dmutate

Mutate a data.frame, adding random variates.

library(dplyr)
library(dmutate)

Univariate examples

Some variables to use in formulae:

low_wt <- 70
high_wt <- 90
mu_wt <- 80
sd <- 60
p.female <- 0.24

Use 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 87.28619
. 2   2 74.41711
. 3   3 80.72592
. 4   4 89.63599
. 5   5 72.58696
. 6   6 86.46300
. 7   7 80.68241
. 8   8 84.47482
. 9   9 78.47417
. 10 10 88.54634

We can simulate from any probability distribution in R

data.frame(ID = 1:10) %>% mutate_random(X ~ rcauchy(0, 0.5))

.    ID           X
. 1   1  3.49562264
. 2   2 -3.87879719
. 3   3 -0.20956182
. 4   4 -0.33935718
. 5   5 -0.02254644
. 6   6 -0.28627923
. 7   7  0.66041153
. 8   8 -0.37298713
. 9   9  0.61288903
. 10 10  1.22101132

We 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 43.69961
. 2   2     0 52.61525
. 3   3     1 43.69961
. 4   4     0 52.61525
. 5   5     1 43.69961
. 6   6     0 52.61525
. 7   7     1 43.69961
. 8   8     0 52.61525
. 9   9     1 43.69961
. 10 10     0 52.61525

Simulate multivariate normal with bounds

mu <- 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)

Simulate

data.frame(ID = 1:10000) %>%
  mutate_random(XY) %>% 
  summary()

.        ID              X                   Y        
.  Min.   :    1   Min.   :2.347e-04   Min.   :200.0  
.  1st Qu.: 2501   1st Qu.:1.598e+00   1st Qu.:210.0  
.  Median : 5000   Median :3.089e+00   Median :221.2  
.  Mean   : 5000   Mean   :3.412e+00   Mean   :225.0  
.  3rd Qu.: 7500   3rd Qu.:4.840e+00   3rd Qu.:236.4  
.  Max.   :10000   Max.   :1.414e+01   Max.   :299.8

An extended example

data.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 78.2    -4.42   1 0.741  2.41
. 2   2     0 82.6     3.85   0 0.185  2.77
. 3   3     1 79.8    -4.42   1 0.649  2.41
. 4   4     0 80.8     3.85   1 0.623  2.77
. 5   5     1 79.9    -4.42   0 0.264  2.41
. 6   6     0 79.9     3.85   0 0.596  2.77
. 7   7     1 78.7    -4.42   0 0.534  2.41
. 8   8     0 81.5     3.85   0 1.290  2.77
. 9   9     1 80.2    -4.42   0 0.180  2.41
. 10 10     0 79.1     3.85   0 0.496  2.77

Create formulae with expr to calculate new columns in the data.frame using dplyr::mutate

We 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 49.7 27.9 12.70  45.8
. 2  2 46.1 26.0 26.10 121.0
. 3  3 53.3 29.6  6.65  57.4