I am trying to automate logistic regression in R.
Basically, my source code will generate a new equation everyday as the input data is updated,
(Variables, data format etc are same) and print out te significant variables with corresponding coefficients.
When I use step function, sometimes the resulting coefficients are not significant. Therefore, I want to update my set of coefficients and get rid of all the ones that are not significant enough.
Is there a function or automated way of doing it?
If not, the only way I can think of is writing a script on another language that takes the coefficients and corresponding P value and checking significance, and rerunning R accordingly. But even for that, do you know how can I get only P values and coefficients of variables. I can either print whole summary of regression result with "summary" function. I can't reach only P values.
Thank you very much
It's a bit hard for me without sample code and data, but you can subset based on variable values like this,
newdata <- data[ which(data$p.value < 0.5), ]
You can inspect your R object using str, see ?str to figure out how to select whatever you want to use in your subset $p.value or $residuals.
If this doesn't answer your question try submitting some sample code and data.
Best,
Eric
Related
I'm running conditional logistic regression models in R as part of a discordant sibling pair analysis and I need to isolate the total n for each model. Also, I need to isolate the number and % of cases of the disease in the exposed and unexposed groups.
In Stata the e(sample) == 1 command gives this info. Is there an equivalent function for accomplishing this in R?
In R, if you run a regression you create a regression object.
RegOb <- lm(y ~ x1 + x2, data)
Often people call "RegOb" which uses the internal "print" method of this type of object. Alternative "summary(RegOb)" is popular (and often people would assign this).
However, RegOb contains many information about the regression. So in Stata you could use -ereturn list- to see what is saved. In R I would recommend to use "str(RegOb)" or "View(RegOb)" you will see everything that is saved. I have forgotten the correct syntax atm, but it will be something like:
RegOb$data
And since you have the original data, you can simply use a logical statement based on the original and the used data which will give you the estimation sample.
I am working with the 'indicspecies' package - multipatt function and am unable to extract summary values of the package. Unfortunately I can't print all the summary and am left with impartial information for my model. The reason is the huge amount of data that needs to be printed from the summary (300.000 different species, 3 groups, 6 comparable combinations).
This is what happens with summary being saved (pre-code incl.):
x <- multipatt(data, ...)
sumx <-summary(x)
sumx
NULL
str(sumx)
NULL
So, the summary does not work exactly like a generic summary. It seems that the function is based around the older indval function from the 'labdsv' package (which is mentioned in the documentation). I found an archived thread where a similar problem is discussed: http://r.789695.n4.nabble.com/extract-values-from-summary-of-function-indval-of-the-package-labdsv-td4637466.html
but it seems not resolved (and is not exactly about the same function, rather the base function indval).
I was wondering if anyone has experience with the indicspecies package and knows a way to either extract the info from the summary.
It is possible to extract significance and other information from the other saved data from the model, but it might be nice to just get a quick complete overview from the data.
ps. I tried
options(max.print=1000000)
but this didn't solve it for me.
I use to capture the summary output for a multipatt object, but don't any more because the p-values reported are not corrected for multiple testing. To answer the OP's question you can capture the summary output using capture.output
ex.
dat.multipatt.summary<-capture.output(summary(dat.multipatt, indvalcomp=TRUE))
Again, I do not recommend this. It is very important to correct the p-values for multiple testing, so the summary output actually isn't helpful. To be clear ?multipatt states:
"sign Data table with results of the best matching pattern, the association value and the degree of statistical significance of the association (i.e. p-values from permutation test). Note that p-values are not corrected for multiple testing."
I just posted an answer for how to correct the p-values here https://stats.stackexchange.com/questions/370724/indiscpecies-multipatt-and-overcoming-multi-comparrisons/401277#401277
I don't have any experience with this package and since you haven't provided the data, it's difficult to reproduce. But since summary is returning NULL, are you sure your x is computed properly? Check the object.size or class or something else of x to see if it indeed has any content.
Also instead of accessing all the contents of summary(x) together, you can use # to access slots of it (similar to $ in dataframe).
If you need further assistance, it'd be better t provide atleast a small subset or some other sample data so that the community can work with it.
First, I gathered from this link Applying a function to multiple columns that using the "function" function would perhaps do what I'm looking for. However, I have not been able to make the leap from thinking about it in the way presented to making it actually work in my situation (or really even knowing where to start). I'm a beginner in R so I apologize in advance if this is a really "newb" question. My data is a data frame that consists of an event variable (tumor recurrence) and a time variable (followup time/time to recurrence) as well as recurrence risk factors (t-stage, tumor size,age at dx, etc.). Some risk factors are categorical and some are continuous. I have been running my univariate analysis by hand, one at a time like this example univariateageatdx<-coxph(survobj~agedx), and then collecting the data. This gets very tedious for multiple factors and doing it for a few different recurrence types. I figured there must be a way to code such that I could basically have one line of code that had the coxph equation and then applied it to all of my variables of interest and spit out a result that had the univariate analysis results for each factor. I tried using cbind to bind variables (i.e x<-cbind("agedx","tumor size") then running cox coxph(recurrencesurvobj~x) but this of course just did the multivariate analysis on these variables and didn't split them out as true univariate analyses.
I also tried the following code based on a similar problem that I found on a different site, but it gave the error shown and I don't know quite what to make of it. Is this on the right track?
f <- as.formula(paste('regionalsurvobj ~', paste(colnames(nodcistradmasvssubcutmasR)[6-9], collapse='+')))
I then ran it has coxph(f)
Gave me the results of a multivariate cox analysis.
Thanks!
**edit: I just fixed the error, I needed to use the column numbers I suppose not the names. Changes are reflected in the code above. However, it still runs the variables selected as a multivariate analysis and not as the true univariate analysis...
If you want to go the formula-route (which in your case with multiple outcomes and multiple variables might be the most practical way to go about it) you need to create a formula per model you want to fit. I've split the steps here a bit (making formulas, making models and extracting data), they can off course be combined this allows you to inspect all your models.
#example using transplant data from survival package
#make new event-variable: death or no death
#to have dichot outcome
transplant$death <- transplant$event=="death"
#making formulas
univ_formulas <- sapply(c("age","sex","abo"),function(x)as.formula(paste('Surv(futime,death)~',x))
)
#making a list of models
univ_models <- lapply(univ_formulas, function(x){coxph(x,data=transplant)})
#extract data (here I've gone for HR and confint)
univ_results <- lapply(univ_models,function(x){return(exp(cbind(coef(x),confint(x))))})
I have this script which does a simple PCA analysis on number of variables and at the end attaches two coordinates and two other columns(presence, NZ_Field) to the output file. I have done this many times before but now its giving me this error:
I understand that it means there are negative eigenvalues. I looked at similar posts which suggest to use na.omit but it didn't work.
I have uploaded the "biodata.Rdata" file here:
covariance matrix is not non-negative definite
https://www.dropbox.com/s/1ex2z72lilxe16l/biodata.rdata?dl=0
I am pretty sure it is not because of missing values in data because I have used the same data with different "presence" and "NZ_Field" column.
Any help is highly appreciated.
load("biodata.rdata")
#save data separately
coords=biodata[,1:2]
biovars=biodata[,3:21]
presence=biodata[,22]
NZ_Field=biodata[,23]
#Do PCA
bpc=princomp(biovars ,cor=TRUE)
#re-attach data with auxiliary data..coordinates, presence and NZ location data
PCresults=cbind(coords, bpc$scores[,1:3], presence, NZ_Field)
write.table(PCresults,file= "hlb_pca_all.txt", sep= ",",row.names=FALSE)
This does appear to be an issue with missing data so there are a few ways to deal with it. One way is to manually do listwise deletion on the data before running the PCA which in your case would be:
biovars<-biovars[complete.cases(biovars),]
The other option is to use another package, specifically psych seems to work well here and you can use principal(biovars), and while the output is bit different it does work using pairwise deletion, so basically it comes down to whether or not you want to use pairwise or listwise deletion. Thanks!
I am working on developing and optimizing a linear model using the lm() function and subsequently the step() function for optimization. I have added a variable to my dataframe by using a random generator of 0s and 1s (50% chance each). I use this variable to subset the dataframe into a training set and a validation set If a record is not assigned to the training set it is assigned to the validation set. By using these subsets I am able to estimate how good the fit of the model is (by using the predict function for the records in the validation set and comparing them to the original values). I am interested in the coefficients of the optimized model and in the results of the KS-test between the distributions of the predicted and actual results.
All of my code was working fine, but when I wanted to test whether my model is sensitive to the subset that I chose I ran into some problems. To do this I wanted to create a for (i in 1:10) loop, each time using a different random subset. This turned out to be quite a challenge for me (I have never used a for loop in R before).
Here's the problem (well actually there are many problems, but here is one of them):
I would like to have separate dataframes for each run in the loop with a unique name (for example: Run1, Run2, Run3). I have been able to create a variable with different strings using paste(("Run",1:10,sep=""), but that just gives you a list of strings. How do I use these strings as names for my (subsetted) dataframes?
Another problem that I expect to encounter:
Subsequently I want to use the fitted coefficients for each run and export these to Excel. By using coef(function) I have been able to retrieve the coefficients, however the number of coefficients included in the model may change per simulation run because of the optimization algorithm. This will almost certainly give me some trouble with pasting them into the same dataframe, any thoughts on that?
Thanks for helping me out.
For your first question:
You can create the strings as before, using
df.names <- paste(("Run",1:10,sep="")
Then, create your for loop and do the following to give the data frames the names you want:
for (i in 1:10){
d.frame <- # create your data frame here
assign(df.name[i], d.frame)
}
Now you will end up with ten data frames with ten different names.
For your second question about the coefficients:
As far as I can tell, these don't naturally fit into your data frame structure. You should consider using lists, as they allow different classes - in other words, for each run, create a list containing a data frame and a numeric vector with your coefficients.
Don't create objects with numbers in their names, and then try and access them in a loop later, using get and paste and assign. The right way to do this is to store your elements in an R list object.