makelags.RdCreate a lag matrix for a time series (or suite of time series) using given E
and tau values. Like in fitGP, either a data frame and column names or vectors/matrices
can be provided. Use forecast=TRUE to generate a forecast matrix. Use
vtimestep=TRUE to use the variable timestep method.
makelags(
data = NULL,
y,
pop = NULL,
E,
tau,
yname = NULL,
forecast = FALSE,
vtimestep = FALSE,
x = NULL,
time = NULL,
augment = FALSE,
Tdiff_max = NULL,
Tdiff_fore = NULL,
nreps = NULL,
append = FALSE
)A data frame, or matrix with named columns.
A vector containing a time series (usually the response variable).
Alternatively, a matrix or data frame where each column is a time series
(usually the response variable and covariates). In the case of multiple
time series, lags will be generated for each variable. If data is
supplied, the column names or indices.
A vector of populations (optional, if not supplied defaults to 1
population). If data is supplied, the column name or index.
Embedding dimension. Required.
Time delay. Required.
Optional, name of the variable if y is input as a vector, or variables
if y is a matrix with unnamed columns. Otherwise not used.
Defaults to "var" if NULL.
Produce a forecast matrix instead of the standard training/test matrix.
Use variable timestep method.
If using vtimestep=TRUE, put the response variable under use y, and
covariates under x (see details). If vtimestep=FALSE, will be appended
to y.
Used to generate forecast time if forecast=TRUE and to calculate
Tdiff if vtimestep=TRUE. If not supplied and vtimestep=TRUE, a numeric index is used.
If vtimestep=TRUE, produce augmentation lag matrix.
If vtimestep=TRUE, the maximum Tdiff value to allow in production
of the lag matrix or augmentation lag matrix.
If vtimestep=TRUE and forecast=TRUE, vector of time units to
forecast beyond the last timestep. Defaults to the minimum time difference between consecutive
timepoints, multipled by tau.
If augment=TRUE, the max number of delay vectors for each
Tdiff value.
Return data with the lags appended (defaults to FALSE). Only
relevant if forecast=FALSE and augment=FALSE.
A matrix with named columns, the appended number indicating the time lag. If
y has named columns, named columns in the lag matrix will match. If
generating forecasts, the output matrix will include a column for
population if there is more than one, and will include a time column if
time is supplied. If using the variable timestep method, the output matrix
will include lags of Tdiff (time difference).
When using the standard (fixed timestep) method, the response variable and
covariates can all be input under y, and lags will be generated for
all variables.
When using the variable timestep method, it is necessary to differentiate
between the response variable and covariates, as they are handled
differently. The response variable should go under y and the
covariates should go under x. The output matrix will include lags of
Tdiff (time difference).
An augmentation matrix for use with the variable timestep method can be
generated by setting vtimestep=TRUE and augment=TRUE. Under
default behavior, the augmentation matrix will include only the Tdiff
combinations observed in the original vtimestep matrix, up to nreps.
If you supply a vector Tdiff_fore, then the augmentation matrix will
include or all possible combinations of the Tdiff values supplied in
Tdiff_fore, even if they weren't in the original vtimestep matrix,
up to nreps.
If generating forecasts, the output matrix will include a column for
population if there is more than one, and will include a time column if
time is supplied. The time increment is based on the minimum
difference between timepoints. If generating forecasts using the variable
timestep method, a vector of time units to forecast beyond the last timestep
can be provided under Tdiff_fore.
set.seed(1)
yrand <- rnorm(20)
site <- rep(c("a","b"),each=10)
dfrand <- data.frame(firstvar=rnorm(20),secondvar=rnorm(20))
makelags(y=yrand,E=3,tau=1)
#> var_1 var_2 var_3
#> [1,] NA NA NA
#> [2,] -0.62645381 NA NA
#> [3,] 0.18364332 -0.62645381 NA
#> [4,] -0.83562861 0.18364332 -0.62645381
#> [5,] 1.59528080 -0.83562861 0.18364332
#> [6,] 0.32950777 1.59528080 -0.83562861
#> [7,] -0.82046838 0.32950777 1.59528080
#> [8,] 0.48742905 -0.82046838 0.32950777
#> [9,] 0.73832471 0.48742905 -0.82046838
#> [10,] 0.57578135 0.73832471 0.48742905
#> [11,] -0.30538839 0.57578135 0.73832471
#> [12,] 1.51178117 -0.30538839 0.57578135
#> [13,] 0.38984324 1.51178117 -0.30538839
#> [14,] -0.62124058 0.38984324 1.51178117
#> [15,] -2.21469989 -0.62124058 0.38984324
#> [16,] 1.12493092 -2.21469989 -0.62124058
#> [17,] -0.04493361 1.12493092 -2.21469989
#> [18,] -0.01619026 -0.04493361 1.12493092
#> [19,] 0.94383621 -0.01619026 -0.04493361
#> [20,] 0.82122120 0.94383621 -0.01619026
makelags(y=dfrand,E=2,tau=2)
#> firstvar_2 firstvar_4 secondvar_2 secondvar_4
#> [1,] NA NA NA NA
#> [2,] NA NA NA NA
#> [3,] 0.91897737 NA -0.1645236 NA
#> [4,] 0.78213630 NA -0.2533617 NA
#> [5,] 0.07456498 0.91897737 0.6969634 -0.1645236
#> [6,] -1.98935170 0.78213630 0.5566632 -0.2533617
#> [7,] 0.61982575 0.07456498 -0.6887557 0.6969634
#> [8,] -0.05612874 -1.98935170 -0.7074952 0.5566632
#> [9,] -0.15579551 0.61982575 0.3645820 -0.6887557
#> [10,] -1.47075238 -0.05612874 0.7685329 -0.7074952
#> [11,] -0.47815006 -0.15579551 -0.1123462 0.3645820
#> [12,] 0.41794156 -1.47075238 0.8811077 0.7685329
#> [13,] 1.35867955 -0.47815006 0.3981059 -0.1123462
#> [14,] -0.10278773 0.41794156 -0.6120264 0.8811077
#> [15,] 0.38767161 1.35867955 0.3411197 0.3981059
#> [16,] -0.05380504 -0.10278773 -1.1293631 -0.6120264
#> [17,] -1.37705956 0.38767161 1.4330237 0.3411197
#> [18,] -0.41499456 -0.05380504 1.9803999 -1.1293631
#> [19,] -0.39428995 -1.37705956 -0.3672215 1.4330237
#> [20,] -0.05931340 -0.41499456 -1.0441346 1.9803999
makelags(y=dfrand,pop=site,E=2,tau=1)
#> firstvar_1 firstvar_2 secondvar_1 secondvar_2
#> [1,] NA NA NA NA
#> [2,] 0.91897737 NA -0.1645236 NA
#> [3,] 0.78213630 0.91897737 -0.2533617 -0.1645236
#> [4,] 0.07456498 0.78213630 0.6969634 -0.2533617
#> [5,] -1.98935170 0.07456498 0.5566632 0.6969634
#> [6,] 0.61982575 -1.98935170 -0.6887557 0.5566632
#> [7,] -0.05612874 0.61982575 -0.7074952 -0.6887557
#> [8,] -0.15579551 -0.05612874 0.3645820 -0.7074952
#> [9,] -1.47075238 -0.15579551 0.7685329 0.3645820
#> [10,] -0.47815006 -1.47075238 -0.1123462 0.7685329
#> [11,] NA NA NA NA
#> [12,] 1.35867955 NA 0.3981059 NA
#> [13,] -0.10278773 1.35867955 -0.6120264 0.3981059
#> [14,] 0.38767161 -0.10278773 0.3411197 -0.6120264
#> [15,] -0.05380504 0.38767161 -1.1293631 0.3411197
#> [16,] -1.37705956 -0.05380504 1.4330237 -1.1293631
#> [17,] -0.41499456 -1.37705956 1.9803999 1.4330237
#> [18,] -0.39428995 -0.41499456 -0.3672215 1.9803999
#> [19,] -0.05931340 -0.39428995 -1.0441346 -0.3672215
#> [20,] 1.10002537 -0.05931340 0.5697196 -1.0441346
makelags(y=yrand,pop=site,E=2,tau=3,forecast = TRUE, yname="SomeName",time=c(1:10,1:10))
#> time pop SomeName_3 SomeName_6
#> 1 11 a 0.7383247 0.32950777
#> 2 12 a 0.5757814 -0.82046838
#> 3 13 a -0.3053884 0.48742905
#> 4 11 b 0.9438362 1.12493092
#> 5 12 b 0.8212212 -0.04493361
#> 6 13 b 0.5939013 -0.01619026
dfrand2 <- cbind.data.frame(Time=c(1:10,1:10),Site=site,dfrand)
makelags(data=dfrand2, y=c("firstvar","secondvar"),pop="Site",E=2,tau=3)
#> firstvar_3 firstvar_6 secondvar_3 secondvar_6
#> [1,] NA NA NA NA
#> [2,] NA NA NA NA
#> [3,] NA NA NA NA
#> [4,] 0.91897737 NA -0.1645236 NA
#> [5,] 0.78213630 NA -0.2533617 NA
#> [6,] 0.07456498 NA 0.6969634 NA
#> [7,] -1.98935170 0.91897737 0.5566632 -0.1645236
#> [8,] 0.61982575 0.78213630 -0.6887557 -0.2533617
#> [9,] -0.05612874 0.07456498 -0.7074952 0.6969634
#> [10,] -0.15579551 -1.98935170 0.3645820 0.5566632
#> [11,] NA NA NA NA
#> [12,] NA NA NA NA
#> [13,] NA NA NA NA
#> [14,] 1.35867955 NA 0.3981059 NA
#> [15,] -0.10278773 NA -0.6120264 NA
#> [16,] 0.38767161 NA 0.3411197 NA
#> [17,] -0.05380504 1.35867955 -1.1293631 0.3981059
#> [18,] -1.37705956 -0.10278773 1.4330237 -0.6120264
#> [19,] -0.41499456 0.38767161 1.9803999 0.3411197
#> [20,] -0.39428995 -0.05380504 -0.3672215 -1.1293631
makelags(data=dfrand2, y=c("firstvar","secondvar"),pop="Site",E=2,tau=3,
forecast = TRUE,time="Time")
#> Time Site firstvar_3 firstvar_6 secondvar_3 secondvar_6
#> 1 11 a -1.4707524 0.61982575 0.7685329 -0.6887557
#> 2 12 a -0.4781501 -0.05612874 -0.1123462 -0.7074952
#> 3 13 a 0.4179416 -0.15579551 0.8811077 0.3645820
#> 4 11 b -0.0593134 -1.37705956 -1.0441346 1.4330237
#> 5 12 b 1.1000254 -0.41499456 0.5697196 1.9803999
#> 6 13 b 0.7631757 -0.39428995 -0.1350546 -0.3672215