matrix_interp | R Documentation |

Function `matrix_interp`

summarizes matrices from `lefkoMat`

,
`lefkoSens`

, `lefkoElas`

, and `lefkoLTRE`

objects in terms
of the magnitudes of their elements. It can also create ordered summaries of
standard matrices and sparse matrices.

```
matrix_interp(object, mat_chosen = 1L, part = 1L, type = 3L)
```

`object` |
A list object in one of |

`mat_chosen` |
The number of the matrix to assess, within the appropriate
matrix list. See |

`part` |
An integer noting whether to provide assessments of which of the
main types of matrices to analyze. In a standard |

`type` |
An integer corresponding to the type of order summary, including
most to least positive ( |

A data frame arranging all elements in the matrix chosen from greatest and smallest. This can be a data frame of only positive elements, of only negative elements, or all elements in order of absolute magnitude.

Argument `mat_chosen`

refers to the number of the matrix within the
list that it is held in. For example, if the function is applied to the
`cont_sd`

portion of a stochastic LTRE, and there are four LTRE
matrices within that list element corresponding to three patch LTRE matrices
and one overall population-level LTRE matrix, then setting this value to
`4`

would focus the function on the overall population-level LTRE
matrix associated with contributions of the standard deviations of elements.
This argument should be left blank if a standard matrix or sparse matrix is
input.

Huge sparse matrices may take more time to process than small, dense matrices.

```
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)
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 <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
aaa <- ltre3(cypmatrix2r, stochastic = TRUE)
matrix_interp(aaa, mat_chosen = 1, part = 2, type = 3)
```

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