print.remoteTS {remotePARTS}

S3 print method for remoteTS class

Description

S3 print method for remoteTS class

S3 summary method for remoteTS class

S3 print method for mapTS class

S3 summary method for mapTS class

helper summary function (matrix)

helper summary function (vector)

Usage

## S3 method for class 'remoteTS'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  ...
)

## S3 method for class 'remoteTS'
summary(
  object,
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  ...
)

## S3 method for class 'mapTS'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'mapTS'
summary(
  object,
  digits = max(3L, getOption("digits") - 3L),
  CL = 0.95,
  na.rm = TRUE,
  ...
)

smry_funM(x, CL = 0.95, na.rm = TRUE)

smry_funV(x, CL = 0.95, na.rm = TRUE)

Arguments

x

numeric matrix

digits

significant digits to show

signif.stars

logical, passed to stats::printCoefmat

...

additional parameters passed to further print methods

object

mapTS object

CL

confidence level (default = .95)

na.rm

logical, should observations with NA be removed?

Value

returns formatted output

returns formatted output, including summary stats

returns formatted output

returns formatted summary stats

summary statistics for each column including quartiles, mean, and upper and lower confidence levels (given by CL)

summary statistics including quartiles, mean, and upper and lower confidence levels (given by CL)

Examples

# simulate dummy data
 time.points = 9 # time series length
 map.width = 5 # square map width
 coords = expand.grid(x = 1:map.width, y = 1:map.width) # coordinate matrix
 ## create empty spatiotemporal variables:
 X <- matrix(NA, nrow = nrow(coords), ncol = time.points) # response
 Z <- matrix(NA, nrow = nrow(coords), ncol = time.points) # predictor
 # setup first time point:
 Z[, 1] <- .05*coords[,"x"] + .2*coords[,"y"]
 X[, 1] <- .5*Z[, 1] + rnorm(nrow(coords), 0, .05) #x at time t
 ## project through time:
 for(t in 2:time.points){
   Z[, t] <- Z[, t-1] + rnorm(map.width^2)
   X[, t] <- .2*X[, t-1] + .1*Z[, t] + .05*t + rnorm(nrow(coords), 0 , .25)
 }

 ## Pixel CLS
 tmp.df = data.frame(x = X[1, ], t = nrow(X), z = Z[1, ])
 CLS <- fitCLS(x ~ z, data = tmp.df)
 print(CLS)
 summary(CLS)
 residuals(CLS)
 coef(CLS)
 logLik(CLS)
 fitted(CLS)
 # plot(CLS) # doesn't work

 ## Pixel AR
 AR <- fitAR(x ~ z, data = tmp.df)
 print(AR)
 summary(AR)
 coef(AR)
 residuals(AR)
 logLik(AR)
 fitted(AR)
 # plot(AR) # doesn't work

 ## Map CLS
 CLS.map <- fitCLS_map(X, coords, y ~ Z, X.list = list(Z = Z), lag.x = 0, resids.only = TRUE)
 print(CLS.map)
 summary(CLS.map)
 residuals(CLS.map)
 # plot(CLS.map)# doesn't work

 CLS.map <- fitCLS_map(X, coords, y ~ Z, X.list = list(Z = Z), lag.x = 0, resids.only = FALSE)
 print(CLS.map)
 summary(CLS.map)
 coef(CLS.map)
 residuals(CLS.map)
 # logLik(CLS.map) # doesn't work
 fitted(CLS.map)
 # plot(CLS.map) # doesn't work

 ## Map AR
 AR.map <- fitAR_map(X, coords, y ~ Z, X.list = list(Z = Z), resids.only = TRUE)
 print(AR.map)
 summary(AR.map)
 residuals(AR.map)
 # plot(AR.map)# doesn't work

 AR.map <- fitAR_map(X, coords, y ~ Z, X.list = list(Z = Z), resids.only = FALSE)
 print(AR.map)
 summary(AR.map)
 coef(AR.map)
 residuals(AR.map)
 # logLik(AR.map) # doesn't work
 fitted(AR.map)
 # plot(AR.map) # doesn't work