I have a dataset with multiple columns with variables that I want to check for correlations. My identifier is country code and the remaining are all variables that are to be studied. The goal is to find the top 5/10 linear regressions among all combinations using r square value as comparison attribute. My data looks like:
Country_code | A1 | A2 | A3 |...... | A193
I want to run a simulation that can run lm in loop and have summaries of all combination of attributes in a list so that I can compare their r square value and publish/plot the top 5/10 correlations.
It should run like
For A1:
lm(A1~A2) then lm(A1~A3).....
Next: lm(A2~A3)....
I know I need to put two loops but either the results are not saved because the summary have too many attributes to be pushed into a list, etc.
Related
I need to perform a correlation analysis on a data frame constructed as follows:
Rows: Features --> gene variants related with different levels of severity of the disease we are studying, in a format of a Boolean matrix
Columns: Observations --> List of patients;
The discriminant of my analysis is, thus, the severity marked as follows:
A: less severe than expected
B: equal to what expected
C: more severe than expected
Suppose I have a lot more features than observations and I want to use the PTM function with a three-level annotation (i.e. A,B,C) as a match template. The function requires you to set the annotation.level.set.high parameter, but it's not clear to me how it works. For example, if I set annotation.level.set.high='A', does that mean I'm making a comparison between A vs B&C? So I can only do a comparison between two groups/classes even if I have multiple levels? Because my goal is to compare all levels with each other (i.e. A vs B vs C), but it is not clear to me how to achieve this comparison, if it is possible.
Thanks
I have a dataset of expression values from 3 groups (proteome expression data as log2).
Now I would like to perform first an ANOVA to check for proteins differentially expressed in any of the 3 groups and next perform a post-test (eg Tukeys) to figure out which proteins are affected in which specific group comparison.
Unfortunately, no resources I found discussed this rather simple case but only much more complicated cases with multiple group combinations (and eg two treatments).
With the HybridMTest package, the ANOVA went fine and I have now the FDR for differentially expressed proteins between the 3 groups (6 samples per group).
But now I'm stuck on how to calculate the posttest for every protein (= rows, n = 3878) between all of the 3 groups.
I could not find an appropriate package or function, maybe one of you could offer a hint on how to solve this? I would like to obtain as a result a df with the protein_id, the comparison group, the FDR and the logFoldChange.
Many thanks!
(and sorry for providing the data only in a very descriptive way. I dont know how to create example expression data but will look into it)
Sebastian
Data description:
expression_df:
rownames(expression_df) = protein_id, colnames(expression_df) = sample_id
pheno_df:
rownames(pheno_df) = sample_id, pheno_df$cluster = cluster group (n=3)
anova_results:
rownames(protein_id), anova_results$FDR = FDR controlled ANOVA result
I have a list of matrices containing association measurements between GPS tracked animals. One matrix in the list is observed association rates, the others are association rates for randomized versions of the GPS tracking trajectories. For example, I currently have 99 permutations of randomized tracking trajectories resulting in a list of 99 animal association matrices, plus the observed association matrix. I am expecting that for the animals that belong to the same pack, the observed association rates will be higher than the randomized association rates. Accordingly, I would like to determine the rank of the observed rates compared to the randomized rates for each dyad (cell). Essentially, I am doing a rank-permutation test. However, since I am only really concerned with determining if the observed association data is greater than the randomized trajectory association data, any result just giving the rank of the observed cells is sufficient.
ls <- list(matrix(10:18,3,3), matrix(18:10,3,3))
I've seen using sapply can get the ranks of particular cells. Could I do the following for all cells and take the final number in the resulting vector to get the rank of the cell in that position in the list (knowing the position of the observed data in the list of matrices, e.g. last).
rank(sapply(ls, '[',1,1))
The ideal result would be a matrix of the same form as those in the list giving the rank of the observed data, although any similar solutions are welcome. Thanks in advance.
You can proceed that way, but there are cleaner and quicker methods to get what you want.
Here's some code that would take your ls produce a 3x3 matrix with the following properties:
if the entry in ls[[1]] is greater than the corresponding entry of ls[[2]], record a 1
if the entry in ls[[1]] is less than the corresponding entry of ls[[2]], record a 2
if the entries are equal, record a 1.5
result <- 1 * (ls[[1]] > ls[[2]]) + 2 * (ls[[1]] < ls[[2]]) + 1.5 * (ls[[1]] == ls[[2]])
How it works: when we do something like ls[[1]] > ls[[2]], we are ripping out the matrices of interest and directly comparing them. The result of this bit of code is a T/F-populated matrix, which is secretly coded as a 0/1 matrix. We can then multiply it by whatever coefficient we want to represent that situation.
I am working with a large federal dataset with thousands of observations and thousands of variables. Replicate weights are provided. I am using the "survey" package in R to apply these weights:
els.weighted=svrepdesign(data=els, repweights = ~els$F3F1PNLWT,
combined.weights = TRUE).
I am interested in some categorical descriptive characteristics of a subset of the population, such as family living arrangements. I want to get these sorted out into a contingency table that shows frequency. I would like to sort people based on four variables (none of which are binary, but all of which are numeric) This is what I would like to get:
.
The blank boxes are where the cross-tabulation/frequency counts would show. (I only put in 3 columns beneath F1COMP for brevity's sake, but it has 9 outcomes – indexed 1-9)
My current code: svyby(~F1FCOMP, ~F1RTRCC +BYS33C +F1A10 +byurban, els.weighted, svytotal)
This code does sort the data, but it sorts every single combination, by default. I want them pared down to represent only specific subpopulations of each variable. I tried:
svyby(~F1FCOMP, ~F1RTRCC==2 |F1RTRCC==3 +BYS33C==1 +F1A10==2 | F1A10==3 +byurban==3, els.weighted, svytotal)
But got stopped:
Error: unexpected '==' in "svyby(~F1FCOMP, ~F1RTRCC==2 |F1RTRCC==3 +BYS33C=="
Additionally, my current version of the code tells me how many cases occur for each combination, This is a picture of what my current output looks like. There are hundreds more rows, 1 for each combination, when I keep scrolling down.
This is a picture of what my current output looks like. There are hundreds more rows, 1 for each combination, when I keep scrolling down
.
You can see in that picture that I only get one number for F1FCOMP per row – the number of cases who fit the specified combination – a specific subpopulation. I want to know more about that subpopulation. That is, F1COMP has nine different outcomes (indexed 1-9), and I want to see how many of each subpopulation fits into each of the 9 outcomes of F1COMP.
The title is not precisely stated but I could not come up with other words which summarizes what I exactly going to ask.
I have a table of the following form:
value (0<v<1) # of events
0.5677 100000
0.5688 5000
0.1111 6000
... ...
0.5688 200000
0.1111 35000
Here are some of the things I like to do with this table: drawing the histogram, computing mean value, fitting the distribution, etc. So far, I could only figure out how to do this with vectors like
v=(0.5677,...,0.5688,...,0.1111,...)
but not with tables.
Since the number of possible values are huge by being almost continuous, I guess making a new table would not be that effective, so doing this without modifying the original table and making another table would be desirable very much. But if it has to be done so, it's okay. Thanks in advance.
Appendix: What I want to figure out is how to treat this table as a usual data vector:
If I had the following vector representing the exact same data as above:
v= (0.5677, ...,0.5677 , 0.5688, ... 0.5688, 0.1111,....,0.1111,....)
------------------ ------------------ ------------------
(100000 times) (5000+200000 times) (6000+35000) times
then we just need to apply the basic functions like plot, mean, or etc to get what I wanted. I hope this makes my question more clear.
Your data consist of a value and a count for that value so you are looking for functions that will use the count to weight the value. Type ?weighted.mean to get information on a function that will compute the mean for weighted (grouped) data. For density plots, you want to use the weights= argument in the density() function. For the histogram, you just need to use cut() to combine values into a small number of groups and then use aggregate() to sum the counts for all the values in the group. You will find a variety of weighted statistical measures in package Hmisc (wtd.mean, wtd.var, wtd.quantile, etc).