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Results tagged with r
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user 68444
Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.
2
votes
Interpreting accuracy results for an ARIMA model fit
Out of all the one simplest to understand is MAPE (Mean absolute percentage error). It considers actual values fed into model and fitted values from the model and calculates absolute difference betwee …
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2
votes
Conjoint Packages for R
If you are looking for models other than logit,
you can use 'survival' package to build conditional multinomial
logit model.
you can use 'bayesm' package to build hierarchical
bayesian(HB) model. S …
0
votes
Difficulty with auto.arima function results
Consider ARIMA with regression errors by passing dummy variables. Lets say if you have monthly data, then include 11 dummy variables and pass these in xreg argument of auto.arima function. This tends …
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2
votes
How to create a nearly orthogonal experimental design in R?
by going to suggested link i created below code and though it might be useful.
###Define factor and level in below code
cand.list = expand.grid(Storage = c("8 GB", "16 GB"),
B …
- 1,124
5
votes
Accepted
Book recommendations for Design of Experiments?
There is a book by John Lawson Design and Analysis of Experiments with R and he has developed a companion library called daewr. It's easy to follow. Good luck …
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4
votes
1
answer
2k
views
Choice based conjoint latent class analysis in R
How can we perform Latent class Analysis for choice based conjoint data, to both segment respondents and determine co-efficients for attributes at the same point of time? I know there is a package poL …
- 1,124
3
votes
Resources and R code for analysing advanced experimental designs?
There is a book by John Lawson Design and Analysis of Experiments with R
That same author has also written a book with same title using SAS Design and Analysis of Experiments with SAS
This might give …
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1
vote
Multiple Correspondence / Correlation Analysis
Here is a tutorial on how to do this kind of analysis in R. …
- 1,124
0
votes
Accepted
Instrumental variable identification for price elasticity
I found the answer myself. IV is uncorrelated with error, however correlated with endogenous variable. So all the variables that fulfill this criteria will be instrumental variable.
Source:http://ho …
- 1,124
1
vote
1
answer
85
views
Scalable machine learning for bigger data
I need suggestion for a book which has worked out implementations or examples of these type of optimized models with R/Python code or pseudo code. …
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2
votes
2
answers
2k
views
Instrumental variable identification for price elasticity
I am working on a price elasticity problem and i am using Log-Log linear model. Below are the set of variables
Dependent variable: logarithm of my quantity sold (LogQ)
Independent variable: logarith …
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4
votes
2
answers
4k
views
Time series with autoregressive distributed lags: Forecasting for future
As stated earlier, i am using auto.arima function from forecast package in R .
My ultimate goal is to predict for next 365 days. …
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5
votes
1
answer
4k
views
How to interpret D-Efficiency in Experimental Design in R OptFederov?
I created an orthogonal design in R using AlgDesign package. I got 14 profiles and some output: D =0.2519353; A = 5.462121; Ge = 0.748; Dea = 0.714.
How to interpret D , A , Ge and Dea? … Also, In regression like we have r square, which value should we be looking for D-Efficiency and how much it should be in order for this design to be usable in a choice based conjoint/Discrete choice model …
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6
votes
Accepted
How to interpret D-Efficiency in Experimental Design in R OptFederov?
Below links have some explanation and i referred the book "Design and Analysis of Experiments with R". Thought this might be useful for those who are looking for answer. … https://stat.ethz.ch/pipermail/r-help/2007-October/143217.html
https://stackoverflow.com/questions/28395486/error-in-hierarchical-bayesn-in-r-bayesn-package …
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5
votes
2
answers
28k
views
Forecasting technique for daily data with monthly and day of week seasonality
Posted here already
I have also tried dummy regression using sales as dependent variable and 11 month dummy variables and 6 day of week dummy variables. i abandoned this as R square was low at 48% and …
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