From: Benjamin Auder <benjamin.auder@somewhere>
Date: Tue, 5 Feb 2019 15:22:07 +0000 (+0100)
Subject: Update vignette + optim code
X-Git-Url: https://git.auder.net/variants/current/doc/css/scripts/getGraph_%22%20%20%20this.image%20%20%20%22.php?a=commitdiff_plain;h=d294ece1cf943b74d96b26cc28b08c00cb191264;p=morpheus.git

Update vignette + optim code
---

diff --git a/patch_Bettina/FLXMRglm.R b/patch_Bettina/FLXMRglm.R
index 6fab5f5..30fbff6 100644
--- a/patch_Bettina/FLXMRglm.R
+++ b/patch_Bettina/FLXMRglm.R
@@ -1,139 +1,148 @@
 FLXMRglm <- function(formula=.~., family=gaussian, offset=NULL)
 {
-    if (is.character(family)) 
-        family <- get(family, mode = "function", envir = parent.frame())
-    if (is.function(family)) 
-        family <- family()
-    if (is.null(family$family)) {
-        print(family)
-        stop("'family' not recognized")
+  if (is.character(family))
+    family <- get(family, mode = "function", envir = parent.frame())
+  if (is.function(family))
+    family <- family()
+  if (is.null(family$family)) {
+    print(family)
+    stop("'family' not recognized")
+  }
+
+  glmrefit <- function(x, y, w) {
+    fit <- c(glm.fit(x, y, weights=w, offset=offset, family=family),
+      list(call = sys.call(), offset = offset,
+        control = eval(formals(glm.fit)$control),
+        method = "weighted.glm.fit"))
+    fit$df.null <- sum(w) + fit$df.null - fit$df.residual - fit$rank
+    fit$df.residual <- sum(w) - fit$rank
+    fit$x <- x
+    fit
+  }
+
+  z <- new("FLXMRglm", weighted=TRUE, formula=formula,
+    name=paste("FLXMRglm", family$family, sep=":"), offset = offset,
+    family=family$family, refit=glmrefit)
+
+  z@preproc.y <- function(x) {
+    if (ncol(x) > 1)
+      stop(paste("for the", family$family, "family y must be univariate"))
+    x
+  }
+
+  if (family$family=="gaussian") {
+    z@defineComponent <- function(para) {
+      predict <- function(x, ...) {
+        dotarg = list(...)
+        if("offset" %in% names(dotarg)) offset <- dotarg$offset
+        p <- x %*% para$coef
+        if (!is.null(offset)) p <-  p + offset
+        family$linkinv(p)
+      }
+
+      logLik <- function(x, y, ...)
+        dnorm(y, mean=predict(x, ...), sd=para$sigma, log=TRUE)
+
+      new("FLXcomponent",
+        parameters=list(coef=para$coef, sigma=para$sigma),
+        logLik=logLik, predict=predict,
+        df=para$df)
     }
-    glmrefit <- function(x, y, w) {
-      fit <- c(glm.fit(x, y, weights=w, offset=offset,
-                       family=family),
-               list(call = sys.call(), offset = offset,
-                    control = eval(formals(glm.fit)$control),            
-                    method = "weighted.glm.fit"))
-      fit$df.null <- sum(w) + fit$df.null - fit$df.residual - fit$rank
-      fit$df.residual <- sum(w) - fit$rank
-      fit$x <- x
-      fit
+
+    z@fit <- function(x, y, w, component){
+      fit <- glm.fit(x, y, w=w, offset=offset, family = family)
+      z@defineComponent(para = list(coef = coef(fit), df = ncol(x)+1,
+        sigma =  sqrt(sum(fit$weights * fit$residuals^2 /
+        mean(fit$weights))/ (nrow(x)-fit$rank))))
     }
-                
-    z <- new("FLXMRglm", weighted=TRUE, formula=formula,
-             name=paste("FLXMRglm", family$family, sep=":"), offset = offset,
-             family=family$family, refit=glmrefit)
-    z@preproc.y <- function(x){
-      if (ncol(x) > 1)
-        stop(paste("for the", family$family, "family y must be univariate"))
+  }
+
+  else if (family$family=="binomial") {
+    z@preproc.y <- function(x) {
+      if (ncol(x) != 2)
+      {
+        stop("for the binomial family, y must be a 2 column matrix\n",
+          "where col 1 is no. successes and col 2 is no. failures")
+      }
+      if (any(x < 0))
+        stop("negative values are not allowed for the binomial family")
       x
     }
 
-    if(family$family=="gaussian"){
-      z@defineComponent <- function(para) {
-        predict <- function(x, ...) {
-          dotarg = list(...)
-          if("offset" %in% names(dotarg)) offset <- dotarg$offset
-          p <- x %*% para$coef
-          if (!is.null(offset)) p <-  p + offset
-          family$linkinv(p)
-        }
-
-        logLik <- function(x, y, ...)
-          dnorm(y, mean=predict(x, ...), sd=para$sigma, log=TRUE)
-
-        new("FLXcomponent",
-            parameters=list(coef=para$coef, sigma=para$sigma),
-            logLik=logLik, predict=predict,
-            df=para$df)
+    z@defineComponent <- function(para) {
+      predict <- function(x, ...) {
+        dotarg = list(...)
+        if("offset" %in% names(dotarg))
+          offset <- dotarg$offset
+        p <- x %*% para$coef
+        if (!is.null(offset))
+          p <- p + offset
+        family$linkinv(p)
       }
+      logLik <- function(x, y, ...)
+      dbinom(y[,1], size=rowSums(y), prob=predict(x, ...), log=TRUE)
 
-      z@fit <- function(x, y, w, component){
-          fit <- glm.fit(x, y, w=w, offset=offset, family = family)
-          z@defineComponent(para = list(coef = coef(fit), df = ncol(x)+1,
-                                        sigma =  sqrt(sum(fit$weights * fit$residuals^2 /
-                                                          mean(fit$weights))/ (nrow(x)-fit$rank))))
-      }
+      new("FLXcomponent",
+        parameters=list(coef=para$coef),
+        logLik=logLik, predict=predict,
+        df=para$df)
     }
-    else if(family$family=="binomial"){
-      z@preproc.y <- function(x){
-        if (ncol(x) != 2)
-          stop("for the binomial family, y must be a 2 column matrix\n",
-               "where col 1 is no. successes and col 2 is no. failures")
-        if (any(x < 0))
-          stop("negative values are not allowed for the binomial family")
-        x
-      }     
-      z@defineComponent <- function(para) {
-        predict <- function(x, ...) {
-          dotarg = list(...)
-          if("offset" %in% names(dotarg)) offset <- dotarg$offset
-          p <- x %*% para$coef
-          if (!is.null(offset)) p <- p + offset
-          family$linkinv(p)
-        }
-        logLik <- function(x, y, ...)
-          dbinom(y[,1], size=rowSums(y), prob=predict(x, ...), log=TRUE)
-
-        new("FLXcomponent",
-            parameters=list(coef=para$coef),
-            logLik=logLik, predict=predict,
-            df=para$df)
-      }
 
-      z@fit <- function(x, y, w, component){
-        fit <- glm.fit(x, y, weights=w, family=family, offset=offset, start=component$coef)
-        z@defineComponent(para = list(coef = coef(fit), df = ncol(x)))
-      }
+    z@fit <- function(x, y, w, component) {
+      fit <- glm.fit(x, y, weights=w, family=family, offset=offset, start=component$coef)
+      z@defineComponent(para = list(coef = coef(fit), df = ncol(x)))
     }
-    else if(family$family=="poisson"){
-      z@defineComponent <- function(para) {
-        predict <- function(x, ...) {
-          dotarg = list(...)
-          if("offset" %in% names(dotarg)) offset <- dotarg$offset
-          p <- x %*% para$coef
-          if (!is.null(offset)) p <- p + offset
-          family$linkinv(p)
-        }
-        logLik <- function(x, y, ...)
-          dpois(y, lambda=predict(x, ...), log=TRUE)
-        
-        new("FLXcomponent",
-            parameters=list(coef=para$coef),
-            logLik=logLik, predict=predict,
-            df=para$df)
-      }
-          
-      z@fit <- function(x, y, w, component){
-        fit <- glm.fit(x, y, weights=w, family=family, offset=offset, start=component$coef)
-        z@defineComponent(para = list(coef = coef(fit), df = ncol(x)))
+  }
+
+  else if (family$family=="poisson") {
+    z@defineComponent <- function(para) {
+      predict <- function(x, ...) {
+        dotarg = list(...)
+        if("offset" %in% names(dotarg)) offset <- dotarg$offset
+        p <- x %*% para$coef
+        if (!is.null(offset)) p <- p + offset
+        family$linkinv(p)
       }
+      logLik <- function(x, y, ...)
+        dpois(y, lambda=predict(x, ...), log=TRUE)
+
+      new("FLXcomponent",
+        parameters=list(coef=para$coef),
+        logLik=logLik, predict=predict,
+        df=para$df)
     }
-    else if(family$family=="Gamma"){
-      z@defineComponent <- function(para) {
-        predict <- function(x, ...) {
-          dotarg = list(...)
-          if("offset" %in% names(dotarg)) offset <- dotarg$offset
-          p <- x %*% para$coef
-          if (!is.null(offset)) p <- p + offset
-          family$linkinv(p)
-        }
-        logLik <- function(x, y, ...)
-          dgamma(y, shape = para$shape, scale=predict(x, ...)/para$shape, log=TRUE)
-        
-        new("FLXcomponent", 
-            parameters = list(coef = para$coef, shape = para$shape),
-            predict = predict, logLik = logLik,
-            df = para$df)
-      }
 
-      z@fit <- function(x, y, w, component){
-        fit <- glm.fit(x, y, weights=w, family=family, offset=offset, start=component$coef)
-        z@defineComponent(para = list(coef = coef(fit), df = ncol(x)+1,
-                              shape = sum(fit$prior.weights)/fit$deviance))
-        
+    z@fit <- function(x, y, w, component) {
+      fit <- glm.fit(x, y, weights=w, family=family, offset=offset, start=component$coef)
+      z@defineComponent(para = list(coef = coef(fit), df = ncol(x)))
+    }
+  }
+
+  else if (family$family=="Gamma") {
+    z@defineComponent <- function(para) {
+      predict <- function(x, ...) {
+        dotarg = list(...)
+        if("offset" %in% names(dotarg)) offset <- dotarg$offset
+        p <- x %*% para$coef
+        if (!is.null(offset)) p <- p + offset
+        family$linkinv(p)
       }
+      logLik <- function(x, y, ...)
+        dgamma(y, shape = para$shape, scale=predict(x, ...)/para$shape, log=TRUE)
+
+      new("FLXcomponent",
+        parameters = list(coef = para$coef, shape = para$shape),
+        predict = predict, logLik = logLik,
+        df = para$df)
     }
-    else stop(paste("Unknown family", family))
-    z
+
+    z@fit <- function(x, y, w, component) {
+      fit <- glm.fit(x, y, weights=w, family=family, offset=offset, start=component$coef)
+      z@defineComponent(para = list(coef = coef(fit), df = ncol(x)+1,
+        shape = sum(fit$prior.weights)/fit$deviance))
+    }
+  }
+
+  else stop(paste("Unknown family", family))
+  z
 }
diff --git a/pkg/R/computeMu.R b/pkg/R/computeMu.R
index 87cc680..2a62793 100644
--- a/pkg/R/computeMu.R
+++ b/pkg/R/computeMu.R
@@ -12,7 +12,7 @@
 #'     \item 'jd_nvects', number of random vectors for joint-diagonalization
 #'       (or 0 for p=d, canonical basis by default)
 #'     \item 'M', moments of order 1,2,3: will be computed if not provided.
-#'     \item 'K', number of populations (estimated with ranks of M2 if not given)
+#'     \item 'K', number of populations (estimated with rank of M2 if not given)
 #'   }
 #'
 #' @return The estimated normalized parameters as columns of a matrix μ of size dxK
diff --git a/pkg/R/multiRun.R b/pkg/R/multiRun.R
index fefdd34..8b70d6d 100644
--- a/pkg/R/multiRun.R
+++ b/pkg/R/multiRun.R
@@ -57,8 +57,7 @@
 #'     library(morpheus)
 #'     K <- fargs$optargs$K
 #'     μ <- computeMu(fargs$X, fargs$Y, fargs$optargs)
-#'     V <- list( p=rep(1/K,K-1), β=μ, b=c(0,0) )
-#'     optimParams(V,fargs$optargs)$β
+#'     optimParams(fargs$K,fargs$link,fargs$optargs)$run(list(β=μ))$β
 #'   },
 #'   # flexmix
 #'   function(fargs) {
@@ -74,7 +73,8 @@
 #'     io = generateSampleIO(fargs$n, fargs$p, fargs$β, fargs$b, fargs$optargs$link)
 #'     fargs$X = io$X
 #'     fargs$Y = io$Y
-#'     fargs$optargs$K = ncol(fargs$β)
+#'     fargs$K = ncol(fargs$β)
+#'     fargs$link = fargs$optargs$link
 #'     fargs$optargs$M = computeMoments(io$X,io$Y)
 #'     fargs
 #'   }, N=10, ncores=3)
diff --git a/pkg/R/optimParams.R b/pkg/R/optimParams.R
index 1ef1494..2eada8f 100644
--- a/pkg/R/optimParams.R
+++ b/pkg/R/optimParams.R
@@ -213,13 +213,23 @@ setRefClass(
 		{
 			"Run optimization from x0 with solver..."
 
-			if (!is.numeric(x0) || any(is.na(x0)) || length(x0) != (d+2)*K-1
-				|| any(x0[1:(K-1)] < 0) || sum(x0[1:(K-1)]) > 1)
-			{
-				stop("x0: numeric vector, no NA, length (d+2)*K-1, sum(x0[1:(K-1) >= 0]) <= 1")
-			}
-
-			op_res = constrOptim( x0, .self$f, .self$grad_f,
+	    if (!is.list(x0))
+		    stop("x0: list")
+      if (is.null(x0$β))
+        stop("At least x0$β must be provided")
+			if (!is.matrix(x0$β) || any(is.na(x0$β)) || ncol(x0$β) != K)
+				stop("x0$β: matrix, no NA, ncol == K")
+      if (is.null(x0$p))
+        x0$p = rep(1/K, K-1)
+      else if (length(x0$p) != K-1 || sum(x0$p) > 1)
+        stop("x0$p should contain positive integers and sum to < 1")
+      # Next test = heuristic to detect missing b (when matrix is called "beta")
+      if (is.null(x0$b) || all(x0$b == x0$β))
+        x0$b = rep(0, K)
+      else if (any(is.na(x0$b)))
+        stop("x0$b cannot have missing values")
+
+			op_res = constrOptim( linArgs(x0), .self$f, .self$grad_f,
 				ui=cbind(
 					rbind( rep(-1,K-1), diag(K-1) ),
 					matrix(0, nrow=K, ncol=(d+1)*K) ),
diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 35e89ff..29a254e 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -48,14 +48,14 @@ plotHist <- function(mr, x, y)
 #' @examples
 #' #See example in ?plotHist
 #' @export
-plotBox <- function(mr, x, y)
+plotBox <- function(mr, x, y, xtitle="")
 {
 	params <- extractParam(mr, x, y)
 	L = length(params)
 	# Plot boxplots side by side
 	par(mfrow=c(1,L), cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
 	for (i in 1:L)
-		boxplot(params[[i]], ylab="Parameter value")
+		boxplot(params[[i]], xlab=xtitle, ylab="Parameter value")
 }
 
 #' plotCoefs
diff --git a/vignettes/report.Rmd b/vignettes/report.Rmd
index 4de2b4d..84dc5b6 100644
--- a/vignettes/report.Rmd
+++ b/vignettes/report.Rmd
@@ -89,24 +89,143 @@ X and Y which contain vector inputs and binary output.
 However, a function is provided in the package to generate such data following a
 pre-defined law:
 
+```{r, results="show", include=TRUE, echo=TRUE}
+library(morpheus)
 io <- generateSampleIO(n=10000, p=1/2, beta=matrix(c(1,0,0,1),ncol=2), b=c(0,0), link="probit")
+# io$X and io$Y contain the sample data
+```
 
-n is the total number of samples (lines in X, number of elements in Y)
-p is a vector of proportions, of size d-1 (because the last proportion is deduced from
-  the others: p elements sums to 1) [TODO: omega or p?]
-beta is the matrix of linear coefficients, as written above in the model.
-b is the vector of intercepts (as in linear regression, and as in the model above)
+$n$ is the total number of samples (lines in X, number of elements in Y)
+$p$ is a vector of proportions, of size $d-1$ (because the last proportion is deduced
+from the others: $p$ elements sums to 1) [TODO: omega or p?]
+$\beta$ is the matrix of linear coefficients, as written above in the model.
+$b$ is the vector of intercepts (as in linear regression, and as in the model above)
 link can be either "logit" or "probit", as mentioned earlier.
 
 This function outputs a list containing in particular the matrices X and Y, allowing to
 use the other functions (which all require either these, or the moments).
 
-TODO: computeMu(), explain input/output
+```{r, results="show", include=TRUE, echo=TRUE}
+mu <- computeMu(io$X, io$Y, optargs=list(K=2))
+```
 
-### Estimation of other parameters
+The optional argument, "optargs", is a list which can provide
+ * the number of clusters $K$,
+ * the moments matrix $M$ (computed with the "computeMoments()" function),
+ * the joint-diagonalisation method ("uwedge" or "jedi"),
+ * the number of random vectors for joint-diagonalization.
+See ?computeMu and the code for more details.
+
+### Estimation of the other parameters
+
+The other parameters are estimated by solving an optimization problem.
+The following function builds and return an optimization algorithm object:
+
+```{r, results="show", include=TRUE, echo=TRUE}
+M <- computeMoments(io$X, io$Y)
+# X and Y must be provided if the moments matrix is not given
+algopt <- optimParams(K=2, link="probit", optargs=list(M=M))
+# Optimization starts at beta = mu, b = 0 and p = uniform distribution
+x0 <- list(beta = mu)
+theta <- algopt$run(x0)
+```
 
-TODO: just run optimParams$run(...)
+Now theta is a list with three slots:
+ * $p$: estimated proportions,
+ * $\beta$: estimated regression matrix,
+ * $b$: estimated bias.
 
 ### Monte-Carlo and bootstrap
 
-TODO: show example comparison with flexmix, show plots.
+The package provides a function to compare methods on several computations on random data.
+It takes in input a list of parameters, then a list of functions which output some quantities
+(on the first example, our "computeMu()" method versus flexmix way of estimating directions),
+and finally a method to prepare the arguments which will be given to the functions in the
+list just mentioned; this allows to run Monte-Carlo estimations with the exact same samples
+for each compared method. The two last arguments to "multiRun()" control the number of runs,
+and the number of cores (using the package parallel).
+
+```{r, results="show", include=TRUE, echo=TRUE}
+beta <- matrix(c(1,-2,3,1), ncol=2)
+io <- generateSampleIO(n=1000, p=1/2, beta=beta, b=c(0,0), "logit")
+mu <- normalize(beta)
+
+# Example 1: bootstrap + computeMu, morpheus VS flexmix; assumes fargs first 3 elts X,Y,K
+mr1 <- multiRun(list(X=io$X,Y=io$Y,optargs=list(K=2,jd_nvects=0)), list(
+  # morpheus
+  function(fargs) {
+    library(morpheus)
+    ind <- fargs$ind
+    computeMu(fargs$X[ind,],fargs$Y[ind],fargs$optargs)
+  },
+  # flexmix
+  function(fargs) {
+    library(flexmix)
+    source("../patch_Bettina/FLXMRglm.R")
+    ind <- fargs$ind
+    K <- fargs$optargs$K
+    dat = as.data.frame( cbind(fargs$Y[ind],fargs$X[ind,]) )
+    out = refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
+      model=FLXMRglm(family="binomial") ) )
+    normalize( matrix(out@coef[1:(ncol(fargs$X)*K)], ncol=K) )
+  } ),
+  prepareArgs = function(fargs,index) {
+    # Always include the non-shuffled dataset
+    if (index == 1)
+      fargs$ind <- 1:nrow(fargs$X)
+    else
+      fargs$ind <- sample(1:nrow(fargs$X),replace=TRUE)
+    fargs
+  }, N=10, ncores=3)
+# The result is correct up to matrices columns permutations; align them:
+for (i in 1:2)
+  mr1[[i]] <- alignMatrices(mr1[[i]], ref=mu, ls_mode="exact")
+```
+
+Several plots are available: histograms, boxplots, or curves of coefficients.
+We illustrate boxplots and curves here (histograms function uses the same arguments,
+see ?plotHist).
+
+```
+# Second row, first column; morpheus on the left, flexmix on the right
+plotBox(mr1, 2, 1, "Target value: -1")
+```
+
+```{r, results="show", include=TRUE, echo=TRUE}
+# Example 2: Monte-Carlo + optimParams from X,Y, morpheus VS flexmix; first args n,p,beta,b
+mr2 <- multiRun(list(n=1000,p=1/2,beta=beta,b=c(0,0),optargs=list(link="logit")), list(
+  # morpheus
+  function(fargs) {
+    library(morpheus)
+    mu <- computeMu(fargs$X, fargs$Y, fargs$optargs)
+    optimParams(fargs$K,fargs$link,fargs$optargs)$run(list(beta=mu))$beta
+  },
+  # flexmix
+  function(fargs) {
+    library(flexmix)
+    source("../patch_Bettina/FLXMRglm.R")
+    dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
+    out <- refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=fargs$K,
+      model=FLXMRglm(family="binomial") ) )
+    sapply( seq_len(fargs$K), function(i) as.double( out@components[[1]][[i]][,1] ) )
+  } ),
+  prepareArgs = function(fargs,index) {
+    library(morpheus)
+    io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$optargs$link)
+    fargs$X = io$X
+    fargs$Y = io$Y
+    fargs$K = ncol(fargs$beta)
+    fargs$link = fargs$optargs$link
+    fargs$optargs$M = computeMoments(io$X,io$Y)
+    fargs
+  }, N=10, ncores=3)
+# As in example 1, align results:
+for (i in 1:2)
+  mr2[[i]] <- alignMatrices(mr2[[i]], ref=beta, ls_mode="exact")
+```
+
+```
+# Second argument = true parameters matrix; third arg = index of method (here "morpheus")
+plotCoefs(mr2, beta, 1)
+# Real params are on the continous line; estimations = dotted line
+```