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knitr::opts_chunk$set(comment='.')Mutate a data.frame, adding random variates.
library(dplyr)
library(dmutate)Some variables to use in formulae:
low_wt <- 70
high_wt <- 90
mu_wt <- 80
sd <- 60
p.female <- 0.24Use mutate_random to implement formulae in data frame.
We can put bounds on any simulated variable
data.frame(ID=1:10) %>%
mutate_random(WT[low_wt,high_wt] ~ rnorm(mu_wt,sd))
. ID WT
. 1 1 80.06845
. 2 2 89.33775
. 3 3 80.10562
. 4 4 84.24226
. 5 5 77.91191
. 6 6 78.10506
. 7 7 87.47608
. 8 8 85.73655
. 9 9 76.68176
. 10 10 73.60327We can simulate from any probability distirbution in
R
data.frame(ID=1:10) %>% mutate_random(X ~ rcauchy(0,0.5))
. ID X
. 1 1 1.90139689
. 2 2 -0.10051859
. 3 3 0.70502454
. 4 4 0.29159943
. 5 5 -1.28752659
. 6 6 -0.17683919
. 7 7 0.10487994
. 8 8 -0.03835449
. 9 9 -0.74823471
. 10 10 -0.05401384We can add the variate at any level
data.frame(ID=1:10) %>%
mutate(GROUP = ID%%2) %>%
mutate_random(STUDY_RE ~ rnorm(50,sqrt(50))|GROUP)
. ID GROUP STUDY_RE
. 1 1 1 35.51964
. 2 2 0 57.52245
. 3 3 1 35.51964
. 4 4 0 57.52245
. 5 5 1 35.51964
. 6 6 0 57.52245
. 7 7 1 35.51964
. 8 8 0 57.52245
. 9 9 1 35.51964
. 10 10 0 57.52245mu <- c(2,200)
Sigma <- diag(c(10,1000))
XY <- X[0,] + Y[200,300] ~ rmvnorm(mu,Sigma)The object
XY
. X[0, ] + Y[200, 300] ~ rmvnorm(mu, Sigma)
. <environment: 0x107283a30>Simulate
data.frame(ID=1:10000) %>%
mutate_random(XY) %>%
summary
. ID X Y
. Min. : 1 Min. : 0.000705 Min. :200.0
. 1st Qu.: 2501 1st Qu.: 1.630148 1st Qu.:209.9
. Median : 5000 Median : 3.093676 Median :221.0
. Mean : 5000 Mean : 3.418346 Mean :224.9
. 3rd Qu.: 7500 3rd Qu.: 4.843010 3rd Qu.:235.9
. Max. :10000 Max. :13.981875 Max. :299.3data.frame(ID=1:10) %>%
mutate(GROUP = ID%%2) %>%
mutate_random(WT[low_wt,high_wt] ~ rnorm(mu_wt,1)) %>%
mutate_random(STUDY_RE ~ rnorm(0,sqrt(50))|GROUP) %>%
mutate_random(SEX ~ rbinomial(p.female)) %>%
mutate_random(sigma ~ rgamma(1,1)) %>%
mutate_random(kappa ~ rgamma(1,1)|GROUP) %>% signif(3)
. ID GROUP WT STUDY_RE SEX sigma kappa
. 1 1 1 78.1 -0.609 0 1.7200 0.045
. 2 2 0 79.6 3.740 0 2.1300 0.193
. 3 3 1 78.7 -0.609 1 0.9670 0.045
. 4 4 0 82.0 3.740 0 0.1240 0.193
. 5 5 1 80.9 -0.609 0 0.0672 0.045
. 6 6 0 79.2 3.740 0 0.5910 0.193
. 7 7 1 81.0 -0.609 1 0.0549 0.045
. 8 8 0 79.8 3.740 0 0.9100 0.193
. 9 9 1 80.0 -0.609 1 0.0262 0.045
. 10 10 0 79.8 3.740 0 1.9900 0.193expr to calculate new columns in the
data.frame using dplyr::mutateWe can easily save formulae to R variables. We collect
formulae together into sets called covset. For better
control for where objects are found, we can specify an environment where
objects can be found.
a <- X ~ rnorm(50,3)
b <- Y ~ expr(X/2 + c)
d <- A+B ~ rlmvnorm(log(c(20,80)),diag(c(0.2,0.2)))
cov1 <- covset(a,b,d)
e <- list(c=3)Notice that b has function expr. This
assigns the column named Y (in this case) to the result of
evaluating the expression in the data frame using
dplyr::dmutate.
.data <- data.frame(ID=1:3)
mutate_random(.data,cov1,envir=e) %>% signif(3)
. ID X Y A B
. 1 1 52.1 29.0 15.6 64.4
. 2 2 41.2 23.6 21.8 100.0
. 3 3 47.6 26.8 11.4 31.9These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.