Block Bootstrap from Subject List

Block bootstrap from subject list

How about something like this:

myfit <- function(x, i) {
   mydata <- do.call("rbind", lapply(i, function(n) subset(Grunfeld, firm==x[n])))
   coefficients(lm(value ~ inv + capital, data = mydata))
}

firms <- unique(Grunfeld$firm)

b0 <- boot(firms, myfit, 999)

How to bootstrap respecting within-subject information?

Just modify your call to boot() like this:

data.boot <- boot(data, boot.huber, 1999, strata=data$Subject)

?boot provides this description of the strata= argument, which does exactly what you are asking for:

strata: An integer vector or factor specifying the strata for
multi-sample problems. This may be specified for any
simulation, but is ignored when ‘sim = "parametric"’. When
‘strata’ is supplied for a nonparametric bootstrap, the
simulations are done within the specified strata.

---

Additional note:

To confirm that it's working as you'd like, you can call debugonce(boot), run the call above, and step through the debugger until the object i (whose rows contain the indices used to resample rows of data to create each bootstrap resample) has been assigned, and then have a look at it.

debugonce(boot)
data.boot <- boot(data, boot.huber, 1999, strata=data$Subject)
# Browse[2]>
## [Press return 34 times]
# Browse[2]> head(i)
#      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
# [1,]    9   10   11   16    9   14   15   16    9     2    15    16     1    10
# [2,]    9   14    7   12    5    6   15    4   13     6    11    16    13     6
# [3,]    5   10   15   16    9    6    3    4    1     2    15    12     5     6
# [4,]    5   10   11    4    9    6   15   16    9    14    11    16     5     2
# [5,]    5   10    3    4    1   10   15   16    9     6     3     8    13    14
# [6,]   13   10    3   12    5   10    3    4    5    14     7    16     5    14
#      [,15] [,16]
# [1,]     7     8
# [2,]    11    16
# [3,]     3    16
# [4,]     3     8
# [5,]     7     8
# [6,]     7    12

(You can enter Q to leave the debugger at any time.)

Block bootstrap for genomic data

So, after a while I came up with an answer to my problem. Here it goes.

You'll need the package dplyr.

l = 1000
teste = freq %>%
  mutate(w = ceiling(POS/l)) %>%
  group_by(CHR, w) %>%
  sample_n(1)

This code creates a new variable named w based on the position in the genome (POS). This variable w is the window to which each row was assigned, and it depends on l, which is the length of your window.

You can repeat this code several times, each time sampling one row per window/CHR (with the sample_n(1)) and apply whatever statistic of interest that you want.

Block sampling according to index in panel data

Apparently in this answer every firm is viewed for exactly 20 years, so I won't have a problem demonstrating:

data("Grunfeld", package="plm") #load data

Solution

#n is the the firms column, df is the dataframe
myfunc <- function(n,df) {      #define function
 unique_firms <- unique(n)      #unique firms
 sample_firms <- sample(unique_firms, size=length(unique_firms), replace=T ) #choose from unique firms randomly with replacement
 new_df <- do.call(rbind, lapply(sample_firms, function(x)  df[df$firm==x,] ))  #fetch all years for each randomly picked firm and rbind
}

a <- myfunc(Grunfeld$firm, Grunfeld) #run function 

Output

> str(a)
'data.frame':   200 obs. of  5 variables:
 $ firm   : int  4 4 4 4 4 4 4 4 4 4 ...
 $ year   : int  1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 ...
 $ inv    : num  40.3 72.8 66.3 51.6 52.4 ...
 $ value  : num  418 838 884 438 680 ...
 $ capital: num  10.5 10.2 34.7 51.8 64.3 67.1 75.2 71.4 67.1 60.5 ...

As you can see dim is exactly the same as the input data.frame

For your data the solution will be:

myfunc <- function(n,df) {      #define function
  unique_firms <- unique(n)      #unique firms
  print(unique_firms)
  sample_firms <- sample(unique_firms, size=length(unique_firms), replace=T ) #choose from unique firms randomly with replacement
  new_df <- do.call(rbind, lapply(sample_firms, function(x)  df[df$country==x,] ))  #fetch all years for each randomly picked firm and rbind
}

and Output:

> str(a)
'data.frame':   848 obs. of  18 variables:
 $ isocode  : Factor w/ 106 levels "AGO","ALB","ARG",..: 82 82 82 82 82 82 82 82 61 61 ...
 $ time     : int  2 3 4 5 6 7 8 9 2 3 ...
 $ country  : num  80 80 80 80 80 80 80 80 59 59 ...
 $ year     : int  1975 1980 1985 1990 1995 2000 2005 2010 1975 1980 ...
 $ gdp      : num  184619 210169 199343 268870 305255 ...
 $ pop      : num  33.4 34.9 36.6 37.8 38.3 ...
 $ gdp_k    : num  5526 6022 5443 7117 7969 ...
 $ co2      : num  340353 431436 426881 431052 350874 ...
 $ co2_k    : num  10191 12333 11674 11407 9128 ...
 $ oecd     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ LI       : int  0 0 0 0 0 0 0 0 0 0 ...
 $ LMI      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ UMI      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ HI       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ gdpk     : num  5531 6018 5449 7118 7971 ...
 $ co2k     : num  10196 12355 11668 11412 9162 ...
 $ co2_k.lag: num  8595 10191 12333 11674 11407 ...
 $ gdp_k.lag: num  4730 5526 6022 5443 7117 ...

R block resampling by unique identifier for bootstrap

something like this could help:

positions<-replicate(1000, sample(1:nrow(df), nrow(df), T))

apply(positions, 2, function(i) lm(yvar[i]~xvar[i], df)$coef)

---

Related Topics