1) do you have the same number of petri dishes per treatment?
2) are you also interested in the effect of light (I assume so, since its in your design).
Dear All
i am stuck at the moment between a rock and a hard place. the short of
the story is this.
I have 84 petri dishes each containing 4 lines of seeds (each line
contains 4 seeds).
these lines contain a wild type plant, and 3 knock out mutant lines.
i have split these 84 plates into 2 groups, a high light and a low
light.
these two groups are then further divided into differing
concentrations of added zinc. (7 in total, 0 , 2, 5, 15, 300, 350 and
400 umol)
each different concentration has 6 petri dishes as repeats.
the plants were all harvesed and weighed as groups i.e 4 plants at a
time.
now i am interested in several things from a statistical point of
view:
A) i am interested in a significant difference between the wild type
and the 3 different mutants (called 1a, 1b and 1c). in this case a
paired T-Test conducted numerous times will tell me if there is a
within concentration difference.
B) i wish to know if there is a significant difference between
concentrations in other words taking the concentration into account.
my problem is that i cant do a paired t-test as plants grown on petri
dishes in diffedrent concentrations are not paired. secondly a simple
anova will not do as the mutant must allways be compared to the wild
type of its own concentration. hence the for a lack of better words a
"paired anova"
... any advice
cheers
Yoma
1) do you have the same number of petri dishes per treatment?
2) are you also interested in the effect of light (I assume so, since its in your design).
The earth is round: P<0.05
Dear bugman
Thanks for the reply.
i am interested in the effect of light yes.
i have the following number of petri dishes:
84 total
42 per light treatment (2 light treatments)
the 42 dishes per light treatment are broken worn into 7 different concentrations of zinc.
each concentration having 6 plates.
to answer your question yes and no, by this i mean that due to infection the occasional plate has been omitted leaving only 5 plates per concentration.
this happens a few times in bot light treatments.
as an update to the forum:
i am using R (of course) and have been trying to carry out a factoral anova (after dismissing a 3 way anova).
my data is formatted like this:
light treatment, Zinc concentration, seed type, weight
(high/low), (0,2,5,15,300,350,400), (wt,1a,1b,1c), average weight of seedling)
the strange thing is that when executing the following command:
model<-aov(weight~seed*ZN*light)
and then
summary.lm(model)
i get:
no mention of 1a or high light!Code:Call: aov(formula = weight ~ seed * ZN * light) Residuals: Min 1Q Median 3Q Max -6.8329 -1.0244 -0.1706 0.9130 23.0957 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.523122 0.698769 2.180 0.030035 * seed1b 6.331149 0.988209 6.407 5.56e-10 *** seed1c 0.348668 0.988209 0.353 0.724459 seedwt 6.881466 0.988209 6.964 2.01e-11 *** ZN -0.003398 0.003028 -1.122 0.262718 lightlow 10.642914 0.967918 10.996 < 2e-16 *** seed1b:ZN -0.017887 0.004283 -4.176 3.86e-05 *** seed1c:ZN -0.001104 0.004283 -0.258 0.796784 seedwt:ZN -0.018851 0.004283 -4.402 1.48e-05 *** seed1b:lightlow -5.687621 1.368843 -4.155 4.22e-05 *** seed1c:lightlow -2.819258 1.368843 -2.060 0.040278 * seedwt:lightlow -5.066436 1.368843 -3.701 0.000254 *** ZN:lightlow -0.027007 0.004195 -6.438 4.63e-10 *** seed1b:ZN:lightlow 0.017588 0.005932 2.965 0.003266 ** seed1c:ZN:lightlow 0.007877 0.005932 1.328 0.185211 seedwt:ZN:lightlow 0.015840 0.005932 2.670 0.007984 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.247 on 308 degrees of freedom Multiple R-squared: 0.7129, Adjusted R-squared: 0.6989 F-statistic: 50.98 on 15 and 308 DF, p-value: < 2.2e-16
i cannot work out why unless i am using the wrong test!
many thanks
--yoma
You're probably missing those because R needs to make the model full rank. The same issue is discussed in this thread and there's a link to a lecture in one of my posts toward the end of that if you want to read more about this.
As for seed weight, is this the final weight? were ALL the initial weights the same / did you weigh them initially?
The earth is round: P<0.05
yes seed weight is the final weight.
all plants were planted and harvested at the same time.
they were not weighed before planting as they were planted as seeds.
cheers
Yoma
@Dason
thanks i will look into that later.
it has been buggin me for a while.
just had a look over it and it is the case that some of my petri dishes had to be ommited due to infection. as a result in some concentration levels there are 5 repeats not 6.
is this what would be causing the problem?
thanks
yoma
Last edited by yoma819; 08-10-2010 at 07:31 AM. Reason: addition
update:
the only problem i am having now is just that the 1a and the high light keeps dissapearing.
does anyone know how i can stop this?
here is my output
any comments would be appreciatedCode:> summary.lm(model) Call: aov(formula = weight ~ seed * zinc * light) Residuals: Min 1Q Median 3Q Max -10.13153 -0.43882 -0.04083 0.43031 19.72397 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.9142 1.1485 1.667 0.096748 . seed1b 9.3119 1.6242 5.733 2.65e-08 *** seed1c 0.3044 1.6242 0.187 0.851457 seedwt 9.3004 1.6242 5.726 2.75e-08 *** zinczn15 -0.8852 1.7035 -0.520 0.603759 zinczn2 0.1953 1.6242 0.120 0.904392 zinczn300 -1.6146 1.6242 -0.994 0.321088 zinczn350 -1.5657 1.6242 -0.964 0.335932 zinczn400 -1.5602 1.7035 -0.916 0.360563 zinczn5 -1.1087 1.7035 -0.651 0.515722 lightlow 8.8017 1.6242 5.419 1.34e-07 *** seed1b:zinczn15 -7.7509 2.4091 -3.217 0.001453 ** seed1c:zinczn15 -0.2004 2.4091 -0.083 0.933752 seedwt:zinczn15 -8.4839 2.4091 -3.522 0.000504 *** seed1b:zinczn2 0.5831 2.2970 0.254 0.799814 seed1c:zinczn2 -0.4285 2.2970 -0.187 0.852166 seedwt:zinczn2 2.0857 2.2970 0.908 0.364689 seed1b:zinczn300 -9.1323 2.2970 -3.976 9.03e-05 *** seed1c:zinczn300 -0.2374 2.2970 -0.103 0.917774 seedwt:zinczn300 -8.6908 2.2970 -3.784 0.000191 *** seed1b:zinczn350 -9.2132 2.2970 -4.011 7.85e-05 *** seed1c:zinczn350 -0.3704 2.2970 -0.161 0.872009 seedwt:zinczn350 -8.9306 2.2970 -3.888 0.000128 *** seed1b:zinczn400 -9.2692 2.4091 -3.848 0.000149 *** seed1c:zinczn400 -0.4113 2.4091 -0.171 0.864575 seedwt:zinczn400 -9.2729 2.4091 -3.849 0.000148 *** seed1b:zinczn5 -6.4224 2.4091 -2.666 0.008146 ** seed1c:zinczn5 0.8729 2.4091 0.362 0.717392 seedwt:zinczn5 -5.1349 2.4091 -2.131 0.033961 * seed1b:lightlow -9.3817 2.2970 -4.084 5.84e-05 *** seed1c:lightlow -1.8265 2.2970 -0.795 0.427212 seedwt:lightlow -8.0725 2.2970 -3.514 0.000517 *** zinczn15:lightlow 1.1885 2.3537 0.505 0.614012 zinczn2:lightlow 2.0043 2.2970 0.873 0.383675 zinczn300:lightlow -7.6043 2.2970 -3.311 0.001059 ** zinczn350:lightlow -7.5731 2.3537 -3.218 0.001452 ** zinczn400:lightlow -7.9828 2.3133 -3.451 0.000649 *** zinczn5:lightlow 4.1310 2.3537 1.755 0.080384 . seed1b:zinczn15:lightlow 9.6429 3.3287 2.897 0.004079 ** seed1c:zinczn15:lightlow 0.7586 3.3287 0.228 0.819889 seedwt:zinczn15:lightlow 12.8477 3.3287 3.860 0.000142 *** seed1b:zinczn2:lightlow -0.8990 3.2484 -0.277 0.782177 seed1c:zinczn2:lightlow -1.3732 3.2484 -0.423 0.672836 seedwt:zinczn2:lightlow -2.6843 3.2484 -0.826 0.409349 seed1b:zinczn300:lightlow 9.7844 3.2484 3.012 0.002843 ** seed1c:zinczn300:lightlow 1.9351 3.2484 0.596 0.551870 seedwt:zinczn300:lightlow 8.7914 3.2484 2.706 0.007240 ** seed1b:zinczn350:lightlow 9.4691 3.3287 2.845 0.004788 ** seed1c:zinczn350:lightlow 1.5498 3.3287 0.466 0.641878 seedwt:zinczn350:lightlow 8.0641 3.3287 2.423 0.016072 * seed1b:zinczn400:lightlow 10.0394 3.2716 3.069 0.002370 ** seed1c:zinczn400:lightlow 1.8637 3.2716 0.570 0.569378 seedwt:zinczn400:lightlow 8.5554 3.2716 2.615 0.009426 ** seed1b:zinczn5:lightlow 7.7549 3.3287 2.330 0.020564 * seed1c:zinczn5:lightlow -3.3832 3.3287 -1.016 0.310368 seedwt:zinczn5:lightlow 3.7101 3.3287 1.115 0.266030 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.813 on 268 degrees of freedom Multiple R-squared: 0.8124, Adjusted R-squared: 0.774 F-statistic: 21.11 on 55 and 268 DF, p-value: < 2.2e-16 >
it was generated by:
thanks againCode:model<-aov(weight~seed*zinc*light)
Yoma
i am probably just thinking aloud to myself but...
levels of signisicance are determined by the control i.e the wild type.
so surely i need to construct 3 anova models and lay them on top of each other?
one to test for significance between weed type and growth (base of wild type)
another to test that significance against zinc
and a last one to test both against light level.
does this sound plausable to anyone?
--yoma
I already mentioned that it's trying to make the model full rank. If you want to learn more about what it's doing and why you can follow the link in my previous post. Notice that you're missing more than just 1a and high_light. You're missing any interaction term where those would be involved. I would suggest learning a little bit about what it's doing. Read lecture 7 found here for more info.
Well you can always do a post-hoc test to do those individual tests so I don't see the point in doing multiple ANOVAs.
Also: It might look like terms are disappearing but it does this for a reason. It might be that it's confusing for a newcomer but that almost might be a good thing because if you don't understand whats going on you're probably not going to interpret things correctly anyways. Note that I'm not saying they do this to confuse people. It really is a good system if you know what's going on. SAS does a similar type of thing to make the model full rank but it drops the last parameter instead of the first parameter. There are other ways to get a full rank model. It's also possible to just not have a full rank model at all (a parameter for everything) but then the individual parameters aren't estimable and then you have to use contrasts to answer any of your questions and really that's more of a pain than just understanding a full rank model in the first place.
Hi Yoma
just a quick follow up. I hope you get this sorted because it sounds like a cool GH experiment!
I just wanted to add that even though you are usuing seeds in this project, it would have been a good idea to have intial weight in your model as a co-variate. I have conducted similar studies on salt marsh plant seed and even moss spores (we used diameter as a proxy for weight) and in each case, the relative growth measurements were strongly related to initial weight inidcating an imbalance of resources or "fitness" from the outset.
Food for thought. Thats all.
The earth is round: P<0.05
Dear all especially Bugman
i am so very close now.
i have found out how to overlay models as i have been helped by a friend.
tell me what you think of this:
what does everyone think?Code:assuming a table is loaded and attached and that R knows that light and zinc are factors > model1<-glm(y~1) > model1<-glm(y~zinc) > anova(modela,modelb,test="Chi") Analysis of Deviance Table Model 1: y ~ 1 Model 2: y ~ zinc Resid. Df Resid. Dev Df Deviance P(>|Chi|) 1 323 11308.8 2 317 6665.6 6 4643.2 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > model3<-glm(y~zinc+light) > anova(mode1b,model3,test="Chi") Error in anova(mode1b, model3, test = "Chi") : object 'mode1b' not found > anova(modelb,model3,test="Chi") Analysis of Deviance Table Model 1: y ~ zinc Model 2: y ~ zinc + light Resid. Df Resid. Dev Df Deviance P(>|Chi|) 1 317 6665.6 2 316 4878.9 1 1786.7 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
yet again thank you to everyone who had a look at this post and especially those that contributed!
--yoma
i really am jumping between the fire and the frying pan haha
i cn now compare models but i can only compare models that i am not interested in.
i think my key to success lies in answering this question:
how do i conduct multiple paired t tests between:
wt and 1a
wt and 1b
wt and 1c
just between seeds on the same zinc levels!
so basically it would be all wild type weights on zinc level 0 paired with all 1a weights on zinc level 0 and so on for wt:1b and wt:1c and then for every zinc concentration.
that would really eventually solve my problem as i could then use an anova model to test for significance between each T-test.
If you wanted to do this run the ANOVA and use the package "multcomp" for planned comparisons.
But what about your light factor?
The earth is round: P<0.05
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