verticalize3  R Documentation 
Function verticalize3()
returns a vertically formatted demographic
data frame organized to create historical projection matrices, given a
horizontally formatted input data frame. It also handles stage assignments
if given an appropriate stageframe.
verticalize3( data, noyears, firstyear = 1, popidcol = 0, patchidcol = 0, individcol = 0, blocksize = NA, xcol = 0, ycol = 0, juvcol = 0, sizeacol, sizebcol = 0, sizeccol = 0, repstracol = 0, repstrbcol = 0, fecacol = 0, fecbcol = 0, indcovacol = 0, indcovbcol = 0, indcovccol = 0, aliveacol = 0, deadacol = 0, obsacol = 0, nonobsacol = 0, censorcol = 0, repstrrel = 1, fecrel = 1, stagecol = 0, stageassign = NA, stagesize = NA, censorkeep = 0, censorRepeat = FALSE, censor = FALSE, coordsRepeat = FALSE, spacing = NA, NAas0 = FALSE, NRasRep = FALSE, NOasObs = FALSE, prebreeding = TRUE, age_offset = 0, reduce = TRUE, a2check = FALSE, quiet = FALSE )
data 
The horizontal data file. A valid data frame is required as input. 
noyears 
The number of years or observation occasions in the dataset. A valid integer is required as input. 
firstyear 
The first year or occasion of observation. Defaults to

popidcol 
A variable name or column number corresponding to the identity of the population for each individual. 
patchidcol 
A variable name or column number corresponding to the identity of the patch or subpopulation for each individual, if patches have been designated within populations. 
individcol 
A variable name or column number corresponding to the identity of each individual. 
blocksize 
The number of variables corresponding to each occasion in
the input dataset designated in 
xcol 
A variable name(s) or column number(s) corresponding to the X coordinate of each individual, or of each individual at each occasion, in Cartesian space. Can refer to the only instance, the first instance, or all instances of X variables. In the last case, the values should be entered as a vector. 
ycol 
A variable name(s) or column number(s) corresponding to the Y coordinate of each individual, or of each individual at each occasion, in Cartesian space. Can refer to the only instance, the first instance, or all instances of Y variables. In the last case, the values should be entered as a vector. 
juvcol 
A variable name(s) or column number(s) that marks individuals
in immature stages within the dataset. This function assumes that immature
individuals are identified in this variable marked with a number equal to or
greater than 
sizeacol 
A variable name(s) or column number(s) corresponding to the
size entry associated with the first year or observation occasion in the
dataset. Can refer to the first instance, or all instances of these
variables. In the latter case, the values should be entered as a vector.
This variable should refer to the first size variable in the stageframe,
unless 
sizebcol 
A second variable name(s) or column number(s) corresponding
to the size entry associated with the first year or observation occasion in
the dataset. Can refer to the first instance, or all instances of these
variables. In the latter case, the values should be entered as a vector.
This variable should refer to the second size variable in the stageframe,
unless 
sizeccol 
A third variable name(s) or column number(s) corresponding to
the size entry associated with the first year or observation occasion in the
dataset. Can refer to the first instance, or all instances of these variables.
In the latter case, the values should be entered as a vector. This variable
should refer to the third size variable in the stageframe, unless

repstracol 
A variable name(s) or column number(s) corresponding to the production of reproductive structures, such as flowers, associated with the first year or observation period in the input dataset. This can be binomial or count data, and is used to analyze the probability of reproduction. Can refer to the first instance, or all instances of these variables. In the latter case, the values should be entered as a vector. 
repstrbcol 
A second variable name(s) or column number(s) corresponding to the production of reproductive structures, such as flowers, associated with the first year or observation period in the input dataset. This can be binomial or count data, and is used to analyze the probability of reproduction. Can refer to the first instance, or all instances of these variables. In the latter case, the values should be entered as a vector. 
fecacol 
A variable name(s) or column number(s) denoting fecundity associated with the first year or observation occasion in the input dataset. This may represent egg counts, fruit counts, seed production, etc. Can refer to the first instance, or all instances of these variables. In the latter case, the values should be entered as a vector. 
fecbcol 
A second variable name(s) or column number(s) denoting fecundity associated with the first year or observation occasion in the input dataset. This may represent egg counts, fruit counts, seed production, etc. Can refer to the first instance, or all instances of these variables. In the latter case, the values should be entered as a vector. 
indcovacol 
A variable name(s) or column number(s) corresponding to an individual covariate to be used in analysis. Can refer to the only instance, the first instance, or all instances of these variables. In the last case, the values should be entered as a vector. 
indcovbcol 
A variable name(s) or column number(s) corresponding to an individual covariate to be used in analysis. Can refer to the only instance, the first instance, or all instances of these variables. In the last case, the values should be entered as a vector. 
indcovccol 
A second variable name(s) or column number(s) corresponding to an individual covariate to be used in analysis. Can refer to the only instance, the first instance, or all instances of these variables. In the last case, the values should be entered as a vector. 
aliveacol 
Variable name(s) or column number(s) providing information
on whether an individual is alive at a given occasion. If used, living status
must be designated as binomial (living = 
deadacol 
Variable name(s) or column number(s) providing information on
whether an individual is alive at a given occasion. If used, dead status must
be designated as binomial (dead = 
obsacol 
A variable name(s) or column number(s) providing information
on whether an individual is in an observable stage at a given occasion. If
used, observation status must be designated as binomial (observed = 
nonobsacol 
A variable name(s) or column number(s) providing
information on whether an individual is in an unobservable stage at a given
occasion. If used, observation status must be designated as binomial (not
observed = 
censorcol 
A variable name(s) or column number(s) corresponding to the
first entry of a censor variable, used to distinguish between entries to use
and entries not to use, or to designate entries with special issues that
require further attention. Can refer to the first instance of a censor status
variable in the dataset, or a full vector of all censor status variables in
temporal order. Can also refer to a single censor status variable used for
the entire individual, if 
repstrrel 
This is a scalar multiplier on variable 
fecrel 
This is a scalar multiplier on variable 
stagecol 
Optional variable name(s) or column number(s) corresponding to life history stage at a given occasion. Can refer to the first instance of a stage identity variable in the dataset, or a full vector of all stage identity variables in temporal order. 
stageassign 
The stageframe object identifying the life history model
being operationalized. Note that if 
stagesize 
A variable name or column number describing which size
variable to use in stage estimation. Defaults to NA, and can also take

censorkeep 
The value of the censor variable identifying data to be
included in analysis. Defaults to 
censorRepeat 
A logical value indicating whether the censor variable
is a single column, or whether it repeats across occasion blocks. Defaults to

censor 
A logical variable determining whether the output data should
be censored using the variable defined in 
coordsRepeat 
A logical value indicating whether X and Y coordinates
correspond to single X and Y columns. If 
spacing 
The spacing at which density should be estimated, if density
estimation is desired and X and Y coordinates are supplied. Given in the same
units as those used in the X and Y coordinates given in 
NAas0 
If 
NRasRep 
If 
NOasObs 
If 
prebreeding 
A logical term indicating whether the life history model
is prebreeding. If so, then 
age_offset 
A number to add automatically to all values of age at time
t. Defaults to 
reduce 
A logical variable determining whether unused variables and
some invariant state variables should be removed from the output dataset.
Defaults to 
a2check 
A logical variable indicating whether to retain all data with
living status at occasion t equal to 0. Defaults to 
quiet 
A logical variable indicating whether to silence warnings.
Defaults to 
If all inputs are properly formatted, then this function will output
a historical vertical data frame (class hfvdata
), meaning that the
output data frame will have three consecutive occasions of size and
reproductive data per individual per row. This data frame is in standard
format for all functions used in lefko3
, and so can be used without
further modification.
Variables in this data frame include the following:
rowid 
Unique identifier for the row of the data frame. 
popid 
Unique identifier for the population, if given. 
patchid 
Unique identifier for patch within population, if given. 
individ 
Unique identifier for the individual. 
year2 
Year or time at occasion t. 
firstseen 
Occasion of first observation. 
lastseen 
Occasion of last observation. 
obsage 
Observed age in occasion t, assuming first observation corresponds to age = 0. 
obslifespan 
Observed lifespan, given as 
xpos1,xpos2,xpos3 
X position in Cartesian space in occasions t1, t, and t+1, respectively, if provided. 
ypos1,ypos2,ypos3 
Y position in Cartesian space in occasions t1, t, and t+1, respectively, if provided. 
sizea1,sizea2,sizea3 
Main size measurement in occasions t1, t, and t+1, respectively. 
sizeb1,sizeb2,sizeb3 
Secondary size measurement in occasions t1, t, and t+1, respectively. 
sizec1,sizec2,sizec3 
Tertiary measurement in occasions t1, t, and t+1, respectively. 
size1added,size2added,size3added 
Sum of primary, secondary, and tertiary size measurements in occasions t1, t, and t+1, respectively. 
repstra1,repstra2,repstra3 
Main numbers of reproductive structures in occasions t1, t, and t+1, respectively. 
repstrb1,repstrb2,repstrb3 
Secondary numbers of reproductive structures in occasions t1, t, and t+1, respectively. 
repstr1added,repstr2added,repstr3added 
Sum of primary and secondary reproductive structures in occasions t1, t, and t+1, respectively. 
feca1,feca2,feca3 
Main numbers of offspring in occasions t1, t, and t+1, respectively. 
fecb1,fecb2, fecb3 
Secondary numbers of offspring in occasions t1, t, and t+1, respectively. 
fec1added,fec2added,fec3added 
Sum of primary and secondary fecundity in occasions t1, t, and t+1, respectively. 
censor1,censor2,censor3 
Censor state values in occasions t1, t, and t+1, respectively. 
juvgiven1,juvgiven2,juvgiven3 
Binomial variable indicating whether
individual is juvenile in occasions t1, t, and t+1.
Only given if 
obsstatus1,obsstatus2,obsstatus3 
Binomial observation state in occasions t1, t, and t+1, respectively. 
repstatus1,repstatus2,repstatus3 
Binomial reproductive state in occasions t1, t, and t+1, respectively. 
fecstatus1,fecstatus2,fecstatus3 
Binomial offspring production state in occasions t1, t, and t+1, respectively. 
matstatus1,matstatus2,matstatus3 
Binomial maturity state in occasions t1, t, and t+1, respectively. 
alive1,alive2,alive3 
Binomial state as alive in occasions t1, t, and t+1, respectively. 
density 
Radial density of individuals per unit designated in

In some datasets on species with unobservable stages, observation status
(obsstatus
) might not be inferred properly if a single size variable
is used that does not yield sizes greater than 0 in all cases in which
individuals were observed. Such situations may arise, for example, in plants
when leaf number is the dominant size variable used, but individuals
occasionally occur with inflorescences but no leaves. In this instances,
it helps to mark related variables as sizeb
and sizec
, because
observation status will be interpreted in relation to all 3 size variables.
Further analysis can then utilize only a single size variable, of the user's
choosing. Similar issues can arise in reproductive status (repstatus
).
Juvenile designation should only be used when juveniles fall outside of the size classification scheme used in determining stages. If juveniles are to be size classified along the size spectrum that adults also fall on, then it is best to treat juveniles as mature but not reproductive.
Warnings that some individuals occur in state combinations that do not match
any stages in the stageframe used to assign stages are common when first
working with a dataset. Typically, these situations can be identified as
NoMatch
entries in stage3
, although such entries may crop up in
stage1
and stage2
, as well. In rare cases, these warnings will
arise with no concurrent NoMatch
entries, which indicates that the
input dataset contained conflicting state data at once suggesting that the
individual is in some stage but is also dead. The latter is removed if the
conflict occurs in occasion t or t1, as only living entries
are allowed in time t and time t1 may involve living entries
as well as nonliving entries immediately prior to birth.
Care should be taken to avoid variables with negative values indicating size, fecundity, or reproductive or observation status. Negative values can be interpreted in different ways, typically reflecting estimation through other algorithms rather than actual measured data. Variables holding negative values can conflict with data management algorithms in ways that are difficult to predict.
Unusual errors (e.g. "Error in .pfj..."
) may occur in cases where the
variables are improperly passed, where seemingly numeric variables include
text, or where the blocksize
is improperly set.
Density estimation is performed as a count of individuals alive and within
the radius specified in spacing
of the respective individual at some
point in time.
If a censor variable is included for each monitoring occasion, and the
blocksize
option is set, then the user must set
censorRepeat = TRUE
in order to censor the correct transitions.
Failing this step will likely lead to the loss of a large portion of the data
as all data for entire individuals will be excluded.
# Lathyrus example using blocksize  when repeated patterns exist in variable # order data(lathyrus) sizevector < c(0, 100, 13, 127, 3730, 3800, 0) stagevector < c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm") repvector < c(0, 0, 0, 0, 0, 1, 0) obsvector < c(0, 1, 1, 1, 1, 1, 0) matvector < c(0, 0, 1, 1, 1, 1, 1) immvector < c(1, 1, 0, 0, 0, 0, 0) propvector < c(1, 0, 0, 0, 0, 0, 0) indataset < c(0, 1, 1, 1, 1, 1, 1) binvec < c(0, 100, 11, 103, 3500, 3800, 0.5) lathframe < sf_create(sizes = sizevector, stagenames = stagevector, repstatus = repvector, obsstatus = obsvector, matstatus = matvector, immstatus = immvector, indataset = indataset, binhalfwidth = binvec, propstatus = propvector) lathvert < verticalize3(lathyrus, noyears = 4, firstyear = 1988, patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9, juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88", fecacol = "Intactseed88", deadacol = "Dead1988", nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea", censorcol = "Missing1988", censorkeep = NA, censor = TRUE) lathsupp3 < supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"), stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"), stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"), eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"), eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"), eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"), givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA), multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA), type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1), stageframe = lathframe, historical = TRUE) ehrlen3 < rlefko3(data = lathvert, stageframe = lathframe, year = "all", stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3, yearcol = "year2", indivcol = "individ") ehrlen3mean < lmean(ehrlen3) ehrlen3mean$A[[1]] # Lathyrus example without blocksize  when no repeated patterns exist in # variable order and all variables names are specified data(lathyrus) sizevector < c(0, 100, 13, 127, 3730, 3800, 0) stagevector < c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm") repvector < c(0, 0, 0, 0, 0, 1, 0) obsvector < c(0, 1, 1, 1, 1, 1, 0) matvector < c(0, 0, 1, 1, 1, 1, 1) immvector < c(1, 1, 0, 0, 0, 0, 0) propvector < c(1, 0, 0, 0, 0, 0, 0) indataset < c(0, 1, 1, 1, 1, 1, 1) binvec < c(0, 100, 11, 103, 3500, 3800, 0.5) lathframe < sf_create(sizes = sizevector, stagenames = stagevector, repstatus = repvector, obsstatus = obsvector, matstatus = matvector, immstatus = immvector, indataset = indataset, binhalfwidth = binvec, propstatus = propvector) lathvert < verticalize3(lathyrus, noyears = 4, firstyear = 1988, patchidcol = "SUBPLOT", individcol = "GENET", juvcol = c("Seedling1988", "Seedling1989", "Seedling1990", "Seedling1991"), sizeacol = c("Volume88", "Volume89", "Volume90", "Volume91"), repstracol = c("FCODE88", "FCODE89", "FCODE90", "FCODE91"), fecacol = c("Intactseed88", "Intactseed89", "Intactseed90", "Intactseed91"), deadacol = c("Dead1988", "Dead1989", "Dead1990", "Dead1991"), nonobsacol = c("Dormant1988", "Dormant1989", "Dormant1990", "Dormant1991"), censorcol = c("Missing1988", "Missing1989", "Missing1990", "Missing1991"), stageassign = lathframe, stagesize = "sizea", censorkeep = NA, censor = TRUE) lathsupp3 < supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"), stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"), stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"), eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"), eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"), eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"), givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA), multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA), type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1), stageframe = lathframe, historical = TRUE) ehrlen3 < rlefko3(data = lathvert, stageframe = lathframe, year = "all", stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3, yearcol = "year2", indivcol = "individ") ehrlen3mean < lmean(ehrlen3) ehrlen3mean$A[[1]] # Cypripedium example using blocksize 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") cyp2mean < lmean(cypmatrix2r) cyp2mean # Cypripedium example using partial repeat patterns with blocksize and part # explicit variable name cast 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 = c("Inf.04", "Inf.05", "Inf.06", "Inf.07", "Inf.08", "Inf.09"), repstrbcol = c("Inf2.04", "Inf2.05", "Inf2.06", "Inf2.07", "Inf2.08", "Inf2.09"), 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") cyp2mean < lmean(cypmatrix2r) cyp2mean
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