I use the following code to generate a matrix
randomdiv <-
function(nchrom, ndivs, size) {
sz <- matrix(nrow = nchrom, ncol = ndivs)
for (j in 1:nchrom) {
n <- size
for (i in 1:ndivs)
{
old_subs <- rbinom (1, n, 0.5)
num_chrom <- rep(1 / nchrom, nchrom)
new_subs <- rmultinom(1, size * nchrom / 2, prob = c(num_chrom))
m <- old_subs + new_subs
sz[j,i] <- m[1,1]
n <- m
}
}
return (sz)
}
>randomdiv(3, 3, 10)
[,1] [,2] [,3]
[1,] 11 13 12
[2,] 6 8 5
[3,] 12 11 9
The only adjustment I need to make is that when a 0 is generated in the column by the rbinom function, I need that occurence to stay as a 0 for the remainder of the matrix, but anything >0 needs to go through the rest of the loop and have new_subs added to it.
I have tried;
randomdiv <- function(nchrom, ndivs, size) {sz <- matrix(nrow = nchrom, ncol = ndivs)
for (j in 1:nchrom) {
n <- size
for (i in 1:ndivs)
{
old_subs <- rbinom (1, n, 0.5)
num_chrom <- rep(1/nchrom, nchrom)
new_subs <- rmultinom(1, size*nchrom/2, prob = c(num_chrom))
m <- ifelse(old_subs>0, old_subs + new_subs, old_subs+0)
sz[j,i] <- m[1,1]
n <- m
}
}
return (replicate(ncell, sz, simplify = FALSE))
}
> randomdiv(3, 3, 10)
#Error in m[1, 1] : incorrect number of dimensions
I've tried a few different tactics with the ifelse function, but I think it only treats the columns as a whole, so if there is a 0 at all, nothing happens for the whole column, whereas I need each value in the columns to be treated individually.
You just need to use if() with an else and skip several lines of code if there's a 0:
randomdiv <-
function(nchrom, ndivs, size) {
sz <- matrix(nrow = nchrom, ncol = ndivs)
for (j in 1:nchrom) {
n <- size
for (i in 1:ndivs)
{
old_subs <- rbinom (1, n, 0.5)
if(old_subs>0){
num_chrom <- rep(1 / nchrom, nchrom)
new_subs <- rmultinom(1, size * nchrom / 2, prob = c(num_chrom))
m <- old_subs + new_subs
sz[j,i] <- m[1,1]
} else sz[j,i] <- old_subs
n <- m
}
}
return (sz)
}
randomdiv(3, 3, 2)
# [,1] [,2] [,3]
# [1,] 2 2 0
# [2,] 1 2 4
# [3,] 1 1 0
Related
I would be super grateful for some help. I don't have a coding background and I am confused by the error message I am getting when running the sppb functions of the WRS2 package. These functions perform a robust mixed ANOVA using bootstrapping.
sppba(formula = score ~ my_between_variable * my_within_variable, id = participant_code, data = df_long_T2)
Error in xmat[, k] <- x[[kv]] :
number of items to replace is not a multiple of replacement length
I get the same error for all three sppb functions. The functions look the same except that instead of sppba the others say sppbb and sppbi. I don't even know what the functions are trying to replace. The functions work for me with other data.
The classes of all the things involved seem fine: score is numeric, order_supplement and time are factors, participant_code is character, df_long_T2 is a dataframe. I have 120 participants, 61 in one group and 59 in the other, with two observations per participant. There are no NAs in the columns involved.
Traceback() just gives me the one line of code above and the error message.
Debug() gives me this and I don't know what to make of it:
"Debug location is approximate because location is not available"
function (formula, id, data, est = "mom", avg = TRUE, nboot = 500,
MDIS = FALSE, ...)
{
if (missing(data)) {
mf <- model.frame(formula)
}
else {
mf <- model.frame(formula, data)
}
cl <- match.call()
est <- match.arg(est, c("mom", "onestep", "median"), several.ok = FALSE)
mf1 <- match.call()
m <- match(c("formula", "data", "id"), names(mf1), 0L)
mf1 <- mf1[c(1L, m)]
mf1$drop.unused.levels <- TRUE
mf1[[1L]] <- quote(stats::model.frame)
mf1 <- eval(mf1, parent.frame())
random1 <- mf1[, "(id)"]
depvar <- colnames(mf)[1]
if (all(length(table(random1)) == table(mf[, 3]))) {
ranvar <- colnames(mf)[3]
fixvar <- colnames(mf)[2]
}
else {
ranvar <- colnames(mf)[2]
fixvar <- colnames(mf)[3]
}
MC <- FALSE
K <- length(table(mf[, ranvar]))
J <- length(table(mf[, fixvar]))
p <- J * K
grp <- 1:p
est <- get(est)
fixsplit <- split(mf[, depvar], mf[, fixvar])
indsplit <- split(mf[, ranvar], mf[, fixvar])
dattemp <- mapply(split, fixsplit, indsplit, SIMPLIFY = FALSE)
data <- do.call(c, dattemp)
x <- data
jp <- 1 - K
kv <- 0
kv2 <- 0
for (j in 1:J) {
jp <- jp + K
xmat <- matrix(NA, ncol = K, nrow = length(x[[jp]]))
for (k in 1:K) {
kv <- kv + 1
xmat[, k] <- x[[kv]]
}
xmat <- elimna(xmat)
for (k in 1:K) {
kv2 <- kv2 + 1
x[[kv2]] <- xmat[, k]
}
}
xx <- x
nvec <- NA
jp <- 1 - K
for (j in 1:J) {
jp <- jp + K
nvec[j] <- length(x[[jp]])
}
bloc <- matrix(NA, nrow = J, ncol = nboot)
mvec <- NA
ik <- 0
for (j in 1:J) {
x <- matrix(NA, nrow = nvec[j], ncol = K)
for (k in 1:K) {
ik <- ik + 1
x[, k] <- xx[[ik]]
if (!avg)
mvec[ik] <- est(xx[[ik]])
}
tempv <- apply(x, 2, est)
data <- matrix(sample(nvec[j], size = nvec[j] * nboot,
replace = TRUE), nrow = nboot)
bvec <- matrix(NA, ncol = K, nrow = nboot)
for (k in 1:K) {
temp <- x[, k]
bvec[, k] <- apply(data, 1, rmanogsub, temp, est)
}
if (avg) {
mvec[j] <- mean(tempv)
bloc[j, ] <- apply(bvec, 1, mean)
}
if (!avg) {
if (j == 1)
bloc <- bvec
if (j > 1)
bloc <- cbind(bloc, bvec)
}
}
if (avg) {
d <- (J^2 - J)/2
con <- matrix(0, J, d)
id <- 0
Jm <- J - 1
for (j in 1:Jm) {
jp <- j + 1
for (k in jp:J) {
id <- id + 1
con[j, id] <- 1
con[k, id] <- 0 - 1
}
}
}
if (!avg) {
MJK <- K * (J^2 - J)/2
JK <- J * K
MJ <- (J^2 - J)/2
cont <- matrix(0, nrow = J, ncol = MJ)
ic <- 0
for (j in 1:J) {
for (jj in 1:J) {
if (j < jj) {
ic <- ic + 1
cont[j, ic] <- 1
cont[jj, ic] <- 0 - 1
}
}
}
tempv <- matrix(0, nrow = K - 1, ncol = MJ)
con1 <- rbind(cont[1, ], tempv)
for (j in 2:J) {
con2 <- rbind(cont[j, ], tempv)
con1 <- rbind(con1, con2)
}
con <- con1
if (K > 1) {
for (k in 2:K) {
con1 <- push(con1)
con <- cbind(con, con1)
}
}
}
if (!avg)
bcon <- t(con) %*% t(bloc)
if (avg)
bcon <- t(con) %*% (bloc)
tvec <- t(con) %*% mvec
tvec <- tvec[, 1]
tempcen <- apply(bcon, 1, mean)
vecz <- rep(0, ncol(con))
bcon <- t(bcon)
temp = bcon
for (ib in 1:nrow(temp)) temp[ib, ] = temp[ib, ] - tempcen +
tvec
bcon <- rbind(bcon, vecz)
if (!MDIS) {
if (!MC)
dv = pdis(bcon, center = tvec)
}
if (MDIS) {
smat <- var(temp)
bcon <- rbind(bcon, vecz)
chkrank <- qr(smat)$rank
if (chkrank == ncol(smat))
dv <- mahalanobis(bcon, tvec, smat)
if (chkrank < ncol(smat)) {
smat <- ginv(smat)
dv <- mahalanobis(bcon, tvec, smat, inverted = T)
}
}
bplus <- nboot + 1
sig.level <- 1 - sum(dv[bplus] >= dv[1:nboot])/nboot
tvec1 <- data.frame(Estimate = tvec)
if (avg) {
tnames <- apply(combn(levels(mf[, fixvar]), 2), 2, paste0,
collapse = "-")
rownames(tvec1) <- tnames
}
else {
fixcomb <- apply(combn(levels(mf[, fixvar]), 2), 2,
paste0, collapse = "-")
rnames <- levels(mf[, ranvar])
tnames <- as.vector(t(outer(rnames, fixcomb, paste)))
rownames(tvec1) <- tnames
}
result <- list(test = tvec1, p.value = sig.level, contrasts = con,
call = cl)
class(result) <- c("spp")
result
}
I expected to get an output like this:
## Test statistics:
## Estimate
## time1-time2 0.3000
##
## Test whether the corrresponding population parameters are the same:
## p-value: 0.37
I am trying to take a derivative of a double sum function. I am running into this error:
Error in deriv.f.1(X = X.data, y = y.vec, alpha = alpha.vector[1, ]) :
object 'L_D_grad' not found
I have tried to move the {} brackets around, double check if I missed a closing/opening bracket, if I have extra opening/closing bracket. However, the error still exists.
# Generate Sample Data
gen.sample <- function(n){
x <- rnorm(n,5,10)
y <- ifelse(x < 2.843,1,-1)
return(data.frame(x,y))
}
##
deriv.f.1 <- function(X,y,alpha){
N <- length(X)
L_D_grad < numeric(N)
xy.alpha.sum <- numeric(N)
for(k in 1:N){
for(l in 1:N){
if(l == k){
xy.alpha.sum[l] = 0}
else{
xy.alpha.sum[l] <- alpha[l]*y[k]*y[l]*X[k]*X[l]}
}
L_D_grad[k] <- 1 - sum(xy.alpha.sum) - alpha[k]*(y[k])^2*(X[k])^2
}
return(L_D_grad)
}
## Illustration
set.seed(4997)
options(digits = 4,scipen = -4)
sample.data <- gen.sample(n=N)
X.data <- sample.data$x
y.vec <- sample.data$y
alpha.vector <- matrix(rep(seq(from=-5,to = 5, length.out = N),N*N),
ncol = N, nrow = N, byrow = TRUE)
alpha_vec <- alpha.vector[1,]
deriv.f.1(X = X.data, y = y.vec, alpha = alpha_vec)
Thanks in advance!
Here is my code:
# Generate Sample Data
gen.sample <- function(n){
x <- rnorm(n,5,10)
y <- ifelse(x < 2.843,1,-1)
return(data.frame(x,y))
}
##
deriv.f.1 <- function(X,y,alpha){
N <- length(X)
L_D_grad <- numeric(N)
xy.alpha.sum <- numeric(N)
for(k in 1:N){
for(l in 1:N){
if(l == k){
xy.alpha.sum[l] = 0}
else{
xy.alpha.sum[l] <- alpha[l]*y[k]*y[l]*X[k]*X[l]}
}
L_D_grad[k] <- 1 - sum(xy.alpha.sum) - alpha[k]*(y[k])^2*(X[k])^2
}
return(L_D_grad)
}
## Illustration
set.seed(4997)
options(digits = 4,scipen = -4)
N=10
sample.data <- gen.sample(n=N)
X.data <- sample.data$x
y.vec <- sample.data$y
alpha.vector <- matrix(rep(seq(from=-5,to = 5, length.out = N),N*N),
ncol = N, nrow = N, byrow = TRUE)
alpha_vec <- alpha.vector[1,]
deriv.f.1(X = X.data, y = y.vec, alpha = alpha_vec)
Where:
#sample.data
#x y
#1 -5.303e+00 1
#2 1.493e+01 -1
#3 9.797e+00 -1
#4 1.991e+01 -1
#5 -1.454e+01 1
#6 1.423e+01 -1
#7 1.025e+01 -1
#8 5.455e+00 -1
#9 3.719e+00 -1
#10 2.021e+01 -1
And deriv.f.1(X = X.data, y = y.vec, alpha = alpha_vec)
# -1.271e+01 -3.759e+01 -2.432e+01 -5.046e+01 -3.659e+01 -3.577e+01 -2.548e+01 -1.310e+01
# -8.612e+00 -5.123e+01
I made two changes:
Assign N a value: N=10
Correct assignment form L_D_grad: L_D_grad <- numeric(N)
I have made a function so that it works when its arguments each have a length >= 2.
But I'm wondering why the function only works when its argument have each have a length of >= 3!
Am I missing something? (Any fix so the function works when length of its args are each of 2 as well?)
[Note: I always expect the output of function (i.e., CI) to be a matrix with 2 columns, length(n) rows, except when length(n) == 2. When length(n) == 2 I expect the output to have 1 row, and 2 columns.]
abc <- function(n, yes, a, b = a){
p <- list()
for(i in 1:length(n)){
p[[i]] <- rbeta(1e3, a[i] + yes[i], b[i] + (n[i] - yes[i]))
}
ps <- combn(p, 2, FUN = function(x) x[[1]]- x[[2]])
CI <- matrix(NA, length(n), 2)
for(i in 1:length(n)){
CI[i, ] <- quantile(ps[, i], c(.025, .975))
}
CI
}
For example:
abc(n = c(10, 20, 30), yes = rep(5, 3), a = rep(1, 3)) # Works well :-)
abc(n = c(10, 20), yes = rep(5, 2), a = rep(1, 2)) # Doesn't work! :-(
# Error in ps[, i] : subscript out of bounds
There is easy fix to problem. Replace length(n) with ncol(ps) while creating result matrix and running for loop to copy values to CI. It makes more sense as number of combinations generate by 'combnwill more than actual length ofn`.
abc <- function(n, yes, a, b = a){
p <- list()
for(i in 1:length(n)){
p[[i]] <- rbeta(1e3, a[i] + yes[i], b[i] + (n[i] - yes[i]))
}
str(p)
ps <- combn(p, 2, FUN = function(x) x[[1]]- x[[2]])
CI <- matrix(NA, ncol(ps), 2)
for(i in 1:ncol(ps)){
CI[i, ] <- quantile(ps[, i], c(.025, .975), na.rm = TRUE)
}
CI
}
#Results
#> abc(n = c(10, 20, 30), yes = rep(5, 3), a = rep(1, 3))
# [,1] [,2]
#[1,] -0.10141014 0.5774627
#[2,] 0.02638096 0.6159326
#[3,] -0.12473451 0.3069135
#> abc(n = c(10, 20), yes = rep(5, 2), a = rep(1, 2))
# [,1] [,2]
#[1,] -0.1228497 0.5304606
I need to write the following in an R program:
X[,1] = (C[1,1]*Y[,1])+(C[1,2]*Y[,2])+(C[1,3]*Y[,3]) + mu[1]
X[,2] = (C[2,1]*Y[,1])+(C[2,2]*Y[,2])+(C[2,3]*Y[,3]) + mu[2]
X[,3] = (C[3,1]*Y[,1])+(C[3,2]*Y[,2])+(C[3,3]*Y[,3]) + mu[3]
I'm writing the following:
for (i in 1:3){
for (j in 1:3) {
X[,i] = sum((C[i,j]*Y[,j]))+ mu[i]
}
}
but the answer is not the same as writing all the above. Can anyone help?
There is no need for for-loop in your calculation.
Let's say that your data are something like this:
set.seed(1)
C = matrix(1:9, nrow = 3, ncol = 3)
C
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
Y = 1:3
mu = rnorm(3)
mu
[1] -0.6264538 0.1836433 -0.8356286
Then simply you can write:
X = C%*%Y + mu
X
[,1]
[1,] 29.37355
[2,] 36.18364
[3,] 41.16437
With your for-loop you actually calculate X[i] as C[i,3]*Y[,3]+ mu[i] because you rewrite the value of X[i] in each j-loop.
If you insist on using for-loop solution, use only one for loop:
for (i in 1:3){
X[i] <- sum(C[i, 1:3]*Y) + mu[i]
}
X
[,1]
[1,] 29.37355
[2,] 36.18364
[3,] 41.16437
Moreover, if the Y is also the matrix, you can try this:
set.seed(1)
C = matrix(1:9, nrow = 3, ncol = 3)
Y = matrix(1:9, nrow = 3, ncol = 3)
mu = rnorm(3)
X = C%*%Y + mu
X <- matrix(0, nrow = 3, ncol = 3)
for (i in 1:3){
for (j in 1:3){
X[i, j] <- sum(C[i, ]*Y[, j]) + mu[i]
}
}
I have 2 given matrices
a1 <- matrix(c(0.4092951, 0.1611806, 0.4283178, 0.001206529), nrow =
1)
a2 <- matrix(c(0.394223557, 0.140443266, 0.463980790, 0.001352387),
nrow = 1)
I have an initial matrix
b <- matrix(c(0.4095868, 0.1612955, 0.4286231, 0.0004946572,
0, 0.2732351, 0.7260891, 0.0006757670,
0, 0, 0.9909494, 0.0090505527,
0, 0, 0, 1), nrow = 4, byrow = T)
I need to update 'b' such that
a1 %*% b = a2
The above is an optimization problem where the
objective function is to minimize
(a1 %*% b - a2)
which would drive the value of the sum(absolute value(a1 %*% b - a2)) to zero, subject to the constraints:
Lower triangle(b) = 0 ;
RowSum(b) = 1
## creating a data vector with a1 and a2
data = c(as.numeric(a1), as.numeric(a2))
## objective function
min_obj <- function(p){
## Creating a matrix to recreate 'b'
p1 <- matrix(rep(0, 16), nrow = 4)
k = 1
for(i in 1:nrow(p1)){
for (j in 1:ncol(p1)){
if(j >= i){
p1[i,j] <- p[k]
k = k+1
}
}
}
actual <- matrix(data[1:(length(data)/2)], nrow = 1)
pred <- matrix(data[(length(data)/ 2 + 1):length(data)], nrow = 1)
s <- (actual %*% p1) - pred
sum(abs(s))
}
## Initializing the initial values for b taking only non-zero values
init <- b[b>0]
opt <- optim(init, min_obj, control = list(trace = T), method =
"L-BFGS-B", lower = rep(0, length(init)), upper = rep(1,
length(init)))
transformed_b <- matrix(rep(0, 16), nrow = 4)
k = 1
for(i in 1:nrow(transformed_b)){
for (j in 1:ncol(transformed_b)){
if(j >= i){
transformed_b[i,j] <- opt$par[k]
k = k+1
}
}
}
transformed_b
The issue with transformed_b is that rowSum of the matrix is not 1. Any help is highly appreciated.
"optim" is the right choice. Since the row sums have to be 1, there are only 6 parameters, not 10 as in your attempt. The diagonal is uniquely determined by the values strictly above the diagonal.
a1 <- matrix(c(0.4092951, 0.1611806, 0.4283178, 0.001206529), nrow =
1)
a2 <- matrix(c(0.394223557, 0.140443266, 0.463980790, 0.001352387),
nrow = 1)
b <- matrix(c(0.4095868, 0.1612955, 0.4286231, 0.0004946572,
0, 0.2732351, 0.7260891, 0.0006757670,
0, 0, 0.9909494, 0.0090505527,
0, 0, 0, 1), nrow = 4, byrow = T)
#======================================================================
# Build an upper triangular matrix with rowsums 1:
B <- function(x)
{
X <- matrix(c(0,x[1:3],0,0,x[4:5],0,0,0,x[6],rep(0,4)),4,4,byrow=TRUE)
diag(X) <- 1-rowSums(X)
return(X)
}
#----------------------------------------------------------------------
# The function we want to minimize:
f <- function(x)
{
return (sum((a1%*%B(x) - a2)^2))
}
#----------------------------------------------------------------------
#Optimization:
opt <- optim( par = c(b[1,2:4],b[2,3:4],b[3,4]),
fn = f,
lower = rep(0,6),
method = "L-BFGS-B" )
optB <- B(opt$par)
Result:
> optB
[,1] [,2] [,3] [,4]
[1,] 0.9631998 0.03680017 0.0000000 0.0000000000
[2,] 0.0000000 0.77820700 0.2217930 0.0000000000
[3,] 0.0000000 0.00000000 0.9998392 0.0001608464
[4,] 0.0000000 0.00000000 0.0000000 1.0000000000
> a1 %*% optB - a2
[,1] [,2] [,3] [,4]
[1,] 9.411998e-06 5.07363e-05 1.684534e-05 -7.696464e-05
> rowSums(optB)
[1] 1 1 1 1
I chose the sum of squares instead of sum of absolute values, since it is differentiable. This makes it easier for "optim" to find the minimum, I guess.