Displaying 5 results from an estimated 5 matches for "tempsum".
2005 May 12
2
tempsum
hi,
i'd like to calculate a temperatursum, adding the value of each element.
let's say the data looks like this:
x<-c(1,2,3,4,5)
what i want to do, is ploting not the sum in the end but all the
subresults, too,
so my vector holds:
x[i]
[1]
1,3,6,10,15
here is what i tried, which seems to be right to me, bu doesn't work out:
x<-c(1,2,3,4,5)
i<-1
j<-1
2007 Feb 18
3
User defined split function in rpart
Dear R community,
I am trying to write my own user defined split function for rpart. I read
the example in the tests directory and I understand the general idea of the
how to implement user defined splitting functions. However, I am having
troubles with addressing the data frame used in calling rpart in my split
functions.
For example, in the evaluation function that is called once per node,
2013 Mar 28
1
make R program faster
...calculates day wise values
(values are depenend from the output of the day before). First I create
a data.frame with NAs. Finally each row contains the daily values.
output <- as.data.frame(matrix(nrow = 365, ncol = 50))
for (day in (1:365)) {
...
r <- list(Date=d,daylength=daylength,TempSum=tempsum, ...)
output[day,] <- r
}
Is there an better (faster) way to do such things in R?
Greetings
Christof
2012 May 31
1
fitting allometric equation using a for a power model
...iley.com/doi/10.1002/etc.5620120618/abstract(page
1130)
but that doesn't seem to help much - if anything the fit looks worse. Data
and code below:
temppow<-lm(log(y)~log(x))
plot(log(y)~log(x))
plot(residuals(temppow), main="pow")
abline(temppow)
plot(y~x, main="pow")
tempsum<-summary(temppow)$adj.r.squared
tempint<-summary(temppow)$coefficients[1,1] #intercept of power function
tempslope<-summary(temppow)$coefficients[2,1] #slope of power function
tempmin<-min(x)
tempmax<-max(x)
lngth<-c(tempmin:tempmax) # vector from the minimum to the maximum values...
2007 Jul 25
0
Function polr and discrete ordinal scale
...ow how to bring the cumulated probabilities back to a discrete ordinal scale.
For the moment I used the predict.polr function with the argument "class". Is there an other way?
polrf <- polrf <- polr_mod(formula = acipenser_gueldenstaedtii ~ Long + poly(Surf, 2, raw = TRUE) + poly(TempSum, 2, raw = TRUE) , data = mydata, method = "logistic", Hess = TRUE, na.action = na.omit)
pred1 <- predict.polr(polrf, newdata = mydata2, type = "class")
predict.polr <- function(object, newdata, type=c("class","probs"), ...)
{
if(!inherits(object,...