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I work with the Silhouette Plot from R Package 'Cluster' and want to relabel the statistics shown on the righthand side of the plot (e.g., clusters should be denoted by 'c' instead of 'j').
Can anyone please provide me the underlying R Code so that I can adjust the labeling? Or are there further plot parameters that allow for adjustments?
Thank you very much for your help.
Florian
Find all methods for "silhouette" class:
> methods(class = "silhouette")
[1] plot.silhouette* summary.silhouette*
Non-visible functions are asterisked
To get code:
getAnywhere(plot.silhouette)
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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)
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I have imported a pimadata.csv to R, I have to create a scatter plot matrix for the first 8 columns in the pimadata; also have to find two variables that seem to have positive correlations. I used this line of code to create the plot;
pairs(pimadata[,1:8])
What should I do to show the correction between the variables?
You could use cor()
cor(pimadata[,1:8])
Since you did not provide the contents of pimadata.csv, I use the iris dataset as an example here.
head(iris)
pairs(iris[,1:4])
scatter plot
cors <- cor(iris[,1:4])
correction output
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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$n)+1.5) Where model represents the model with number of cluster solutions selected. Keeping this question in the hope that it may help someone in the future.
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For the auto.arima function in forecast package of R, is there a way to let the function omit a model of arima(0,0,0), as I simply assume there must be some correlation within the dataset.
You could try looking at the help for the function
auto.arima(). Check the arguments start.p, start.q,
d,max.d
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I have data that contains 14 columns of predictors and 1 column of solution variable(y).
I wanted to know if there are any inbuilt functions to normalize and denormalize data in R.
Thank you.
normDataWithin of package {Rmisc} can be used: http://www.inside-r.org/packages/cran/Rmisc/docs/normDataWithin
Else following methods can be used:
(variable-mean)/sd . Following code can be used for a data.frame:
mydata$myNormalizedVar<-(mydata$myvar-mean(mydata$myvar))/sd(myvar)
log (log10), log2, and square root (sqrt)
Normal quantile normalization or normal quantile transformation. Try:
quantNorm = function(x){qnorm(rank(x,ties.method = "average")/(length(x)+1))}
hist(quantNorm(1:10000),100)