Related
I am trying to customise the plot generated by:
plot(irf(VECMcoeff, n.ahead = 20, cumulative = FALSE, ortho = TRUE))
Current figure:
Not yet allowed to post figures.
Is there a way, using R's basis plotting functions, to adjust this plot? More specifically;
I would like to have plots stand-alone from each other.
Adjust x- and y-axis titles.
Adjust the main title.
Remove the '95 % Bootstrap CI, 100 runs'.
Thanks for any help!
Data:
dput(head(combined,25))
structure(c(3.12378036948822, 3.24514490963516, 2.54231015523096,
3.10758964326189, 3.26905177146087, 3.39086921629928, 3.39867627597089,
3.063339608249, 2.82158440194456, 3.00060851536641, 2.87498214357332,
2.73447964251719, 2.51961774067125, 2.43535838893541, 2.53536474393679,
2.11458263713232, 2.08443293370433, 1.70951997715485, 1.6939353104687,
1.99402766681289, 2.17851574489578, 2.02035721460859, 2.19849725222166,
2.12385225312224, 2.13870052300126, 2.53563259854902, 2.71236841778707,
2.80602806173539, 2.44978220282482, 2.22240349195674, 2.6269002941349,
2.55424892433652, 2.84227347851153, 3.00695212249206, 2.56409065301929,
2.11958065079056, 1.93021828518557, 1.91149187923047, 2.12824458610721,
1.99034383037538, 1.85993728242216, 1.78831122085649, 1.70508421574581,
1.34148894168009, 1.26428948883955, 1.53707667916106, 1.40125321322403,
1.56189928398736, 1.59267901471992, 1.29435444758231, 2.88357952825106,
3.2967949657277, 2.71315870827614, 2.88194083947586, 2.55384396254808,
2.48162552588286, 2.43461752858767, 2.60895931242784, 2.88699097436266,
3.06774759389068, 2.92820858177705, 1.9236817467793, 1.30469143981917,
1.63412478606386, 1.32569634585868, 1.66411340281953, 1.811114177636,
1.32324449480086, 0.683740288067047, 0.506428412402278, 0.244160570695116,
-0.0614637978267916, 0.11100051693192, 0.107431188637327, -0.0946163941762501,
1.56887584570782, 2.2953989716194, 2.3913948824343, 1.60366568545365,
2.14074303245166, 1.42821783272864, 1.14416900596202, 1.32550310805691,
1.06775704738626, 1.1754985484452, 1.30819594680082, 1.57801107586324,
1.57465869540119, 1.52953051921855, 1.59632502092932, 1.51164066108273,
1.74699133577352, 1.89513403376172, 1.50403737650093, 1.69077755145674,
1.51619819345532, 1.7908456553931, 1.63120428277988, 1.72264300428443,
1.91016040082409, 2.93953881174612, 0.573867521584496, 1.36693966408554,
1.33745582274447, 2.00217541671565, 1.47500074486359, 1.54892810099376,
1.52596101747453, 1.85097710190023, 1.8027452973638, 1.71255671138078,
1.78801314649281, 1.73039561596535, 1.7797925346833, 1.68662137367852,
2.10887254895115, 2.47630376444312, 2.10728662380876, 1.99939507617536,
2.1661652656972, 1.97780409080129, 2.08116163569287, 2.33934227442197,
2.38773088163046, 2.39899888596041), .Dim = c(25L, 5L), .Dimnames = list(
NULL, c("rstar.nl2", "rstar.ger2", "rstar.fr2", "rstar.sp2",
"rstar.it2")))
Somewhat general advice:
plot() is a generic function that actually calls a more specific function (called a "method") depending on what you are trying to plot (see this chapter from Hadley Wickham's Advanced R book for details). In this case, you are feeding-in an object of class "varirf" to plot(). You can see this by running, e.g.:
out <- irf(your_arguments_go_here)
class(out)
The generic function plot() is calling the method plot.varirf() because you are feeding in an object of type "varirf". To see which parameters of plot.varirf() you can control, check out that function's help page
?plot.varirf
If this doesn't provide you with sufficient control over what you want your plot to look like, then you should abandon trying to use plot.varif() and construct your desired plot manually, as in:
plot(x=my_x_vals, y=my_y_vals, main="My Title", pch=20, col="red", etc.)
In manually creating your plot, you may find it useful to see how plot.varirf plots are created so you can implement some of the same formatting. To view the source code for plot.varirf, use:
getAnywhere(plot.varirf)
Here is an example:
plot(x, plot.type = "single", names = NULL, main = "IRF to a one-standard deviation shock to APP", sub = NULL, lty = NULL, lwd = NULL, col = NULL, ylim = NULL, ylab = "Eonia", xlab = "Number of periods", mar.multi = c(0, 4, 0, 4), oma.multi = c(6, 4, 6, 4), adj.mtext = NA, padj.mtext = NA, col.mtext = NA)
where, x is your varest object,
Good luck!
Is it possible to draw a log price chart in the chart.Posn() or chart.Reconcile() functions of blotter? I tried adding log.scale = TRUE to the function call without success. Is the underlying chart_Series function still too "experimental" to support this functionality or is the function call not correct?
chart.Posn(Portfolio = portfolio.st, Symbol = "GSPC", log.scale = TRUE)
Update: I have been trying to use the chart_Series() function directly, setting the ylog graphical parameter:
par(ylog=TRUE)
chart_Series(Cl(GSPC))
But I receive an error "log scale needs positive bounds" despite the data being all positive.
Btw, GSPC is an OHLCV time-series xts of the S&P 500 that plots in chartSeries() and chart_Series(), but just not with log-scale for either charting functions.
I found this old post not as a solution but as an alternative:
Does chart_Series() work with logarithmic axis?
I don't think there is any parameter like log.scale that chart_Series recognises. You could simply do chart_Series(log(Cl(GSPC)). You could also do some basic modifications to chart.Posn to put things on the log scale. Use as a starting point the source code for chart.Posn.
Here is an example of a modified function you could make. You can obviously modify it further in any way you please.
# We need an example. So,
# Source this code from the directory containing quantstrat, or at least source the macd.R demo in quantstrat.
source("demo/macd.R")
log.chart.Posn <- function(Portfolio, Symbol, Dates = NULL, env = .GlobalEnv) {
pname<-Portfolio
Portfolio<-getPortfolio(pname)
x <- get(Symbol, env)
Prices <- log(x)
chart_Series(Prices)
#browser()
if(is.null(Dates)) Dates<-paste(first(index(Prices)),last(index(Prices)),sep='::')
#scope the data by Dates
Portfolio$symbols[[Symbol]]$txn<-Portfolio$symbols[[Symbol]]$txn[Dates]
Portfolio$symbols[[Symbol]]$posPL<-Portfolio$symbols[[Symbol]]$posPL[Dates]
Trades = Portfolio$symbols[[Symbol]]$txn$Txn.Qty
Buys = log(Portfolio$symbols[[Symbol]]$txn$Txn.Price[which(Trades>0)])
Sells = log(Portfolio$symbols[[Symbol]]$txn$Txn.Price[which(Trades<0)])
Position = Portfolio$symbols[[Symbol]]$txn$Pos.Qty
if(nrow(Position)<1) stop ('no transactions/positions to chart')
if(as.POSIXct(first(index(Prices)))<as.POSIXct(first(index(Position)))) Position<-rbind(xts(0,order.by=first(index(Prices)-1)),Position)
Positionfill = na.locf(merge(Position,index(Prices)))
CumPL = cumsum(Portfolio$symbols[[Symbol]]$posPL$Net.Trading.PL)
if(length(CumPL)>1)
CumPL = na.omit(na.locf(merge(CumPL,index(Prices))))
else
CumPL = NULL
if(!is.null(CumPL)) {
CumMax <- cummax(CumPL)
Drawdown <- -(CumMax - CumPL)
Drawdown<-rbind(xts(-max(CumPL),order.by=first(index(Drawdown)-1)),Drawdown)
} else {
Drawdown <- NULL
}
if(!is.null(nrow(Buys)) && nrow(Buys) >=1 ) (add_TA(Buys,pch=2,type='p',col='green', on=1));
if(!is.null(nrow(Sells)) && nrow(Sells) >= 1) (add_TA(Sells,pch=6,type='p',col='red', on=1));
if(nrow(Position)>=1) {
(add_TA(Positionfill,type='h',col='blue', lwd=2))
(add_TA(Position,type='p',col='orange', lwd=2, on=2))
}
if(!is.null(CumPL)) (add_TA(CumPL, col='darkgreen', lwd=2))
if(!is.null(Drawdown)) (add_TA(Drawdown, col='darkred', lwd=2, yaxis=c(0,-max(CumMax))))
plot(current.chob())
}
log.chart.Posn(Portfolio = portfolio.st, Sym = "AAPL", Dates = NULL, env = .GlobalEnv)
add_MACD() # Simply added to make the plot almost identical to what is in demo/macd.R
This is what the original chart looks like:
New plot, with log scales:
I'm working with the hpfilter from the mFilter package and I can't seem to find a simple way to convert the list of Time-Series objects by hpfilter to a format I can use with ggplot2. I realize I can take it all apart and put it back together, but I imagine there's some simple way I have overlooked? I tried the code suggested in the SO discussion R list to data frame. However I couldn't find a way to convert the list of Time-Series objects to a data.frame in any simple way. The final goal is to reproduce the default plot produced by the mFilter object (see below)
Here's some example code
# install.packages(c("mFilter"), dependencies = TRUE)
library(mFilter)
data(unemp)
unemp.hp <- hpfilter(unemp, type=c("lambda"), freq = 1606)
# str(unemp.hp)
class(unemp.hp)
# [1] "mFilter"
plot(unemp.hp)
Hit <Return> to see next plot:
Also, why am I asked to " Hit <Return>" to see the plot?
The plot function calls plot.mFilter which has parameter ask=interactive() and it is set as TRUE for interactive sessions,
you could disable this by ask=FALSE in call for plot
plot(unemp.hp,ask=FALSE)
Data:
library(mFilter)
library(ggplot2)
library(gridExtra)
# library(zoo)
data(unemp)
unemp.hp <- hpfilter(unemp, type=c("lambda"), freq = 1606)
# str(unemp.hp)
class(unemp.hp)
# [1] "mFilter"
plot(unemp.hp,ask=FALSE)
To check for slots of object unemp.hp
names(unemp.hp)
# [1] "cycle" "trend" "fmatrix" "title" "xname" "call" "type" "lambda" "method"
#[10] "x"
The relevant objects are x (the main unemp series) , trend and cycle. All three objects are of class ts, we first convert them to
data.frame using custom function and plot using ggplot and gridExtra (for grid.arrange)
objectList = list(unemp.hp$x,unemp.hp$trend,unemp.hp$cycle)
names(objectList) = c("unemp","trend","cycle")
sapply(objectList,class)
#unemp trend cycle
# "ts" "ts" "ts"
Conversion from ts to data.frame:
fn_ts_to_DF = function(x) {
DF = data.frame(date=zoo::as.Date(time(objectList[[x]])),tseries=as.matrix(objectList[[x]]))
colnames(DF)[2]=names(objectList)[x]
return(DF)
}
DFList=lapply(seq_along(objectList),fn_ts_to_DF)
names(DFList) = c("unemp","trend","cycle")
seriesTrend = merge(DFList$unemp,DFList$trend,by="date")
cycleSeries = DFList$cycle
Plots:
gSeries = ggplot(melt(seriesTrend,"date"),aes(x=date,y=value,color=variable)) + geom_line() +
ggtitle('Hodrick-Prescot Filter for unemp') +
theme(legend.title = element_blank(),legend.justification = c(0.1, 0.8), legend.position = c(0, 1),
legend.direction = "horizontal",legend.background = element_rect(fill="transparent",size=.5, linetype="dotted"))
gCycle = ggplot(cycleSeries,aes(x=date,y=cycle)) + geom_line(color="#619CFF") + ggtitle("Cyclical component (deviations from trend)")
gComb = grid.arrange(gSeries,gCycle,nrow=2)
I tried to use the prior answer, didn't worked for me.
I was getting the trend and cycle from a GDP quarterly series.
This data was a time series, so I did this, and worked for me:
list <- list(gdp_ln$x, gdp_ln$trend, gdp_ln$cycle)
names(list)=c("gdp","trend","cycle")
gdp<- data.frame((sapply(list,c)))
Data:
> dput(gdp_ln)
structure(c(16.0275785360442, 16.0477176062761, 16.0718936895007,
16.0899963371452, 16.0875707712141, 16.0981391378223, 16.0988601288276,
16.1110815092797, 16.1244321329861, 16.1384685077996, 16.1451472350838,
16.148178781735, 16.161163569502, 16.1418894206861, 16.1634877625667,
16.1965372621761, 16.2216815829736, 16.2387677536829, 16.249412380526,
16.2690521777631, 16.2812185880068, 16.2951024427095, 16.2964024092233,
16.3127733881018, 16.3233290487177, 16.3369922768377, 16.3486515031696,
16.3489275708763, 16.3451264371757, 16.3524856433069, 16.3666338513045,
16.3801691039135, 16.3959993202765, 16.4135937981601, 16.4321203154987,
16.4488104165345, 16.4344524213544, 16.4302554348621, 16.4240722287677,
16.425087582257, 16.4350803035092, 16.4507216431126, 16.4670532627455,
16.4985227751756, 16.5094864456079, 16.5352746165004, 16.5504689966469,
16.5594976247513, 16.5754312535087, 16.592641573353, 16.6003340665324,
16.6063100774853, 16.6163655606058, 16.6370227688187, 16.6564363783854,
16.6577160570216, 16.6543595214556, 16.6773721241902, 16.6911082706925,
16.6935398489076, 16.6956102943815, 16.6798673418354, 16.6772670544553,
16.6678707780266, 16.6606889172344, 16.6678398460835, 16.6668473810049,
16.676020524389, 16.6775934319312, 16.6882821147755, 16.6957985899994,
16.7032334217472, 16.6926036544774, 16.7027214366522, 16.7103625977254,
16.7105344224572, 16.7042504851486, 16.7063913529457, 16.7100598555556,
16.6960591147037, 16.686477079594, 16.5740423808036, 16.6181175035946
), .Tsp = c(2000, 2020.5, 4), class = "ts")
Hallo everyone can anybody help me to upgrade my code with possibility of insering additional data into my map. This is the code that draw me a map with intensity of migration, and I am trying to add ehtnic information of every region (many small pie charts).
to draw a map
con <- url("http://biogeo.ucdavis.edu/data/gadm2/R/UKR_adm1.RData")
print(load(con))
close(con)
name<-gadm$VARNAME_1
value<-c(4,2,5,2,1,2,4,2,2,4,1,1,1,4,3,3,1,1,3,1,2,4,5,3,4,2,1)
gadm$VARNAME_1<-as.factor(value)
col<- colorRampPalette(c('cadetblue4','cadetblue1','mediumseagreen','tan2','tomato3'))(260)
spplot(gadm, "VARNAME_1", main="Ukraine", scales = list(draw = TRUE), col.regions=col)
sp.label <- function(x, label) {
list("sp.text", coordinates(x), label)
}
NAME.sp.label <- function(x) {
sp.label(x, x$NAME_1)
}
draw.sp.label <- function(x) {
do.call("list", NAME.sp.label(x))
}
spplot(gadm, 'VARNAME_1', sp.layout = draw.sp.label(gadm), col.regions=col,
colorkey = list(labels = list( labels = c("Very low","Low", "Average",
"High","Very high"),
width = 1, cex = 1)))
and this is a part of df, that I am trying to add to that map as pie charts or bar charts, with every latitude (lat) and longitude (long) to locate mu bar or pie charts.
df<-data.frame(region=c('Kiev oblast', 'Donezk oblast'),
rus=c(45,35), ukr=c(65,76), mold=c(11,44),long=c(50.43,48),
lat=c(30.52, 37.82))
i found one example and another but... can't figure out how to use it in ma case.
Hope for your help, thank you.
only that solution i have discovered by now, but it doesn't upgrade my code(((
mapPies( df,nameX="lat", nameY="long", nameZs=c('rus','ukr','mold'),
xlim=c(30,33), ylim=c(44,53), symbolSize = 2)
perhaps this will help:
pieSP The function provide SpatialPolygonsDataFrame depending on few attributes, ready to use for plotGoogleMaps or spplot.
library(plotGoogleMaps)
data(meuse)
coordinates(meuse)<-~x+y
proj4string(meuse) <- CRS('+init=epsg:28992')
pies <- pieSP(meuse,zcol=c('zinc','lead','copper'), max.radius=120)
pies$pie <- rep(c('zinc','lead','copper'),155)
pies$pie2 <- rep(1:3,155)
spplot(pies, 'pie2')
I'd like to add name-labels for regions on an spplot().
Example:
load(url('http://gadm.org/data/rda/FRA_adm0.RData'))
FR <- gadm
FR <- spChFIDs(FR, paste("FR", rownames(FR), sep = "_"))
load(url('http://gadm.org/data/rda/CHE_adm0.RData'))
SW <- gadm
SW <- spChFIDs(SW, paste("SW", rownames(SW), sep = "_"))
load(url('http://gadm.org/data/rda/DEU_adm0.RData'))
GE <- gadm
GE <- spChFIDs(GE, paste("GE", rownames(GE), sep = "_"))
df <- rbind(FR, SW, GE)
## working
plot(df)
text(getSpPPolygonsLabptSlots(df), labels = c("FR", "SW", "GE"))
## not working
spplot(df[1-2,])
text((getSpPPolygonsLabptSlots(df), labels = c("FR", "SW"))
The second one probably doesn't work because of lattice!?
However, I need the spplot-functionality.
How would I get the labels on the plot?
Standard way of adding some text is using the function ltext of lattice, but the coordinates given there are always absolute. In essence, you can't really rescale the figure after adding the text. Eg :
data(meuse.grid)
gridded(meuse.grid)=~x+y
meuse.grid$g = factor(sample(letters[1:5], 3103, replace=TRUE),levels=letters[1:10])
meuse.grid$f = factor(sample(letters[6:10], 3103, replace=TRUE),levels=letters[1:10])
spplot(meuse.grid, c("f","g"))
ltext(100,200,"Horror")
Produces these figures (before and after scaling)
You can use a custom panel function, using the coordinates within each panel :
myPanel <- function(x,y,xx,yy,labels,...){
panel.xyplot(x,y,...)
ltext(xx,yy,labels)
}
xyplot(1:10 ~ 1:10,data=quakes,panel=myPanel,
xx=(1:5),yy=(1:5)+0.5,labels=letters[1:5])
(run it for yourself to see how it looks)
This trick you can use within the spplot function as well, although you really have to check whatever plotting function you use. In the help files on spplot you find the possible options (polygonsplot, gridplot and pointsplot), so you have to check whether any of them is doing what you want. Continuing with the gridplot above, this becomes :
myPanel <- function(x,y,z,subscripts,xx,yy,labels,...){
panel.gridplot(x,y,z,subscripts,...)
ltext(xx,yy,labels)
}
# I just chose some coordinates
spplot(meuse.grid, c("f","g"),panel=myPanel,xx=180000,yy=331000,label="Hooray")
which gives a rescalable result, where the text is added in each panel :
Thank you, Gavin Simpson!
I finally found a way.
In the hope it helps others in the future, I post my solution:
sp.label <- function(x, label) {
list("sp.text", coordinates(x), label)
}
ISO.sp.label <- function(x) {
sp.label(x, row.names(x["ISO"]))
}
make.ISO.sp.label <- function(x) {
do.call("list", ISO.sp.label(x))
}
spplot(df['ISO'], sp.layout = make.ISO.sp.label(df))