I am using some simple forecasting method such as Naive for my project. I am using accuracy(naive_train, test) to measure the accuracy of these method. Right now I have this as my output of meanf method.
However, I want to create a dataframe like this to compare the different methods.
How can I make a data frame like this and adding the extra column on the right indicating which method this is? Thank you!
I have run a GAM and obtained a summary output. I want to extract the output of summary into a table, so that I can use it in a document.
Let's say your gam model is called gamfit.
You can use l <- as.list(summary(gamfit)) to store this in a list.
Then access the parts with l[[1]], l[[2]], ...
The problem with this is that, depending on how many variables are used in the analysis, the data you want may move around and the list items aren't named. If the model isn't going to change, then it's fine.
Also note that all the information about the model is stored in gamfit.
Explore this in the Rstudio Environment window or at the command line with names(gamfit), followed by gamfit$name_of_interest.
I am training multiple 'treebag' models in R. I loop through a data set, where each iteration I define a specific subset based on a feature in the set and train on that subset. I could save each result to disk, but I was hoping to save all the models to a single data frame or data table. I am not sure if this is at all possible. The data frame/table could have numerous classes (numeric and character), however I would like to add a completed model.
To start, is it even possible to assign multiple models to a single column, where each model is assigned to a different row in a data frame or data table?
Any ideas on how this could work is greatly appreciated.
The sample for a survey I am analysing was not selected randomly and so I need to apply a vector of weights to make the findings representative of the population. I have used wtd.table() (from gmodels) successfully to create frequency tables but now want to create a contingency table to compare two categorical variables and conduct a Chi-sqrd test. I'm struggling to find the right function. The svytable() function in the survey package sounds promising but I don't see where I should input the weight vector. I'm new to R. Could anyone explain how to use svytable() or suggest an alternative?
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.