I have a couple of questions regarding to the piece of code shown below, the function called "Func1" will return a matrix as a result, the size of the matrix will be 50 rows and 15 columns, I called it "M", and "M2" is just the transpose of it. W0 is the initial value for the next part of the code, if I run the function called "Rowresult", then it also give me a 50*15 matrix.
My first question is: if I want to run the "Rowresult" function for different W0 values,such as W0 = 10,20,30. and I want to have 3 matrices in the size of 50*15 with different W0 values as results,how could I achieve it?
My second question is : if you tried my code in R, you will see a matrix called "wealth_result 2" as a result. once I got this big matrix, I would like to divide it (50*15 matrix) into three same size matrix, each matrix has a size of 50*5 (so they share the same rows but different columns, the first matrix takes the first 5 columns, the second takes 6-10 columns, third one takes 11-15 columns),and then I want to work out how many positive rows (rows with all numbers positive) among each of the 50 *5 matrix? How could I achieve this?
N=15
func1<-function(N){
alpha1 = 8.439e-02
beta1 = 8.352e-01
mu = 7.483e-03
omega = 1.343e-04
X_0 = -3.092031e-02
sigma_0 = 0.03573968
eps = rt (N,7.433e+00)
# loops
Xn= numeric (N)
sigma= numeric (N)
sigma[1] = sigma_0
Xn[1] = X_0
for (t in 2:N){
sigma[t] = sqrt (omega + alpha1 * (Xn[t-1])^2 + beta1* (sigma[t-1])^2)
Xn[t] = sigma[t] * eps[t]
}
Y = mu + Xn
}
# return matrix
M<-replicate(50,func1(N))
# returns matrix
M2<-t(M)
View(M2)
# wealth with initial wealth 10
W0=10
# 10,20,30,40
r= c(0.101309031, -0.035665516, -0.037377270, -0.005928941, 0.036612849,
0.062404039, 0.124240950, -0.034843633, 0.004770613, 0.005018101,
0.097685945, -0.090660099, 0.004863099, 0.029215984, 0.020835366)
Rowresult<- function(r){
const = exp(cumsum(r))
exp.cum = cumsum(1/const)
wealth=const*(W0 - exp.cum)
wealth
}
# wealth matrix
wealth_result <-apply(M2,1,Rowresult)
wealth_result2 <-t(wealth_result )
View(wealth_result2)
This delivers the desired counds of (all) "positive rows":
> sapply(1:3, function(m) sum( rowSums( wealth_result2[ , (1:5)+(m-1)*5 ] >0 )) )
[1] 250 230 2
Related
While working on an Rcpp program, I used the sample() function, which gave me the following error: "NAs not allowed in probability." I traced this issue to the fact that the probability vector I used had NA values in it. I have no idea how. Below is some R code that captures the errors:
n.0=20
n.1=20
n.reps=1
beta0.vals=rep(seq(-.3,.1,,n.0),n.reps)
beta1.vals=rep(seq(-7,0,,n.1),n.reps)
beta.grd=as.matrix(expand.grid(beta0.vals,beta1.vals))
n.rnd=200
beta.rnd.grd=cbind(runif(n.rnd,min(beta0.vals),max(beta0.vals)),runif(n.rnd,min(beta1.vals),max(beta1.vals)))
beta.grd=rbind(beta.grd,beta.rnd.grd)
N = 22670
count = 0
for(i in 1:dim(beta.grd)[1]){ # iterate through 600 possible beta values in beta grid
beta.ind = 0 # indicator for current pair of beta values
for(j in 1:N){ # iterate through all possible Nsums
logit = beta.grd[i,1]/N*(j - .1*N)^2 + beta.grd[i,2];
phi01 = exp(logit)/(1 + exp(logit))
if(is.na(phi01)){
count = count + 1
}
}
}
cat("Total number of invalid probabilities: ", count)
Here, $\beta_0 \in (-0.3, 0.1), \beta_1 \in (-7, 0), N = 22670, N_\text{sum} \in (1, N)$. Note that $N$ and $N_\text{sum}$ are integers, whereas the beta values may not be.
Since mathematically, $\phi_{01} \in (0,1)$, I'm assuming that NAs are arising because R is not liking extremely small values. I am receiving an overwhelming amount of NA values, too. More so than numbers. Why would I be getting NAs in this code?
Include print(logit) next to count = count + 1 and you will find lots of logit > 1000 values. exp(1000) == Inf so you divide Inf by Inf which will get you a NaN and NaN is NA:
> exp(500)
[1] 1.403592e+217
> Inf/Inf
[1] NaN
> is.na(NaN)
[1] TRUE
So your problems are not too small but to large numbers coming first out of the evaluation of exp(x) with x larger then roughly 700:
> exp(709)
[1] 8.218407e+307
> exp(710)
[1] Inf
Bernhard's answer correctly identifies the problem:
If logit is large, exp(logit) = Inf.
Here is a solution:
for(i in 1:dim(beta.grd)[1]){ # iterate through 600 possible beta values in beta grid
beta.ind = 0 # indicator for current pair of beta values
for(j in 1:N){ # iterate through all possible Nsums
logit = beta.grd[i,1]/N*(j - .1*N)^2 + beta.grd[i,2];
## This one isn't great because exp(logit) can be very large
# phi01 = exp(logit)/(1 + exp(logit))
## So, we say instead
## phi01 = 1 / ( 1 + exp(-logit) )
phi01 = plogis(logit)
if(is.na(phi01)){
count = count + 1
}
}
}
cat("Total number of invalid probabilities: ", count)
# Total number of invalid probabilities: 0
We can use the more stable 1 / (1 + exp(-logit)
(to convince yourself of this, multiply your expression with exp(-logit) / exp(-logit)),
and luckily either way, R has a builtin function plogis() that can calculate these probabilities quickly and accurately.
You can see from the help file (?plogis) that this function evaluates the expression I gave, but you can also double check to assure yourself
x = rnorm(1000)
y = 1 / (1 + exp(-x))
z = plogis(x)
all.equal(y, z)
[1] TRUE
I have to calculate cosine similarity (patient similarity metric) in R between 48k patients data with some predictive variables. Here is the equation: PSM(P1,P2) = P1.P2/ ||P1|| ||P2||
where P1 and P2 are the predictor vectors corresponding to two different patients, where for example P1 index patient and P2 will be compared with index (P1) and finally pairwise patient similarity metric PSM(P1,P2) will be calculated.
This process will go on for all 48k patients.
I have added sample data-set for 300 patients in a .csv file. Please find the sample data-set here.https://1drv.ms/u/s!AhoddsPPvdj3hVTSbosv2KcPIx5a
First things first: You can find more rigorous treatments of cosine similarity at either of these posts:
Find cosine similarity between two arrays
Creating co-occurrence matrix
Now, you clearly have a mixture of data types in your input, at least
decimal
integer
categorical
I suspect that some of the integer values are Booleans or additional categoricals. Generally, it will be up to you to transform these into continuous numerical vectors if you want to use them as input into the similarity calculation. For example, what's the distance between admission types ELECTIVE and EMERGENCY? Is it a nominal or ordinal variable? I will only be modelling the columns that I trust to be numerical dependent variables.
Also, what have you done to ensure that some of your columns don't correlate with others? Using just a little awareness of data science and biomedical terminology, it seems likely that the following are all correlated:
diasbp_max, diasbp_min, meanbp_max, meanbp_min, sysbp_max and sysbp_min
I suggest going to a print shop and ordering a poster-size printout of psm_pairs.pdf. :-) Your eyes are better at detecting meaningful (but non-linear) dependencies between variable. Including multiple measurements of the same fundamental phenomenon may over-weight that phenomenon in your similarity calculation. Don't forget that you can derive variables like
diasbp_rage <- diasbp_max - diasbp_min
Now, I'm not especially good at linear algebra, so I'm importing a cosine similarity function form the lsa text analysis package. I'd love to see you write out the formula in your question as an R function. I would write it to compare one row to another, and use two nested apply loops to get all comparisons. Hopefully we'll get the same results!
After calculating the similarity, I try to find two different patients with the most dissimilar encounters.
Since you're working with a number of rows that's relatively large, you'll want to compare various algorithmic methodologies for efficiency. In addition, you could use SparkR/some other Hadoop solution on a cluster, or the parallel package on a single computer with multiple cores and lots of RAM. I have no idea whether the solution I provided is thread-safe.
Come to think of it, the transposition alone (as I implemented it) is likely to be computationally costly for a set of 1 million patient-encounters. Overall, (If I remember my computational complexity correctly) as the number of rows in your input increases, the performance could degrade exponentially.
library(lsa)
library(reshape2)
psm_sample <- read.csv("psm_sample.csv")
row.names(psm_sample) <-
make.names(paste0("patid.", as.character(psm_sample$subject_id)), unique = TRUE)
temp <- sapply(psm_sample, class)
temp <- cbind.data.frame(names(temp), as.character(temp))
names(temp) <- c("variable", "possible.type")
numeric.cols <- (temp$possible.type %in% c("factor", "integer") &
(!(grepl(
pattern = "_id$", x = temp$variable
))) &
(!(
grepl(pattern = "_code$", x = temp$variable)
)) &
(!(
grepl(pattern = "_type$", x = temp$variable)
))) | temp$possible.type == "numeric"
psm_numerics <- psm_sample[, numeric.cols]
row.names(psm_numerics) <- row.names(psm_sample)
psm_numerics$gender <- as.integer(psm_numerics$gender)
psm_scaled <- scale(psm_numerics)
pair.these.up <- psm_scaled
# checking for independence of variables
# if the following PDF pair plot is too big for your computer to open,
# try pair-plotting some random subset of columns
# keep.frac <- 0.5
# keep.flag <- runif(ncol(psm_scaled)) < keep.frac
# pair.these.up <- psm_scaled[, keep.flag]
# pdf device sizes are in inches
dev <-
pdf(
file = "psm_pairs.pdf",
width = 50,
height = 50,
paper = "special"
)
pairs(pair.these.up)
dev.off()
#transpose the dataframe to get the
#similarity between patients
cs <- lsa::cosine(t(psm_scaled))
# this is super inefficnet, because cs contains
# two identical triangular matrices
cs.melt <- melt(cs)
cs.melt <- as.data.frame(cs.melt)
names(cs.melt) <- c("enc.A", "enc.B", "similarity")
extract.pat <- function(enc.col) {
my.patients <-
sapply(enc.col, function(one.pat) {
temp <- (strsplit(as.character(one.pat), ".", fixed = TRUE))
return(temp[[1]][[2]])
})
return(my.patients)
}
cs.melt$pat.A <- extract.pat(cs.melt$enc.A)
cs.melt$pat.B <- extract.pat(cs.melt$enc.B)
same.pat <- cs.melt[cs.melt$pat.A == cs.melt$pat.B ,]
different.pat <- cs.melt[cs.melt$pat.A != cs.melt$pat.B ,]
most.dissimilar <-
different.pat[which.min(different.pat$similarity),]
dissimilar.pat.frame <- rbind(psm_numerics[rownames(psm_numerics) ==
as.character(most.dissimilar$enc.A) ,],
psm_numerics[rownames(psm_numerics) ==
as.character(most.dissimilar$enc.B) ,])
print(t(dissimilar.pat.frame))
which gives
patid.68.49 patid.9
gender 1.00000 2.00000
age 41.85000 41.79000
sysbp_min 72.00000 106.00000
sysbp_max 95.00000 217.00000
diasbp_min 42.00000 53.00000
diasbp_max 61.00000 107.00000
meanbp_min 52.00000 67.00000
meanbp_max 72.00000 132.00000
resprate_min 20.00000 14.00000
resprate_max 35.00000 19.00000
tempc_min 36.00000 35.50000
tempc_max 37.55555 37.88889
spo2_min 90.00000 95.00000
spo2_max 100.00000 100.00000
bicarbonate_min 22.00000 26.00000
bicarbonate_max 22.00000 30.00000
creatinine_min 2.50000 1.20000
creatinine_max 2.50000 1.40000
glucose_min 82.00000 129.00000
glucose_max 82.00000 178.00000
hematocrit_min 28.10000 37.40000
hematocrit_max 28.10000 45.20000
potassium_min 5.50000 2.80000
potassium_max 5.50000 3.00000
sodium_min 138.00000 136.00000
sodium_max 138.00000 140.00000
bun_min 28.00000 16.00000
bun_max 28.00000 17.00000
wbc_min 2.50000 7.50000
wbc_max 2.50000 13.70000
mingcs 15.00000 15.00000
gcsmotor 6.00000 5.00000
gcsverbal 5.00000 0.00000
gcseyes 4.00000 1.00000
endotrachflag 0.00000 1.00000
urineoutput 1674.00000 887.00000
vasopressor 0.00000 0.00000
vent 0.00000 1.00000
los_hospital 19.09310 4.88130
los_icu 3.53680 5.32310
sofa 3.00000 5.00000
saps 17.00000 18.00000
posthospmort30day 1.00000 0.00000
Usually I wouldn't add a second answer, but that might be the best solution here. Don't worry about voting on it.
Here's the same algorithm as in my first answer, applied to the iris data set. Each row contains four spatial measurements of the flowers form three different varieties of iris plants.
Below that you will find the iris analysis, written out as nested loops so you can see the equivalence. But that's not recommended for production with large data sets.
Please familiarize yourself with starting data and all of the intermediate dataframes:
The input iris data
psm_scaled (the spatial measurements, scaled to mean=0, SD=1)
cs (the matrix of pairwise similarities)
cs.melt (the pairwise similarities in long format)
At the end I have aggregated the mean similarities for all comparisons between one variety and another. You will see that comparisons between individuals of the same variety have mean similarities approaching 1, and comparisons between individuals of the same variety have mean similarities approaching negative 1.
library(lsa)
library(reshape2)
temp <- iris[, 1:4]
iris.names <- paste0(iris$Species, '.', rownames(iris))
psm_scaled <- scale(temp)
rownames(psm_scaled) <- iris.names
cs <- lsa::cosine(t(psm_scaled))
# this is super inefficient, because cs contains
# two identical triangular matrices
cs.melt <- melt(cs)
cs.melt <- as.data.frame(cs.melt)
names(cs.melt) <- c("enc.A", "enc.B", "similarity")
names(cs.melt) <- c("flower.A", "flower.B", "similarity")
class.A <-
strsplit(as.character(cs.melt$flower.A), '.', fixed = TRUE)
cs.melt$class.A <- sapply(class.A, function(one.split) {
return(one.split[1])
})
class.B <-
strsplit(as.character(cs.melt$flower.B), '.', fixed = TRUE)
cs.melt$class.B <- sapply(class.B, function(one.split) {
return(one.split[1])
})
cs.melt$comparison <-
paste0(cs.melt$class.A , '_vs_', cs.melt$class.B)
cs.agg <-
aggregate(cs.melt$similarity, by = list(cs.melt$comparison), mean)
print(cs.agg[order(cs.agg$x),])
which gives
# Group.1 x
# 3 setosa_vs_virginica -0.7945321
# 7 virginica_vs_setosa -0.7945321
# 2 setosa_vs_versicolor -0.4868352
# 4 versicolor_vs_setosa -0.4868352
# 6 versicolor_vs_virginica 0.3774612
# 8 virginica_vs_versicolor 0.3774612
# 5 versicolor_vs_versicolor 0.4134413
# 9 virginica_vs_virginica 0.7622797
# 1 setosa_vs_setosa 0.8698189
If you’re still not comfortable with performing lsa::cosine() on a scaled, numerical dataframe, we can certainly do explicit pairwise calculations.
The formula you gave for PSM, or cosine similarity of patients, is expressed in two formats at Wikipedia
Remembering that vectors A and B represent the ordered list of attributes for PatientA and PatientB, the PSM is the dot product of A and B, divided by (the scalar product of [the magnitude of A] and [the magnitude of B])
The terse way of saying that in R is
cosine.sim <- function(A, B) { A %*% B / sqrt(A %*% A * B %*% B) }
But we can rewrite that to look more similar to your post as
cosine.sim <- function(A, B) { A %*% B / (sqrt(A %*% A) * sqrt(B %*% B)) }
I guess you could even re-write that (the calculations of similarity between a single pair of individuals) as a bunch of nested loops, but in the case of a manageable amount of data, please don’t. R is highly optimized for operations on vectors and matrices. If you’re new to R, don’t second guess it. By the way, what happened to your millions of rows? This will certainly be less stressful now that your down to tens of thousands.
Anyway, let’s say that each individual only has two elements.
individual.1 <- c(1, 0)
individual.2 <- c(1, 1)
So you can think of individual.1 as a line that passes between the origin (0,0) and (0, 1) and individual.2 as a line that passes between the origin and (1, 1).
some.data <- rbind.data.frame(individual.1, individual.2)
names(some.data) <- c('element.i', 'element.j')
rownames(some.data) <- c('individual.1', 'individual.2')
plot(some.data, xlim = c(-0.5, 2), ylim = c(-0.5, 2))
text(
some.data,
rownames(some.data),
xlim = c(-0.5, 2),
ylim = c(-0.5, 2),
adj = c(0, 0)
)
segments(0, 0, x1 = some.data[1, 1], y1 = some.data[1, 2])
segments(0, 0, x1 = some.data[2, 1], y1 = some.data[2, 2])
So what’s the angle between vector individual.1 and vector individual.2? You guessed it, 0.785 radians, or 45 degrees.
cosine.sim <- function(A, B) { A %*% B / (sqrt(A %*% A) * sqrt(B %*% B)) }
cos.sim.result <- cosine.sim(individual.1, individual.2)
angle.radians <- acos(cos.sim.result)
angle.degrees <- angle.radians * 180 / pi
print(angle.degrees)
# [,1]
# [1,] 45
Now we can use the cosine.sim function I previously defined, in two nested loops, to explicitly calculate the pairwise similarities between each of the iris flowers. Remember, psm_scaled has already been defined as the scaled numerical values from the iris dataset.
cs.melt <- lapply(rownames(psm_scaled), function(name.A) {
inner.loop.result <-
lapply(rownames(psm_scaled), function(name.B) {
individual.A <- psm_scaled[rownames(psm_scaled) == name.A, ]
individual.B <- psm_scaled[rownames(psm_scaled) == name.B, ]
similarity <- cosine.sim(individual.A, individual.B)
return(list(name.A, name.B, similarity))
})
inner.loop.result <-
do.call(rbind.data.frame, inner.loop.result)
names(inner.loop.result) <-
c('flower.A', 'flower.B', 'similarity')
return(inner.loop.result)
})
cs.melt <- do.call(rbind.data.frame, cs.melt)
Now we repeat the calculation of cs.melt$class.A, cs.melt$class.B, and cs.melt$comparison as above, and calculate cs.agg.from.loops as the mean similarity between the various types of comparisons:
cs.agg.from.loops <-
aggregate(cs.agg.from.loops$similarity, by = list(cs.agg.from.loops $comparison), mean)
print(cs.agg.from.loops[order(cs.agg.from.loops$x),])
# Group.1 x
# 3 setosa_vs_virginica -0.7945321
# 7 virginica_vs_setosa -0.7945321
# 2 setosa_vs_versicolor -0.4868352
# 4 versicolor_vs_setosa -0.4868352
# 6 versicolor_vs_virginica 0.3774612
# 8 virginica_vs_versicolor 0.3774612
# 5 versicolor_vs_versicolor 0.4134413
# 9 virginica_vs_virginica 0.7622797
# 1 setosa_vs_setosa 0.8698189
Which, I believe is identical to the result we got with lsa::cosine.
So what I'm trying to say is... why wouldn't you use lsa::cosine?
Maybe you should be more concerned with
selection of variables, including removal of highly correlated variables
scaling/normalizing/standardizing the data
performance with a large input data set
identifying known similars and dissimilars for quality control
as previously addressed
I am using the following R code, taken from a published paper (citation below). This is the code:
int2=function(x,r,n,p) {
(1+x)^((n-1-p)/2)*(1+(1-r^2)*x)^(-(n-1)/2)*x^(-3/2)*exp(-n/(2*x))}
integrate(f=int2,lower=0,upper=Inf,n=530,r=sqrt(.245),p=3, stop.on.error=FALSE)
When I run it, I get the error "non-finite function value". Yet Maple is able to compute this as 4.046018765*10^27.
I tried using "integral" in package pracma, which gives me a different error:
Error in if (delta < tol) break : missing value where TRUE/FALSE needed
The overall goal is to compute a ratio of two integrals, as described in Wetzels & Wagenmakers (2012) "A default Bayesian hypothesis test for correlations" (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3505519/). The entire function is as follows:
jzs.pcorbf = function(r0, r1, p0, p1, n) {
int = function(r,n,p,g) {
(1+g)^((n-1-p)/2)*(1+(1-r^2)*g)^(-(n-1)/2)*g^(-3/2)*exp(-n/(2*g))};
bf10=integrate(int, lower=0,upper=Inf,r=r1,p=p1,n=n)$value/
integrate(int,lower=0,upper=Inf,r=r0,p=p0,n=n)$value;
return(bf10)
}
Thanks!
The issue is that your integral function is generating NaN values when called with x values in its domain. You're integrating from 0 to Infinity, so let's check a valid x value of 1000:
int2(1000, sqrt(0.245), 530, 3)
# [1] NaN
Your objective multiplies four pieces:
x <- 1000
r <- sqrt(0.245)
n <- 530
p <- 3
(1+x)^((n-1-p)/2)
# [1] Inf
(1+(1-r^2)*x)^(-(n-1)/2)
# [1] 0
x^(-3/2)
# [1] 3.162278e-05
exp(-n/(2*x))
# [1] 0.7672059
We can now see that the issue is that you're multiplying infinity by 0 (or rather something numerically equal to infinity times something numerically equal to 0), which is causing the numerical issues. Instead of calculating a*b*c*d, it will be more stable to calculate exp(log(a) + log(b) + log(c) + log(d)) (using the identity that log(a*b*c*d) = log(a)+log(b)+log(c)+log(d)). One other quick note -- the value x=0 needs a special case.
int3 = function(x, r, n, p) {
loga <- ((n-1-p)/2) * log(1+x)
logb <- (-(n-1)/2) * log(1+(1-r^2)*x)
logc <- -3/2 * log(x)
logd <- -n/(2*x)
return(ifelse(x == 0, 0, exp(loga + logb + logc + logd)))
}
integrate(f=int3,lower=0,upper=Inf,n=530,r=sqrt(.245),p=3, stop.on.error=FALSE)
# 1.553185e+27 with absolute error < 2.6e+18
I would like to construct a sequence with length 50 of the following type:
Xn+1=4*Xn*(1-Xn). For your information, this is the Logistic Map for r=4. In the case of the Logistic Map with parameter r = 4 and an initial state in (0,1), the attractor is also the interval (0,1) and the probability measure corresponds to the beta distribution with parameters a = 0.5 and b = 0.5. (The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations). How can I do this in R?
There are some ready to use solution on the net. I cite the general solution of mage's blog where you can find more detailed description.
logistic.map <- function(r, x, N, M){
## r: bifurcation parameter
## x: initial value
## N: number of iteration
## M: number of iteration points to be returned
z <- 1:N
z[1] <- x
for(i in c(1:(N-1))){
z[i+1] <- r *z[i] * (1 - z[i])
}
## Return the last M iterations
z[c((N-M):N)]
}
For OP example:
logistic.map(4,0.2,50,49)
This isn't really an R question, is it? More basic programming. Anyway, you probably need an accumulator and a value to process.
values <- 0.2 ## this accumulates as a vector, starting with 0.2
xn <- values ## xn gets the first value
for (it in 2:50) { ## start the loop from the second iteration
xn <- 4L*xn*(1L-xn) ## perform the sequence function
values <- c(values, xn) ## add the new value to the vector
}
values
# [1] 0.2000000000 0.6400000000 0.9216000000 0.2890137600 0.8219392261 0.5854205387 0.9708133262 0.1133392473 0.4019738493 0.9615634951 0 .1478365599 0.5039236459
# [13] 0.9999384200 0.0002463048 0.0009849765 0.0039360251 0.0156821314 0.0617448085 0.2317295484 0.7121238592 0.8200138734 0.5903644834 0 .9673370405 0.1263843622
# [25] 0.4416454208 0.9863789723 0.0537419811 0.2034151221 0.6481496409 0.9122067356 0.3203424285 0.8708926280 0.4497546341 0.9899016128 0 .0399856390 0.1535471506
# [37] 0.5198816927 0.9984188732 0.0063145074 0.0250985376 0.0978744041 0.3531800204 0.9137755744 0.3151590962 0.8633353611 0.4719496615 0 .9968527140 0.0125495222
# [49] 0.0495681269 0.1884445109
I am using the following R code, taken from a published paper (citation below). This is the code:
int2=function(x,r,n,p) {
(1+x)^((n-1-p)/2)*(1+(1-r^2)*x)^(-(n-1)/2)*x^(-3/2)*exp(-n/(2*x))}
integrate(f=int2,lower=0,upper=Inf,n=530,r=sqrt(.245),p=3, stop.on.error=FALSE)
When I run it, I get the error "non-finite function value". Yet Maple is able to compute this as 4.046018765*10^27.
I tried using "integral" in package pracma, which gives me a different error:
Error in if (delta < tol) break : missing value where TRUE/FALSE needed
The overall goal is to compute a ratio of two integrals, as described in Wetzels & Wagenmakers (2012) "A default Bayesian hypothesis test for correlations" (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3505519/). The entire function is as follows:
jzs.pcorbf = function(r0, r1, p0, p1, n) {
int = function(r,n,p,g) {
(1+g)^((n-1-p)/2)*(1+(1-r^2)*g)^(-(n-1)/2)*g^(-3/2)*exp(-n/(2*g))};
bf10=integrate(int, lower=0,upper=Inf,r=r1,p=p1,n=n)$value/
integrate(int,lower=0,upper=Inf,r=r0,p=p0,n=n)$value;
return(bf10)
}
Thanks!
The issue is that your integral function is generating NaN values when called with x values in its domain. You're integrating from 0 to Infinity, so let's check a valid x value of 1000:
int2(1000, sqrt(0.245), 530, 3)
# [1] NaN
Your objective multiplies four pieces:
x <- 1000
r <- sqrt(0.245)
n <- 530
p <- 3
(1+x)^((n-1-p)/2)
# [1] Inf
(1+(1-r^2)*x)^(-(n-1)/2)
# [1] 0
x^(-3/2)
# [1] 3.162278e-05
exp(-n/(2*x))
# [1] 0.7672059
We can now see that the issue is that you're multiplying infinity by 0 (or rather something numerically equal to infinity times something numerically equal to 0), which is causing the numerical issues. Instead of calculating a*b*c*d, it will be more stable to calculate exp(log(a) + log(b) + log(c) + log(d)) (using the identity that log(a*b*c*d) = log(a)+log(b)+log(c)+log(d)). One other quick note -- the value x=0 needs a special case.
int3 = function(x, r, n, p) {
loga <- ((n-1-p)/2) * log(1+x)
logb <- (-(n-1)/2) * log(1+(1-r^2)*x)
logc <- -3/2 * log(x)
logd <- -n/(2*x)
return(ifelse(x == 0, 0, exp(loga + logb + logc + logd)))
}
integrate(f=int3,lower=0,upper=Inf,n=530,r=sqrt(.245),p=3, stop.on.error=FALSE)
# 1.553185e+27 with absolute error < 2.6e+18