inst/doc/Introduction.R

In CMTFtoolbox: Create (Advanced) Coupled Matrix and Tensor Factorization Models

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 12,
  fig.height = 6
)

## ----setup--------------------------------------------------------------------
library(CMTFtoolbox)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(rTensor)

set.seed(123)

## ----helper functions---------------------------------------------------------
simTensorMatrixData = function(I=108, J=100, K=10, L=100, numComponents=2){
  
  A = array(rnorm(I*numComponents), c(I, numComponents))  # shared subject mode
  B = array(rnorm(J*numComponents), c(J, numComponents))  # distinct feature mode of X1
  C = array(rnorm(K*numComponents), c(K, numComponents))  # distinct condition mode of X1
  D = array(rnorm(L*numComponents), c(L, numComponents))  # distinct feature mode of X2
  
  df1 = reinflateTensor(A, B, C)
  df2 = reinflateMatrix(A, D)
  datasets = list(df1, df2)
  modes = list(c(1,2,3), c(1,4))
  Z = setupCMTFdata(datasets, modes, normalize=TRUE)
  
  return(list("Z"=Z, "A"=A, "B"=B, "C"=C, "D"=D))
}

simTwoTensorData = function(I=108, J=100, K=10, L=100, M=10, numComponents=2){

  A = array(rnorm(I*numComponents), c(I, numComponents))  # shared subject mode
  B = array(rnorm(J*numComponents), c(J, numComponents))  # distinct feature mode of X1
  C = array(rnorm(K*numComponents), c(K, numComponents))  # distinct condition mode of X1
  D = array(rnorm(L*numComponents), c(L, numComponents))  # distinct feature mode of X2
  E = array(rnorm(M*numComponents), c(M, numComponents))  # distinct condition mode of X2

  df1 = reinflateTensor(A, B, C)
  df2 = reinflateTensor(A, D, E)
  datasets = list(df1, df2)
  modes = list(c(1,2,3), c(1,4,5))
  Z = setupCMTFdata(datasets, modes, normalize=TRUE)
  
  return(list("Z"=Z, "A"=A, "B"=B, "C"=C, "D"=D, "E"=E))
}

## ----simulate data tensor-matrix----------------------------------------------
tensorMatrixData = simTensorMatrixData(I=108, J=100, K=10, L=100, numComponents=2)

## ----run CMTF with nvec tensor-matrix-----------------------------------------
result_nvec = cmtf_opt(tensorMatrixData$Z, 2, initialization="nvec")

## ----plot result_nvec tensor-matrix-------------------------------------------
a = cbind(tensorMatrixData$A, result_nvec$Fac[[1]]) %>% as_tibble() %>% ggplot(aes(x=V2,y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("CMTF component 1") + ggtitle("Subject mode (shared)") + stat_cor()
b = cbind(tensorMatrixData$B, result_nvec$Fac[[2]]) %>% as_tibble() %>% ggplot(aes(x=V2, y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("") + ggtitle("Feature mode X1") + stat_cor()
c = cbind(tensorMatrixData$C, result_nvec$Fac[[3]]) %>% as_tibble() %>% ggplot(aes(x=V2,y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("") + ggtitle("Condition mode X1") + stat_cor()
d = cbind(tensorMatrixData$D, result_nvec$Fac[[4]]) %>% as_tibble() %>% ggplot(aes(x=V2, y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("") + ggtitle("Feature mode X2") + stat_cor()

e = cbind(tensorMatrixData$A, result_nvec$Fac[[1]]) %>% as_tibble() %>% ggplot(aes(x=V1,y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("CMTF component 2") + stat_cor()
f = cbind(tensorMatrixData$B, result_nvec$Fac[[2]]) %>% as_tibble() %>% ggplot(aes(x=V1, y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("") + stat_cor()
g = cbind(tensorMatrixData$C, result_nvec$Fac[[3]]) %>% as_tibble() %>% ggplot(aes(x=V1,y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("") + stat_cor()
h = cbind(tensorMatrixData$D, result_nvec$Fac[[4]]) %>% as_tibble() %>% ggplot(aes(x=V1, y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("") + stat_cor()

ggarrange(a,b,c,d,e,f,g,h, nrow=2, ncol=4)


## ----simulate two-tensor data-------------------------------------------------
twoTensorData = simTwoTensorData(I=108, J=100, K=10, L=100, M=10, numComponents=2)

## ----run CMTF with nvec two-tensor--------------------------------------------
result_nvec = cmtf_opt(twoTensorData$Z, 2, initialization="nvec")

## ----plot result_nvec two-tensor----------------------------------------------
a = cbind(twoTensorData$A, result_nvec$Fac[[1]]) %>% as_tibble() %>% ggplot(aes(x=V2,y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("CMTF component 1") + ggtitle("Subject mode (shared)") + stat_cor()
b = cbind(twoTensorData$B, result_nvec$Fac[[2]]) %>% as_tibble() %>% ggplot(aes(x=V2, y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("") + ggtitle("Feature mode X1") + stat_cor()
c = cbind(twoTensorData$C, result_nvec$Fac[[3]]) %>% as_tibble() %>% ggplot(aes(x=V2,y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("") + ggtitle("Condition mode X1") + stat_cor()
d = cbind(twoTensorData$D, result_nvec$Fac[[4]]) %>% as_tibble() %>% ggplot(aes(x=V2, y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("") + ggtitle("Feature mode X2") + stat_cor()
e = cbind(twoTensorData$E, result_nvec$Fac[[5]]) %>% as_tibble() %>% ggplot(aes(x=V2,y=V3)) + geom_point() + xlab("Simulated loadings") + ylab("") + ggtitle("Condition mode X2") + stat_cor()

f = cbind(twoTensorData$A, result_nvec$Fac[[1]]) %>% as_tibble() %>% ggplot(aes(x=V1,y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("CMTF component 2") + stat_cor()
g = cbind(twoTensorData$B, result_nvec$Fac[[2]]) %>% as_tibble() %>% ggplot(aes(x=V1, y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("") + stat_cor()
h = cbind(twoTensorData$C, result_nvec$Fac[[3]]) %>% as_tibble() %>% ggplot(aes(x=V1,y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("") + stat_cor()
i = cbind(twoTensorData$D, result_nvec$Fac[[4]]) %>% as_tibble() %>% ggplot(aes(x=V1, y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("") + stat_cor()
j = cbind(twoTensorData$E, result_nvec$Fac[[5]]) %>% as_tibble() %>% ggplot(aes(x=V1,y=V4)) + geom_point() + xlab("Simulated loadings") + ylab("") + stat_cor()

ggarrange(a,b,c,d,e,f,g,h,i,j, nrow=2, ncol=5)