I will try to explain my problem as best as i can. I am trying to externally validate a prediction model, made by one of my colleagues. For the external validation, I have collected data from a set of new patients.
I want to test the accuracy of the prediction model on this new dataset. Online i have found a way to do so, using the coef.orig function to extract de coefficients of the original prediction model (see the picture i added). Here comes the problem, it is impossible for me to repeat the steps my colleague did to obtain the original prediction model. He used multiple imputation and bootstrapping for the development and internal validation, making it very complex to repeat his steps. What I do have, is the computed intercept and coefficients from the original model. Step 1 from the picture i added, could therefor be skipped.
My question is, how can I add these coefficients into the regression model, without the use of the 'coef()' function?
Steps to externally validate the prediction model:
The coefficients I need to use:
I thought that the offset function would possibly be of use, however this does not allow me to set the intercept and all the coefficients for the variables at the same time
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Need a hand analysing a model which I've built. I've built a model which analyses the effects of a number of variables on the chances that someone quits smoking. The output of the model is as follows:
I want to run anova on the model using Chi squared. The current way I am doing this is as follows, where each term is added sequentially:
As well as the effect of dependance, I also want to see the effect of each other variables in the same way so that they're able to be compared. At the moment, if I am to This means that I need to do one of the following:
Do anova, adding each term first sequentially. At the moment, the only way I can think to do this is to write a new model for each variable, adding this variable in first e.g.
Run anova, but add each term comparing the model with it to the model without it. How I do this I'm not sure though...
Any help or advice on how to achieve any of these would be great! Please ask for any more details!
I am trying to build a model that can predict SalePrice using independent variables that denote various house features. I used Multiple Regression Model, and also found that some predictor variables needed to be transformed, as well as the response variable.
My final model is as follows;
Model Output
How do I interpret this result? Can I conclude that a one unit increase in Years Since Remodel causes a -2.905e-03 change in log of Sale Price? How do I make this interpretation easier to understand? Thank you.
Professor wanted us to run some 10 fold cross validation on a data set to get the lowest RMSE and use the coefficients of that to make a function that takes in parameters and predicts and returns a "Fitness Factor" Score which ranges between 25-75.
He encouraged us to try transforming the data, so I did. I used scale() on the entire data set to standardize it and then ran my regression and 10 fold cross validation. I then found the model I wanted and copied the coefficients over. The problem is my function predictions are WAY off when i put unstandardized parameters into it to predict a y.
Did I completely screw this up by standardizing the data to a mean of 0 and sd of 1? Is there anyway I can undo this mess if I did screw up?
My coefficients are extremely small numbers and I feel like I did something wrong here.
Build a proper pipeline, not just a hack with some R functions.
The problem is that you treat scaling as part of loading the data, not as part of the prediction process.
The proper protocol is as follows:
"Learn" the transformation parameters
Transform the training data
Train the model
Transform the new data
Predict the value
Inverse-transform the predicted value
During cross-validation these need to run separately for each fold, or you may overestimate (overfit) your quality.
Standardization is a linear transform, so the inverse is trivial to find.
I am trying to investigate the relationship between some Google Trends Data and Stock Prices.
I performed the augmented ADF Test and KPSS test to make sure that both time series are integrated of the same order (I(1)).
However, after I took the first differences, the ACF plot was completely insigificant (except for 1 of course), which told me that the differenced series are behaving like white noise.
Nevertheless I tried to estimate a VAR model which you can see attached.
As you can see, only one constant is significant. I have already read that because Stocks.ts.l1 is not significant in the equation for GoogleTrends and GoogleTrends.ts.l1 is not significant in the equation for Stocks, there is no dynamic between the two time series and both can also be models independently from each other with a AR(p) model.
I checked the residuals of the model. They fulfill the assumptions (normally distributed residuals are not totally given but ok, there is homoscedasticity, its stable and there is no autocorrelation).
But what does it mean if no coefficient is significant as in the case of the Stocks.ts equation? Is the model just inappropriate to fit the data, because the data doesn't follow an AR process. Or is the model just so bad, that a constant would describe the data better than the model? Or a combination of the previous questions? Any suggestions how I could proceed my analysis?
Thanks in advance
Does anyone here know how I can specify additional external variables to an ARIMA model ?
In my case I am trying to make a volatility model and I would like to add the squared returns to model an ARCH.
The reason I am not using GARCH models, is that I am only interested in the volatility forecasts and the GARCH models present their errors on their returns which is not the subject of my study.
I would like to add an external variable and see the R^2 and p-values to see if the coefficient is statistically significant.
I know that this is a very old question but for people like me who were wondering this you need to use cbind with xreg.
For Example:
Arima(X,order=c(3,1,3),xreg = cbind(ts1,ts2,ts3))
Each external time series should be the same length as the original.