I created a split plot model and I got the below result as below:-
However I have no idea how to interpret them. Can you guys please shine some lights on? Thanks!
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I would appreciate help interpreting the following pairwise scatterplots of predictor variables to check for multicollinearity and then fit the data to the results to avoid this occurring.
Background: I am working on a task where I have to carry out multilinear regression. In this task I have three explanatory variables, tar, nico and weight and want to predict CO, so CO is the response variable (dependent variable). The data comes from 25 American cigarette brands where tar, nico and weight are the respective brand's content of tar, nicotine and weight per cigarette. And CO is how much carbon monoxide a cigarette emits.
Question: In the task, I will now plot all the explanatory variables in pairs against each other to look for multicollinearity and find an observation that is questionable to include in the regression. Which I have done, see the picture above. But how should I interpret this image?
My thoughts: I have understood that multicollinearity would not exist if all the images in this plot looked different, but I can clearly see that this is not the case here. For example, three out of four plots after "tar" are similar and this form also appears in one plot after "nico" and two plots after "weight". But does this then mean that the three predictor variables are multicollinear? Or that some data in "tar" is collinear with another data in "tar"? After I figure out where this collinearity (possibly) arises, I need to fit the data and run a new multilinear regression on the reduced data set for which the questionable observation has been removed. I think this is done by setting the value of the dubious observation to NA, but then I have to find this one first.
Finally: How should I interpret the image and then fit the data to get rid of any collinearity?
Any thoughts and tips on this are welcomed! Thanks in advance!
I am trying to understand how to run PERMANOVA using Adonis2 in R to analyse some data that I have collected. I have been looking online, but as it often happens, explanations are a bit convoluted, so I am asking for your help, if you can help me. I have got some fish and coral groups as columns, as well as 3 independent variables (reef age, depth, and material). Snapshot of my dataset structure I think I have understood that p-values are not the only important bit of the output, and that the R2 values indicate how much each variable contributes to the model. Is there something wrong or that I am missing here? Also, I think I understood that I should check for homogeneity of variance, but I have not understood, again, if I should check for it on each variable independently, or if I should include them all in the same bit of code (which does not seem to work). Here are the bit of code that I am using to run the PERMANOVA (1), and the one that I am trying to use to assess homogeneity of variance - which does not work (2).
(1) adonis2(species ~ Age+Material+Depth,data=data.var,by="margin)
'Species' is the subset of the dataset including all the species'count, while 'data.var'is the subset including the 3 independent variables. Also what is the difference in using '+' or '' in the code? When I use '' it gives me - 'Error in qr.X(object$CCA$QR) :
need larger value of 'ncol' as pivoting occurred'. What does this mean?
(2) variance.check<-betadisper(species.distance,data.var, type=c("centroid"), bias.adjust= FALSE)
'species.distance' is a matrix calculated through 'vegdist' using Bray-Curtis method. I used 'data.var'to check variance on all the 3 independent variables, but it does not work, while it works if I check them independently (3). Why is that?
(3) variance.check<-betadisper(species.distance, data$Depth, type=c("centroid"), bias.adjust= FALSE)
Thank you in advance for your responses, and for your help. It will really help me to get my head around it (and sorry for the many questions).
I set up two identical models in lme (R) and apply it to two different dependent variables. However, in one of the models one of the predictors is renamed, a 1 is added to the name. I assume this means, that the predictor is somehow adjusted, but I do not know what is going on and how to suppress this behaviour? Could you please provide me with some information on this or a hint on where to find help on this topic?
Thank you!
I have repeated measures model but the nested factor is a random factor. Now, every example of repeated measures anova with the aov function I came across has a fixed factor. Their model looks like :
aov(values~ A +Error(Subject/A),long)
Using the logic behind the Error term, I figured if my factor A is random, I should include A in the Error term, like this :
aov(values~ Error(A + (Subject/A)),long)
This seems to work and doesn't produce any error in R, but I have never seen anyone write it this way before ? Does it make sense ?
Any help is appreciated, Thanks !
I have a panel dataset, which is unbalanced. I created a pooled model and now need to predict and input the missing values of the dataset. How can it be done?
Here is a printscreen of my data: https://imagizer.imageshack.us/v2/1366x440q90/661/RAH3uh.jpg
Thank you!
First of all it looks like you have a too broad question in here. If you're really asking about how you should predict values for your spreadsheet (i.e cells: Z6,AA6,...,AM22,...); yes you have a HUGE questions =]. Just a hint, in your following questions, you should be more specific, like: I have THIS data related to Households in Belarus. I've searched about predicting models for that and tried XPTO1 and XPTO2. How can I decide which one is better?
So, what I really mean here is that predicting is not exactly a function like SUM, that you can apply to your data and that's it. Prediction is a whole discipline, with a bunch of methods that should be tested to different cases. For example, to predict the Z6 cell in your data, you should to ask yourself what other data can contribute to infer data missing information? In some cases the simple average value for the past 5 years will be enough, in some other, a lot more should be considered.
I recommend you to first take a look at some basic material that covers simple models, like linear models, play with them, try to understand the accuracy of obtained predictions... That will finally solve your problem, or will at least help you to ask the community more "answerable" questions.
One last tip: there is a new SO's sister Q&A community that may be more appropriate to ask questions about prediction models: https://datascience.stackexchange.com/
Good luck.