I am beginner in R doing modelling in R, I loaded excel sheet in R, i have
chosen x elements and y elements then fitted model for linear and second order
regression. Now I have both models. I am bit confused how to calculate vif for
each term in model like
e.g model1<-lm(y1~x1+x2+.x9) wh
Hi ,
I am little confused about how to covert entire dataset to numeric .
As I read data like..
Xelements =read.csv(file. Choose(),header = T, stringsAsFactors=FALSE)
str(xelements )
> str(xelements)
'data.frame': 731 obs. of 4 variables:
$ Engine.Speed : chr "rpm" "ES" "rpm" "1049" ...
$
Sorry it was my mistake.
I tried to do like this
rm.outliers = function(model,xsys)
{
rst = rstudent(model)
outliers<<-vector("numeric",731)
xsys<<-xsys
for(i in 1:length(rst))
{
if(rst[i]<=3 & rst[i]>=-3) #condition for identifying outlier
{
print("this is not outlier")
Hi
I am trying to make some changes in data frame and return it to function .this
is my function
rm.outliers = function(model,xsys)
{
rst = rstudent(model)
outliers<<-vector("numeric",10)
xsys<<-xsys
for(i in 1:length(rst))
{
if(rst[i]<-3 & rst[i]>=-3)
{
#print("this is n
Hi, this is my function to find rstudent of model which will give me outlier
But I wonder if I could find out the exact no. of outlier in data set.
Like outlierTest() does from car package .
rm.outliers = function(dataset,model){
dataset$rstudent = rstudent(model)
for(i in 1:length(dataset$rstu
Hi ,
I am working on a function .
rm.outliers = function(dataset,model){
dataset$predicted = predict(model)
dataset$stdres = rstudent(model)
m = 1
for(i in 1:length(dataset$stdres)){
dataset$outlier_counter[i] = if(dataset$stdres[i] >= 3 |
Hi all,
Thanks for your support. sorry for inconvenience cause due to lack of
information. here is my all r- program .
read in data
raw=read.csv(file.choose(),header = T, stringsAsFactors=FALSE)
str(raw)
conversion from factor to numeric of xs
raw$Engine.Speed <- as.numeric(raw$Engin
Hi all, I am stuck on outlier, while doing regression analysis. I done up to
modelling ,I got lm model for each y and x.
Now I want to find out outlier in that models. How do I find out outlier and
remove them.
for(i in 1:10){
t1=print(outlierTest(fitted.modely1.temp.l ,cutoff=0.05, n.max=1, or
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