I did not get negative values for prop of healthy vs diseased either   Do you think I could use the results (neg corr) from the analysis including number of total eggs (log transformed) and prop of diseased (without log trans) in the same model with gaussian dist, or was this analysis incorrect ? (because you should not compare counts with proportions) Thank you Steve ... View more

Oh noooo!!! now is the problem solved, but I only get positive corr values (I used log without any base) 1 1.0000 0.6982 0.4875 0.3404 0.2377 2 1.0000 0.1012 0.01025 0.001037 0.000105 3 0.6982 1.0000 0.6982 0.4875 0.3404 4 0.1012 1.0000 0.1012 0.01025 0.001037 5 0.4875 0.6982 1.0000 0.6982 0.4875 6 0.01025 0.1012 1.0000 0.1012 0.01025 7 0.3404 0.4875 0.6982 1.0000 0.6982 8 0.001037 0.01025 0.1012 1.0000 0.1012 9 0.2377 0.3404 0.4875 0.6982 1.0000 10 0.000105 0.001037 0.01025 0.1012 1.0000 11 1.0000 0.6982 0.4875 0.3404 0.2377 12 1.0000 0.1012 0.01025 0.001037 0.000105 13 0.6982 1.0000 0.6982 0.4875 0.3404 14 0.1012 1.0000 0.1012 0.01025 0.001037 15 0.4875 0.6982 1.0000 0.6982 0.4875 16 0.01025 0.1012 1.0000 0.1012 0.01025 17 0.3404 0.4875 0.6982 1.0000 0.6982 18 0.001037 0.01025 0.1012 1.0000 0.1012 19 0.2377 0.3404 0.4875 0.6982 1.0000 20 0.000105 0.001037 0.01025 0.1012 1.0000 the logit transformation for the proportions yielded some negative values. Thank you Steve! ... View more

Thanks again Steve! I tried transforming the number of total eggs and he proportion of diseased eggs with log10, and dist=gaussian but did not work ERROR: QUANEW Optimization cannot be completed Optimization routine cannot improve the function value ... View more

Now I compared the proportion of diseased eggs with the number of total eggs (log10 transformed), using dist=gaussian (you did not mean using gaussian and binomial at the same time right?) and it worked!!!! I finally get negative values and the values in the table goes in the right direction (I guess) 1 1.0000 -0.03965 0.001573 -0.00006 2.473E-6 2 1.0000 0.6982 0.4875 0.3404 0.2377 3 -0.03965 1.0000 -0.03965 0.001573 -0.00006 4 0.6982 1.0000 0.6982 0.4875 0.3404 5 0.001573 -0.03965 1.0000 -0.03965 0.001573 6 0.4875 0.6982 1.0000 0.6982 0.4875 7 -0.00006 0.001573 -0.03965 1.0000 -0.03965 8 0.3404 0.4875 0.6982 1.0000 0.6982 9 2.473E-6 -0.00006 0.001573 -0.03965 1.0000 10 0.2377 0.3404 0.4875 0.6982 1.0000 11 1.0000 -0.03965 0.001573 -0.00006 2.473E-6 12 1.0000 0.6982 0.4875 0.3404 0.2377 13 -0.03965 1.0000 -0.03965 0.001573 -0.00006 14 0.6982 1.0000 0.6982 0.4875 0.3404 15 0.001573 -0.03965 1.0000 -0.03965 0.001573 16 0.4875 0.6982 1.0000 0.6982 0.4875 17 -0.00006 0.001573 -0.03965 1.0000 -0.03965 18 0.3404 0.4875 0.6982 1.0000 0.6982 19 2.473E-6 -0.00006 0.001573 -0.03965 1.0000 20 0.2377 0.3404 0.4875 0.6982 1.0000 ... View more

I tried healthy eggs with disease eggs. Significant interactions including eggtype, but still no negative values. I also compared proportion of healthy eggs with proportion of disease eggs, but no negative values. I would appreciate if you could have a look, maybe I am doing something wrong. (harEggsT means he number of total eggs. I also divided 4 because I need the numbers in 100g soil and he raw data is in 400g) data one; input nem$ blk$    year eggtype$ harEggs harEggsT; harEggs=harEggs/4; harEggs=harEggs+1; harEggsT=harEggsT/4; harEggsT=harEggsT+1; harPerEggs=100*harEggs/harEggsT; cards; Chav    A    2010    healthy    1400    8700 Chav    A    2010    diseased    7300    8700 Chav    A    2011    healthy    27375    35250 Chav    A    2011    diseased    7875    35250 Chav    A    2012    healthy    9700    27900 Chav    A    2012    diseased    18200    27900 Chav    A    2013    healthy    60625    68875 Chav    A    2013    diseased    8250    68875 Chav    A    2014    healthy    19350    34425 Chav    A    2014    diseased    15075    34425 Chav    B    2010    healthy    3000    10800 Chav    B    2010    diseased    7800    10800 Chav    B    2011    healthy    17100    21700 Chav    B    2011    diseased    4600    21700 Chav    B    2012    healthy    11625    28275 Chav    B    2012    diseased    16650    28275 Chav    B    2013    healthy    71750    81500 Chav    B    2013    diseased    9750    81500 Chav    B    2014    healthy    25500    46800 Chav    B    2014    diseased    21300    46800 Chav    C    2010    healthy    27200    36000 Chav    C    2010    diseased    8800    36000 Chav    C    2011    healthy    44250    66500 Chav    C    2011    diseased    22250    66500 Chav    C    2012    healthy    13500    26500 Chav    C    2012    diseased    13000    26500 Chav    C    2013    healthy    82800    97650 Chav    C    2013    diseased    14850    97650 Chav    C    2014    healthy    29400    38300 Chav    C    2014    diseased    8900    38300 Chav    D    2010    healthy    5600    22400 Chav    D    2010    diseased    16800    22400 Chav    D    2011    healthy    43500    53100 Chav    D    2011    diseased    9600    53100 Chav    D    2012    healthy    17800    43900 Chav    D    2012    diseased    26100    43900 Chav    D    2013    healthy    62200    70600 Chav    D    2013    diseased    8400    70600 Chav    D    2014    healthy    35125    47875 Chav    D    2014    diseased    12750    47875 Delm    A    2010    healthy    800    12600 Delm    A    2010    diseased    11800    12600 Delm    A    2011    healthy    2850    24750 Delm    A    2011    diseased    21900    24750 Delm    A    2012    healthy    9150    34200 Delm    A    2012    diseased    25050    34200 Delm    A    2013    healthy    21500    30000 Delm    A    2013    diseased    8500    30000 Delm    A    2014    healthy    8750    16850 Delm    A    2014    diseased    8100    16850 Delm    B    2010    healthy    7000    46800 Delm    B    2010    diseased    39800    46800 Delm    B    2011    healthy    28000    62000 Delm    B    2011    diseased    34000    62000 Delm    B    2012    healthy    12400    41200 Delm    B    2012    diseased    28800    41200 Delm    B    2013    healthy    28350    37275 Delm    B    2013    diseased    8925    37275 Delm    B    2014    healthy    50300    72100 Delm    B    2014    diseased    21800    72100 Delm    C    2010    healthy    17250    55050 Delm    C    2010    diseased    37800    55050 Delm    C    2011    healthy    44700    88650 Delm    C    2011    diseased    43950    88650 Delm    C    2012    healthy    9400    51700 Delm    C    2012    diseased    42300    51700 Delm    C    2013    healthy    54375    89750 Delm    C    2013    diseased    35375    89750 Delm    C    2014    healthy    28200    56700 Delm    C    2014    diseased    28500    56700 Delm    D    2010    healthy    .    . Delm    D    2010    diseased    .    . Delm    D    2011    healthy    .    . Delm    D    2011    diseased    .    . Delm    D    2012    healthy    .    . Delm    D    2012    diseased    .    . Delm    D    2013    healthy    .    . Delm    D    2013    diseased    .    . Delm    D    2014    healthy    .    . Delm    D    2014    diseased    .    . Proc glimmix data=one; harPerEggsp=harPerEggs/100; class nem blk year eggtype; model harPerEggsp= eggtype|nem|year/dist=beta ddfm=kr; random intercept/subject=blk; random year/residual subject=blk*nem type=ar(1) group=eggtype vcorr; run; Thank you very much Steve!! ... View more

I tried healthy eggs with disease eggs. Significant interactions including eggtype, but still no negative values. I also compared proportion of healthy eggs with proportion of disease eggs, but no negative values. I would appreciate if you could have a look, maybe I am doing something wrong. (harEggsT means he number of total eggs. I also divided 4 because I need the numbers in 100g soil and he raw data is in 400g) data one; input nem$ blk$    year eggtype$ harEggs harEggsT; harEggs=harEggs/4; harEggs=harEggs+1; harEggsT=harEggsT/4; harEggsT=harEggsT+1; harPerEggs=100*harEggs/harEggsT; cards; Chav    A    2010    healthy    1400    8700 Chav    A    2010    diseased    7300    8700 Chav    A    2011    healthy    27375    35250 Chav    A    2011    diseased    7875    35250 Chav    A    2012    healthy    9700    27900 Chav    A    2012    diseased    18200    27900 Chav    A    2013    healthy    60625    68875 Chav    A    2013    diseased    8250    68875 Chav    A    2014    healthy    19350    34425 Chav    A    2014    diseased    15075    34425 Chav    B    2010    healthy    3000    10800 Chav    B    2010    diseased    7800    10800 Chav    B    2011    healthy    17100    21700 Chav    B    2011    diseased    4600    21700 Chav    B    2012    healthy    11625    28275 Chav    B    2012    diseased    16650    28275 Chav    B    2013    healthy    71750    81500 Chav    B    2013    diseased    9750    81500 Chav    B    2014    healthy    25500    46800 Chav    B    2014    diseased    21300    46800 Chav    C    2010    healthy    27200    36000 Chav    C    2010    diseased    8800    36000 Chav    C    2011    healthy    44250    66500 Chav    C    2011    diseased    22250    66500 Chav    C    2012    healthy    13500    26500 Chav    C    2012    diseased    13000    26500 Chav    C    2013    healthy    82800    97650 Chav    C    2013    diseased    14850    97650 Chav    C    2014    healthy    29400    38300 Chav    C    2014    diseased    8900    38300 Chav    D    2010    healthy    5600    22400 Chav    D    2010    diseased    16800    22400 Chav    D    2011    healthy    43500    53100 Chav    D    2011    diseased    9600    53100 Chav    D    2012    healthy    17800    43900 Chav    D    2012    diseased    26100    43900 Chav    D    2013    healthy    62200    70600 Chav    D    2013    diseased    8400    70600 Chav    D    2014    healthy    35125    47875 Chav    D    2014    diseased    12750    47875 Delm    A    2010    healthy    800    12600 Delm    A    2010    diseased    11800    12600 Delm    A    2011    healthy    2850    24750 Delm    A    2011    diseased    21900    24750 Delm    A    2012    healthy    9150    34200 Delm    A    2012    diseased    25050    34200 Delm    A    2013    healthy    21500    30000 Delm    A    2013    diseased    8500    30000 Delm    A    2014    healthy    8750    16850 Delm    A    2014    diseased    8100    16850 Delm    B    2010    healthy    7000    46800 Delm    B    2010    diseased    39800    46800 Delm    B    2011    healthy    28000    62000 Delm    B    2011    diseased    34000    62000 Delm    B    2012    healthy    12400    41200 Delm    B    2012    diseased    28800    41200 Delm    B    2013    healthy    28350    37275 Delm    B    2013    diseased    8925    37275 Delm    B    2014    healthy    50300    72100 Delm    B    2014    diseased    21800    72100 Delm    C    2010    healthy    17250    55050 Delm    C    2010    diseased    37800    55050 Delm    C    2011    healthy    44700    88650 Delm    C    2011    diseased    43950    88650 Delm    C    2012    healthy    9400    51700 Delm    C    2012    diseased    42300    51700 Delm    C    2013    healthy    54375    89750 Delm    C    2013    diseased    35375    89750 Delm    C    2014    healthy    28200    56700 Delm    C    2014    diseased    28500    56700 Delm    D    2010    healthy    .    . Delm    D    2010    diseased    .    . Delm    D    2011    healthy    .    . Delm    D    2011    diseased    .    . Delm    D    2012    healthy    .    . Delm    D    2012    diseased    .    . Delm    D    2013    healthy    .    . Delm    D    2013    diseased    .    . Delm    D    2014    healthy    .    . Delm    D    2014    diseased    .    . Proc glimmix data=one; harPerEggsp=harPerEggs/100; class nem blk year eggtype; model harPerEggsp= eggtype|nem|year/dist=beta ddfm=kr; random intercept/subject=blk; random year/residual subject=blk*nem type=ar(1) group=eggtype vcorr; run; Thank you very much Steve!! ... View more

Thanks a lot Steve! Thank you for the explanation. You are right, the counts are large numbers. Unfortunately I do not see any single negative value in that table I would now try with healthy eggs and disease eggs. Do you think is it correct to look for negative corr between number of total eggs and proportion of disease eggs (instead of number of disease eggs)? I appreciate your great help Steve!! Caroline ... View more

Hi Steve, When I used a lognormal distribution I get a Chi-Square/DF=1, but when I use negbin do not converge, so I will stay with the lognormal. It seems that your code worked. There is a significant effect for eggtype. Anyhow I do not know how to interpret the results, because I get a table called "Estimated V Correlation Matrix for blk A" with rows and columns.  Why only for blk A? I also get the Type III Tests of Fixed Effects table, a Cov Parameter Estimates table, but nothing else :smileyplain: Thank you dear Steve! Caroline ... View more

Hi Steve! I am so glad you came to help me . Thank you!! I already check the distribution and the lognormal was the most suitable (is usually the case for nematode counts). harEggsT (total eggs) is one of the dependent variable that I want to test for negative corr with harEggsD (diseased eggs), so I did not know how to include both dependent variables in the same model, but I will try what you are suggesting. Thank you very much Steve, Caroline ... View more

Thank you dear Steve!  method=laplace works wonderful for binomial and negbin when analyzing the sequences as univariate.  I tried the multivariate approach analyzing the two nematode populations separately, due to the hugh differences we observed between both, but still did not work.  I think that the problem is due to the high variability among replicates within a population.  For most bacterial species (dependent var) happens that for one replicate 400 sequences (more or less) were registered whereas for the other 4 replicates 0 sequences were found. I guess that is also the reason why for most of the species (in the univariate analysis) I do not find any significant differences between the two populations. Caroline ... View more

Hi Steve, thank you very much for your good Ideas and suggestions.  I´m sorry I could not reply before, but my SAS License expired.  You are right, I did not expected such a differences between the two populations, so I understand the problems you were facing when trying with different optimizations.  The graphs and the tables were for sure a good Idea to see those differences.  I think before getting deeper and deeper with this multivariate approach including the 2 populations, I would rather leave this on standby and go ahead with an univariate analyze for comparing the two populations, and a multivariate analyze for each population separately. In relation to the univariate analyze, I would like to ask you something; when should you use the option method=quad or laplace for a binomial or negbin distribution?  Is it preferable in order to optimize your model? Thanks a lot Steve!! Caroline ... View more

Mmmm.... it cannot be, I am probably doing something wrong or we are missing something. I reduced the species to 5 but still cannot converge.  I tried the genmode, changing the code a little bit due to the offset statement (I hope was ok), but did not work either.  I will send you my data attached so you can better see if we are missing something (the analysis with 10 species). Thank  you for still helping me Steve!! Caroline ... View more

I have 2 populations of nematodes with 5 replicates each, which makes a total of 10 experimental units.  In long format, I have 140 rows;14 bacteria species (dependent variable) evaluated for each exp. unit. I tried the negbin dist, but unfortunately did also not converge.  Do you think it may have something to do that the dependent var "species" in the random statement is not a numeric variable? Thanks a lot Steve! Caroline ... View more

Thank you very much for your help Steve.  I followed your first advice changing to V, but unfortunately did not work.  I reduced then the species number from 26 to 14, trying this 2 codes, but still getting following warnings; proc glimmix data=one method=laplace; class blk pop species; model No_seq/total_seq=pop species pop*species/dist=binomial link=logit; random species/sub=blk*pop type=chol GCORR; run; WARNING: The initial estimates did not yield a valid objective function. proc glimmix data=one; class blk pop species; model No_seq/total_seq=pop species pop*species/dist=binomial link=logit; random species/residual sub=blk*pop type=chol V; run; WARNING: Pseudo-likelihood update fails in outer iteration 0. Do you think would be a good Idea to try with a negbin distribution (because of the zeroes)? Or should reduce the number of species to 10? Thank you Steve!! Caroline ... View more