just a basic question concerning k-means clustering analysis on survival data, like this one:
I am doing k-means clustering to identify clusters which Gene influences the survival most... However do I include the survival time into my k-means function or should I leave it out? So should I put it into the kmeans() function e.g. in R?
Kind regards,
Hashriama
I think that your approach is not the best one. Your goal is to select genes associated with censored/uncensored survival. The use of supervised methods seems the most suitable. Using a k-means will only cluster genes by similarities without regard to survival, and even if you wanted to add survival in your modeling it would not make sense because you are omitting censoring.
There are Cox regressions to which an L1 penalty is added, allowing variable selection without omitting censoring. This kind of approach seems more appropriate to accomplish your goal and fits better in your context. To learn more, here is an article from Jiang Gui & Hongzhe Li that uses penalized Cox regression (look at the R package biospear too if needed):
https://academic.oup.com/bioinformatics/article/21/13/3001/196819
Related
I want to run a linear regression model with a large number of variables and I want an R function to iterate on good combinations of these variables and give me the best combination.
The glmulti package will do this fairly effectively:
Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.
Unsolicited advice follows:
HOWEVER. Please be aware that while this approach can find the model that minimizes within-sample error (the goodness of fit on your actual data), it has two major problems that should make you think twice about using it.
this type of data-driven model selection will almost always destroy your ability to make reliable inferences (compute p-values, confidence intervals, etc.). See this CrossValidated question.
it may overfit your data (although using the information criteria listed in the package description will help with this)
There are a number of different ways to characterize a "best" model, but AIC is a common one, and base R offers step(), and package MASS offers stepAIC().
summary(lm1 <- lm(Fertility ~ ., data = swiss))
slm1 <- step(lm1)
summary(slm1)
slm1$anova
I am working on quantile forecasting with time-series data. The model I am using is ARIMA(1,1,2)-ARCH(2) and I am trying to get quantile regression estimates of my data.
So far, I have found "quantreg" package to perform quantile regression, but I have no idea how to put ARIMA-ARCH models as the model formula in function rq.
rq function seems to work for regressions with dependent and independent variables but not for time-series.
Is there some other package that I can put time-series models and do quantile regression in R? Any advice is welcome. Thanks.
I just put an answer on the Data Science forum.
It basically says that most of the ready made packages are using so called exact test based on assumption on the distribution (independent identical normal-Gauss distribution, or wider).
You also have a family of resampling methods in which you simulate a sample with a similar distribution of your observed sample, perform your ARIMA(1,1,2)-ARCH(2) and repeat the process a great number of times. Then you analyze this great number of forecast and measure (as opposed to compute) your confidence intervals.
The resampling methods differs in the way to generate the simulated samples. The most used are:
The Jackknife: in which you "forget" one point, that is you simulate a n samples of size n-1 (if n is the size of the observed sample).
The Bootstrap: in which you simulate a sample by taking n values of the original sample with replacements: some will be taken once, some twice or more, some never,...
It is a (not easy) theorem that the expectation of the confidence intervals, as most of the usual statistical estimators, are the same on the simulated sample than on the original sample. With the difference that you can measure them with a great number of simulations.
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I can try to address your question, although this is hard since you don't provide any code/data. Also, I guess by "put ARIMA-ARCH models" you actually mean that you want to make an integrated series stationary using an ARIMA(1,1,2) plus an ARCH(2) filters.
For an overview of the R time-series capabilities you can refer to the CRAN task list.
You can easily apply these filters in R with an appropriate function.
For instance, you could use the Arima() function from the forecast package, then compute the residuals with residuals() from the stats package. Next, you can use this filtered series as input for the garch() function from the tseries package. Other possibilities are of course possible. Finally, you can apply quantile regression on this filtered series. For instance, you can check out the dynrq() function from the quantreg package, which allows time-series objects in the data argument.
Is it possible to train Hidden Markov Model in R?
I have a set of observations with its corresponding labels. And I need to train HMM in order to get the Markov parameters (i.e. the transition probabilities matrix, emission probabilities matrix and initial distribution). So, I can predict for the future observations.
In other words, I need the opposite of Forward_Backward Algorithm..
Yes, you can. R is a good tool for simulation and statistical analysis. There are many nice packages available. You do not need to implement at all (of course you can), just learn to use them.
An example of using package.
An example of implementing HMM is here. Here DNA sequence is modeled using HMM.
Similar question is asked here as well.
I would like to make classification trees to predict the presence/absence of 1 bird species based on several variables. I know that rpart handles univariate partitioning and mvpart handles multivariate partitioning, but I'd like to use mvpart for my one-variable tree because of its more flexible output. Does anyone know of a reason that I should not do this? Will the splits be different in rpart vs mvpart with the same exact input?
It cannot be guaranteed that the splits will be the same; mvpart() is minimising the within groups sums of squares whereas rpart for a classification tree will be minimising the Gini coefficient (by default IIRC).
You may end up with the same model/splits but as the two functions are using two different measures of node impurity this may just be a fluke.
FYI, mvpart is fitting a regression model but you want a classification model.
Finally, consider using the party package and its function ctree; it has much nicer outputs than rpart by default but is, again, doing something slightly different in terms of model fitting.
As an aside, also look into the plotmo package which includes enhanced plots for a number of tree-like models including, IIRC, rpart ones.
In R language is there a predict function in clustering like the way we have in classification?
What can we conclude from the clustering graph result that we get from R, other that comparing two clusters?
Clustering does not pay attention to prediction capabilities. It just tries to find objects that seem to be related. That is why there is no "predict" function for clustering results.
However, in many situations, learning classifiers based on the clusters offers an improved performance. For this, you essentially train a classifier to assign the object to the appropriate cluster, then classify it using a classifier trained only on examples from this cluster. When the cluster is pure, you can even skip this second step.
The reason is the following: there may be multiple types that are classified with the same label. Training a classifier on the full data set may be hard, because it will try to learn both clusters at the same time. Splitting the class into two groups, and training a separate classifier for each, can make the task significantly easier.
Many packages offer predict methods for cluster object. One of such examples is clue, with cl_predict.
The best practice when doing this is applying the same rules used to cluster training data. For example, in Kernel K-Means you should compute the kernel distance between your data point and the cluster centers. The minimum determines cluster assignment (see here for example). In Spectral Clustering you should project your data point dissimilarity into the eigenfunctions of the training data, compare the euclidean distance to K-Means centers in that space, and a minimum should determine your cluster assignment (see here for example).