I am new to machine learning and R. I want to run a statistical model to predict daily hours of supply of electricity (y). I have several x variables to use for prediction. I have three goals to achieve:
I want to use some sort of regularization to choose the x variables that should go in the model.
y is bounded between 0 and 24. So I want the predictions to also be bounded within this range.
The data has spatial attributes and I want to use spatial cross-validation to re-sample while tuning regularization parameters.
I am planning to use the mlr package in R. Which learner can I use that can achieve the above three goals?
Many thanks.
Related
I want to build a survival model then calculate the X-year (e.g. 10-year) risk of survival.
Is there a way to do this using coxph or survreg? Is this possible using random survival forest (e.g. ranger)?
P.S. not sure if important but data is wide (~100 features - mostly continuous) and 17k samples.
For anyone else trying to do the same. If you build a cox-model with survival::coxph or rms::cph you can use the function pec::predictSurvProb.
I have a dataset containing information about weather, air pollution and healthoutcomes. I want to regress temperature (T) and temperature lag (T1) against cardiac deaths (CVD). I have previously used the glm model in R using the following script:
#for mean daily temperature and temperature lags separately.
modelT<-glm(cvd~T, data=datapoisson, family=poisson(link="log"), na=na.omit)
I get the effect estimates and standard error values which i used to convert to risk ratio.
Now i want to use dynamic linear model or distributed linear model for check the predictor-outcome and lagged predictor outcome association. However, i can't find the script for running the model in R.
I installed the DLM package in R, but still can't figure out how to build a model using DLM package in R.
I would appreciate if someone can help with it.
Could you try least squares multiple regression to predict the outcome? I used that method when I tried to 'predict' which factors influenced power in a floating offshore wind turbine. It is good for correlating multiple parameters.
They fit a plane to a set of points, but it seems like a similar idea.
https://math.stackexchange.com/questions/99299/best-fitting-plane-given-a-set-of-points
I am using R to do some evaluations for two different forecasting models. The basic idea of the evaluation is do the comparison of Pearson correlation and it corresponding p-value using the function of cor.() . The graph below shows the final result of the correlation coefficient and its p-value.
we suggestion that model which has lower correlation coefficient with corresponding lower p-value(less 0,05) is better(or, higher correlation coefficient but with pretty high corresponding p-value).
so , in this case, overall, we would say that the model1 is better than model2.
but the question here is, is there any other specific statistic method to quantify the comparison?
Thanks a lot !!!
Assuming you're working with time series data since you called out a "forecast". I think what you're really looking for is backtesting of your forecast model. From Ruey S. Tsay's "An Introduction to Analysis of Financial Data with R", you might want to take a look at his backtest.R function.
backtest(m1,rt,orig,h,xre=NULL,fixed=NULL,inc.mean=TRUE)
# m1: is a time-series model object
# orig: is the starting forecast origin
# rt: the time series
# xre: the independent variables
# h: forecast horizon
# fixed: parameter constriant
# inc.mean: flag for constant term of the model.
Backtesting allows you to see how well your models perform on past data and Tsay's backtest.R provides RMSE and Mean-Absolute-Error which will give you another perspective outside of correlation. Caution depending on the size of your data and complexity of your model, this can be a very slow running test.
To compare models you'll normally look at RMSE which is essentially the standard deviation of the error of your model. Those two are directly comparable and smaller is better.
An even better alternative is to set up training, testing, and validation sets before you build your models. If you train two models on the same training / test data you can compare them against your validation set (which has never been seen by your models) to get a more accurate measurement of your model's performance measures.
One final alternative, if you have a "cost" associated with an inaccurate forecast, apply those costs to your predictions and add them up. If one model performs poorly on a more expensive segment of data, you may want to avoid using it.
As a side-note, your interpretation of a p value as less is better leaves a little to be [desired] quite right.
P values address only one question: how likely are your data, assuming a true null hypothesis? It does not measure support for the alternative hypothesis.
I am using support vector regression in R to forecast future values for a uni-variate time series. Splitting the historical data into test and train sets, I find a model by using svm function in R to the test data and then use the predict() command with train data to predict values for the train set. We can then compute prediction errors. I wonder what happens then? we have a model and by checking the model on the train data, we see the model is efficient. How can I use this model to predict future values out of train data? Generally speaking, we use predict function in R and give it a forecast horizon (h=12) to predict 12 future values. Based on what I saw, the predict() command for SVM does not have such coomand and needs a train dataset. How should I build a train data set for predicting future data which is not in our historical data set?
Thanks
Just a stab in the dark... SVM is not for prediction but for classification, specifically supervised. I am guessing you are trying to predict stock values, no? How about classify your existing data, using some size of your choice say 100 values at a time, for noise (N), up (U), big up (UU), down (D), and big down (DD). In this way as your data comes in you slide your classification frame and get it to tell you if the upcoming trend is N, U, UU, D, DD.
What you can do is to build a data frame with columns representing the actual stock price and its n lagged values. And use it as a train set/test set (the actual value is the output and the previous values the explanatory variables). With this method you can do a 1-day (or whatever the granularity is) into the future forecast and then you can use your prediction to make another one and so on.
I have two temporal processes. I would like to see if one temporal process (X_{t,2}) can be used to perform better forecast of the other process (X_{t,1}). I have multiple sources providing temporal data on X_{t,2}, (e.g. 3 time series measuring X_{t,2}). All time series require a seasonal component.
I found MARSS' notation to be pretty natural to fit this type of model and the code looks like this:
Z=factor(c("R","S","S","S")) # observation matrix
B=matrix(list(1,0,"beta",1),2,2) #evolution matrix
A="zero" #demeaned
R=matrix(list(0),4,4); diag(R)=c("r","s","s","s")
Q="diagonal and unequal"
U="zero"
period = 12
per.1st = 1 # Now create factors for seasons
c.in = diag(period)
for(i in 2:(ceiling(TT/period))) {c.in = cbind(c.in,diag(period))}
c.in = c.in[,(1:TT)+(per.1st-1)]
rownames(c.in) = month.abb
C = "unconstrained" #2 x 12 matrix
dlmfit = MARSS(data, model=list(Z=Z,B=B,Q=Q,C=C, c=c.in,R=R,A=A,U=U))
I got a beta estimate implying that the second temporal process is useful in forecasting the first process but to my dismay, MARSS gives me an error when I use MARSSsimulate to forecast because one of the matrices (related to seasonality) is time-varying.
Anyone, knows a way around this issue of the MARSS package? And if not, any tips on fitting an analogous model using, say the dlm package?
I was able to represent my state-space model in a form adequate to use with the dlm package. But I encountered some problems using dlm too. First, the ML estimates are VERY unstable. I bypassed this issue by constructing the dlm model based on marss estimates. However, dlmFilter is not working properly. I think the issue is that dlmFilter is not designed to deal with models with multiple sources for one time series, and additional seasonal components. dlmForecast gives me forecasts that I need!!!
In summary for my multivariate time series model (with multiple sources providing data for one of the temporal processes), the MARSS library gave me reasonable estimates of the parameters and allowed me to obtain filtered and smoothed values of the states. Forecast values were not possible. On the other hand, dlm gave fishy estimates for my model and the dlmFilter didn't work, but I was able to use dlmForecast to forecast values using the model I fitted in MARSS and reexpressed in dlm appropriate form.