Plot prediction errors vs model size using forward stepwise and cross validation
I need to plot the prediction errors by model size using cross validation, after doing forward selection. I have subset the data in half and used the leaps package to find the best model for each size. However, I cannot figure out how to get the necessary prediction errors. The code I tried gives an error:
1 linear dependencies foundError in val_matrix[, names(coefi)] : subscript out of bounds
n = 400
p = 200
s = 10
X = matrix(rnorm(n*p),n,p)
X = scale(X, center = FALSE, scale = sqrt(colSums(X^2)))
beta = c(rep(5,10), rep(0,p-10))
Y = X%*%beta + rnorm(n)
tr <- sample(1:400, 200, replace = FALSE)
train <- X[tr,]
validation <- X[-tr,]
d <- regsubsets(Y[tr,]~train, nvmax=30, data = as.data.frame(train), method = c("forward"))
val_matrix <- model.matrix(Y[-tr,]~validation, data = as.data.frame(validation))
val_errors = rep(0,30)
for (i in 1:30)
coefi = coef(d, id=i)
predi = val_matrix[,names(coefi)]%*%coefi
val_errors[i] = Y[-tr,] - predi
r regression
edited Nov 13 '18 at 5:02
DTYK
6781119
asked Nov 13 '18 at 4:29
Danny Katz
12
add a comment |
I need to plot the prediction errors by model size using cross validation, after doing forward selection. I have subset the data in half and used the leaps package to find the best model for each size. However, I cannot figure out how to get the necessary prediction errors. The code I tried gives an error:
1 linear dependencies foundError in val_matrix[, names(coefi)] : subscript out of bounds
n = 400
p = 200
s = 10
X = matrix(rnorm(n*p),n,p)
X = scale(X, center = FALSE, scale = sqrt(colSums(X^2)))
beta = c(rep(5,10), rep(0,p-10))
Y = X%*%beta + rnorm(n)
tr <- sample(1:400, 200, replace = FALSE)
train <- X[tr,]
validation <- X[-tr,]
d <- regsubsets(Y[tr,]~train, nvmax=30, data = as.data.frame(train), method = c("forward"))
val_matrix <- model.matrix(Y[-tr,]~validation, data = as.data.frame(validation))
val_errors = rep(0,30)
for (i in 1:30)
coefi = coef(d, id=i)
predi = val_matrix[,names(coefi)]%*%coefi
val_errors[i] = Y[-tr,] - predi
r regression
edited Nov 13 '18 at 5:02
DTYK
6781119
asked Nov 13 '18 at 4:29
Danny Katz
12
add a comment |
I need to plot the prediction errors by model size using cross validation, after doing forward selection. I have subset the data in half and used the leaps package to find the best model for each size. However, I cannot figure out how to get the necessary prediction errors. The code I tried gives an error:
1 linear dependencies foundError in val_matrix[, names(coefi)] : subscript out of bounds
n = 400
p = 200
s = 10
X = matrix(rnorm(n*p),n,p)
X = scale(X, center = FALSE, scale = sqrt(colSums(X^2)))
beta = c(rep(5,10), rep(0,p-10))
Y = X%*%beta + rnorm(n)
tr <- sample(1:400, 200, replace = FALSE)
train <- X[tr,]
validation <- X[-tr,]
d <- regsubsets(Y[tr,]~train, nvmax=30, data = as.data.frame(train), method = c("forward"))
val_matrix <- model.matrix(Y[-tr,]~validation, data = as.data.frame(validation))
val_errors = rep(0,30)
for (i in 1:30)
coefi = coef(d, id=i)
predi = val_matrix[,names(coefi)]%*%coefi
val_errors[i] = Y[-tr,] - predi
r regression
edited Nov 13 '18 at 5:02
DTYK
6781119
asked Nov 13 '18 at 4:29
Danny Katz
12
I need to plot the prediction errors by model size using cross validation, after doing forward selection. I have subset the data in half and used the leaps package to find the best model for each size. However, I cannot figure out how to get the necessary prediction errors. The code I tried gives an error:
1 linear dependencies foundError in val_matrix[, names(coefi)] : subscript out of bounds
n = 400
p = 200
s = 10
X = matrix(rnorm(n*p),n,p)
X = scale(X, center = FALSE, scale = sqrt(colSums(X^2)))
beta = c(rep(5,10), rep(0,p-10))
Y = X%*%beta + rnorm(n)
tr <- sample(1:400, 200, replace = FALSE)
train <- X[tr,]
validation <- X[-tr,]
d <- regsubsets(Y[tr,]~train, nvmax=30, data = as.data.frame(train), method = c("forward"))
val_matrix <- model.matrix(Y[-tr,]~validation, data = as.data.frame(validation))
val_errors = rep(0,30)
for (i in 1:30)
coefi = coef(d, id=i)
predi = val_matrix[,names(coefi)]%*%coefi
val_errors[i] = Y[-tr,] - predi
r regression
r regression
edited Nov 13 '18 at 5:02
DTYK
6781119
asked Nov 13 '18 at 4:29
Danny Katz
12
edited Nov 13 '18 at 5:02
DTYK
6781119
asked Nov 13 '18 at 4:29
Danny Katz
12
edited Nov 13 '18 at 5:02
DTYK
6781119
edited Nov 13 '18 at 5:02
DTYK
6781119
edited Nov 13 '18 at 5:02
DTYK
6781119
6781119
asked Nov 13 '18 at 4:29
Danny Katz
12
asked Nov 13 '18 at 4:29
Danny Katz
12
asked Nov 13 '18 at 4:29
Danny Katz
12
12
add a comment |
add a comment |
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