Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 3 years ago.
Improve this question
Is it possible to calculate the Approximate Weight of Evidence (AWE) from information obtained via the mclust R package?
According to R documentation, you should have access to function awe(tree, data) since version R1.1.7.
From the example on the linked page (in case of broken link),
data(iris)
iris.m _ iris[,1:4]
awe.val <- awe(mhtree(iris.m), iris.m)
plot(awe.val)
Following the formula from Banfield, J. and Raftery, A. (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics, 49, 803-821. -2*model$loglik + model$d*(log(model$n)+1.5) Where model represents the model with number of cluster solutions selected. Keeping this question in the hope that it may help someone in the future.
Related
Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 1 year ago.
Improve this question
can anyone explain to me the tsSmooth function in R?
I would like to use it to obtain a univariate time series with a linear trend
Please note that on your code:
x<-rt(n=30,df=3, ncp=10)
y<-rt(n=20,df=3,ncp=20)
myseries<-c(x,y)
tsSmooth<-c(x,y)
newseries<-tsSmooth
you didn't apply the tsSmooth() function to your data. You simply created a vector named tsSmooth and another vector named newseries
tsSmooth() function uses a specific data input and doesn't provide much explanation.
There is this discussion that might help https://stats.stackexchange.com/questions/125946/generate-a-time-series-comprising-seasonal-trend-and-remainder-components-in-r
In addition, you could generate a simple trend using moving average. But I am not sure if it has all the statistical features you are looking for.
library("TTR")
plot.ts(myseries)
trendSMA <- SMA(myseries)
plot.ts(trendSMA)
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
Does anyone know what package to use for AICc (Akaike Information Criterion corrected) in r? I am currently using the package 'MASS' and function 'step' to find the best AIC, but I would like to find the best AICc because my sample size is small.
Thank you in advance.
Package MuMIn wil calculate it with the function AICc.
https://www.rdocumentation.org/packages/MuMIn/versions/1.43.15/topics/AICc
https://cran.r-project.org/web/packages/MuMIn/index.html
It is build into the R core stats package. No extra packages need to be installed.
https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/AIC
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 3 years ago.
Improve this question
I'm working with a large, national survey that was collected using complex survey methods. As such, I'm needing to account for sample weights and other survey design features (e.g., sampling strata). I'm new to this methodology, so apologies if the answers here are obvious.
I've had success running path analysis models using the 'lavaan' package paired with the 'lavaan.survey' package. However, some of my models involve only a subset of the data (e.g., only female participants).
How can I adjust the sample weights to reflect the fact that I am only analyzing a subsample (e.g., females)?
The subset() function in the survey package handles subpopulations correctly, and since lavaan.survey uses the survey package to get the basic standard errors for the population covariance matrix, it should all flow through properly.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 6 years ago.
Improve this question
I have 22 companies response about 22 questions/parameters in a 22x22 matrix. I applied clustering technique which gives me different groups with similarities.
Now I would like to find correlations between parameters and companies preferences. Which technique is more suitable in R?
Normally we build Bayesian network to find a graphical relationship between different parameters from data. As this data is very limited, how i can build Bayesian Network for it?
Any suggestion to analyze this data.
Try looking at Feature selection and Feature Importance in R, it's simple,
this could lead you: http://machinelearningmastery.com/feature-selection-with-the-caret-r-package/
Some packages are good: https://cran.r-project.org/web/packages/FSelector/FSelector.pdf
, https://cran.r-project.org/web/packages/varSelRF/varSelRF.pdf
this is good SE question with good answers: https://stats.stackexchange.com/questions/56092/feature-selection-packages-in-r-which-do-both-regression-and-classification
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 8 years ago.
Improve this question
I could not find any documentation on how to perform All vs All multi-class classification with kernlab package in R. Any kind of help would be appreciated.
Well apparently the ksvm function of the package does it automatically as it says here .
This is how to use (I quote from the link above):
svp <- ksvm(xtrain,ytrain,type="C-svc",kernel=’vanilladot’,C=100,scaled=c())
And this is the comment below:
"Question 12
Test the ability of a SVM to predict the class of the disease from gene expression. Check the influence of the parameters.
Finally, we may want to predict the type and stage of the diseases. We are then confronted with a multi-class problem, since the variable to predict can take more than two values:
y <- ALL$BT
print(y)
Fortunatelly, kernlab implements automatically multi-class SVM by an all-versus-all strategy to combine several binary SVM."