I'm using lpsolveAPI in R and would like to set coefficients for specified columns and rows (coefficient for specified constraint number and decision variable number).
However, whereas I can add new column (new decision variable) or set existing column, I can't edit the column as that option will remove all previous coefficients in that column.
For example, let it be 5 constraints and 2 decision variables. Then:
lps.model <- make.lp(5, 2) #create lp model
#set coefficients for the first 3 constraint for both variables
for (i in seq(1,2)) set.column(lps.model, i, c(1,2,3), indices = c(1,2,3))
The model looks like this:
Model name:
C1 C2 C3 C4
Minimize 0 0 0 0
R1 1 1 0 0 free 0
R2 2 2 0 0 free 0
R3 3 3 0 0 free 0
R4 0 0 0 0 free 0
R5 0 0 0 0 free 0
Kind Std Std Std Std
Type Real Real Real Real
Upper Inf Inf Inf Inf
Lower 0 0 0 0
Now I want to add coefficients for 4th and 5th constraints.
for (i in seq(1,2)) set.column(lps.model, i, c(4,5), indices = c(4,5))
The code will rewrite the model since set.column functions sets all coefficients that were not listed within the function parameters to 0.
Model name:
C1 C2
Minimize 0 0
R1 0 0 free 0
R2 0 0 free 0
R3 0 0 free 0
R4 4 4 free 0
R5 5 5 free 0
Kind Std Std
Type Real Real
Upper Inf Inf
Lower 0 0
I have a big matrix of constraints and decision variables and need to run alike loops for different sets of variables. Is there any way to edit existing columns without rewriting them?
You could use set.mat for this to set values in your A matrix one at a time. See the help here.
For example:
> set.mat(lps.model, 4,5,3)
Will make the value of 4th row, 5th column to be 3, without overwriting anything else. So you can call set.mat within a double loop and change individual values.
However, it would be much more efficient if you can create whole columns at a time (preprocessing to create the list of coefficients) and then adding them to the lps.model in one shot using set.column especially since you say you have a large matrix of decision variables.
Related
I'm working on data from a pre-post survey: the same participants have been asked the same questions at 2 different times (so the sample are not independant). I have 19 categorical variables (Likert scale with 7 levels).
For each question, I want to know if there is a significant difference between the "pre" and "post" answer. To do this, I want to compare proportions in each of the 7 categories between pre and post results.
I have two data bases (one 'pre' and one 'post') which I have merged as in the following example (I've made sure that the categorical variables have the same levels for PRE and POST):
prepost <- data.frame(ID = c(1:7),
Quest1_PRE = c('5_SomeA','1_StronglyD','3_SomeD','4_Neither','6_Agree','2_Disagree','7_StronglyA'),
Quest1_POST = c('1_StronglyD','7_StronglyA','6_Agree','7_StronglyA','3_SomeD','5_SomeA','7_StronglyA'))
I tried to perform a McNemar test:
temp <- table(prepost_S1$Quest1_PRE,prepost_S1$Quest1_POST)
mcnemar.test(temp)
> McNemar's Chi-squared test
data: temp
McNemar's chi-squared = NaN, df = 21, p-value = NA
But whatever the question, the test always return NA values. I think it is because the pivot table (temp) has very low frequencies (I only have 24 participants).
One exemple of a pivot table (I have 22 participants):
> temp
1_StronglyD 2_Disagree 3_SomeD 4_Neither 5_SomeA 6_Agree 7_StronglyA
1_StronglyD 0 0 0 0 0 1 0
2_Disagree 0 0 0 0 1 0 0
3_SomeD 0 0 0 0 0 1 1
4_Neither 0 0 1 1 2 2 2
5_SomeA 0 0 0 0 1 1 2
6_Agree 0 0 0 0 0 3 2
7_StronglyA 0 0 0 0 0 1 2
I've tried aggregating the variables' levels into 5 instead of 7 ("1_Disagree", "2_SomeD", "3_Neither", "4_SomeA", "5_Agree") but it still doesn't work.
Is there an equivalent of Fisher's exact test for paired sample? I've done research but I couldn't find anything helpful.
If not, could you think of any other test that could answer my question (= Do the answers differ significantly between the pre and post survey)?
Thanks!
I use missMDA package to fill in multiple categorical columns. However, I cannot get any result from these two functions: estim_ncpMCA(test_fill) and imputeMCA(test_fill). The program keeps running without any progress bar or results popped out.
This is the sample from the dataset.
Hybrid G1 G5 G8 G9 G10
P1000:P2030 0 -1 0 1 0
P1006:P1384 0 0 0 0 1
P1006:P1401 0 NA NA NA 1
P1006:P1412 0 0 0 0 1
P1006:P1594 0 0 0 0 1
P1013:P1517 0 0 0 0 1
I am working on a genetic project in R. In this dataset, there are 497 rows and 11,226 columns. Every row is a genetic marker series for a particular hybrid, and every column is a genetic marker ("G1", "G2" and etc) with value 1, 0, -1 and NA. There are total 746,433 of missing values and I am trying to fill in the missing values by imputeMCA.
I also made some transformations on test_fill before running imputeMCA.
test_fill = as.matrix(test_fill)
test_fill[, -1] <- lapply(test_fill[, -1], as.numeric)
I am wondering whether this is the result of too many columns in my dataset. And do I need to transpose my columns and rows.
I don't know if you found your answer, but I think your function doesn't run because of your first column, which seems to be the label of the individuals. You can specify that it should not be taken into the analysis.
estim_ncpMCA(test_fill[,2:11226], ncp.max = 5)
imputeMCA(test_fill[,2:11226], ncp = X)
I hope this can help.
I am using principal component analysis (PCA) based on ~30 variables to compose an index that classifies individuals in 3 different categories (top, middle, bottom) in R.
I have a dataframe of ~2000 individuals with 28 binary and 2 continuous variables.
Now, I would like to use the loading factors from PC1 to construct an
index that classifies my 2000 individuals for these 30 variables in 3 different groups.
Problem: Despite extensive research, I could not find out how to extract the loading factors from PCA_loadings, give each individual a score (based on the loadings of the 30 variables), which would subsequently allow me to rank each individual (for further classification). Does it make sense to display the loading factors in a graph?
I've performed the following steps:
a) Ran a PCA using PCA_outcome <- prcomp(na.omit(df1), scale = T)
b) Extracted the loadings using PCA_loadings <- PCA_outcome$rotation
c) Removed all the variables for which the loading factors were close to 0.
I have considered creating 30 new variable, one for each loading factor, which I would sum up for each binary variable == 1 (though, I am not sure how to proceed with the continuous variables). Consequently, I would assign each individual a score. However, I would not know how to assemble the 30 values from the loading factors to a score for each individual.
R code
df1 <- read.table(text="
educ call house merge_id school members
A 1 0 1 12_3 0 0.9
B 0 0 0 13_3 1 0.8
C 1 1 1 14_3 0 1.1
D 0 0 0 15_3 1 0.8
E 1 1 1 16_3 3 3.2", header=T)
## Run PCA
PCA_outcome <- prcomp(na.omit(df1), scale = T)
## Extract loadings
PCA_loadings <- PCA_outcome$rotation
## Explanation: A-E are 5 of the 2000 individuals and the variables (education, call, house, school, members) represent my 30 variables (binary and continuous).
Expected results:
- Get a rank score for each individual
- Subsequently, assign a category 1-3 to each individual.
I'm not 100% sure what you're asking, but here's an answer to the question I think you're asking.
First of all, PC1 of a PCA won't necessarily provide you with an index of socio-economic status. As explained here, PC1 simply "accounts for as much of the variability in the data as possible". PC1 may well work as a good metric for socio-economic status for your data set, but you'll have to critically examine the loadings and see if this makes sense. Depending on the signs of the loadings, it could be that a very negative PC1 corresponds to a very positive socio-economic status. As I say: look at the results with a critical eye. An explanation of how PC scores are calculated can be found here. Anyway, that's a discussion that belongs on Cross Validated, so let's get to the code.
It sounds like you want to perform the PCA, pull out PC1, and associate it with your original data frame (and merge_ids). If that's your goal, here's a solution.
# Create data frame
df <- read.table(text = "educ call house merge_id school members
A 1 0 1 12_3 0 0.9
B 0 0 0 13_3 1 0.8
C 1 1 1 14_3 0 1.1
D 0 0 0 15_3 1 0.8
E 1 1 1 16_3 3 3.2", header = TRUE)
# Perform PCA
PCA <- prcomp(df[, names(df) != "merge_id"], scale = TRUE, center = TRUE)
# Add PC1
df$PC1 <- PCA$x[, 1]
# Look at new data frame
print(df)
#> educ call house merge_id school members PC1
#> A 1 0 1 12_3 0 0.9 0.1000145
#> B 0 0 0 13_3 1 0.8 1.6610864
#> C 1 1 1 14_3 0 1.1 -0.8882381
#> D 0 0 0 15_3 1 0.8 1.6610864
#> E 1 1 1 16_3 3 3.2 -2.5339491
Created on 2019-05-30 by the reprex package (v0.2.1.9000)
As you say you have to use PCA, I'm assuming this is for a homework question, so I'd recommend reading up on PCA so that you get a feel of what it does and what it's useful for.
I downloaded the R package RVAideMemoire in order to use the G.test.
> head(bio)
Date Trt Treated Control Dead DeadinC AliveinC
1 23Ap citol 1 3 1 0 13
2 23Ap cital 1 5 3 1 6
3 23Ap gerol 0 3 0 0 9
4 23Ap mix 0 5 0 0 8
5 23Ap cital 0 5 1 0 13
6 23Ap cella 0 5 0 1 4
So, I make subsets of the data to look at each treatment, because the G.test result will need to be pooled for each one.
datamix<-subset(bio, Trt=="mix")
head(datamix)
Date Trt Treated Control Dead DeadinC AliveinC
4 23Ap mix 0 5 0 0 8
8 23Ap mix 0 5 1 0 8
10 23Ap mix 0 2 3 0 5
20 23Ap mix 0 0 0 0 18
25 23Ap mix 0 2 1 0 15
28 23Ap mix 0 1 0 0 12
So for the G.test(x) to work if x is a matrix, it must be constructed as 2 columns containing numbers, with 1 row per population. If I use the apply() function I can run the G,test on each row if my data set contains only two columns of numbers. I want to look only at the treated and control for example, but I'm not sure how to omit columns so the G.test can ignore the headers, and other columns. I've tried using the following but I get an error:
apply(datamix, 1, G.test)
Error in match.fun(FUN) : object 'G.test' not found
I have also thought about trying to use something like this rather than creating subsets.
by(bio, Trt, rowG.test)
The G.test spits out this, when you compare two numbers.
G-test for given probabilities
data: counts
G = 0.6796, df = 1, p-value = 0.4097
My other question is, is there someway to add all the df and G values that I get for each row (once I'm able to get all these numbers) for each treatment? Is there also some way to have R report the G, df and p-values in a table to be summed rather than like above for each row?
Any help is hugely appreciated.
You're really close. This seems to work (hard to tell with such a small sample though).
by(bio,bio$Trt,function(x)G.test(as.matrix(x[,3:4])))
So first, the indices argument to by(...) (the second argument) is not evaluated in the context of bio, so you have to specify bio$Trt instead of just Trt.
Second, this will pass all the columns of bio, for each unique value of bio$Trt, to the function specified in the third argument. You need to extract only the two columns you want (columns 3 and 4).
Third, and this is a bit subtle, passing x[,3:4] to G.test(...) causes it to fail with an unintelligible error. Looking at the code, G.test(...) requires a matrix as it's first argument, whereas x[,3:4] in the code above is a data.frame. So you need to convert with as.matrix(...).
I have a matrix which contains the genes and the mrna.
ID_REF GSM362168 GSM362169 GSM362170 GSM362171 GSM362172 GSM362173 GSM362174
244901_at 5.171072 5.207896 5.191145 5.067809 5.010239 5.556884 4.879528
244902_at 5.296012 5.460796 5.419633 5.440318 5.234789 7.567894 6.908795
I wanted to find the differentially expressed genes from the matrix using t test and i carried out the following.
stat=mt.teststat(control,classlabel,test="t",na=.mt.naNUM,nonpara="n")
and I get the following error
Error in is.factor(classlabel) : object 'classlabel' not found.
I am not sure how I have to assign the classlabels.Is it the right way to find the differentially expressed genes.
The classlabel should be a vector of integers corresponding to observation (column) class labels. I do not understand what that is.
If you open the documentation for mt.teststat:
?mt.teststat
and scroll down to the end, you'll see an example using the "Golub data":
data(golub)
teststat <- mt.teststat(golub, golub.cl)
If you look at golub.cl,it will become clear what the classlabel vector should look like:
golub.cl
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
In this case, 0 or 1 are labels for two classes of sample. There should be as many values in the vector as you have samples, in the same order that the samples appear in the data matrix. You can also look at:
?golub
golub.cl: numeric vector indicating the tumor class, 27 acute
lymphoblastic leukemia (ALL) cases (code 0) and 11 acute
myeloid leukemia (AML) cases (code 1).
So you need to create a similar vector, with labels (0, 1, ...) for however many classes you have for your own data.