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How to generate random data (synthetic data) from a generalized gamma distribution with three parameters (scale, shape and shape)?. Using Matlab or R.
https://en.wikipedia.org/wiki/Generalized_gamma_distribution
Have a look at the VGAM::gengamma function.
See http://www.inside-r.org/packages/cran/vgam/docs/gengamma
Or flexsurv::GenGamma
see http://artax.karlin.mff.cuni.cz/r-help/library/flexsurv/html/GenGamma.html
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〖〖rx〗(t+12)〗^((k))= α_1 + 〖α_2〗^((k) ) 1(zt≤c^((k) ))+〖β_1〗^((k)) 〖s_t〗^((k))+ 〖β_2〗^((k)) 〖s_t〗^((k)) 1(zt≤c^((k) ))+〖ε(t+12)〗^((k)), That is the regression model
I cant figure out exactly how to input the correct R code for this model using nested minimisation technique
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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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Is it possible to create correlation matrix in ggplot2 package that will include histograms and scatterplots of included variables?
I'm looking for something similar to the following graph generated with the gpairs() function from the YaleToolkit package.
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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)
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i want to fit a tweedie compound Poisson Gamma to my loss data using ptweedie.series R command. I am getting problems how to start with my fitting in R. Thanks in advance.
Performing such a fit is illustrated here:
library(tweedie)
example("tweedie-package")