R version 3

R version 3.2.1 (2015-06-18) -- "World-Famous Astronaut"

Copyright (C) 2015 The R Foundation for Statistical Computing

Platform: x86_64-w64-mingw32/x64 (64-bit)

R es un software libre y viene sin GARANTIA ALGUNA.

Usted puede redistribuirlo bajo ciertas circunstancias.

Escriba 'license()' o 'licence()' para detalles de distribucion.

R es un proyecto colaborativo con muchos contribuyentes.

Escriba 'contributors()' para obtener más información y

'citation()' para saber cómo citar R o paquetes de R en publicaciones.

Escriba 'demo()' para demostraciones, 'help()' para el sistema on-line de ayuda,

o 'help.start()' para abrir el sistema de ayuda HTML con su navegador.

Escriba 'q()' para salir de R.

> 2+2

[1] 4

> x

Error: objeto 'x' no encontrado

> x<-2

> x

[1] 2

> x+x

[1] 4

> y<-x+x

> y

[1] 4

> peso<-c(60,70,80,90,100)

> peso

[1] 60 70 80 90 100

> Enorm(15)

Error: no se pudo encontrar la función "Enorm"

> Ernorm(15)

Error: no se pudo encontrar la función "Ernorm"

> Enorm(15)

Error: no se pudo encontrar la función "Enorm"

> rnorm(15)

[1] -0.09156132 0.27116751 1.14782468 -0.93962827 1.82457948 -0.67277553

[7] 0.59714 248 -1.28884141 2.40275865 -0.25590020 -0.47069998 -0.08833632

[13] 0.66677849 0.51115291 -1.96689498

> al<-rnorm(15)

> al

[1] 0.22074328 -2.03384002 1.48783896 0.87069384 2.84172017 -0.73843798

[7] 1.11238844 -0.09468982 -0.54074205 1.82273762 1.60791268 1.20035212

[13] -0.25661125 0.71722739 -0.95860322

> al[13]

[1] -0.2566113

> mean(al)

[1] 0.4839127

> max(al)

[1] 2.84172

> min(al)

[1] -2.03384

> sum(al)

[1] 7.25869

> al[al>0]

[1] 0.2207433 1.4878390 0.8706938 2.8417202 1.1123884 1.8227376 1.6079127

[8] 1.2003521 0.7172274

> max(peso)

[1] 100

> demo()

> demo(graphica)

Error in demo(graphica) : No demo found for topic ‘graphica’

> demo(graphics)

demo(graphics)

---- ~~~~~~~~

Type <Return> to start :

> # Copyright (C) 1997-2009 The R Core Team

>

> require(datasets)

> require(grDevices); require(graphics)

> ## Here is some code which illustrates some of the differences between

> ## R and S graphics capabilities. Note that colors are generally specified

> ## by a character string name (taken from the X11 rgb.txt file) and that line

> ## textures are given similarly. The parameter "bg" sets the background

> ## parameter for the plot and there is also an "fg" parameter which sets

> ## the foreground color.

>

>

> x <- stats::rnorm(50)

> opar <- par(bg = "white")

> plot(x, ann = FALSE, type = "n")

Esperando para confirmar cambio de página...

> abline(h = 0, col = gray(.90))

> lines(x, col = "green4", lty = "dotted")

> points(x, bg = "limegreen", pch = 21)

> title(main = "Simple Use of Color In a Plot",

+ xlab = "Just a Whisper of a Label",

+ col.main = "blue", col.lab = gray(.8),

+ cex.main = 1.2, cex.lab = 1.0, font.main = 4, font.lab = 3)

> ## A little color wheel. This code just plots equally spaced hues in

> ## a pie chart. If you have a cheap SVGA monitor (like me) you will

> ## probably find that numerically equispaced does not mean visually

> ## equispaced. On my display at home, these colors tend to cluster at

> ## the RGB primaries. On the other hand on the SGI Indy at work the

> ## effect is near perfect.

>

> par(bg = "gray")

> pie(rep(1,24), col = rainbow(24), radius = 0.9)


> title(main = "A Sample Color Wheel", cex.main = 1.4, font.main = 3)

> title(xlab = "(Use this as a test of monitor linearity)",

+ cex.lab = 0.8, font.lab = 3)

> ## We have already confessed to having these. This is just showing off X11

> ## color names (and the example (from the postscript manual) is pretty "cute".

>

> pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)

> names(pie.sales) <- c("Blueberry", "Cherry",

+ "Apple", "Boston Cream", "Other", "Vanilla Cream")

> pie(pie.sales,

+ col = c("purple","violetred1","green3","cornsilk","cyan","white"))


> title(main = "January Pie Sales", cex.main = 1.8, font.main = 1)

> title(xlab = "(Don't try this at home kids)", cex.lab = 0.8, font.lab = 3)

> ## Boxplots: I couldn't resist the capability for filling the "box".

> ## The use of color seems like a useful addition, it focuses attention

> ## on the central bulk of the data.

>

> par(bg="cornsilk")

> n <- 10

> g <- gl(n, 100, n*100)

> x <- rnorm(n*100) + sqrt(as.numeric(g))

> boxplot(split(x,g), col="lavender", notch=TRUE)


> title(main="Notched Boxplots", xlab="Group", font.main=4, font.lab=1)

> ## An example showing how to fill between curves.

>

> par(bg="white")

> n <- 100

> x <- c(0,cumsum(rnorm(n)))

> y <- c(0,cumsum(rnorm(n)))

> xx <- c(0:n, n:0)

> yy <- c(x, rev(y))

> plot(xx, yy, type="n", xlab="Time", ylab="Distance")


> polygon(xx, yy, col="gray")

> title("Distance Between Brownian Motions")

> ## Colored plot margins, axis labels and titles. You do need to be

> ## careful with these kinds of effects. It's easy to go completely

> ## over the top and you can end up with your lunch all over the keyboard.

> ## On the other hand, my market research clients love it.

>

> x <- c(0.00, 0.40, 0.86, 0.85, 0.69, 0.48, 0.54, 1.09, 1.11, 1.73, 2.05, 2.02)

> par(bg="lightgray")

> plot(x, type="n", axes=FALSE, ann=FALSE)


> usr <- par("usr")

> rect(usr[1], usr[3], usr[2], usr[4], col="cornsilk", border="black")

> lines(x, col="blue")

> points(x, pch=21, bg="lightcyan", cex=1.25)

> axis(2, col.axis="blue", las=1)

> axis(1, at=1:12, lab=month.abb, col.axis="blue")

> box()

> title(main= "The Level of Interest in R", font.main=4, col.main="red")

> title(xlab= "1996", col.lab="red")

> ## A filled histogram, showing how to change the font used for the

> ## main title without changing the other annotation.

>


> x <- rnorm(1000)

> hist(x, xlim=range(-4, 4, x), col="lavender", main="")


> title(main="1000 Normal Random Variates", font.main=3)

> ## A scatterplot matrix

> ## The good old Iris data (yet again)

>

> pairs(iris[1:4], main="Edgar Anderson's Iris Data", font.main=4, pch=19)


> pairs(iris[1:4], main="Edgar Anderson's Iris Data", pch=21,

+ bg = c("red", "green3", "blue")[unclass(iris$Species)])


> ## Contour plotting

> ## This produces a topographic map of one of Auckland's many volcanic "peaks".

>

> x <- 10*1:nrow(volcano)

> y <- 10*1:ncol(volcano)

> lev <- pretty(range(volcano), 10)

> par(bg = "lightcyan")

> pin <- par("pin")

> xdelta <- diff(range(x))

> ydelta <- diff(range(y))

> xscale <- pin[1]/xdelta

> yscale <- pin[2]/ydelta

> scale <- min(xscale, yscale)

> xadd <- 0.5*(pin[1]/scale - xdelta)

> yadd <- 0.5*(pin[2]/scale - ydelta)

> plot(numeric(0), numeric(0),

+ xlim = range(x)+c(-1,1)*xadd, ylim = range(y)+c(-1,1)*yadd,

+ type = "n", ann = FALSE)


> usr <- par("usr")

> rect(usr[1], usr[3], usr[2], usr[4], col="green3")

> contour(x, y, volcano, levels = lev, col="yellow", lty="solid", add=TRUE)

> box()

> title("A Topographic Map of Maunga Whau", font= 4)

> title(xlab = "Meters North", ylab = "Meters West", font= 3)

> mtext("10 Meter Contour Spacing", side=3, line=0.35, outer=FALSE,

+ at = mean(par("usr")[1:2]), cex=0.7, font=3)

> ## Conditioning plots

>


> coplot(lat ~ long | depth, data = quakes, pch = 21, bg = "green3")


> par(opar)

>

> demo(colors)

demo(colors)

---- ~~~~~~


> ### ----------- Show (almost) all named colors ---------------------

>

> ## 1) with traditional 'graphics' package:

> showCols1 <- function(bg = "gray", cex = 0.75, srt = 30) {

+ m <- ceiling(sqrt(n <- length(cl <- colors())))

+ length(cl) <- m*m; cm <- matrix(cl, m)

+ ##

+ require("graphics")

+ op <- par(mar=rep(0,4), ann=FALSE, bg = bg); on.exit(par(op))

+ plot(1:m,1:m, type="n", axes=FALSE)

+ text(col(cm), rev(row(cm)), cm, col = cl, cex=cex, srt=srt)

+ }

> showCols1()


> ## 2) with 'grid' package:

> showCols2 <- function(bg = "grey", cex = 0.75, rot = 30) {

+ m <- ceiling(sqrt(n <- length(cl <- colors())))

+ length(cl) <- m*m; cm <- matrix(cl, m)

+ ##

+ require("grid")

+ grid.newpage(); vp <- viewport(w = .92, h = .92)

+ grid.rect(gp=gpar(fill=bg))

+ grid.text(cm, x = col(cm)/m, y = rev(row(cm))/m, rot = rot,

+ vp=vp, gp=gpar(cex = cex, col = cm))

+ }

> showCols2()

Loading required package: grid


> showCols2(bg = "gray33")


> ###

>

> ##' @title Comparing Colors

> ##' @param col

> ##' @param nrow

> ##' @param ncol

> ##' @param txt.col

> ##' @return the grid layout, invisibly

> ##' @author Marius Hofert, originally

> plotCol <- function(col, nrow=1, ncol=ceiling(length(col) / nrow),

+ txt.col="black") {

+ stopifnot(nrow >= 1, ncol >= 1)

+ if(length(col) > nrow*ncol)

+ warning("some colors will not be shown")

+ require(grid)

+ grid.newpage()

+ gl <- grid.layout(nrow, ncol)

+ pushViewport(viewport(layout=gl))

+ ic <- 1

+ for(i in 1:nrow) {

+ for(j in 1:ncol) {

+ pushViewport(viewport(layout.pos.row=i, layout.pos.col=j))

+ grid.rect(gp= gpar(fill=col[ic]))

+ grid.text(col[ic], gp=gpar(col=txt.col))

+ upViewport()

+ ic <- ic+1

+ }

+ }

+ upViewport()

+ invisible(gl)

+ }

> ## A Chocolate Bar of colors:

> plotCol(c("#CC8C3C", paste0("chocolate", 2:4),

+ paste0("darkorange", c("",1:2)), paste0("darkgoldenrod", 1:2),

+ "orange", "orange1", "sandybrown", "tan1", "tan2"),

+ nrow=2)


> ##' Find close R colors() to a given color {original by Marius Hofert)

> ##' using Euclidean norm in (HSV / RGB / ...) color space

> nearRcolor <- function(rgb, cSpace = c("hsv", "rgb255", "Luv", "Lab"),

+ dist = switch(cSpace, "hsv" = 0.10, "rgb255" = 30,

+ "Luv" = 15, "Lab" = 12))

+ {

+ if(is.character(rgb)) rgb <- col2rgb(rgb)

+ stopifnot(length(rgb <- as.vector(rgb)) == 3)

+ Rcol <- col2rgb(.cc <- colors())

+ uniqC <- !duplicated(t(Rcol)) # gray9 == grey9 (etc)

+ Rcol <- Rcol[, uniqC] ; .cc <- .cc[uniqC]

+ cSpace <- match.arg(cSpace)

+ convRGB2 <- function(Rgb, to)

+ t(convertColor(t(Rgb), from="sRGB", to=to, scale.in=255))

+ ## the transformation, rgb{0..255} --> cSpace :

+ TransF <- switch(cSpace,

+ "rgb255" = identity,

+ "hsv" = rgb2hsv,

+ "Luv" = function(RGB) convRGB2(RGB, "Luv"),

+ "Lab" = function(RGB) convRGB2(RGB, "Lab"))

+ d <- sqrt(colSums((TransF(Rcol) - as.vector(TransF(rgb)))^2))

+ iS <- sort.list(d[near <- d <= dist])# sorted: closest first

+ setNames(.cc[near][iS], format(d[near][iS], digits=3))

+ }

> nearRcolor(col2rgb("tan2"), "rgb")

0.0 21.1 25.8 29.5

"tan2" "tan1" "sandybrown" "sienna1"

> nearRcolor(col2rgb("tan2"), "hsv")

0.0000 0.0410 0.0618 0.0638 0.0667 0.0766

"tan2" "sienna2" "coral2" "tomato2" "tan1" "coral"

0.0778 0.0900 0.0912 0.0918

"sienna1" "sandybrown" "coral1" "tomato"

> nearRcolor(col2rgb("tan2"), "Luv")

0.00 7.42 7.48 12.41 13.69

"tan2" "tan1" "sandybrown" "orange3" "orange2"

> nearRcolor(col2rgb("tan2"), "Lab")

0.00 5.56 8.08 11.31

"tan2" "tan1" "sandybrown" "peru"

> nearRcolor("#334455")

0.0867

"darkslategray"

> ## Now, consider choosing a color by looking in the

> ## neighborhood of one you know :

>

> plotCol(nearRcolor("deepskyblue", "rgb", dist=50))


> plotCol(nearRcolor("deepskyblue", dist=.1))


> install.packages("gstat")

Warning in install.packages("gstat") :

'lib = "C:/Program Files/R/R-3.2.1/library"' is not writable

--- Please select a CRAN mirror for use in this session ---

also installing the dependencies ‘xts’, ‘intervals’, ‘sp’, ‘zoo’, ‘spacetime’, ‘FNN’

probando la URL 'http://cran.revolutionanalytics.com/bin/windows/contrib/3.2/xts_0.9-7.zip'

Content type 'application/zip' length 662241 bytes (646 KB)

downloaded 646 KB

probando la URL 'http://cran.revolutionanalytics.com/bin/windows/contrib/3.2/intervals_0.15.0.zip'


downloaded 638 KB

probando la URL 'http://cran.revolutionanalytics.com/bin/windows/contrib/3.2/sp_1.1-1.zip'

Content type 'application/zip' length 1519801 bytes (1.4 MB)

downloaded 1.4 MB

probando la URL 'http://cran.revolutionanalytics.com/bin/windows/contrib/3.2/zoo_1.7-12.zip'


downloaded 876 KB

probando la URL 'http://cran.revolutionanalytics.com/bin/windows/contrib/3.2/spacetime_1.1-4.zip'


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probando la URL 'http://cran.revolutionanalytics.com/bin/windows/contrib/3.2/FNN_1.1.zip'


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package ‘xts’ successfully unpacked and MD5 sums checked

package ‘intervals’ successfully unpacked and MD5 sums checked

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The downloaded binary packages are in

C:\Users\Isela\AppData\Local\Temp\RtmpMNf0b5\downloaded_packages

> q()

>

> demo(gstat)

Error in demo(gstat) : No demo found for topic ‘gstat’

> demo(gstat)

Error in demo(gstat) : No demo found for topic ‘gstat’

> library (gstat)

Warning message:

package ‘gstat’ was built under R version 3.2.2

> demo()

> demo(examples)

demo(examples)

---- ~~~~~~~~


> # $Id: examples.R,v 1.6 2006-02-10 19:05:02 edzer Exp $

> ## ex01.cmd, ex02.cmd:

> ##

> ## Two variables with (initial estimates of) variograms,

> ## calcute sample variogram and plot fitted model

> ##

>

> library(sp)

> par(ask = TRUE)

> data(meuse)

> coordinates(meuse)=~x+y

> x <- variogram(zinc ~ 1, meuse)

> v <- vgm(140000, "Sph", 800, nug = 10000)

> plot(x, model = v)


> plot(x, model = fit.variogram(x, model = v))


> x <- variogram(log(zinc) ~ 1, meuse)

> v <- vgm(.5, "Sph", 800, nug = .1)

> plot(x, model = v)


> plot(x, model = fit.variogram(x, model = v))


> ##

> ## ex03.cmd:

> ## Inverse distance interpolation on a mask map

> ##

> data(meuse.grid)

> gridded(meuse.grid) = ~x+y

> x <- krige(zinc ~ 1, meuse, meuse.grid, model = NULL)

[inverse distance weighted interpolation]

> library(lattice)

> spplot(x[1])


> ##

> ## ex04.cmd

> ## Local ordinary block kriging at non-gridded locations

> ##

> ## the gstat "classic" radius maps into the gstat "S" maxdist argument

> ##

> new.locs <- SpatialPoints(cbind(x = c(181170, 180310, 180205, 178673, 178770, 178270),

+ y = c(333250, 332189, 331707, 330066, 330675, 331075)))

> krige(zinc ~ 1, meuse, new.locs,

+ model = vgm(1.34e5, "Sph", 800, nug = 2.42e4),

+ block = c(40,40), nmax = 40, nmin = 20,

+ maxdist = 1000)

[using ordinary kriging]

coordinates var1.pred var1.var

1 (181170, 333250) 268.7576 19447.67

2 (180310, 332189) 663.4915 16991.33

3 (180205, 331707) 251.4606 21579.71

4 (178673, 330066) 532.5776 73807.91

5 (178770, 330675) 664.4039 23589.17

6 (178270, 331075) 647.6618 175360.21

> ##

> ## ex05.cmd

> ##

> ## Local simple point kriging on a mask map

> ##

> v <- vgm(0.581, "Sph", 900, nug = 0.0554)

> x <- krige(log(zinc) ~ 1, meuse, meuse.grid, model = v,

+ nmax = 40, nmin = 20, maxdist = 1000, beta = 5.9)

[using simple kriging]

> spplot(x[1], main = "log(zinc) simple kriging prediction")


> x$se = sqrt(x$var1.var)

> spplot(x["se"], main = "log(zinc) simple kriging standard errors")


> ##

> ## ex06.cmd

> ##

> ## Unconditional Gaussian simulation on a mask

> ## (local neigbourhoods, simple kriging)

> ##

> x <- krige(log(zinc) ~ 1, locations = NULL, newdata = meuse.grid,

+ model = v, nmax = 20, beta = c(5.9), nsim = 5, dummy = TRUE)

[using unconditional Gaussian simulation]

> spplot(x, main = "five unconditional realisations of a correlated Gaussian field")


> ##

> ## ex07.cmd

> ##

> ## Gaussian simulation, conditional upon data

> ## (local neighbourhoods, simple and ordinary kriging)

> ##

> x <- krige(log(zinc) ~ 1, meuse, meuse.grid,

+ model = v, nmax = 20, beta = c(5.9), nsim = 5)

[using conditional Gaussian simulation]

> spplot(x, main = "five conditional realisations of a correlated Gaussian field")


> ##

> ## ex08.cmd

> ##

> ## Change of support: local ordinary block kriging on a mask

> ##

> x <- krige(log(zinc) ~ 1, meuse, meuse.grid,

+ model = v, nmax = 40, nmin = 20, maxdist = 1000,

+ block = c(40,40))


> spplot(x[1], main = "ordinary block kriging predictions")


> x$se = sqrt(x$var1.var)

> spplot(x["se"], main = "ordinary block kriging prediction standard errors")


> ##

> ## ex09.cmd

> ##

> ## Obtain map values at data() locations

> ## (Point-map overlay)

> ##

> # we trick here by using inv.weighted distance interpolation, using the

> # single nearest observation. It will not fail on points outside the grid.

> # Note that we reversed meuse.grid and meuse to get these results.

> x <- krige(part.a ~ 1, meuse.grid, meuse, model = NULL, nmax = 1)


> meuse$part.a = x$var1.pred

> x <- krige(part.b ~ 1, meuse.grid, meuse, model = NULL, nmax = 1)


> meuse$part.b = x$var1.pred

> ##

> ## ex10.cmd

> ##

> ## Multiple kriging: prediction on more than one variable

> ## (ordinary kriging of two variables)

> ## (note that zinc_map.eas wass obtained through ex09.gst)

> ##

> x <- variogram(dist~1,meuse)

> v.dist <- fit.variogram(x, model = vgm(1,"Gau",100))

> plot(x, model = v.dist)


> g <- gstat(id = "ln.zinc", form = log(zinc) ~ 1,

+ data = meuse, nmax = 40, nmin = 20, maxdist = 1000, model = v)

> g <- gstat(g, id = "dist", form = dist ~ 1,

+ data = meuse, nmax = 40, nmin = 20, maxdist = 1000,

+ model = vgm(.01, "Nug", 0, add.to = v.dist))

> # the added nugget variance is necessary to avoid near-singular covariances

> x <- predict(g, meuse.grid)


> spplot(x["ln.zinc.pred"], main = "log(zinc) ordinary kriging predictions")


> x$ln.zinc.se = sqrt(x$ln.zinc.var)

> spplot(x["ln.zinc.se"], main = "log(zinc) ordinary kriging prediction standard errors")


> spplot(x["dist.pred"], main = "dist ordinary kriging predictions")


> x$dist.se = sqrt(x$dist.var)

> spplot(x["dist.se"], main = "dist ordinary kriging prediction standard errors")


> ##

> ## ex11.cmd

> ##

> ## Multivariable kriging: ordinary local cokriging of two variables

> ## For examples of fitting an LMC: see demo(cokriging)

> ##

> g <- gstat(id = "ln.zinc", form = log(zinc) ~ 1,


+ model = vgm(0.581, "Sph", 900, 0.0554))

> g <- gstat(g, id = "sq.dist", form = sqrt(dist) ~ 1,


+ model = vgm(0.0631, "Sph", 900, 0.0001))

> g <- gstat(g, id = c("ln.zinc", "sq.dist"),

+ model = vgm(-0.156, "Sph", 900, 1e-5))

> # small nugget necessary to let gstat recognize LMC

> x <- predict(g, meuse.grid)

Linear Model of Coregionalization found. Good.

[using ordinary cokriging]

> spplot(x["ln.zinc.pred"], main = "log(zinc) ordinary cokriging predictions")


> x$ln.zinc.se = sqrt(x$ln.zinc.var)

> spplot(x["ln.zinc.se"], main = "log(zinc) ordinary cokriging prediction standard errors")


> spplot(x["sq.dist.pred"], main = "dist ordinary cokriging predictions")


> x$sq.dist.se = sqrt(x$sq.dist.var)

> spplot(x["sq.dist.se"], main = "dist ordinary cokriging prediction standard errors")


> ##

> ## ex12.cmd

> ##

> ## Stratified ordinary kriging (within-category ordinary kriging)

> ##

>

> # find out in which part the data are:

> meuse$part.a = krige(part.a~1, meuse.grid, meuse, nmax=1)$var1.pred


> x1 = krige(log(zinc)~1, meuse[meuse$part.a == 0,],

+ meuse.grid[meuse.grid$part.a == 0,], model = vgm(.548, "Sph", 900, .0654),

+ nmin = 20, nmax = 40, maxdist = 1000)


> x2 = krige(log(zinc)~1, meuse[meuse$part.a == 1,],

+ meuse.grid[meuse.grid$part.a == 1,], model = vgm(.716, "Sph", 900),

+ nmin = 20, nmax = 40, maxdist = 1000)


> x = rbind(as.data.frame(x1), as.data.frame(x2))

> gridded(x) = ~x+y

> spplot(x["var1.pred"], main = "stratified kriging predictions")


> ##

> ## ex13.cmd

> ##

> ## Local universal kriging, using one continuous variable

> ###

> ## the variogram should be that of the residual:

> x <- krige(log(zinc) ~ sqrt(dist), meuse, meuse.grid,

+ model = vgm(.149, "Sph", 700, .0674),

+ nmax = 40, nmin = 20, maxdist = 1000)

[using universal kriging]

> spplot(x["var1.pred"], main = "universal kriging predictions")


> x$var1.se = sqrt(x$var1.var)

> spplot(x["var1.se"], main = "universal kriging prediction standard errors")


> ##

> ## ex14.cmd

> ##

> ## Universal kriging, using one continuous and

> ## two binary variables.

> ##

> x <- krige(log(zinc) ~ -1 + sqrt(dist)+ part.a + part.b,

+ meuse, meuse.grid, model = vgm(.149, "Sph", 700, .0674))

[using universal kriging]

> spplot(meuse.grid["part.a"], main = "the areas defining part.a (1) and part.b (0)")


> spplot(x["var1.pred"], main = "universal kriging predictions")



> spplot(x["var1.se"], main = "universal kriging prediction standard errors")


> ##

> ## ex14a.cmd

> ##

> ## stratified universal kriging:

> ## (again: not implemented)

> ##

>

> ## ex15.cmd

> ##

> ## Local linear model, using one continuous variable

> ##

> x <- krige(log(zinc) ~ sqrt(dist), meuse, meuse.grid,

+ model = NULL, nmax = 40, nmin = 20, maxdist = 1000)

[ordinary or weighted least squares prediction]

> spplot(x["var1.pred"], main = "IID local linear model kriging predictions")



> spplot(x["var1.se"], main = "IID local linear model prediction standard errors")


> ##

> ## ex16.cmd

> ##

> ## Multivariable indicator cosimulation

> ## ==>> see demo(cosimulation) for an extended example how to do this

> ##

>

> ##

> ## ex17.cmd

> ##

> ## global coordinate polynomial trend surfaces

> ## trend orders 0-3 ==>> better use lm() for this

> ##

Warning message:

package ‘sp’ was built under R version 3.2.2

> x<-c(1,2,3,4,5,6,7,8,9,10)

> x$

+

+

+

+

+ c

Error in x$c : $ operator is invalid for atomic vectors

> x^2

[1] 1 4 9 16 25 36 49 64 81 100

> x<-seq(0,4,0.1)

> x

[1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

[20] 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7

[39] 3.8 3.9 4.0

> mean(x)

[1] 2

> plot(x,x^2,type="l")

> plot(x,x^2,type="p")

> plot(x,x^2,type="l"

+ c

Error: unexpected symbol in:

"plot(x,x^2,type="l"

c"

> plot(x,x^2,type="l")

> plot(x,x^2)

> plot(x,x^2,type="l",col="blue")

> x=rnom(30)

Error: no se pudo encontrar la función "rnom"

> x=rnorm (30)

> p<-rnorm (30)

> p

[1] -0.891861978 -0.122828218 0.877440176 1.267248900 0.627286959

[6] -0.378100966 0.858271393 0.303717256 -0.565343522 -1.928233269

[11] 0.746375452 -0.206070941 -0.010140040 -0.473720599 -2.022354366

[16] 0.216574821 -0.601776872 0.002840592 -0.442315720 -1.672318091

[21] -1.050505918 0.375925133 -0.394971374 0.281650376 -1.589366158

[26] 0.182030517 1.888707124 2.660406643 -0.911409240 -0.576548984

> plot(x,x^2,type="l",col="blue")

> hist(p)

> e<-rnorm(30)

> plot(p)

> plot(e,p,type="l")

> p+e

[1] -0.85103940 1.40573141 1.11172503 0.54711536 0.81270268 -0.66928198

[7] 0.84658020 0.46128848 0.53052053 -1.79054859 0.83191301 -0.90060065

[13] 0.63033389 -1.63290437 -1.10382111 1.87431871 0.46317276 0.65025830

[19] -0.20072392 -1.43168431 -0.63078173 0.00272631 -1.32219185 -0.13892253

[25] -2.53684916 -0.59669804 0.66247343 3.19140369 -0.71840342 -0.49216694

> a=p,e

Error: inesperado ',' in "a=p,"

> r<-(p,e)

Error: inesperado ',' in "r<-(p,"

> p,e

Error: inesperado ',' in "p,"

> boxplot(p)

R version 3

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