?lda explains the object produced: please do study it.
Hint: you asked for leave-one-out cross-validation, and what is the output
from cross-validation of a classifer? The predicted class for each
observation. How many observations do you have?
You are using software from a contributed package without credit, and that
software is support for a book (see library(help=MASS) and the help page).
Please consult the book for the background.
On Thu, 27 Dec 2007, [EMAIL PROTECTED] wrote:
Hi all,
I'm working with some data: 54 variables and a column of classes, each
observation as one of a possible seven different classes:
var.can3<-lda(x=dados[,c(1:28,30:54)],grouping=dados[,55],CV=TRUE)
Warning message:
In lda.default(x, grouping, ...) : variables are collinear
summary(var.can3)
Length Class Mode
class 30000 factor numeric ### why?? I don't understand it
posterior 210000 -none- numeric
call 4 -none- call ## what's this?
var.can<-lda(dados[,c(1:28,30:54)],dados[,55])#porque a variavel 29 é constante
Warning message:
In lda.default(x, grouping, ...) : variables are collinear
summary(var.can)
Length Class Mode
prior 7 -none- numeric
counts 7 -none- numeric
means 371 -none- numeric
scaling 318 -none- numeric
lev 7 -none- character
svd 6 -none- numeric
N 1 -none- numeric
call 3 -none- call
(normalizar<-function(matriz){ n<-dim(matriz)[1]; m<-dim(matriz)[2];
normas<-sqrt(colSums(matriz*matriz));
matriz.normalizada<-matriz/t(matrix(rep(normas,n),m,n));return(matriz.normalizada)})
function(matriz){ n<-dim(matriz)[1]; m<-dim(matriz)[2];
normas<-sqrt(colSums(matriz*matriz));
matriz.normalizada<-matriz/t(matrix(rep(normas,n),m,n));return(matriz.normalizada)}
var.canonicas<-as.matrix(dados[,c(1:28,30:54)])%*%(normalizar(var.can$scaling))
summary(var.canonicas)
LD1 LD2 LD3 LD4
Min. :-21.942 Min. :-6.820 Min. :-10.138 Min. :-6.584
1st Qu.:-20.014 1st Qu.:-5.480 1st Qu.: -8.280 1st Qu.: 0.872
Median :-19.495 Median :-5.007 Median : -7.800 Median : 1.083
Mean :-18.827 Mean :-4.760 Mean : -7.803 Mean : 1.134
3rd Qu.:-18.975 3rd Qu.:-4.456 3rd Qu.: -7.278 3rd Qu.: 1.311
Max. : -7.886 Max. : 3.116 Max. : -1.619 Max. : 5.556
LD5 LD6
Min. :-11.083 Min. :-4.4972
1st Qu.: -1.237 1st Qu.:-1.6497
Median : -1.100 Median :-1.0909
Mean : -1.100 Mean :-0.9808
3rd Qu.: -0.957 3rd Qu.:-0.4598
Max. : 4.712 Max. : 7.5356
I don't know wether I need to specify a training set and a testing set,
I also don't know the error nor the classifier; shouldn't the lenght of
class of var.can3 be 7 since I only have 7 different classes?
Best regards,
Pedro Marques
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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and provide commented, minimal, self-contained, reproducible code.
