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I am trying to fit a curve to a cumulative mortality curve (logistic) where y is the cumulative proportion of mortalities, and t is the time in hours (see below). Asym. at 0 and 1 > y [1] 0.00000000 0.04853859 0.08303777 0.15201970 0.40995074 0.46444992 0.62862069 0.95885057 1.00000000 [10] 1.00000000 1.00000000 > t [1] 0 13 20 24 37 42 48 61 72 86 90 I tried to find starting values for

Hello again R-folks, I'm trying to apply a loop to multiple data frames and then organize the data in a manageable form. I have the code for my loop figured out (thanks to help from this list)...it runs up to 2000 iterations of a "while" loop until it finds a 40-row "d2p" column sum >5 n<-nrow(dsub.f1) test<-0 for(k in 1:2001){ if(k>2000){ break} #stops

Hi all, I have a dataframe of behavioral observations from 360 fish, each with 241 observation points(rows), which looks like this: > head(d) fish treatment tank trial video tid pid ang.chg abs.ac t len vel d2p x y 1 1 3 1 1 1 1 1 NA NA 0.0 0.000 NA NA 5.169 9.617 2

Hello R-universe... I am having trouble writing a function which contains a loop so I can sapply() it to a list of data frames Each data frame has 241 observations of 15 variables. My loop takes a random sample of one row until the 40 consecutive rows after the sample have a d2p(variable) sum greater than 5. here is my loop code (it works fine when applied to a 241 observation data frame):

Hello R experts, I have having a difficult time figuring out how to perform and interpret an ANCOVA of my nested experimental data and would love any suggestions that you might have. Here is the deal: 1) I have twelve tanks of fish (1-12), each with a bunch of fish in them 2) I have three treatments (1-3); 4 tanks per treatment. (each tank only has one treatment applied to it) 3) I sampled