jd_method = ifelse(!is.null(optargs$jd_method), optargs$jd_method, "uwedge")
V =
if (jd_nvects > 1) {
- #NOTE: increasing itermax does not help to converge, thus we suppress warnings
+ # NOTE: increasing itermax does not help to converge, thus we suppress warnings
suppressWarnings({jd = jointDiag::ajd(M2_t, method=jd_method)})
-# if (jd_method=="uwedge") jd$B else solve(jd$A)
if (jd_method=="uwedge") jd$B else MASS::ginv(jd$A)
}
else
for (i in seq_len(K))
M2_t[,,i] = .T_I_I_w(M[[3]],V[,i])
suppressWarnings({jd = jointDiag::ajd(M2_t, method=jd_method)})
-# U = if (jd_method=="uwedge") solve(jd$B) else jd$A
U = if (jd_method=="uwedge") MASS::ginv(jd$B) else jd$A
μ = normalize(U[,1:K])
#' # Bootstrap + computeMu, morpheus VS flexmix ; assumes fargs first 3 elts X,Y,K
#' io <- generateSampleIO(n=1000, p=1/2, β=β, b=c(0,0), "logit")
#' μ <- normalize(β)
-#' res <- multiRun(list(X=io$X,Y=io$Y,optargs=list(K=2,jd_nvects=0)), list(
+#' res <- multiRun(list(X=io$X,Y=io$Y,optargs=list(K=2)), list(
#' # morpheus
#' function(fargs) {
#' library(morpheus)
c(L$p[1:(K-1)], as.double(L$β), L$b)
},
- #TODO: compare with R version?
- #D <- diag(d) #matrix of ej vectors
- #Y * X
- #Y * ( t( apply(X, 1, function(row) row %o% row) ) - Reduce('+', lapply(1:d, function(j) as.double(D[j,] %o% D[j,])), rep(0, d*d)))
- #Y * ( t( apply(X, 1, function(row) row %o% row %*% row) ) - Reduce('+', lapply(1:d, function(j) ), rep(0, d*d*d)))
computeW = function(θ)
{
- #require(MASS)
+ #return (diag(c(rep(6,d), rep(3, d^2), rep(1,d^3))))
+ require(MASS)
dd <- d + d^2 + d^3
M <- Moments(θ)
Omega <- matrix( .C("Compute_Omega",
X=as.double(X), Y=as.double(Y), M=as.double(M),
pn=as.integer(n), pd=as.integer(d),
W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd )
- W <<- MASS::ginv(Omega, tol=1e-4)
- NULL #avoid returning W
+ MASS::ginv(Omega)
},
Moments = function(θ)
"Gradient of f, dimension (K-1) + d*K + K = (d+2)*K - 1"
L <- expArgs(θ)
- -2 * t(grad_M(L)) %*% W %*% as.matrix((Mhat - Moments(L)))
+ -2 * t(grad_M(L)) %*% W %*% as.matrix(Mhat - Moments(L))
},
grad_M = function(θ)
stop("θ0: list")
if (is.null(θ0$β))
stop("At least θ0$β must be provided")
- if (!is.matrix(θ0$β) || any(is.na(θ0$β)) || ncol(θ0$β) != K)
- stop("θ0$β: matrix, no NA, ncol == K")
+ if (!is.matrix(θ0$β) || any(is.na(θ0$β))
+ || nrow(θ0$β) != d || ncol(θ0$β) != K)
+ {
+ stop("θ0$β: matrix, no NA, nrow = d, ncol = K")
+ }
if (is.null(θ0$p))
θ0$p = rep(1/K, K-1)
- else if (length(θ0$p) != K-1 || sum(θ0$p) > 1)
- stop("θ0$p should contain positive integers and sum to < 1")
- # Next test = heuristic to detect missing b (when matrix is called "beta")
- if (is.null(θ0$b) || all(θ0$b == θ0$β))
+ else if (!is.numeric(θ0$p) || length(θ0$p) != K-1
+ || any(is.na(θ0$p)) || sum(θ0$p) > 1)
+ {
+ stop("θ0$p: length K-1, no NA, positive integers, sum to <= 1")
+ }
+ if (is.null(θ0$b))
θ0$b = rep(0, K)
- else if (any(is.na(θ0$b)))
- stop("θ0$b cannot have missing values")
+ else if (!is.numeric(θ0$b) || length(θ0$b) != K || any(is.na(θ0$b)))
+ stop("θ0$b: length K, no NA")
# TODO: stopping condition? N iterations? Delta <= epsilon ?
for (loop in 1:10)
{
rbind( rep(-1,K-1), diag(K-1) ),
matrix(0, nrow=K, ncol=(d+1)*K) ),
ci=c(-1,rep(0,K-1)) )
-
- computeW(expArgs(op_res$par))
- # debug:
- #print(W)
- print(op_res$value)
- print(expArgs(op_res$par))
+ W <<- computeW(expArgs(op_res$par))
+ print(op_res$value) #debug
+ print(expArgs(op_res$par)) #debug
}
expArgs(op_res$par)
#' described by the corresponding column parameter in the matrix β + intercept b.
#'
#' @param n Number of individuals
-#' @param p Vector of K-1 populations relative proportions (sum <= 1)
+#' @param p Vector of K(-1) populations relative proportions (sum (<)= 1)
#' @param β Vectors of model parameters for each population, of size dxK
#' @param b Vector of intercept values (use rep(0,K) for no intercept)
#' @param link Link type; "logit" or "probit"
stop("n: positive integer")
if (!is.matrix(β) || !is.numeric(β) || any(is.na(β)))
stop("β: real matrix, no NAs")
- K = ncol(β)
- if (!is.numeric(p) || length(p)!=K-1 || any(is.na(p)) || any(p<0) || sum(p) > 1)
- stop("p: positive vector of size K-1, no NA, sum<=1")
- p <- c(p, 1-sum(p))
+ K <- ncol(β)
+ if (!is.numeric(p) || length(p)<K-1 || any(is.na(p)) || any(p<0) || sum(p) > 1)
+ stop("p: positive vector of size >= K-1, no NA, sum(<)=1")
+ if (length(p) == K-1)
+ p <- c(p, 1-sum(p))
if (!is.numeric(b) || length(b)!=K || any(is.na(b)))
stop("b: real vector of size K, no NA")
- #random generation of the size of each population in X~Y (unordered)
- classes = rmultinom(1, n, p)
+ # Random generation of the size of each population in X~Y (unordered)
+ classes <- rmultinom(1, n, p)
- d = nrow(β)
- zero_mean = rep(0,d)
- id_sigma = diag(rep(1,d))
- # Always consider an intercept (use b=0 for none)
- d = d + 1
- β = rbind(β, b)
- X = matrix(nrow=0, ncol=d)
- Y = c()
- index = c()
- for (i in 1:ncol(β))
+ d <- nrow(β)
+ zero_mean <- rep(0,d)
+ id_sigma <- diag(rep(1,d))
+ X <- matrix(nrow=0, ncol=d)
+ Y <- c()
+ index <- c()
+ for (i in 1:K)
{
- index = c(index, rep(i, classes[i]))
- newXblock = cbind( MASS::mvrnorm(classes[i], zero_mean, id_sigma), 1 )
- arg_link = newXblock %*% β[,i] #β
- probas =
+ index <- c(index, rep(i, classes[i]))
+ newXblock <- MASS::mvrnorm(classes[i], zero_mean, id_sigma)
+ arg_link <- newXblock %*% β[,i] + b[i]
+ probas <-
if (link == "logit")
{
e_arg_link = exp(arg_link)
else #"probit"
pnorm(arg_link)
probas[is.nan(probas)] = 1 #overflow of exp(x)
- #probas = rowSums(p * probas)
- X = rbind(X, newXblock)
- #Y = c( Y, vapply(probas, function(p) (ifelse(p >= .5, 1, 0)), 1) )
- Y = c( Y, vapply(probas, function(p) (rbinom(1,1,p)), 1) )
+ X <- rbind(X, newXblock)
+ Y <- c( Y, vapply(probas, function(p) (rbinom(1,1,p)), 1) )
}
- shuffle = sample(n)
- # Returned X should not contain an intercept column (it's an argument of estimation
- # methods)
- list("X"=X[shuffle,-d], "Y"=Y[shuffle], "index"=index[shuffle])
+ shuffle <- sample(n)
+ list("X"=X[shuffle,], "Y"=Y[shuffle], "index"=index[shuffle])
}
g[j] -= Y[i] * X[mi(i,idx1,n,d)];
g[j] += Y[i] * X[mi(i,idx1,n,d)]*X[mi(i,idx2,n,d)]*X[mi(i,idx3,n,d)] - M[j];
}
-
- // TODO: 1/n des gj empirique doit tendre vers 0
// Add 1/n t(gi) %*% gi to W
for (int j=0; j<dim; j++)
{
}
}
})
+
+# TODO: test computeW
+# computeW = function(θ)
+# {
+# require(MASS)
+# dd <- d + d^2 + d^3
+# M <- Moments(θ)
+# Id <- as.double(diag(d))
+# E <- diag(d)
+# v1 <- Y * X
+# v2 <- Y * t( apply(X, 1, function(Xi) Xi %o% Xi - Id) )
+# v3 <- Y * t( apply(X, 1, function(Xi) { return (Xi %o% Xi %o% Xi
+# - Reduce('+', lapply(1:d, function(j) as.double(Xi %o% E[j,] %o% E[j,])), rep(0, d*d*d))
+# - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% Xi %o% E[j,])), rep(0, d*d*d))
+# - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% E[j,] %o% Xi)), rep(0, d*d*d))) } ) )
+# Wtmp <- matrix(0, nrow=dd, ncol=dd)
+#
+#
+#g <- matrix(nrow=n, ncol=dd); for (i in 1:n) g[i,] = c(v1[i,], v2[i,], v3[i,]) - M
+#
+#
+#
+#
+#
+#
+# p <- θ$p
+# β <- θ$β
+# b <- θ$b
+#
+#
+#
+#
+## # Random generation of the size of each population in X~Y (unordered)
+## classes <- rmultinom(1, n, p)
+##
+## #d <- nrow(β)
+## zero_mean <- rep(0,d)
+## id_sigma <- diag(rep(1,d))
+## X <- matrix(nrow=0, ncol=d)
+## Y <- c()
+## for (i in 1:ncol(β)) #K = ncol(β)
+## {
+## newXblock <- MASS::mvrnorm(classes[i], zero_mean, id_sigma)
+## arg_link <- newXblock %*% β[,i] + b[i]
+## probas <-
+## if (li == "logit")
+## {
+## e_arg_link = exp(arg_link)
+## e_arg_link / (1 + e_arg_link)
+## }
+## else #"probit"
+## pnorm(arg_link)
+## probas[is.nan(probas)] <- 1 #overflow of exp(x)
+## X <- rbind(X, newXblock)
+## Y <- c( Y, vapply(probas, function(p) (rbinom(1,1,p)), 1) )
+## }
+#
+#
+#
+#
+#
+#
+#
+#
+# Mhatt <- c(
+# colMeans(Y * X),
+# colMeans(Y * t( apply(X, 1, function(Xi) Xi %o% Xi - Id) )),
+# colMeans(Y * t( apply(X, 1, function(Xi) { return (Xi %o% Xi %o% Xi
+# - Reduce('+', lapply(1:d, function(j) as.double(Xi %o% E[j,] %o% E[j,])), rep(0, d*d*d))
+# - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% Xi %o% E[j,])), rep(0, d*d*d))
+# - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% E[j,] %o% Xi)), rep(0, d*d*d))) } ) ) ))
+# λ <- sqrt(colSums(β^2))
+# β2 <- apply(β, 2, function(col) col %o% col)
+# β3 <- apply(β, 2, function(col) col %o% col %o% col)
+# M <- c(
+# β %*% (p * .G(li,1,λ,b)),
+# β2 %*% (p * .G(li,2,λ,b)),
+# β3 %*% (p * .G(li,3,λ,b)) )
+# print(sum(abs(Mhatt - M)))
+#
+#save(list=c("X", "Y"), file="v2.RData")
+#
+#
+#
+#
+#browser()
+# for (i in 1:n)
+# {
+# gi <- t(as.matrix(c(v1[i,], v2[i,], v3[i,]) - M))
+# Wtmp <- Wtmp + t(gi) %*% gi / n
+# }
+# Wtmp
+# #MASS::ginv(Wtmp)
+# },
+#
+# #TODO: compare with R version?
+# computeW_orig = function(θ)
+# {
+# require(MASS)
+# dd <- d + d^2 + d^3
+# M <- Moments(θ)
+# Omega <- matrix( .C("Compute_Omega",
+# X=as.double(X), Y=as.double(Y), M=as.double(M),
+# pn=as.integer(n), pd=as.integer(d),
+# W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd )
+# Omega
+# #MASS::ginv(Omega) #, tol=1e-4)
+# },
+#
+# Moments = function(θ)
+# {
+# "Vector of moments, of size d+d^2+d^3"
+#
+# p <- θ$p
+# β <- θ$β
+# λ <- sqrt(colSums(β^2))
+# b <- θ$b
+#
+# # Tensorial products β^2 = β2 and β^3 = β3 must be computed from current β1
+# β2 <- apply(β, 2, function(col) col %o% col)
+# β3 <- apply(β, 2, function(col) col %o% col %o% col)
+#
+# c(
+# β %*% (p * .G(li,1,λ,b)),
+# β2 %*% (p * .G(li,2,λ,b)),
+# β3 %*% (p * .G(li,3,λ,b)))
+# },
+#
projects / morpheus.git / commitdiff