I want to create a custom bootstrap function because I want to better understand what bootstrap is doing and it seems like the other bootstrap libraries out there does not solve my issue.
The Problem: I would like to create my own wald confidence interval function where it takes in the bootstrap data, outputs the confidence interval, test the confidence interval is within a range, and gets the coverage.
Right now, I am getting this type of error:
Error in bootresults[i,}<-waldCI(y=bootdata[i], n=numTrials):number of
items to replace is not a multiple of replacement length
The goal: My goal is to get the bootresults dataset to return 4 columns(p value,One that shows the upper bound, lower bound, and whether or not the p is in the interval) and get a graph similar to this one:
Wald interval chart
Code:
set.seed(42)
samples10 <- list()
i <- 1
while(i < 100) {
sample10[[i]] <- rbinom(1500, size=10, prob=i*.01) ## rows=1500 ;columns=10
i <- i + 1
}
sample10 <- data.frame(samples10)
colnames(sample10) <- c(seq(.01, .99, .01)) ## p-values
waldconfidenceinterval <- function(y, n, alpha=0.05) {
p <- colSums(y)/(n*200)
sd <- sqrt(p*((1 - p)/(n*200)))
z <- qnorm(c(alpha/2, 1 - alpha/2))
ci <- p + z*sd
return(ci)
}
B <- 200
numTrials <- 10
bootresults <- matrix(ncol=length(sample10), nrow=B) ## rows=200, cols=99
## empty matrix in the beginning
set.seed(42)
for(i in seq_len(B)) {
bootdata <- sample10[sample(B, replace=T), ]
bootresults[i, ] <- waldCI(y=bootdata[i], n=numTrials)
## Pseudocode:
# boot_test_data$in_interval <-
# ifelse(boot_test_data$lower1 < i/100 & i/100 < boot_test_data$upper1, 1, 0)
# coverage[i] <- sum(boot_test_data$in_interval) / length(boot_test_data$in_interval)
}
Any help is greatly appreciated since I am fairly new to R.
Looks like that you want to initialize a three-dimensional array bootresults rather than a two-dimensional matrix. In your waldCI() you may use colMeans.
waldCI <- function(y, alpha=0.05) {
p <- colMeans(y)
se <- sqrt(p*(1 - p)/nrow(y))
z <- qnorm(1 - alpha/2)
ci <- p + z*se %*% cbind(lower=-1, upper=1)
return(ci)
}
B <- 200
numTrials <- 10
## initialize array
bootresults1 <- array(dim=c(ncol(samples10), 4, B),
dimnames=list(c(), c("p.values", "lower", "upper", "in.int"), c()))
set.seed(42)
for(i in seq_len(B)) {
samp <- samples10[sample(nrow(samples10), numTrials, replace=F), ]
ci <- waldCI(samp)
bootresults1[,,i] <- cbind(p.values, ci, in.int=ci[, 1] < p.values & p.values < ci[, 2])
}
coverage <- rowMeans(bootresults[,4,])
plot(p.values, coverage, type="l", main="My Plot")
Similar approach, more R-ish, though:
p.values <- seq(.01, .99, .01)
set.seed(42)
samples10 <- `colnames<-`(sapply(p.values, function(pr) rbinom(1.5e3, 1, pr)), p.values)
BOOT <- function(numTrials, ...) {
samp <- samples10[sample(nrow(samples10), numTrials, replace=F), ]
ci <- waldCI(samp, ...)
cbind(p.values, ci, in.int=ci[, 1] < p.values & p.values < ci[, 2])
}
B <- 200
numTrials <- 10
set.seed(42)
bootresults2 <- replicate(B, BOOT(numTrials=10))
stopifnot(all.equal(bootresults1, bootresults2))
Data:
Note, that I used rbinom(..., size=1, ...) to create your sample data. The use of "p" as an object name suggested that the data should be binomial.
set.seed(42)
samples10 <- matrix(nrow=1500, ncol=99, dimnames=list(c(), c(seq(.01, .99, .01))))
i <- 1
while (i < 100) {
samples10[, i] <- rbinom(1500, size=1, prob=i*.01) ## rows=1500 ;columns=10
i <- i + 1
}
Without a while loop, you could proceed vectorized:
p.values <- seq(.01, .99, .01)
set.seed(42)
samples10 <- `colnames<-`(sapply(p.values, function(pr) rbinom(1.5e3, 1, pr)), p.values)
Related
I'm trying to run the following function mentioned below using OptimParallel in R on a certain data set. The code is as follows:
install.packages("optimParallel")
install.packages('parallel')
library(parallel)
library(optimParallel)
library(doParallel)
library(data.table)
library(Rlab)
library(HDInterval)
library(mvtnorm)
library(matrixStats)
library(dplyr)
library(cold)
## Bolus data:
data("bolus")
d1 <- bolus
d1$group <- ifelse(d1$group == "2mg",1,0)
colnames(d1) <- c("index",'group',"time","y")
d2 <- d1 %>% select(index, y, group, time)
colnames(d2) <- c('index','y','x1','x2') ### Final data
## Modification of the objective function:
## Another approach:
dpd_poi <- function(x,fixed = c(rep(FALSE,5))){
params <- fixed
dpd_1 <- function(p){
params[!fixed] <- p
alpha <- params[1]
beta_0 <- params[2]
beta_1 <- params[3]
beta_2 <- params[4]
rho <- params[5]
add_pi <- function(d){
k <- beta_0+(d[3]*beta_1)+(d[4]*beta_2)
k1 <- exp(k) ## for Poisson regression
d <- cbind(d,k1)
}
dat_split <- split(x , f = x$index)
result <- lapply(dat_split, add_pi)
result <- rbindlist(result)
result <- as.data.frame(result)
colnames(result) <- c('index','y','x1','x2','lamb')
result_split <- split(result, f = result$index)
expression <- function(d){
bin <- as.data.frame(combn(d$y , 2))
pr <- as.data.frame(combn(d$lamb , 2))
## Evaluation of the probabilities:
f_jk <- function(u,v){
dummy_func <- function(x,y){
ppois(x, lambda = y)
}
dummy_func_1 <- function(x,y){
ppois(x-1, lambda = y)
}
k <- mapply(dummy_func,u,v)
k_1 <- mapply(dummy_func_1,u,v)
inv1 <- inverseCDF(as.matrix(k), pnorm)
inv2 <- inverseCDF(as.matrix(k_1), pnorm)
mean <- rep(0,2)
lower <- inv2
upper <- inv1
corr <- diag(2)
corr[lower.tri(corr)] <- rho
corr[upper.tri(corr)] <- rho
prob <- pmvnorm(lower = lower, upper = upper, mean = mean, corr = corr)
prob <- (1+(1/alpha))*(prob^alpha)
## First expression: (changes for Poisson regression)
lam <- as.vector(t(v))
v1 <- rpois(1000, lambda = lam[1])
v2 <- rpois(1000, lambda = lam[2])
all_possib <- as.data.frame(rbind(v1,v2))
new_func <- function(u){
k <- mapply(dummy_func,u,v)
k_1 <- mapply(dummy_func_1,u,v)
inv1_1 <- inverseCDF(as.matrix(k), pnorm)
inv2_1 <- inverseCDF(as.matrix(k_1), pnorm)
mean1 <- rep(0,2)
lower1 <- inv2_1
upper1 <- inv1_1
corr1 <- diag(2)
corr1[lower.tri(corr1)] <- rho
corr1[upper.tri(corr1)] <- rho
prob1 <- pmvnorm(lower = lower1, upper = upper1, mean = mean1, corr = corr1)
prob1 <- prob1^(alpha)
}
val <- apply(all_possib, 2, new_func)
val_s <- mean(val) ## approximation
return(val_s - prob)
}
final_res <- mapply(f_jk, bin, pr)
final_value <- sum(final_res)
}
u <- sapply(result_split,expression)
return(sum(u))
}
}
## run the objective function:
cl <- makeCluster(25)
setDefaultCluster(cl=cl)
clusterExport(cl,c('d2','val'))
clusterEvalQ(cl,c(library(data.table), library(Rlab),library(HDInterval),library(mvtnorm),library(matrixStats),library(dplyr),library(cold)))
val <- dpd_poi(d2, c(0.5,FALSE,FALSE,FALSE,FALSE))
optimParallel(par = c(beta_0 =1, beta_1 =0.1 ,beta_2 = 1,rho=0.2),fn = val ,method = "L-BFGS-B",lower = c(-10,-10,-10,0),upper = c(Inf,Inf,Inf,1))
stopCluster(cl)
After running for some time, it returns the following error:
checkForRemoteErrors(val)
9 nodes produced errors; first error: missing value where TRUE/FALSE needed
However, when I make a minor change in the objective function (pick 2 random numbers from rpois instead of 1000) and run the same code using optim, it converges and gives me a proper result. This is a Monte Carlo simulation and it does not make sense to draw so few Poisson variables. I have to use optimParllel, otherwise, it takes way too long to converge. I could also run this code using simulated data.
I'm unable to figure out where the issue truly lies. I truly appreciate any help in this regard.
I have wrote a simulation code for censored observations to find bootstrap-t confidence interval. However, I encountered some problem where my 'btAlpha' and 'btLambda' cannot compute the correct answer hence I cannot go to the next step which is to calculate the total error probabilities.
This is my code :
#BOOTSTRAP-T (20%)
library(survival)
n <- 100
N <- 1000
alpha <- 1
lambda <- 0.5
alphaHat <- NULL
lambdaHat <- NULL
cp <- NULL
btAlpha <- matrix (NA, nrow=N, ncol=2)
btLambda <- matrix (NA, nrow=N, ncol=2)
for (i in 1:1000) {
u <- runif(n)
c1 <- rexp(n, 0.1)
t1 <- -(log(1 - u^(1/alpha))/lambda)
t <- pmin(t1, c1)
ci <- 1*(t1 < c1) #censored data
cp[i] <- length(ci[ci == 0])/n #censoring proportion
#FUNCTION TO CALL OUT
estBoot < -function(data, j) {
dat <- data [j, ]
data0 <- dat[which(dat$ci == 0), ] # right censored data
data1 <- dat[which(dat$ci == 1), ] # uncensored data
dat
#MAXIMUM LIKELIHOOD ESTIMATION
library(maxLik)
LLF <- function(para) {
alpha <- para[1]
lambda <- para[2]
a <- sum(log(alpha*lambda*(1 - exp(-lambda*data1$t1))^(alpha - 1)*
exp(-lambda*data1$t1)))
b <- sum(log(1 - (1 - exp(-lambda*data0$t1)^(alpha))))
l <- a + b
return(l)
}
mle <- maxLik(LLF, start=c(alpha=1, lambda=0.5))
alphaHat <- mle$estimate[1]
lambdaHat <- mle$estimate[2]
observedDi <- solve(-mle$hessian)
return(c(alphaHat, lambdaHat, observedDi[1, 1], observedDi[2, 2]))
}
library(boot)
bt <- boot(dat, estBoot, R=1000)
bootAlphaHat <- bt$t[, 1] #t is from bootstrap
bootAlphaHat0 <- bt$t0[1] #t0 is from original set
seAlphaHat <- sqrt(bt$t[, 2])
seAlphaHat0 <- sqrt(bt$t0[2]) #same as 'original' in bt
zAlpha <- (bootAlphaHat - bootAlphaHat0)/seAlphaHat
kAlpha <- zAlpha[order(zAlpha)]
ciAlpha <- c(kAlpha[25], kAlpha[975])
btAlpha[i, ] <- rev(bootAlphaHat0 - ciAlpha*seAlphaHat0)
bootLambdaHat <- bt$t[, 2]
bootLambdaHat0 <- bt$t0[2]
seLambdaHat <- sqrt(bt$t[, 4])
seLambdaHat0 <- sqrt(bt$t0[4])
zLambda <- (bootLambdaHat - bootLambdaHat0)/seLambdaHat
kLambda <- zLambda[order(zLambda)]
ciLambda <- c(kLambda[25], kLambda[975])
btLambda[i, ] <- rev(bootLambdaHat0 - ciLambda*seLambdaHat0)
}
leftAlpha <- sum(btAlpha[, 1] > alpha)/N
rightAlpha <- sum(btAlpha[, 2] < alpha)/N
totalEAlpha <- leftAlpha + rightAlpha
leftLambda <- sum(btLambda[, 1] > lambda)/N
rightLambda <- sum(btLambda[, 2] < lambda)/N
totalELambda <- leftLambda + rightLambda
#alpha=0.05
sealphaHat <- sqrt(0.05*(1 - 0.05)/N)
antiAlpha <- totalEAlpha > (0.05 + 2.58*sealphaHat)
conAlpha <- totalEAlpha < (0.05 - 2.58*sealphaHat )
asymAlpha <- (max(leftAlpha, rightAlpha)/min(leftAlpha, rightAlpha)) > 1.5
antiLambda <- totalELambda > (0.05 + 2.58 *sealphaHat)
conLambda <- totalELambda < (0.05 - 2.58 *sealphaHat)
asymLambda <- (max(leftLambda, rightLambda)/min(leftLambda, rightLambda)) > 1.5
anti <- antiAlpha + antiLambda
con <- conAlpha + conLambda
asym <- asymAlpha + asymLambda
My 'btAlpha[i,]' and 'btLambda[i,]' is two matrix data frame and only computed NA values hence I cannot calculate the next step which is total error probabilities etc. It should be simulated 1000 values through specified formula but I didnt get the desired output. I have tried to run this without using loops and same problems encountered. Do you guys have any idea? I could really use and truly appreciate your help.
I'm trying to create a simulation to calculate the confidence interval for a binomial proportion. So far I have a function that calculates the lower and upper bounds and I have generated and stored the type of data I want (in a matrix, I'm not sure about that).
How can I create a loop that generates samples with different sizes. I'd like to test how the formula performs when calculating the intervals with sample sizes n=10, 11, 12,... up to 100.
My code so far:
## functions that calculate lower and upper bounds
ll <- function(x, cl=0.95) {
n <- length(x)
p.est <- mean(x)
z = abs(qnorm((1-cl)/2))
return((p.est) - z*sqrt(p.est*(1-p.est)/n))
}
ul <- function(x, cl=0.95) {
n <- length(x)
p.est <- mean(x)
z = abs(qnorm((1-cl)/2))
return((p.est) + z*sqrt(p.est*(1-p.est)/n))
}
## my simulation for n=10 and 200 repetitions.
p <- 0.4
n <- 10
rep <- 200
dat <- rbinom(rep*n,1,p)
x <- matrix(dat, ncol=rep)
ll.res <- apply(x, 2, ll)
ul.res <- apply(x, 2, ul)
hits <- ll.res <= p & p <= ul.res
sum(hits==1)/rep
I'm not sure which values do you want to compare between different sample sizes. But I guess wrapping your simulation in a for and using lists to store the results should work:
nrep=200
hits=list()
value=NULL
ll.res = list()
ul.res = list()
ns = c(10:100)
for(i in 1:length(ns)){
p <- 0.4
n <- ns[i]
rep <- 200
dat <- rbinom(rep*n,1,p)
x <- matrix(dat, ncol=nrep)
ll.res[[i]] <- apply(x, 2, ll)
ul.res[[i]] <- apply(x, 2, ul,cl=0.95)
hits[[i]] <- ll.res[[i]] <= p & p <= ul.res[[i]]
value[i] = sum(hits[[i]]==1)/rep
}
I am trying to code gradient descent in R. The goal is to collect a data frame of each estimate so I can plot the algorithm's search through the parameter space.
I am using the built-in dataset data(cars) in R. Unfortunately something is way off in my function. The estimates just increase linearly with each iteration! But I cannot figure out where I err.
Any tips?
Code:
GradientDescent <- function(b0_start, b1_start, x, y, niter=10, alpha=0.1) {
# initialize
gradient_b0 = 0
gradient_b1 = 0
x <- as.matrix(x)
y <- as.matrix(y)
N = length(y)
results <- matrix(nrow=niter, ncol=2)
# gradient
for(i in 1:N){
gradient_b0 <- gradient_b0 + (-2/N) * (y[i] - (b0_start + b1_start*x[i]))
gradient_b1 <- gradient_b1 + (-2/N) * x[i] * (y[i] - (b0_start + b1_start*x[i]))
}
# descent
b0_hat <- b0_start
b1_hat <- b1_start
for(i in 1:niter){
b0_hat <- b0_hat - (alpha*gradient_b0)
b1_hat <- b1_hat - (alpha*gradient_b1)
# collect
results[i,] <- c(b0_hat,b1_hat)
}
# return
df <- data.frame(results)
colnames(df) <- c("b0", "b1")
return(df)
}
> test <- GradientDescent(0,0,cars$speed, cars$dist, niter=1000)
> head(test,2); tail(test,2)
b0 b1
1 8.596 153.928
2 17.192 307.856
b0 b1
999 8587.404 153774.1
1000 8596.000 153928.0
Here is a solution for cars dataset:
# dependent and independent variables
y <- cars$dist
x <- cars$speed
# number of iterations
iter_n <- 100
# initial value of the parameter
theta1 <- 0
# learning rate
alpha <- 0.001
m <- nrow(cars)
yhat <- theta1*x
# a tibble to record the parameter update and cost
library(tibble)
results <- data_frame(theta1 = as.numeric(),
cost = NA,
iteration = 1)
# run the gradient descent
for (i in 1:iter_n){
theta1 <- theta1 - alpha * ((1 / m) * (sum((yhat - y) * x)))
yhat <- theta1*x
cost <- (1/m)*sum((yhat-y)^2)
results[i, 1] = theta1
results[i, 2] <- cost
results[i, 3] <- i
}
# print the parameter value after the defined iteration
print(theta1)
# 2.909132
Checking whether cost is decreasing:
library(ggplot2)
ggplot(results, aes(x = iteration, y = cost))+
geom_line()+
geom_point()
I wrote a more detailed blog post here.
I have written a custom likelihood function that fits a multi-data model that integrates mark-recapture and telemetry data (sensu Royle et al. 2013 Methods in Ecology and Evolution). The likelihood function is designed to be flexible in terms of whether and how many covariates are specified for different linear models in different likelihood components which is determined by values supplied as function arguments (i.e., data matrices "detcovs" and "dencovs" in my code). The likelihood function works when I directly supply it to optimization functions (e.g., optim or nlm), but does not play nice with the mle2 function in the bbmle package. My problem is that I continually run into the following error: "some named arguments in 'start' are not arguments to the specified log-likelihood function". This is my first attempt at writing custom likelihood functions so I'm sure there are general coding conventions of which I'm unaware that make such tasks much more efficient and amendable to the mle2 function. Below is my likelihood function, code creating the staring value objects, and code calling the mle2 function. Any advice how to solve the error problem and general comments on writing cleaner functions is welcome. Many thanks in advance.
Edit: As requested, I have simplified the likelihood function and provided code to simulate reproducible data to which the model can be fit. Included in the simulation code are 2 custom functions and use of the raster function from the raster package. Hopefully, I have sufficiently simplified everything to enable others to troubleshoot. Again, many thanks for your help!
Jared
Likelihood function:
CSCR.RSF.intlik2.EXAMPLE <- function(alpha0,sigma,alphas=NULL,betas=NULL,n0,yscr=NULL,K=NULL,X=X,trapcovs=NULL,Gden=NULL,Gdet=NULL,ytel=NULL,stel=NULL,
dencovs=NULL,detcovs=NULL){
#
# this version of the code handles a covariate on log(Density). This is starting value 5
#
# start = vector of starting values
# yscr = nind x ntraps encounter matrix
# K = number of occasions
# X = trap locations
# Gden = matrix with grid cell coordinates for density raster
# Gdet = matrix with gride cell coordinates for RSF raster
# dencovs = all covariate values for all nGden pixels in density raster
# trapcovs = covariate value at trap locations
# detcovs = all covariate values for all nGrsf pixels in RSF raster
# ytel = nguys x nGdet matrix of telemetry fixes in each nGdet pixels
# stel = home range center of telemetered individuals, IF you wish to estimate it. Not necessary
# alphas = starting values for RSF/detfn coefficients excluding sigma and intercept
# alpha0 = starting values for RSF/detfn intercept
# sigma = starting value for RSF/detfn sigma
# betas = starting values for density function coefficients
# n0 = starting value for number of undetected individuals on log scale
#
n0 = exp(n0)
nGden = nrow(Gden)
D = e2dist(X,Gden)
nGdet <- nrow(Gdet)
alphas = alphas
loglam = alpha0 -(1/(2*sigma*sigma))*D*D + as.vector(trapcovs%*%alphas) # ztrap recycled over nG
psi = exp(as.vector(dencovs%*%betas))
psi = psi/sum(psi)
probcap = 1-exp(-exp(loglam))
#probcap = (exp(theta0)/(1+exp(theta0)))*exp(-theta1*D*D)
Pm = matrix(NA,nrow=nrow(probcap),ncol=ncol(probcap))
ymat = yscr
ymat = rbind(yscr,rep(0,ncol(yscr)))
lik.marg = rep(NA,nrow(ymat))
for(i in 1:nrow(ymat)){
Pm[1:length(Pm)] = (dbinom(rep(ymat[i,],nGden),rep(K,nGden),probcap[1:length(Pm)],log=TRUE))
lik.cond = exp(colSums(Pm))
lik.marg[i] = sum( lik.cond*psi )
}
nv = c(rep(1,length(lik.marg)-1),n0)
part1 = lgamma(nrow(yscr)+n0+1) - lgamma(n0+1)
part2 = sum(nv*log(lik.marg))
out = -1*(part1+ part2)
lam = t(exp(a0 - (1/(2*sigma*sigma))*t(D2)+ as.vector(detcovs%*%alphas)))# recycle zall over all ytel guys
# lam is now nGdet x nG!
denom = rowSums(lam)
probs = lam/denom # each column is the probs for a guy at column [j]
tel.loglik = -1*sum( ytel*log(probs) )
out = out + tel.loglik
out
}
Data simulation code:
library(raster)
library(bbmle)
e2dist <- function (x, y){
i <- sort(rep(1:nrow(y), nrow(x)))
dvec <- sqrt((x[, 1] - y[i, 1])^2 + (x[, 2] - y[i, 2])^2)
matrix(dvec, nrow = nrow(x), ncol = nrow(y), byrow = F)
}
spcov <- function(R) {
v <- sqrt(nrow(R))
D <- as.matrix(dist(R))
V <- exp(-D/2)
cov1 <- t(chol(V)) %*% rnorm(nrow(R))
Rd <- as.data.frame(R)
colnames(Rd) <- c("x", "y")
Rd$C <- as.numeric((cov1 - mean(cov1)) / sd(cov1))
return(Rd)
}
set.seed(1234)
co <- seq(0.3, 0.7, length=5)
X <- cbind(rep(co, each=5),
rep(co, times=5))
B <- 10
co <- seq(0, 1, length=B)
Z <- cbind(rep(co, each=B), rep(co, times=B))
dencovs <- cbind(spcov(Z),spcov(Z)[,3]) # ordered as reading raster image from left to right, bottom to top
dimnames(dencovs)[[2]][3:4] <- c("dencov1","dencov2")
denr.list <- vector("list",2)
for(i in 1:2){
denr.list[[i]] <- raster(
list(x=seq(0,1,length=10),
y=seq(0,1,length=10),
z=t(matrix(dencovs[,i+2],10,10,byrow=TRUE)))
)
}
B <- 20
co <- seq(0, 1, length=B)
Z <- cbind(rep(co, each=B), rep(co, times=B))
detcovs <- cbind(spcov(Z),spcov(Z)[,3]) # ordered as reading raster image from left to right, bottom to top
dimnames(detcovs)[[2]][3:4] <- c("detcov1","detcov2")
detcov.raster.list <- vector("list",2)
trapcovs <- matrix(0,J,2)
for(i in 1:2){
detr.list[[i]] <- raster(
list(x=seq(0,1,length=20),
y=seq(0,1,length=20),
z=t(matrix(detcovs[,i+2],20,20,byrow=TRUE)))
)
trapcovs[,i] <- extract(detr.list[[i]],X)
}
alpha0 <- -3
sigma <- 0.15
alphas <- c(1,-1)
beta0 <- 3
betas <- c(-1,1)
pixelArea <- (dencovs$y[2] - dencovs$y[1])^2
mu <- exp(beta0 + as.matrix(dencovs[,3:4])%*%betas)*pixelArea
EN <- sum(mu)
N <- rpois(1, EN)
pi <- mu/sum(mu)
s <- dencovs[sample(1:nrow(dencovs), size=N, replace=TRUE, prob=pi),1:2]
J <- nrow(X)
K <- 10
yc <- d <- p <- matrix(NA, N, J)
D <- e2dist(s,X)
loglam <- t(alpha0 - t((1/(2*sigma*sigma))*D*D) + as.vector(trapcovs%*%alphas))
p <- 1-exp(-exp(loglam))
for(i in 1:N) {
for(j in 1:J) {
yc[i,j] <- rbinom(1, K, p[i,j])
}
}
detected <- apply(yc>0, 1, any)
yscr <- yc[detected,]
ntel <- 5
nfixes <- 100
poss.tel <- which(s[,1]>0.2 & s[,1]<0.8 & s[,2]>0.2 & s[,2]<0.8)
stel.id <- sample(poss.tel,ntel)
stel <- s[stel.id,]
ytel <- matrix(NA,ntel,nrow(detcovs))
d <- e2dist(stel,detcovs[,1:2])
lam <- t(exp(1 - t((1/(2*sigma*sigma))*d*d) + as.vector(as.matrix(detcovs[,3:4])%*%alphas)))
for(i in 1:ntel){
ytel[i,] <- rmultinom(1,nfixes,lam[i,]/sum(lam[i,]))
}
Specify starting values and call mle2 function:
start1 <- list(alpha0=alpha0,sigma=sigma,alphas=alphas,betas=betas,n0=log(N-nrow(yscr)))
parnames(CSCR.RSF.intlik2.EXAMPLE) <- names(start)
out1 <- mle2(CSCR.RSF.intlik2.EXAMPLE,start=start1,method="SANN",optimizer="optim",
data=list(yscr=yscr,K=K,X=X,trapcovs=trapcovs,Gden=dencovs[,1:2],Gdet=detcovs[,1:2],
ytel=ytel,stel=stel,dencovs=as.matrix(dencovs[,3:4]),detcovs=as.matrix(detcovs[,3:4]))
)