Hi I have the following code in Scilab:
>Tc=0;
>Tm=1;
>Tf=1800;
>t=(Tc:1:Tf)';
where t is a vector of 1800 components.
And I am asked to do a piecewise function that satisfies certain conditions,
My first try was to do something on the line of
> function vg=simula_vg(t,Tcg,Tfg,Ag)
> if (t<Tcg | t>Tfg) then
> vg=0;
> else
> vg=Ag*Ag*(1-cos(2*%pi*(t-Tcg)/(Tfg-Tcg)));
>end
>endfunction
But it doesnt work as I am asking it to compare vector and scalars.
Then I tried to write this
>for i=[Tc:1:Tf]
>function vg=simula_vg(t,Tcg,Tfg,Ag)
> vg(t<Tcg)=0
>vg(t>Tfg)=0
>vg((Tcg<=t)&(t<=Tfg))=sin(t(i))
>endfunction
>end
But I doesnt work either and I have run out of ideas, is there anything else I can do? All the variables are well defined
>vm=10;
>Ag=2;
>Tcg=200;
>Tfg=400;
>Ar=2;
>Tcr=1000;
>Tfr=1500;
>As=2;
>fs=0.0008;
>h=20;
>d=0.6;
There are more because there are more functions similar to that one that I have to define and I dont know how. Any suggestions on how to do it?
You can do it like this, where the zero values are defined afterwards:
function vg=simula_vg(t,Tcg,Tfg,Ag)
vg=Ag*Ag*(1-cos(2*%pi*(t-Tcg)/(Tfg-Tcg)));
vg(t<Tcg|t>Tfg)=0;
endfunction
Ag=2;
Tcg=200;
Tfg=400;
Tc=0;
Tm=1;
Tf=1800;
t=Tc:1:Tf;
vg = simula_vg(t,Tcg,Tfg,Ag);
plot(t,vg)
I have been given following code for R, but I am having trouble understanding what it is doing. In fact I can not even run it in R because of its syntax. I assume the syntax is for lower level code behind R. If someone could help explain what's happening here and translate this into executable R code that would be very helpful.
soft_thresholding = function(x,a){
result a)] a)] - a
result[which(x < -a)] = x[which(x < -a)] + a
return(result)}
Here is a summary of the findings. This is not a definite answer but could help the questioner.
If one uses wordpress, then x <- a will look like x < -a. Check this URL that confirms this assumption
Upon further online search with the function name in the question "soft_thresholding", shows that this function is probably attempting to do soft thresholding defined here.
Some more online searching about soft thresholding lands on a CRAN package that is present here.
Further deepdive into the r folder in the package binaries shows the following.
soft.threshold <- function(x,sumabs=1)
return(soft(x, BinarySearch(x,sumabs)))
The function above seems very close to the code in the question.
Furthermore, the soft.threshold function uses another internal function BinarySearch that looks like this.
BinarySearch <-
function(argu,sumabs){
if(norm2(argu)==0 || sum(abs(argu/norm2(argu)))<=sumabs) return(0)
lam_max = max(abs(argu))
lam1 <- 0
lam2 <- lam_max
iter <- 1
while(iter < 500){
su <- soft(argu,(lam1+lam2)/2)
if(sum(abs(su/norm2(su)))<sumabs){
lam2 <- (lam1+lam2)/2
} else {
lam1 <- (lam1+lam2)/2
}
if((lam2-lam1)/lam1 < 1e-10){
if (lam2 != lam_max){
return(lam2)
}else{
return(lam1)
}
}
iter <- iter+1
}
warning("Didn't quite converge")
return((lam1+lam2)/2)
}
This recursive research leads one to believe that the function is perhaps attempting to mimic the function soft.threshold in the CRAN package "RGCCA"
Hope it helps
I have the same problem with this one but no solutions found yet
.
Error in if (more || nchar(output) > 80) { : missing value where TRUE/FALSE needed
I am conducting analysis using the mgcv package.
model1<-gam(fm_xsetz~total_pm2.5, data=analysis)
I can get the results by using the summary(). But when I try to open the model in the global environment, I get the warning:
Error in if (more || nchar(output) > 80) { : missing value where
TRUE/FALSE needed
Is anyone has the same problem?
FYI,when you use the following code:
library(geostatsp)
data(swissRain)
same problem happens!
I have/had a similar problem when I tried to view a List generated trough a function that computes the intersect/difference of two sets of 23000 observations each.
The function in question:
jeepers.creepers<-function(dfx,dfy,by.x,by.y){
SetX<-dfx[[by.x]]
SetY<-dfy[[by.y]]
Union.X.Y<-intersect(SetX,SetY)
Difference.in.X<-setdiff(SetX,Union.X.Y)
Difference.in.Y<-setdiff(SetY,Union.X.Y)
result<-list(Union.X.Y,Difference.in.X,Difference.in.Y)
names(result)<-c("Union of SetX and SetY",
"Unique in SetX",
"Unique in SetY")
return(result)
}
It gave me this error:
Error in if (more || nchar(output) > 80) { :
missing value where TRUE/FALSE needed
Nevertheless I could view the elements individually with
View(list$element)
I had a similar problem yet I could view it with:
view(as.data.frame(df))
I am currently trying to run some code (if you need to know the purpose to help me, ask me, but I'm trying to keep this question short). This is the code:
par<-c(a=.5,b=rep(1.3,4))
est<-rep(TRUE,length(par))
ncat<-5
Theta<-matrix(c(-6,-5.8,-5.6,-5.4,-5.2,-5,-4.8,-4.6,-4.4,-4.2,-4,-3.8,-3.6,-3.4,-3.2,-3,-2.8,-2.6,-2.4,-2.2,-2,-1.8,-1.6,-1.4,-1.2,-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6,2.8,3,3.2,3.4,3.6,3.8,4,4.2,4.4,4.6,4.8,5,5.2,5.4,5.6,5.8,6))
p.grm<-function(par,Theta,ncat){
a<-par[1]
b<-par[2:length(par)]
z<-matrix(0,nrow(Theta),ncat)
y<-matrix(0,nrow(Theta),ncat)
y[,1]<-1
for(i in 1:ncat-1){
y[,i+1]<-(exp(a*(Theta-b[i])))/(1+exp(a*(Theta-b[i])))
}
for(i in 1:ncat-1){
z[,i]<-y[,i]-y[,i+1]
}
z[,ncat]<-y[,ncat]
z
}
However, when I try to run the code:
p.grm(par=par,Theta=Theta,ncat=ncat)
I get the following error:
Error: dims [product 61] do not match the length of object [0]
Traceback tells me that the error is occurring in the first for loop in the line:
y[,i+1]<-(exp(a*(Theta-b[i])))/(1+exp(a*(Theta-b[i])))
Could someone point me to what I'm doing wrong? When I try to run this code step by step outside of the custom p.grm function, everything seems to work fine.
It is a common mistake. When you write the for loop and you want it from 1 to ncat -1 remember to write it as for (i in 1:(ncat-1)) instead of for(i in 1:ncat-1) they are completly different.
You may also add to the function something to return return(z). Here it is the corrected code:
par<-c(a=.5,b=rep(1.3,4))
est<-rep(TRUE,length(par))
ncat<-5
Theta<-matrix(c(-6,-5.8,-5.6,-5.4,-5.2,-5,-4.8,-4.6,-4.4,-4.2,-4,-3.8,-3.6,-3.4,-3.2,-3,-2.8,-2.6,-2.4,-2.2,-2,-1.8,-1.6,-1.4,-1.2,-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6,2.8,3,3.2,3.4,3.6,3.8,4,4.2,4.4,4.6,4.8,5,5.2,5.4,5.6,5.8,6))
p.grm<-function(par,Theta,ncat){
a<-par[1]
b<-par[2:length(par)]
z<-matrix(0,nrow(Theta),ncat)
y<-matrix(0,nrow(Theta),ncat)
y[,1]<-1
for(i in 1:(ncat-1)){
y[,i+1]<-(exp(a*(Theta-b[i])))/(1+exp(a*(Theta-b[i])))
}
for(i in 1:(ncat-1)){
z[,i]<-y[,i]-y[,i+1]
}
z[,ncat]<-y[,ncat]
return(z)
}
p.grm(par=par,Theta=Theta,ncat=ncat)
I'm trying to make a confidence interval for practice and I keep getting an error referring to:
an 'invalid number of breaks' at hist.default(boot.dist).
I'm pretty sure the problem is somewhere here.
Any advice or help would be very much appreciated at this point.
b=1000
boot.dist = rep(0,b)
for (i in 1:b) {
boot.sample = sample(ACS$Income, replace = TRUE)
boot.dist[i] = mean(boot.sample)
}
hist(boot.dist)
The problem is that ACS$Income is array of NA.
Example, this code will reproduce error exactly like yours:
boot.dist[1:1000]<-NA
hist(boot.dist )
Error in hist.default(boot.dist) : invalid number of 'breaks'