Displaying 5 results from an estimated 5 matches for "reggression".
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regression
2010 Dec 22
5
regression
Hi dear all,
suppose that s is a statistic code;
i have a matrix (x) which has 7 columns (1=x1,2=x23=x3,4=x4,5=x5,6=x6
and7=y)
and has 20 rows. i want to do linear reggression like
reg<-lm(x[,7]~1+x[,1]+x[,2]+.......+x[,6])
but i want to do delete i th row for nrows times and create regression model
like above and compute each models' "s" statistics and list them. but i
could not do. i always get only one model and statistic.
How can i do this
Thank...
2009 May 12
0
Bootstrap error rate of logistic disrmination model
...icriminant function error
bias estimation but when using it for my logistic models i come into
problems with the predict function calculating probabilities instead
of group allocation as for lda(). In that case maybe someone knows of a
function which predicts goup allocations for data in a logistic reggression
that i can use.
df=function(data,index){
boot.df=lda(x=white[index,4:6],group=white[index,3])
boot.pr=predict(boot.df)
boot.resub=sum(boot.pr$class!=white[index,3])/nrow(white)
data.pr=predict(boot.df,white[,4:6])
data.resub=sum(data.pr$class!=white[,3])/nrow(white)
bias=data.resub-boot.resub
bias...
2009 May 12
0
Bootstrap error rate for logistic disrmination model
...icriminant function error
bias estimation but when using it for my logistic models i come into
problems with the predict function calculating probabilities instead
of group allocation as for lda(). In that case maybe someone knows of a
function which predicts goup allocations for data in a logistic reggression
that i can use.
df=function(data,index){
boot.df=lda(x=white[index,4:6],group=white[index,3])
boot.pr=predict(boot.df)
boot.resub=sum(boot.pr$class!=white[index,3])/nrow(white)
data.pr=predict(boot.df,white[,4:6])
data.resub=sum(data.pr$class!=white[,3])/nrow(white)
bias=data.resub-boot.resub
bias...
2007 Jun 12
3
Appropriate regression model for categorical variables
Dear users,
In my psychometric test i have applied logistic regression on my data. My
data consists of 50 predictors (22 continuous and 28 categorical) plus a
binary response.
Using glm(), stepAIC() i didn't get satisfactory result as misclassification
rate is too high. I think categorical variables are responsible for this
debacle. Some of them have more than 6 level (one has 10 level).
2011 Apr 08
2
lars - lasso problem
hi,
I have problem in following code, error is occurred. I have attached my data
herewith. and my code is as following,
> library(lars)
Loaded lars 0.9-8
Warning message:
package 'lars' was built under R version 2.12.2
> x<- read.table("D:/spring '11/james reggression/NewFeature.txt")
> y<-read.table("D:/spring '11/lars/RFU.txt")
> out<- lars(x,y, type = "lasso")
Error in one %*% x : requires numeric/complex matrix/vector arguments
Please can you help me?
Thank u in advance.
Awaiting for reply,
Gauri C. Jape
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