I am using Netlogo and I would like to use the time extension by Colin Sheppard to export automatically data generated by my model as time series, so that it will look like this.
Normally, one could export all data by using export-all-plots, but the data are not well sorted for later use in R.
Unfortunately, I do not understand well the documentation (available here) about how to export data generated by the model into a .csv file as the documentation describes only how to load existing data into the model.
Does somebody has experiences about how to export files as time series from Netlogo or understand the documentation linked above?
Might it be adequate to your needs to use BehaviorSpace to export the data (in table format), with the current time as one of the metrics? This will give you a CSV file with the time (and your other metrics) reported each tick.
Related
I am struggling a bit with an analysis I need to do. I have collected data consisting of little owl calls that were recorded along transects. I want to analyse these recordings for similarity, in order to see which recorded calls are from the same owls and which are from different owls. In that way I can make an estimate of the size of the population at my study area.
I have done a bit of research and it seems that the package warbleR seems to be suitable for this. However, I am far from an R expert and am struggling a bit with how to go about this. Do any of you have experience with these types of analyses and maybe have example scripts? It seems to me that I could use the function cross_correlation and maybe make a pca, however in the warbleR vignette I looked at they only do this for different types of calls and not for the same type call from different individuals, so I am not sure if it would work.
to be able to run analyses with warbleR you need to input the data using the "selection_table" format. Take a look at the example data "lbh_selec_table" to get a sense of the format:
library(warbleR)
data(lbh_selec_table)
head(lbh_selec_table)
The whole point of these objects is to tell R the time location in your sound files (in seconds) of the signals you want to analyze. Take a look at this link for more details on this object structure and how to import it into R.
I want to save all the parameter values (more than 100) used for a certain analysis for the purpose of
reference: to make a little report that can be read by anyone (non-programmers)
loading all these variables the next time to run the same analysis (so the can easily be loaded into the workspace)
I was thinking of
a simple text file/pdf/word document
some kind dynamic structure like in matlab: a big variable that holds all other parameter values (or a workspace file, in matlab that would be .mat and I would only want to save a small part of the current workspace)
Which variable type can I best use for this and which are the best tools for exporting text info in a nicely formatted way (for purpose 1).
Thx!
I am new to R and have just started to use it. I am currently experimenting with the quantmod, rugarch and rmgarch packages.
In particular, I'm implementing the last package to make a multivariate portfolio analysis for the case of the european markets. In this sense, I need to download the 3-month german treasury bills, in order to use them as risk free rate. However, as far as I known, I can´t download the the mentioned data serie from Yahoo, Google or FDRA databases, so I have already downloaded them from investing.com and I want to load them in R.
The fact here is, my data is different from the ones downloaded by the getsymbols () function of yahoo, because in this case I only have 2 columns, the date column and the closing price column. To sump up, the question arises here is, is there any way to load this type of data in R for rmgarch purposes??
thanks in advance
Not sure if this is the issue, but this is how you might go about getting the data from a csv file.
data <- read.csv(file="file/path/data.csv")
head(data) # Take a look at your data
# Do this if you want the data only replacing ColumnName with the proper name
data_only <- data$ColumnName
It looks like the input data for rugarch needs to be an xts vector. So, you might want to take a look at this. You might also want to take a look at ?read.csv.
I apologise if this question has been asked already (I haven't been able to find it). I was under the impression that I could access datasets in R using data(), for example, from the datasets package. However, this doesn't work for time series objects. Are there other examples where this is not the case? (And why?)
data("ldeaths") # no dice
ts("ldeaths") # works
(However, this works for data("austres"), which is also a time-series object).
The data function is designed to load package data sets and all their attributes, time series or otherwise.
I think the issue your having is that there is no stand-alone data set called ldeaths in the datasets package. ldeaths does exist as 1 of 3 data sets in the UKLungDeaths data set. The other two are fdeaths and mdeaths.
The following should lazily load all data sets.
data(UKLungDeaths)
Then, typing ldeaths in the console or using it as an argument in some function will load it.
str(ldeaths)
While it is uncommon for package authors to include multiple objects in 1 data set, it does happen. This line from the data function documentation gives on a 'heads up' about this:
"For each given data set, the first two types (‘.R’ or ‘.r’, and ‘.RData’ or ‘.rda’ files) can create several variables in the load environment, which might all be named differently from the data set"
That is the case here, as while there are three time series objects contained in the data set, not one of them is named UKLungDeaths.
This choice occurs when the package author uses the save function to write multiple R objects to an external file. In the wild, I've seen folks use the save function to bundle a description file with the data set, although this would not be the proper way to document something in a full on package. If your really curious, go read the documentation on the save function.
Justin
r
I created an xml file using pmml function from pmml library in R.
adamodel_iOS=ada(label~.,data=train_iOS, iter=ntrees, verbose=TRUE, loss="ada", bag.frac=0.7, nu=0.1, control=defctrl, type="real")
Ptrain_iOS = predict(adamodel_iOS,newdata=train_iOS, type="prob")
library(pmml)
adapmml_iOS=pmml(adamodel_iOS)
saveXML(adapmml_iOS,"model_iOS.xml")
save.image()
After, training model in the first line, I found the corresponding probabilities for the training data.
Now I want to use this xml file to generate predictions on a set of data(basically the training set again). How do I do that in R? I see that in java and spark, we can load xml file generated by pmml function and then there are functions which can make predictions.
Basically, I am looking for a function in R that can take this xml file as an input and then return an object which in turn takes some datapoints as input and return their probabilities of having label 0 and 1.
I found a link:
Can PMML models be read in R?
but it does not help
Check this link for the list of PMML producers and consumers. As you can see R is listed as producer not consumer. Also, algorithms for which R can produce the corresponding PMML files are listed.
The most comprehensive tool for PMML validator, convertor, and also for scoring data using PMML models is ADAPA, which is not free.
KNIME is an open source drag & drop analytics tool which supports both import and export of PMML files (not for all models and the features are limited.) It supports R, Python, and Java too.
Although it's a long time ago, I still want to share that you can use the "reticulate" to call the python pypmml package to implement your ideas in R, and in order to be more friendly and make the prediction look more like the predict function in R, I will It is encapsulated, the address of the package is here enter link description here