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I have a glm model and use the following script:
prob=predict(myglm,type=("response"))
If i export this prob vector i get 1 column with all the probabilities.
prob=predict(myglm,type=("terms"))
This will provide me the terms for each observation in my data set.
My question is how can I export the data set with the
response probability column added to the end of the file?
Thanks is advance!
Is all you want to add a column with the predicted probability to the dataframe used to build the model? If so you do it this way:
mydata$prob <- predict(myglm, type = "response")
Please read An Introduction to R to learn the basics of the R language, it is the standard tutorial.
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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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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")