 8, parameters: 25 32
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idh(TEST).ROW [25:32] initialized.
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Forming 3892 equations: 9 dense.
<br />
Initial updates will be shrunk by factor 0.316
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Notice: ReStartValues taken from C:\Users\nasir\Desktop\ACE1\asreml2\out/ACE9_VOL.rsv
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<br />
Notice: # is honoured as a special white-out character in data file
<br />
Notice: Use !SPECIALCHAR to treat # as normal character in data file
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* This job may use 12 of the 12 processor cores. !MP *
<br />
Notice: LogL values are reported relative to a base of -30000.000
<br />
Notice: 1 singularities detected in design matrix.
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1 LogL=-8505.14 S2= 1.0000 11508 df
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2 LogL=-8505.14 S2= 1.0000 11508 df
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3 LogL=-8505.14 S2= 1.0000 11508 df
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<br />
- - - Results from analysis of VOL - - -
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Akaike Information Criterion 77060.28 (assuming 25 parameters).
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Bayesian Information Criterion 77244.05
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<br />
Model_Term Sigma Sigma Sigma/SE % C
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giv1(CLONE) GRM_V 2043 31.4027 31.4027 9.38 0 P
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sat(TEST,1).id(units) 1899 effects
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Residual_1 SCA_V 1899 145.562 145.562 28.08 0 P
<br />
sat(TEST,2).id(units) 1583 effects
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Residual_2 SCA_V 1583 920.123 920.123 26.95 0 P
<br />
sat(TEST,3).id(units) 1743 effects
<br />
Residual_3 SCA_V 1743 603.684 603.684 28.20 0 P
<br />
sat(TEST,4).id(units) 985 effects
<br />
Residual_4 SCA_V 985 129.879 129.879 19.64 0 P
<br />
sat(TEST,5).id(units) 1732 effects
<br />
Residual_5 SCA_V 1732 266.113 266.113 27.52 0 P
<br />
sat(TEST,6).id(units) 1561 effects
<br />
Residual_6 SCA_V 1561 512.861 512.861 26.01 0 P
<br />
sat(TEST,7).id(units) 1316 effects
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Residual_7 SCA_V 1316 86.5134 86.5134 22.08 0 P
<br />
sat(TEST,<img src="images/smiles/icon_cool.gif" alt="Cool" border="0" />.id(units) 697 effects
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Residual_8 SCA_V 697 48.6964 48.6964 14.59 0 P
<br />
idh(TEST).COL 672 effects
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TEST DIAG_V 1 11.6155 11.6155 3.84 0 P
<br />
TEST DIAG_V 2 4.24047 4.24047 0.57 0 P
<br />
TEST DIAG_V 3 1.46150 1.46150 0.34 0 P
<br />
TEST DIAG_V 4 6.72778 6.72778 2.29 0 P
<br />
TEST DIAG_V 5 1.20975 1.20975 0.64 0 P
<br />
TEST DIAG_V 6 84.5800 84.5800 2.65 0 P
<br />
TEST DIAG_V 7 1.07176 1.07176 0.96 0 P
<br />
TEST DIAG_V 8 0.797852 0.797852 0.66 0 P
<br />
idh(TEST).ROW 1168 effects
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TEST DIAG_V 1 64.9776 64.9776 4.20 0 P
<br />
TEST DIAG_V 2 20.4466 20.4466 2.01 0 P
<br />
TEST DIAG_V 3 7.24004 7.24004 1.49 0 P
<br />
TEST DIAG_V 4 14.6032 14.6032 3.12 0 P
<br />
TEST DIAG_V 5 13.0553 13.0553 3.07 0 P
<br />
TEST DIAG_V 6 3.41688 3.41688 0.54 0 P
<br />
TEST DIAG_V 7 6.52739 6.52739 2.64 0 P
<br />
TEST DIAG_V 8 0.593520 0.593520 0.77 0 P
<br />
<br />
Wald F statistics
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Source of Variation NumDF F-inc
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12 mu 1 416.42
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3 TEST 7 928.47
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<br />
Solution Standard Error T-value T-prev
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3 TEST
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2 39.0418 1.74864 22.33
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3 42.5612 1.56328 27.23 2.68
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4 15.4518 1.58553 9.75 -25.00
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5 12.1835 1.53027 7.96 -3.16
<br />
6 28.8744 2.68743 10.74 6.94
<br />
7 7.00266 1.50244 4.66 -9.16
<br />
8 -12.4166 1.41803 -8.76 -27.89
<br />
12 mu
<br />
1 28.4362 2.13185 13.34
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16 idh(TEST).COL 672 effects fitted ( 191 are zero)
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15 idh(TEST).ROW 1168 effects fitted ( 771 are zero)
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13 giv1(CLONE) 2043 effects fitted
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* This job used at least 2100 of the 16026 Mbyte of primary workspace. *
<br />
SLOPES FOR LOG(ABS(RES)) on LOG(PV) for Section 11
<br />
0.69
<br />
7 possible outliers: see .res file
<br />
SLOPES FOR LOG(ABS(RES)) on LOG(PV) for Section 13
<br />
0.59
<br />
1 possible outliers: see .res file
<br />
SLOPES FOR LOG(ABS(RES)) on LOG(PV) for Section 14
<br />
0.65
<br />
SLOPES FOR LOG(ABS(RES)) on LOG(PV) for Section 15
<br />
0.49
<br />
2 possible outliers: see .res file
<br />
1 possible outliers: see .res file
<br />
SLOPES FOR LOG(ABS(RES)) on LOG(PV) for Section 18
<br />
0.52
<br />
Finished: 09 Jun 2020 12:24:42.381 LogL ConvergedAnnouncementshttp://forum.vsni.co.uk//posting.php?mode=reply&t=1928nasirshaliziTue, 09 Jun 2020 20:28:19 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5598#5598ASReml - Bug with asreml R 4 - constraints mislabeled
http://forum.vsni.co.uk/viewtopic.php?p=5594#5594
In asreml R 3 the constraint column for "fa1" and "fa2" components was labeled as "U" for unconstrained. I believe this is correct.
<br />
<br />
<div class="code">Code:<div class="inside_code">
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genotype:fa(site, 2)!site.BLA3071.fa1 0.2055 0.01283 16 U
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genotype:fa(site, 2)!site.MTA3071.fa1 0.2377 0.02049 12 U
<br />
genotype:fa(site, 2)!site.PNA3071.fa1 0.1013 0.006714 15 U
<br />
genotype:fa(site, 2)!site.RSA3071.fa1 0.2403 0.026 9.2 U
<br />
genotype:fa(site, 2)!site.WTA3071.fa1 0.2273 0.0123 18 U
<br />
genotype:fa(site, 2)!site.BLA3071.fa2 0 NA NA F
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genotype:fa(site, 2)!site.MTA3071.fa2 0.05263 0.02275 2.3 U
<br />
genotype:fa(site, 2)!site.PNA3071.fa2 -0.006081 0.007743 -0.79 U
<br />
genotype:fa(site, 2)!site.RSA3071.fa2 0.3043 0.01994 15 U
<br />
genotype:fa(site, 2)!site.WTA3071.fa2 -0.03982 0.01505 -2.6 U
<br />
</div></div>
<br />
<br />
In asreml R 4 the bound column is labeled as "P" for positive. Shouldnt this be "U" instead of P? You can even see that the row for the 3rd fa2 component has a value of -.003199, which is negative, NOT positive.
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<br />
<div class="code">Code:<div class="inside_code">
<br />
genotype:fa(site, 2)!BLA3071!fa1 0.2091 0.01289 16 P 0
<br />
genotype:fa(site, 2)!MTA3071!fa1 0.232 0.0232 10 P 0
<br />
genotype:fa(site, 2)!PNA3071!fa1 0.1032 0.006689 15 P 0
<br />
genotype:fa(site, 2)!RSA3071!fa1 0.2113 0.02349 9 P 0
<br />
genotype:fa(site, 2)!WTA3071!fa1 0.2226 0.01226 18 P 0
<br />
genotype:fa(site, 2)!BLA3071!fa2 0 NA NA F NA
<br />
genotype:fa(site, 2)!MTA3071!fa2 0.2201 0.02428 9.1 P 0
<br />
genotype:fa(site, 2)!PNA3071!fa2 -0.003199 0.009583 -0.33 P 0
<br />
genotype:fa(site, 2)!RSA3071!fa2 0.11 0.03205 3.4 P 0
<br />
genotype:fa(site, 2)!WTA3071!fa2 0.002106 0.01852 0.11 P 0
<br />
</div></div>
<br />
<br />
Possible fix: Change the bound labels for factor analytic terms from "P" to "U" (except for boundary/fixed labels).ASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1926kwstatTue, 21 Apr 2020 19:12:51 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5594#5594ASReml - Bug with asreml R 4 - rotate.fa not working?
http://forum.vsni.co.uk/viewtopic.php?p=5593#5593
<div class="code">Code:<div class="inside_code">
<br />
When I fit a factor analytic model, I get the same results with rotate.fa=FALSE and rotate.fa=TRUE. If I understand the manual, I expect to get different results. Am I doing something wrong?
<br />
<br />
library(agridat)
<br />
library(asreml) # version 4
<br />
<br />
dat <- besag.met
<br />
dat <- transform(dat, xf=factor(col), yf=factor(row))
<br />
dat <- dat[order(dat$county, dat$xf, dat$yf), ]
<br />
<br />
# ---
<br />
asreml.options(rotate.fa=FALSE)
<br />
# First, AR1xAR1
<br />
m1 <- asreml(yield ~ county, data=dat,
<br />
random = ~ gen:county,
<br />
resid = ~ dsum( ~ ar1(xf):ar1(yf)|county))
<br />
# Add FA1
<br />
m2 <- update(m1, random=~gen:fa(county,1)) # rotate.FA=FALSE
<br />
# FA2
<br />
m3 <- update(m2, random=~gen:fa(county,2))
<br />
asreml.options(extra=100)
<br />
m3 <- update(m3, maxit=100)
<br />
asreml.options(extra=0)
<br />
<br />
v3 <- summary(m3)$varcomp
<br />
v3[13:18,]
<br />
sum( v3[7:12,1] * v3[13:18,1] )
<br />
<br />
gen:fa(county, 2)!C1!fa1 8.9280685 NA NA F 0.0
<br />
gen:fa(county, 2)!C2!fa1 3.4138813 1.44655165 2.360013423 P 0.0
<br />
gen:fa(county, 2)!C3!fa1 6.4681237 1.49612348 4.323255277 P 0.0
<br />
gen:fa(county, 2)!C4!fa1 6.1091640 6.44926570 0.947265044 P 0.0
<br />
gen:fa(county, 2)!C5!fa1 9.2815434 5.96665609 1.555568688 P 0.0
<br />
gen:fa(county, 2)!C6!fa1 3.0671903 4.26143390 0.719755461 P 0.0
<br />
gen:fa(county, 2)!C1!fa2 -0.1029065 14.32967412 -0.007181359 P 0.8
<br />
gen:fa(county, 2)!C2!fa2 0.6094157 5.63006905 0.108243028 P 0.1
<br />
gen:fa(county, 2)!C3!fa2 0.2541841 10.50137099 0.024204847 P 0.2
<br />
gen:fa(county, 2)!C4!fa2 3.9401840 9.86942202 0.399231480 P 0.0
<br />
gen:fa(county, 2)!C5!fa2 -3.7116785 14.87131329 -0.249586464 P 0.0
<br />
gen:fa(county, 2)!C6!fa2 2.4622861 5.37691282 0.457936768 P 0.0
<br />
<br />
<br />
#---
<br />
asreml.options(rotate.fa=TRUE)
<br />
# First, AR1xAR1
<br />
m5 <- asreml(yield ~ county, data=dat,
<br />
random = ~ gen:county,
<br />
resid = ~ dsum( ~ ar1(xf):ar1(yf)|county))
<br />
# Add FA1
<br />
m6 <- update(m5, random=~gen:fa(county,1))
<br />
# FA2
<br />
m7 <- update(m6, random=~gen:fa(county,2), rotate.fa=TRUE)
<br />
asreml.options(extra=100)
<br />
m7 <- update(m7, maxit=100)
<br />
asreml.options(extra=0)
<br />
<br />
v7 <- summary(m7)$varcomp
<br />
v7[13:18,]
<br />
gen:fa(county, 2)!C1!fa1 8.9280685 NA NA F 0.0
<br />
gen:fa(county, 2)!C2!fa1 3.4138813 1.44655165 2.360013423 P 0.0
<br />
gen:fa(county, 2)!C3!fa1 6.4681237 1.49612348 4.323255277 P 0.0
<br />
gen:fa(county, 2)!C4!fa1 6.1091640 6.44926570 0.947265044 P 0.0
<br />
gen:fa(county, 2)!C5!fa1 9.2815434 5.96665609 1.555568688 P 0.0
<br />
gen:fa(county, 2)!C6!fa1 3.0671903 4.26143390 0.719755461 P 0.0
<br />
gen:fa(county, 2)!C1!fa2 -0.1029065 14.32967412 -0.007181359 P 0.8
<br />
gen:fa(county, 2)!C2!fa2 0.6094157 5.63006905 0.108243028 P 0.1
<br />
gen:fa(county, 2)!C3!fa2 0.2541841 10.50137099 0.024204847 P 0.2
<br />
gen:fa(county, 2)!C4!fa2 3.9401840 9.86942202 0.399231480 P 0.0
<br />
gen:fa(county, 2)!C5!fa2 -3.7116785 14.87131329 -0.249586464 P 0.0
<br />
gen:fa(county, 2)!C6!fa2 2.4622861 5.37691282 0.457936768 P 0.0
<br />
<br />
# dot product of FA1 and FA2 is NOT 0, so these vectors are NOT orthogonal
<br />
sum( v7[7:12,1] * v7[13:18,1] )
<br />
</div></div>[/code]ASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1925kwstatTue, 21 Apr 2020 19:10:43 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5593#5593Announcements - Announcement: VSN has abandoned this forum
http://forum.vsni.co.uk/viewtopic.php?p=5591#5591
I recently contacted VSN about the status of this forum and was told:
<br />
<br />
"The Forum was built several years ago and is sadly now on an unsupported platform. We are currently evaluating a new system and sadly this means there is currently a gap within our services. We are sorry about this and hopefully will be able to launch a new and improved Forum later this year."
<br />
<br />
This forum is still working, so it is not clear to me why VSN has abandoned this site without first creating a replacement.
<br />
<br />
I suggest posting questions to <a href="https://stats.stackexchange.com/" target="_blank" class="postlink">https://stats.stackexchange.com/</a> with an "asreml" tag attached to the question.Announcementshttp://forum.vsni.co.uk//posting.php?mode=reply&t=1923kwstatWed, 18 Mar 2020 19:53:48 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5591#5591ASReml - RE: Spatial bivariate animal model
http://forum.vsni.co.uk/viewtopic.php?p=5575#5575
Hi, wondering if anyone knows how to specify trait specific spatial autocorrelation structures in a bivariate model, as a route to dealing with this? Something along the lines of the below (which doesn't work).
<br />
<br />
+ at(trait, 1):ar1v(col):ar1(row)
<br />
+ at(trait, 2):ar1v(col):ar1(row)ASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1903srevansSun, 09 Feb 2020 23:31:01 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5575#5575ASReml - DHGLM in ASReml 4.2
http://forum.vsni.co.uk/viewtopic.php?p=5574#5574
Is there new documentation on how to fit DHGLM in version 4.2? I found some documentation here:
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<a href="https://niasra.uow.edu.au/content/groups/public/@web/@inf/@math/documents/mm/uow225970.pdf" target="_blank">https://niasra.uow.edu.au/content/groups/public/@web/@inf/@math/documents/mm/uow225970.pdf</a>
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And also in the document “What's new in ASReml 4.2” dated November 2017 (Build 4.2 mv). But I am wondering if there is more elaborated examples and help for fitting DHGLM in ASReml 4.2. I am trying to run a DHGLM in which there are random intercept and slopes to the mean part of the model and only random intercept fitted to the dispersion part, with covariances fitted between all 3.
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If this is the code to allow a covariance between mean and dispersion effects at sow level:
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<span style="font-weight: bold">!HGLM HSection
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LS dev(LS) ∼ Trait Trait.age !r us(Trait).nrm(sow)</span>
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Then what would be the code to add a random slope according to age only to the mean part of the model, while estimating all of the covariances (i.e., a 3 x 3 co-variance matrix with intercept and slope variance fitted to the mean part and intercept variance fitted to the dispersion part)? Would that be the correct code?:
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<span style="font-weight: bold">!HGLM HSection
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LS dev(LS) ∼ Trait Trait.age !r us(Trait).nrm(sow) !r str(Trait.sow at(Trait,1).age.sow us(3).nrm(sow))
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</span>ASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1922vincent_careauWed, 29 Jan 2020 21:02:24 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5574#5574ASReml - Doubly multivariate analysis with ASREML-R 4.1
http://forum.vsni.co.uk/viewtopic.php?p=5557#5557
Good day:
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I have a doubly multivariate dataset that consists of root length data at 4 depths, captured over time during two growing seasons. This is superimposed on a split-plot design where CO2 concentration is the main plot, and cultivar is the subplot. I have been able to analyse the aggregated root length over time, and I have analysed each date separately as a typical repeated measure with ASREML-R 4.1. However, it looks like the two cultivars do show differences in root length distribution over time, and I'm not capturing this with analyses over separate samplings. I would like to analyse this with the entire data set if possible.
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Some of the issues I am running into is
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1) the software doesn't seem to like it when I use the time factor (days after planting) as a numerical value. When I use this in the residual, I get an error message that says "Error in spc("cor", "cor", 0.1, numeric(0), NA, obj, init, data) : DAP must be a factor".
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2)At the moment, I'm only using the DAP correlation in the residual term, but I'm not sure if I should have the Depth too?
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3) I have a lot of zeros, especially at the beginning of the season as roots have not yet grown into the deeper layers. Could that be a problem? My residuals plots sure look funky.
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4) Since this is a growth curve, I was thinking of using a power error structure, however, regardless of the correlation error structure, I always get the same loglikelihood and residuals plots. Is that normal?
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Any advice?
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Thanks in advance,
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Maryse BASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1920marysebMon, 25 Nov 2019 19:08:54 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5557#5557ASReml - VPREDICT failing to read asr file
http://forum.vsni.co.uk/viewtopic.php?p=5537#5537
Hello,
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I am trying to calculate hertibility after running a binomial gblup model. I'm using a standalone ASReml4 on a Mac.
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The model runs to convergence but when it gets to manipulating the variance components I get the error below
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<span style="font-style: italic">Failed to interpret line: Reading pedigree file M048_Dead_and_Alive_Pedigree.
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txt: ski
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Failed to interpret line: Reading pedigree file M048_Dead_and_Alive_Pedigree.
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txt: ski
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Program to calculate functions of variance components from ASReml output.
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Reads <xx>.asr, <xx>.vvp and <xx>.pin, writes <xx>.pvc
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Usage ASREML -P <xx>[.pin]
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or ASREML -P<xx>[.asr] <xx>[.pin]
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<xx>.pin gives the function of components to be calculated
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The input file may have a different extension (not PIN).
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Fields may be TAB SPACE or COMMA separated.
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" characters are ignored; Anything after # is ignored
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Input consists of lines of three types
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Each line consists of a code in column 1 (P, H or R),
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a label and a list of arguments (space separated)
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P label c calculates c'v, Cov(c'v,v) and Var(c'v)
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where v is the existing variance components
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c is coefficients for a linear function
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It is used to construct linear functions of variances
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NB each P line extends v by one term
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H label n d calculates v(n)/v(d) and Var[v(n)/v(d)]
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i.e. is used to calculate heritability
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R label a ab b calculates v(ab)/sqrt[v(a)v(b)] and its
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variance (i.e. a correlation)</span>[/i]
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Making a separate pin file resulted in the same error.
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I can see that there is a problem reading the asr file, but I can't understand why that would be since the model ran to completion. Any advice on how to fix this is greatly appreciated.
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Thanks,
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JoshuaASRemlhttp://forum.vsni.co.uk//posting.php?mode=reply&t=1919JoshuaWed, 30 Oct 2019 17:52:48 GMThttp://forum.vsni.co.uk/viewtopic.php?p=5537#5537