Re: [R] Making a function and applying it over a list(?)
You could store your first approach in a function and lapply it to
your by_hour variable:
df <- data.frame(
hour = factor(rep(1:5,4)),
id = factor(rep(c("supply", "demand"), each = 10)),
price= c(5,7,9,11,13,15,17,19,21,23,
20,18,16,14,12,10,8,6,4,2 ),
quantity = c(3,5,7,13,19,31,37,53,61,67,6,18,20,24,40,46,66,70,76,78)
)
myfu <- function(x){
df <- x # for simplicity
quantity_points <- with(
df,
seq(min(quantity), max(quantity), length.out = 500)
)
by_id <- split(df[, c("price", "quantity")], df$id)
interpolated_price <- lapply(
by_id,
function(x)
{
with(
x,
approx(
quantity,
price,
xout = quantity_points
)
)$y
}
)
index_of_equality <- with(interpolated_price, which.min(abs(supply - demand)))
quantity_points[index_of_equality]
}
by_hour <- split(df,df$hour)
lapply(by_hour,myfu)
Was that what you were looking for?
Cheers,
Christoph
2013/10/24 Lasse Thorst :
> Hi All
>
> I've gotten some awesome help getting a formular that finds the intersection
> of two vectors. This works brilliantly, but I can't figure out how to make it
> run over another factor. A simple example looks likes this:
>
> df <- data.frame(
> id = factor(rep(c("supply", "demand"), each = 10)),
> price= c(5,7,9,11,13,15,17,19,21,23,20,18,16,14,12,10,8,6,4,2 ),
> quantity = c(3,5,7,13,19,31,37,53,61,67,6,18,20,24,40,46,66,70,76,78)
> )
>
> quantity_points <- with(
> df,
> seq(min(quantity), max(quantity), length.out = 500)
> )
>
> by_id <- split(df[, c("price", "quantity")], df$id)
>
> interpolated_price <- lapply(
> by_id,
> function(x)
> {
> with(
> x,
> approx(
> quantity,
> price,
> xout = quantity_points
> )
> )$y
> }
> )
>
> index_of_equality <- with(interpolated_price, which.min(abs(supply - demand)))
> quantity_points[index_of_equality]
>
> Question: I need to run this over a larger data frame, where I have the same
> data, but also a new factor variable (called hour). So if you have the
> original data frame and add:
>
> df <- data.frame(
> hour = factor(seq(1:20)),
> id = factor(rep(c("supply", "demand"), each = 10)),
> price= c(5,7,9,11,13,15,17,19,21,23,20,18,16,14,12,10,8,6,4,2 ),
> quantity = c(3,5,7,13,19,31,37,53,61,67,6,18,20,24,40,46,66,70,76,78)
> )
>
> How can I run it for each hour? I tried using:
> by_hour <- split(df[, c("price", "quantity")], df$hour)
> mapply(fx, by_hour)
>
> And gathering the above into a fx <- function(){"the neat code"}, but I can't
> get it to work.
>
> Kind Regards,
> Lasse
>
> [[alternative HTML version deleted]]
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] matrix values linked to vector index
Just a further suggestion: vec <- c(3,2,5,0,1) mat <- t(sapply(vec,">=",1:max(vec))) ifelse(mat,1,0) Cheers, Christoph 2013/10/11 arun : > Hi, > > In the example you showed: > > m1<- matrix(0,length(vec),max(vec)) > 1*!upper.tri(m1) > > #or > m1[!upper.tri(m1)] <- rep(rep(1,length(vec)),vec) > > #But, in a case like below, perhaps: > vec1<- c(3,4,5) > > m2<- matrix(0,length(vec1),max(vec1)) > indx <- > cbind(rep(seq_along(vec1),vec1),unlist(tapply(vec1,list(vec1),FUN=seq),use.names=FALSE)) > m2[indx]<- 1 > m2 > # [,1] [,2] [,3] [,4] [,5] > #[1,]11100 > #[2,]11110 > #[3,]11111 > > > > > A.K. > > > Hi- > > I'd like to create a matrix of 0's and 1's where the number of > 1's in each row defined by the value indexed in another vector, and > where the (value-1) is back-filled by 0's. > > For example, given the following vector: > vec= c(1,2,3) > > I'd like to produce a matrix with dimensions (length(vec), max(vec)): > > 1,0,0 > 1,1,0 > 1,1,1 > > Thank you! > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] GLM: Defining non-constant variance for (gaussian) family
Dear all,
I want to fit some observations Y to a set of predictor variables X_i. (and
proceed with model selection with support of the second-order AIC
(AICc)...)
I (think I) "know" that the distribution of Y[i] is Gaussian and has a
variance, which is proportional to its value Y[i]. Say:
x1 <- rnorm(1:100)*2
x2 <- runif(1:100)*0.5
etc...
Y <- exp(3.2 + 0.45*x1 - 0.5*x2) + rnorm(100,0,(exp(3.2 + 0.45*x1 -
0.5*x2))^0.5)
So, I thought, I could change the family object as following:
GaussVar <- gaussian("log")
GaussVar$variance <- function(mu) mu
GaussVar$dev.resids <- function(y, mu, wt) wt * ((y - mu)^2/mu)
GaussVar$aic <- function (y, n, mu, wt, dev){
nobs <- length(y);-2 * sum(dnorm(y,mu,sqrt(dev*mu/nobs), log = TRUE) * wt)
+ 2
}
And the resulting Regression:
reg <- glm(Y~x1+x2,family=GaussVar)
Is this a valid modification of the "family" object?
Am I missing something?
Is the calculation of the AIC complete rubbish?
I have to admit, I'm very new to the topic of regression, so please be
kind! :-)
Thanks in advance,
Christoph
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