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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)
Related
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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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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 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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Given the following setup:
area.factor <- cut(state.x77[,"Area"],
breaks=quantile(state.x77[,"Area"],c(0,.25,.75,1)),
labels=c("small","medium","large"),
include.lowest=TRUE)
state <- data.frame(pop=state.x77[,"Population"],
inc=state.x77[,"Income"],
area=area.factor,
region=state.region)
pop.area.region <- with(state,ftable(pop,area,region))
The following two lines of code are show the same result:
head(ftable(prop.table(pop.area.region,margin=2)))
head(prop.table(pop.area.region,margin=2))
I don't understand what effect adding ftable has, if any, in:
head(ftable(prop.table(pop.area.region,margin=2)))
Adding ftable witll try to coerce the pop.area.region to a ftable class. Here
No need to add ftable since pop.area.region is already an ftable.
identical(ftable(prop.table(pop.area.region,margin=2)),
prop.table(pop.area.region,margin=2))
TRUE