Displaying 5 results from an estimated 5 matches for "i_b".
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2009 Nov 22
1
How to make a matrix of a number of factors?
I use the following code to generate a matrix of factors. I'm
wondering if there is a way to make it more general so that I can have
any number of factors (not necessarily 5).
a=3
b=4
c=5
d=6
e=7
A=1:a
B=1:b
C=1:c
D=1:d
E=1:e
X=matrix(nr=a*b*c*d*e,nc=5)
for(i_a in 1:a-1) {
for(i_b in 1:b-1) {
for(i_c in 1:c-1) {
for(i_d in 1:d-1) {
for(i_e in 1:e-1) {
X[(((i_a * b + i_b) * c + i_c) * d + i_d) * e + i_e + 1, ] =
c(i_a+1, i_b+1, i_c+1, i_d+1, i_e+1)
}
}
}
}
}
print(X)
2010 Feb 09
1
"1 observation deleted due to missingness" from summary() on the result of aov()
...05 0.9589 0.52442
Residuals 119 102.642 0.86254
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
1 observation deleted due to missingness
#######################
a=3
b=4
c=5
A=1:a
B=1:b
C=1:c
n=3
X=matrix(nr=a*b*c*n,nc=n)
colnames(X)=LETTERS[1:n]
for(i_a in 1:a-1) {
for(i_b in 1:b-1) {
for(i_c in 1:c-1) {
for(i_n in 1:n-1) {
X[((i_a * b + i_b) * c + i_c) * n + i_n + 1, ] = c(i_a+1, i_b+1, i_c+1)
}
}
}
}
set.seed(0)
Y=matrix(nr=a*b*c*n,nc=1)
for(i in 1:(a*b*c)) {
for(i_n in 1:n-1) {
fa=X[i,'A']
fb=X[i,'B']
fc...
2009 Nov 22
1
Why F value and Pr are not show in summary() of an aov() result?
...I'm wondering why summary() doesn't show F
value and Pr?
Rscript multi_factor.R
> a=3
> b=4
> c=5
> d=6
> e=7
>
> A=1:a
> B=1:b
> C=1:c
> D=1:d
> E=1:e
>
> X=matrix(nr=a*b*c*d*e,nc=5)
> colnames(X)=LETTERS[1:5]
>
> for(i_a in 1:a-1) {
+ for(i_b in 1:b-1) {
+ for(i_c in 1:c-1) {
+ for(i_d in 1:d-1) {
+ for(i_e in 1:e-1) {
+ X[(((i_a * b + i_b) * c + i_c) * d + i_d) * e + i_e + 1, ]
= c(i_a+1, i_b+1, i_c+1, i_d+1, i_e+1)
+ }
+ }
+ }
+ }
+ }
>
> Y=matrix(nr=a*b*c*d*e,nc=1)
> for(i in 1...
2010 Apr 06
1
estimating the starting value within a ODE using nls and lsoda
...cal combined fitting procedure using nls and lsoda (alternatively rk4), I first defined the ODE model:
minmod <- function(t, y, parms) {
G <- y[1]
X <- y[2]
with(as.list(parms),{
I_t <- approx(time, I.input, t)$y
dG <- -1*(p1 + X)*G +p1*G_b
dX <- -1*p2*X + p3*(I_t-I_b)
list(c(dG, dX))
})
}
Then I estimated the parameters of the model using nls:
fit.rk4 <- nls(noisy ~ rk4(p4, time, minmod, parms=c(p1,p2,p3))
However, my goal is to not only estimate p1, p2 and p3, but also to estimate the starting value p4 from the data.
I am currently using the follo...
2020 Feb 27
2
[PATCH] Update the 5 year logo to 10 year logo
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