Yeah, sorry you are suffering confusion from my confusion. This stuff is a little foreign to me, and I'm not exactly clear on the correct order of operations, I guess. Let me give it another shot:
Background: I am working with telemetry data and doing a CJS analysis to investigate factors influencing departure rates from a migratory stopover. I am defining Phi as the probability of remaining in the study area and 1-Phi the departure probability.
I have a daily encounter (capture) history for 186 individuals over four years. There are 102 days per year. I have defined the year in which the individual is captured as a group, as well as sex, and age. I also have weight (measured at capture), z-standarized weight, and an average rspf value (averaged over the duration the individual stayed in the study area) for each individual (the three individual covariates; not time-varying).
The 11 time-varying covariates are daily measurements of environmental variables (avg. wind speed, min. temp, avg. barometric pressure, etc.), which if I am understanding correctly are design covariates.
Here is the code I've been trying. I included intermediary steps just in case I have assigned something as a factor when I should not have.
- Code: Select all
amwo <- read.csv("~/Dropbox/Thesis/nj_amwo/all_years/data_csv/daily_ch_rmark.csv", header=T)
rspf.cjs <- read.csv("~/Dropbox/Thesis/nj_amwo/all_years/data_csv/rspf_cjs.csv") # generated from chapt1_rsf.R script
names(rspf.cjs) <- c("uid", "rspf")
wx.moon <- read.csv("/Users/brianballen/Dropbox/Thesis/nj_amwo/all_years/data_csv/wx.moon.csv")
amwo2 <- join(amwo, rspf.cjs, by= "uid", type= "left")
amwo2$z.rspf<- scale(amwo2$rspf)
amwo2$z.rspf[is.na(amwo2$z.rspf)] <- 0
amwo.cjs <- amwo2[c("uid", "ch", "Year","Age", "Sex", "Wt", "Z_Wt", "z.rspf")]
amwo.cjs$ch <- as.character(amwo$ch)
amwo.cjs$Year <- as.factor(amwo$Year)
amwo.process <- process.data(amwo.cjs, model="CJS", groups = c("Year"))
amwo.ddl <- make.design.data(amwo.process)
wx.moon$group <- as.factor(wx.moon$group)
wx.moon$X <- NULL
amwo.ddl$Phi <- merge_design.covariates(amwo.ddl$Phi,wx.moon, bytime = TRUE, bygroup = TRUE)
Output
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> amwo <- read.csv("~/Dropbox/Thesis/nj_amwo/all_years/data_csv/daily_ch_rmark.csv", header=T)
> rspf.cjs <- read.csv("~/Dropbox/Thesis/nj_amwo/all_years/data_csv/rspf_cjs.csv") # generated from chapt1_rsf.R script
> names(rspf.cjs) <- c("uid", "rspf")
>
>
> wx.moon <- read.csv("/Users/brianballen/Dropbox/Thesis/nj_amwo/all_years/data_csv/wx.moon.csv")
>
>
> amwo2 <- join(amwo, rspf.cjs, by= "uid", type= "left")
> amwo2$z.rspf<- scale(amwo2$rspf)
> amwo2$z.rspf[is.na(amwo2$z.rspf)] <- 0
> amwo.cjs <- amwo2[c("uid", "ch", "Year","Age", "Sex", "Wt", "Z_Wt", "z.rspf")]
> amwo.cjs$ch <- as.character(amwo$ch)
> amwo.cjs$Year <- as.factor(amwo$Year)
>
> amwo.process <- process.data(amwo.cjs, model="CJS", groups = c("Year"))
> amwo.ddl <- make.design.data(amwo.process)
>
> wx.moon$group <- as.factor(wx.moon$group)
> wx.moon$X <- NULL
> amwo.ddl$Phi <- merge_design.covariates(amwo.ddl$Phi,wx.moon, bytime = TRUE, bygroup = TRUE)
Warning message:
In `[<-.factor`(`*tmp*`, ri, value = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, :
invalid factor level, NA generated
And here is the head of amwo.cjs and wx.moon. I have a feeling this will provide insight into the issue.
> head(amwo.cjs)
uid
1 164.033_10a
2 164.043_10a
3 164.052_10a
4 164.093_10a
5 164.113_10a
6 164.122_10a
ch Year Age
1 ...000001100000100100010111000000000000000000000000000000000000000000000000000000..................... 2010 HY
2 ...100000000000000000000000000000000000000000000000000000000000000000000000000000..................... 2010 HY
3 ...011001000000100100001111001110000000000000000000000000000000000000000000000000..................... 2010 AHY
4 ...000001110100000100011111000000000000000000000000000000000000000000000000000000..................... 2010 HY
5 ...000001110100101100011111000000000000000000000000000000000000000000000000000000..................... 2010 HY
6 ...000000110100101100011111001100000111110011111001111100110100000000000000000000..................... 2010 HY
Sex Wt Z_Wt z.rspf
1 F 183 0.5933 -0.81063965
2 F 230 2.6292 0.00000000
3 M 143 -1.1395 -2.62820102
4 F 182 0.5499 -0.78084812
5 F 179 0.4200 -0.41633140
6 F 183 0.5933 0.01694618
> head(wx.moon)
time group moon min.temp precip avg.bar avg.wind max.wind avg.cc avg.ch avg.wnd.dir
1 1 2010 1.3529332 0 0 0 0 0 0 0 0
2 2 2010 1.2393887 0 0 0 0 0 0 0 0
3 3 2010 1.0974580 0 0 0 0 0 0 0 0
4 4 2010 0.8987551 0 0 0 0 0 0 0 0
5 5 2010 0.6716660 0 0 0 0 0 0 0 0
6 6 2010 0.3878047 0 0 0 0 0 0 0 0
You'll notice in the code only Year is a group and I had stated Year, Age, and Sex were groups. I thought having Age and Sex as groups was the issue so I decided to define them as individual covariates, since it doesn't affect my analysis.