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fix: some smaller stuff

This commit is contained in:
Daniel Kapla 2021-02-10 19:05:35 +01:00
parent 25b20984d5
commit 8e95902e79
4 changed files with 10 additions and 10 deletions
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10
CVE/R/CVE.R
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@ -123,10 +123,10 @@
#' # y = f(B'x) + err #' # y = f(B'x) + err
#' # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2) #' # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2)
#' y <- (x %*% b1)^2 + 2 * (x %*% b2) + 0.25 * rnorm(100) #' y <- (x %*% b1)^2 + 2 * (x %*% b2) + 0.25 * rnorm(100)
#' # calculate cve with method 'simple' for k unknown in 1, ..., 4 #' # calculate cve with method 'mean' for k unknown in 1, ..., 4
#' cve.obj.s <- cve(y ~ x, max.dim = 4) # default method 'simple' #' cve.obj.s <- cve(y ~ x, max.dim = 4) # default method 'mean'
#' # calculate cve with method 'weighed' for k = 2 #' # calculate cve with method 'weighed' for k = 2
#' cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted') #' cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted.mean')
#' # estimate dimension from cve.obj.s #' # estimate dimension from cve.obj.s
#' khat <- predict_dim(cve.obj.s)$k #' khat <- predict_dim(cve.obj.s)$k
#' # get cve-estimate for B with dimensions (p, k = khat) #' # get cve-estimate for B with dimensions (p, k = khat)
@ -145,13 +145,13 @@
#' # projection matrix on span(B) #' # projection matrix on span(B)
#' # same as B %*% t(B) since B is semi-orthogonal #' # same as B %*% t(B) since B is semi-orthogonal
#' PB <- B %*% solve(t(B) %*% B) %*% t(B) #' PB <- B %*% solve(t(B) %*% B) %*% t(B)
#' # cve estimates for B with simple and weighted method #' # cve estimates for B with mean and weighted method
#' B.s <- coef(cve.obj.s, k = 2) #' B.s <- coef(cve.obj.s, k = 2)
#' B.w <- coef(cve.obj.w, k = 2) #' B.w <- coef(cve.obj.w, k = 2)
#' # same as B.s %*% t(B.s) since B.s is semi-orthogonal (same vor B.w) #' # same as B.s %*% t(B.s) since B.s is semi-orthogonal (same vor B.w)
#' PB.s <- B.s %*% solve(t(B.s) %*% B.s) %*% t(B.s) #' PB.s <- B.s %*% solve(t(B.s) %*% B.s) %*% t(B.s)
#' PB.w <- B.w %*% solve(t(B.w) %*% B.w) %*% t(B.w) #' PB.w <- B.w %*% solve(t(B.w) %*% B.w) %*% t(B.w)
#' # compare estimation accuracy of simple and weighted cve estimate by #' # compare estimation accuracy of mean and weighted cve estimate by
#' # Frobenius norm of difference of projections. #' # Frobenius norm of difference of projections.
#' norm(PB - PB.s, type = 'F') #' norm(PB - PB.s, type = 'F')
#' norm(PB - PB.w, type = 'F') #' norm(PB - PB.w, type = 'F')

6
CVE/R/directions.R
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@ -26,11 +26,11 @@ directions <- function(object, k, ...) {
#' # y = f(B'x) + err #' # y = f(B'x) + err
#' # with f(x1) = x1 and err ~ N(0, 0.25^2) #' # with f(x1) = x1 and err ~ N(0, 0.25^2)
#' y <- x %*% B + 0.25 * rnorm(100) #' y <- x %*% B + 0.25 * rnorm(100)
#' # calculate cve with method 'simple' for k = 1 #' # calculate cve with method 'mean' for k = 1
#' set.seed(21) #' set.seed(21)
#' cve.obj.simple <- cve(y ~ x, k = 1, method = 'simple') #' cve.obj.mean <- cve(y ~ x, k = 1, method = 'mean')
#' # get projected data for k = 1 #' # get projected data for k = 1
#' x.proj <- directions(cve.obj.simple, k = 1) #' x.proj <- directions(cve.obj.mean, k = 1)
#' # plot y against projected data #' # plot y against projected data
#' plot(x.proj, y) #' plot(x.proj, y)
#' #'

2
CVE/man/cve.Rd
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@ -109,7 +109,7 @@ y <- (x \%*\% b1)^2 + 2 * (x \%*\% b2) + 0.25 * rnorm(100)
# calculate cve with method 'simple' for k unknown in 1, ..., 4 # calculate cve with method 'simple' for k unknown in 1, ..., 4
cve.obj.s <- cve(y ~ x, max.dim = 4) # default method 'simple' cve.obj.s <- cve(y ~ x, max.dim = 4) # default method 'simple'
# calculate cve with method 'weighed' for k = 2 # calculate cve with method 'weighed' for k = 2
cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted') cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted.mean')
# estimate dimension from cve.obj.s # estimate dimension from cve.obj.s
khat <- predict_dim(cve.obj.s)$k khat <- predict_dim(cve.obj.s)$k
# get cve-estimate for B with dimensions (p, k = khat) # get cve-estimate for B with dimensions (p, k = khat)

2
CVE/man/directions.cve.Rd
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@ -35,7 +35,7 @@ x <- matrix(rnorm(500), 100, 5)
y <- x \%*\% B + 0.25 * rnorm(100) y <- x \%*\% B + 0.25 * rnorm(100)
# calculate cve with method 'simple' for k = 1 # calculate cve with method 'simple' for k = 1
set.seed(21) set.seed(21)
cve.obj.simple <- cve(y ~ x, k = 1, method = 'simple') cve.obj.simple <- cve(y ~ x, k = 1, method = 'mean')
# get projected data for k = 1 # get projected data for k = 1
x.proj <- directions(cve.obj.simple, k = 1) x.proj <- directions(cve.obj.simple, k = 1)
# plot y against projected data # plot y against projected data