View source: R/matrixcreation.R
| summary.lefkoMatList | R Documentation |
A function to simplify the viewing of basic information describing the
bootstrapped matrices produced through functions flefko3(),
flefko2(), rlefko3(), rlefko2(),
aflefko2(), rleslie(), and
fleslie() when using bootstrapped data input in format
hfvlist.
## S3 method for class 'lefkoMatList'
summary(
object,
elem_summaries = FALSE,
colsums = TRUE,
check_cycle = TRUE,
...
)
object |
An object of class |
elem_summaries |
A logical value indicating whether to include
summaries of all |
colsums |
If |
check_cycle |
If |
... |
Other parameters. |
A general summary of the lefkoMatList object, showing the
number and type of lefkoMat objects included, the mean number of
annual matrices per lefkoMat element, the mean size of each matrix,
the mean number of estimated (non-zero) elements across all matrices and per
matrix, the mean number of unique transitions in the bootstrapped datasets,
the mean number of individuals, and summaries of the column sums of the survival-transition
matrices across all lefkoMat objects.
if elem_summaries = TRUE, then summary.lefkoMat{} output
is also shown for each lefkoMat object per entered lefkoMatList
object.
Stage discontinuities may also be also checked across all lefkoMat
objects with function cycle_check. This function will also yield
warnings if any survival-transition matrices include elements outside of the
interval [0,1], if any fecundity matrices contain negative elements, and if
any matrices include NA values.
Under the Gaussian and gamma size distributions, the number of estimated
parameters may differ between the two ipm_method settings. Because
the midpoint method has a tendency to incorporate upward bias in the
estimation of size transition probabilities, it is more likely to yield non-
zero values when the true probability is extremely close to 0. This will
result in the summary.lefkoMat function yielding higher numbers of
estimated parameters than the ipm_method = "CDF" yields in some cases.
data(cypdata)
sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",
"XLg")
repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)
cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
propstatus = propvector, immstatus = immvector, indataset = indataset,
binhalfwidth = binvec)
cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
NRasRep = TRUE)
cypraw_v1_boot <- bootstrap3(cypraw_v1, reps = 3)
# Here we use supplemental() to provide overwrite and reproductive info
cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",
"XSm", "Sm", "SD", "P1"),
stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",
"rep"),
eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),
eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),
type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),
stageframe = cypframe_raw, historical = FALSE)
cypmatrix2r_boot <- rlefko2(data = cypraw_v1_boot, stageframe = cypframe_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
summary(cypmatrix2r_boot)
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