Last updated: 2020-04-29

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Knit directory: FLASHvestigations/

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Rmd 8420f5d Jason Willwerscheid 2020-04-29 wflow_publish(“analysis/ebnm_npmle.Rmd”)

I want to test out approximations of NPMLEs using a dense ashr grid. Let \(x_1, \ldots, x_n\) be \(n\) observations with standard errors equal to 1. Dicker and Zhao show that when the true NPMLE has compact support, then a good approximation can be obtained by optimizing over the family of distributions that’s supported on \(\sqrt{n}\) equally spaced points between \(\min (x)\) and \(\max (x)\). Instead of using point masses, I use an ashr grid with \(\sqrt{n}\) uniform components of equal width. Let’s see how it works in practice.

Here’s the true distribution which I’ll be sampling from. It’s bimodal with peaks at -5 and 5, so a unimodal prior family wouldn’t work very well.

suppressMessages(library(tidyverse))

true_g <- ashr::normalmix(pi = rep(0.1, 10),
                          mean = c(rep(-5, 5), rep(5, 5)),
                          sd = c(0:4, 0:4))
cdf_grid <- seq(-20, 20, by = 0.1)
true_cdf <- drop(ashr::mixcdf(true_g, cdf_grid))
ggplot(tibble(x = cdf_grid, y = true_cdf), aes(x = x, y = y)) + geom_line()

Now I sample 100, 1000, and 10000 observations from the true distribution, add normally distributed noise, and call ebnm_npmle.

samp_from_g <- function(g, n) {
  comp <- sample(1:length(g$pi), n, replace = TRUE, prob = g$pi)
  mean <- g$mean[comp]
  sd <- g$sd[comp]
  return(rnorm(n, mean = mean, sd = sd))
}

estimate_g <- function(true_g, n, verbose = FALSE) {
  samp <- samp_from_g(true_g, n) + rnorm(n) # Add N(0, 1) noise
  ebnm_res <- ebnm::ebnm_npmle(samp, control = list(verbose = verbose))
  return(ebnm_res$fitted_g)
}

set.seed(666)

g100 <- estimate_g(true_g, 100)
g1000 <- estimate_g(true_g, 1000)
g10000 <- estimate_g(true_g, 10000)

cdf100 <- drop(ashr::mixcdf(g100, cdf_grid))
cdf1000 <- drop(ashr::mixcdf(g1000, cdf_grid))
cdf10000 <- drop(ashr::mixcdf(g10000, cdf_grid))

res <- tibble(x = rep(cdf_grid, 4), 
              y = c(true_cdf, cdf100, cdf1000, cdf10000), 
              cdf = c(rep("True", length(cdf_grid)),
                      rep("n = 100", length(cdf_grid)),
                      rep("n = 1000", length(cdf_grid)),
                      rep("n = 10000", length(cdf_grid))))

ggplot(res, aes(x = x, y = y, col = cdf)) + geom_line()

When \(n\) gets too large, mixsqp gets bogged down. Compare \(n = 10000\):

g10000 <- estimate_g(true_g, 10000, verbose = TRUE)
#> Running mix-SQP algorithm 0.3-31 on 10000 x 100 matrix
#> convergence tol. (SQP):     1.0e-08
#> conv. tol. (active-set):    1.0e-10
#> zero threshold (solution):  1.0e-08
#> zero thresh. (search dir.): 1.0e-14
#> l.s. sufficient decrease:   1.0e-02
#> step size reduction factor: 7.5e-01
#> minimum step size:          1.0e-08
#> max. iter (SQP):            1000
#> max. iter (active-set):     20
#> number of EM iterations:    20
#> Computing SVD of 10000 x 100 matrix.
#> Matrix is not low-rank; falling back to full matrix.
#> iter        objective max(rdual) nnz stepsize max.diff nqp nls
#>    1 +2.030462224e+00  -- EM --  100 1.00e+00 1.98e-02  --  --
#>    2 +2.012745932e+00  -- EM --  100 1.00e+00 6.26e-03  --  --
#>    3 +2.009553447e+00  -- EM --  100 1.00e+00 3.17e-03  --  --
#>    4 +2.008362079e+00  -- EM --  100 1.00e+00 1.99e-03  --  --
#>    5 +2.007711941e+00  -- EM --  100 1.00e+00 1.45e-03  --  --
#>    6 +2.007298230e+00  -- EM --  100 1.00e+00 1.15e-03  --  --
#>    7 +2.007016171e+00  -- EM --  100 1.00e+00 9.60e-04  --  --
#>    8 +2.006815351e+00  -- EM --  100 1.00e+00 8.24e-04  --  --
#>    9 +2.006667310e+00  -- EM --  100 1.00e+00 7.20e-04  --  --
#>   10 +2.006554748e+00  -- EM --  100 1.00e+00 6.37e-04  --  --
#>   11 +2.006466694e+00  -- EM --  100 1.00e+00 5.69e-04  --  --
#>   12 +2.006395982e+00  -- EM --  100 1.00e+00 5.13e-04  --  --
#>   13 +2.006337819e+00  -- EM --  100 1.00e+00 4.65e-04  --  --
#>   14 +2.006288934e+00  -- EM --  100 1.00e+00 4.25e-04  --  --
#>   15 +2.006247056e+00  -- EM --  100 1.00e+00 3.90e-04  --  --
#>   16 +2.006210580e+00  -- EM --  100 1.00e+00 3.60e-04  --  --
#>   17 +2.006178354e+00  -- EM --  100 1.00e+00 3.34e-04  --  --
#>   18 +2.006149537e+00  -- EM --  100 1.00e+00 3.12e-04  --  --
#>   19 +2.006123508e+00  -- EM --  100 1.00e+00 2.92e-04  --  --
#>   20 +2.006099797e+00  -- EM --  100 1.00e+00 2.74e-04  --  --
#>    1 +2.006078047e+00 +1.332e-02 100  ------   ------   --  --
#>    2 +2.006057850e+00 +1.326e-02  80 1.00e+00 2.61e-03  20   1
#>    3 +2.006039264e+00 +1.287e-02  60 1.00e+00 1.92e-02  20   1
#>    4 +2.006019180e+00 +1.182e-02  40 1.00e+00 1.02e-01  20   1
#>    5 +2.005511332e+00 +2.118e-03  28 1.00e+00 1.18e-01  20   1
#>    6 +2.005496997e+00 +2.665e-04  30 1.00e+00 2.56e-02  20   1
#>    7 +2.005460778e+00 +1.722e-04  30 1.00e+00 1.50e-01  20   1
#>    8 +2.005452812e+00 +1.484e-04  30 1.00e+00 3.73e-02  20   1
#>    9 +2.005437216e+00 +9.627e-05  28 1.00e+00 4.61e-02  20   1
#>   10 +2.005432708e+00 +5.942e-05  27 1.00e+00 3.49e-02  20   1
#>   11 +2.005432478e+00 +6.458e-05  29 1.00e+00 3.71e-03  20   1
#>   12 +2.005430497e+00 +4.191e-05  29 1.00e+00 5.46e-02  20   1
#>   13 +2.005429142e+00 +1.908e-05  29 1.00e+00 2.41e-02  20   1
#>   14 +2.005429142e+00 +1.908e-05  29 1.00e+00 7.53e-08  20   1
#>   15 +2.005429142e+00 +1.908e-05  29 1.00e+00 3.38e-11  20   1
#>   16 +2.005429142e+00 +1.908e-05  29 1.00e+00 2.01e-12  20   1
#>   17 +2.005429142e+00 +1.908e-05  29 1.00e+00 1.80e-12  20   1
#>   18 +2.005429142e+00 +1.908e-05  29 1.00e+00 1.77e-12  20   1
#>   19 +2.005429142e+00 +1.908e-05  29 1.00e+00 2.49e-12  20   1
#>   20 +2.005429142e+00 +1.908e-05  29 1.00e+00 9.50e-13  20   1
#>   21 +2.005429142e+00 +1.908e-05  29 1.00e+00 5.77e-13  20   1
#>   22 +2.005429142e+00 +1.908e-05  29 1.00e+00 5.39e-13  20   1
#>   23 +2.005429142e+00 +1.908e-05  29 1.00e+00 9.52e-13  20   1
#>   24 +2.005429142e+00 +1.908e-05  29 1.00e+00 1.70e-12  20   1
#>   25 +2.005429142e+00 +1.908e-05  29 1.00e+00 1.21e-12  20   1
#>   26 +2.005429142e+00 +1.908e-05  29 1.00e+00 1.32e-12  20   1
#>   27 +2.005429142e+00 +1.908e-05  29 1.00e+00 9.14e-13  20   1
#>   28 +2.005429142e+00 +1.908e-05  29 1.00e+00 1.10e-12  20   1
#>   29 +2.005429142e+00 +1.908e-05  29 1.00e+00 9.27e-13  20   1
#>   30 +2.005429137e+00 +1.918e-05  30 1.00e+00 5.73e-04  20   1
#>   31 +2.005429137e+00 +1.918e-05  30 1.00e+00 5.78e-07  20   1
#>   32 +2.005429137e+00 +1.918e-05  30 1.00e+00 6.30e-10  20   1
#>   33 +2.005429137e+00 +1.918e-05  30 1.00e+00 8.29e-13  20   1
#>   34 +2.005429137e+00 +1.918e-05  30 1.00e+00 4.11e-12  20   1
#>   35 +2.005429137e+00 +1.918e-05  30 1.00e+00 4.02e-12  20   1
#>   36 +2.005429137e+00 +1.918e-05  30 1.00e+00 2.41e-12  20   1
#>   37 +2.005429137e+00 +1.918e-05  30 1.00e+00 1.10e-12  20   1
#>   38 +2.005429137e+00 +1.918e-05  30 1.00e+00 9.77e-13  20   1
#>   39 +2.005429137e+00 +1.918e-05  30 1.00e+00 3.06e-12  20   1
#>   40 +2.005429137e+00 +1.918e-05  30 1.00e+00 2.58e-12  20   1
#>   41 +2.005429137e+00 +1.918e-05  30 1.00e+00 1.01e-12  20   1
#>   42 +2.005429002e+00 -5.999e-06  30 1.00e+00 9.49e-03  20   1
#> Optimization took 3.34 seconds.
#> Convergence criteria met---optimal solution found.

and \(n = 100000\):

g100000 <- estimate_g(true_g, 100000, verbose = TRUE)
#> Running mix-SQP algorithm 0.3-31 on 100000 x 317 matrix
#> convergence tol. (SQP):     1.0e-08
#> conv. tol. (active-set):    1.0e-10
#> zero threshold (solution):  1.0e-08
#> zero thresh. (search dir.): 1.0e-14
#> l.s. sufficient decrease:   1.0e-02
#> step size reduction factor: 7.5e-01
#> minimum step size:          1.0e-08
#> max. iter (SQP):            1000
#> max. iter (active-set):     20
#> number of EM iterations:    20
#> Computing SVD of 100000 x 317 matrix.
#> SVD computation took 28.23 seconds.
#> Rank of matrix is estimated to be 80.
#> iter        objective max(rdual) nnz stepsize max.diff nqp nls
#>    1 +2.025005873e+00  -- EM --  317 1.00e+00 8.17e-03  --  --
#>    2 +2.005919110e+00  -- EM --  317 1.00e+00 2.44e-03  --  --
#>    3 +2.002278085e+00  -- EM --  317 1.00e+00 1.24e-03  --  --
#>    4 +2.000870033e+00  -- EM --  317 1.00e+00 7.75e-04  --  --
#>    5 +2.000107437e+00  -- EM --  317 1.00e+00 5.55e-04  --  --
#>    6 +1.999636078e+00  -- EM --  317 1.00e+00 4.31e-04  --  --
#>    7 +1.999326526e+00  -- EM --  317 1.00e+00 3.50e-04  --  --
#>    8 +1.999115017e+00  -- EM --  317 1.00e+00 2.93e-04  --  --
#>    9 +1.998965586e+00  -- EM --  317 1.00e+00 2.49e-04  --  --
#>   10 +1.998856663e+00  -- EM --  317 1.00e+00 2.15e-04  --  --
#>   11 +1.998774855e+00  -- EM --  317 1.00e+00 1.88e-04  --  --
#>   12 +1.998711637e+00  -- EM --  317 1.00e+00 1.66e-04  --  --
#>   13 +1.998661462e+00  -- EM --  317 1.00e+00 1.47e-04  --  --
#>   14 +1.998620653e+00  -- EM --  317 1.00e+00 1.32e-04  --  --
#>   15 +1.998586726e+00  -- EM --  317 1.00e+00 1.20e-04  --  --
#>   16 +1.998557972e+00  -- EM --  317 1.00e+00 1.09e-04  --  --
#>   17 +1.998533194e+00  -- EM --  317 1.00e+00 1.01e-04  --  --
#>   18 +1.998511540e+00  -- EM --  316 1.00e+00 9.33e-05  --  --
#>   19 +1.998492389e+00  -- EM --  316 1.00e+00 8.70e-05  --  --
#>   20 +1.998475286e+00  -- EM --  316 1.00e+00 8.16e-05  --  --
#>    1 +1.998459886e+00 +1.302e-02 316  ------   ------   --  --
#>    2 +1.998445889e+00 +1.239e-02 296 1.00e+00 7.59e-05  20   1
#>    3 +1.998433145e+00 +1.178e-02 276 1.00e+00 8.88e-05  20   1
#>    4 +1.998421482e+00 +1.119e-02 256 1.00e+00 1.09e-04  20   1
#>    5 +1.998410753e+00 +1.063e-02 236 1.00e+00 4.56e-04  20   1
#>    6 +1.998400863e+00 +1.008e-02 216 1.00e+00 9.47e-04  20   1
#>    7 +1.998391701e+00 +9.538e-03 196 1.00e+00 2.94e-03  20   1
#>    8 +1.998383206e+00 +9.022e-03 176 1.00e+00 1.94e-03  20   1
#>    9 +1.998375309e+00 +8.535e-03 156 1.00e+00 2.14e-03  20   1
#>   10 +1.998367951e+00 +8.072e-03 136 1.00e+00 3.22e-03  20   1
#>   11 +1.998361093e+00 +7.643e-03 116 1.00e+00 5.87e-03  20   1
#>   12 +1.998354621e+00 +7.223e-03  96 1.00e+00 1.02e-02  20   1
#>   13 +1.998348497e+00 +7.140e-03  76 1.00e+00 1.68e-02  20   1
#>   14 +1.998342127e+00 +6.999e-03  56 1.00e+00 4.50e-02  20   1
#>   15 +1.998256234e+00 +4.139e-03  36 1.00e+00 1.13e-01  20   1
#>   16 +1.998224811e+00 +3.176e-03  35 1.00e+00 2.94e-02  20   1
#>   17 +1.998222421e+00 +2.119e-04  35 1.00e+00 1.23e-03  20   1
#>   18 +1.998222016e+00 +2.157e-04  34 1.00e+00 6.01e-04  20   1
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#>   21 +1.998219530e+00 +1.514e-04  35 1.00e+00 1.92e-02  20   1
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#>   23 +1.998216133e+00 +7.796e-05  35 1.00e+00 4.36e-03  20   1
#>   24 +1.998215900e+00 +7.869e-05  35 1.00e+00 2.19e-03  20   1
#>   25 +1.998214921e+00 +6.126e-05  36 1.00e+00 2.19e-02  20   1
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#>   27 +1.998214534e+00 +6.132e-05  35 1.00e+00 2.90e-07  20   1
#>   28 +1.998214534e+00 +6.132e-05  35 1.00e+00 6.37e-10  20   1
#>   29 +1.998214534e+00 +6.132e-05  35 1.00e+00 2.82e-12  20   1
#>   30 +1.998214534e+00 +6.132e-05  35 1.00e+00 1.44e-12  20   1
#>   31 +1.998214534e+00 +6.132e-05  35 1.00e+00 4.28e-13  20   1
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#>   33 +1.998214534e+00 +6.132e-05  35 1.00e+00 1.08e-12  20   1
#>   34 +1.998214513e+00 +6.135e-05  34 1.00e+00 2.48e-04  20   1
#>   35 +1.998214513e+00 +6.135e-05  34 1.00e+00 6.00e-07  20   1
#>   36 +1.998214513e+00 +6.135e-05  34 1.00e+00 8.12e-09  20   1
#>   37 +1.998214513e+00 +6.135e-05  34 1.00e+00 3.29e-10  20   1
#>   38 +1.998214513e+00 +6.135e-05  34 1.00e+00 1.36e-11  20   1
#>   39 +1.998214513e+00 +6.135e-05  34 1.00e+00 1.41e-12  20   1
#>   40 +1.998214513e+00 +6.135e-05  34 1.00e+00 3.21e-13  20   1
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#>   42 +1.998214513e+00 +6.135e-05  34 1.00e+00 1.62e-12  20   1
#>   43 +1.998214513e+00 +6.135e-05  34 1.00e+00 5.30e-13  20   1
#>   44 +1.998214513e+00 +6.135e-05  34 1.00e+00 3.29e-13  20   1
#>   45 +1.998214513e+00 +6.135e-05  34 1.00e+00 1.20e-13  20   1
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#>  853 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.30e-12  20   1
#>  854 +1.998208372e+00 +5.379e-06  36 1.00e+00 8.80e-12  20   1
#>  855 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.99e-13  20   1
#>  856 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.84e-12  20   1
#>  857 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.61e-12  20   1
#>  858 +1.998208372e+00 +5.379e-06  36 1.00e+00 3.62e-12  20   1
#>  859 +1.998208372e+00 +5.379e-06  36 1.00e+00 8.00e-12  20   1
#>  860 +1.998208372e+00 +5.379e-06  36 1.00e+00 8.56e-12  20   1
#>  861 +1.998208372e+00 +5.379e-06  36 1.00e+00 7.29e-13  20   1
#>  862 +1.998208372e+00 +5.379e-06  36 1.00e+00 9.45e-12  20   1
#>  863 +1.998208372e+00 +5.379e-06  36 1.00e+00 9.51e-12  20   1
#>  864 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.37e-12  20   1
#>  865 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.47e-12  20   1
#>  866 +1.998208372e+00 +5.379e-06  36 1.00e+00 3.02e-12  20   1
#>  867 +1.998208372e+00 +5.379e-06  36 1.00e+00 5.84e-12  20   1
#>  868 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.98e-12  20   1
#>  869 +1.998208372e+00 +5.379e-06  36 1.00e+00 5.38e-12  20   1
#>  870 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.69e-12  20   1
#>  871 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.03e-11  20   1
#>  872 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.56e-12  20   1
#>  873 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.29e-12  20   1
#>  874 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.35e-12  20   1
#>  875 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.84e-12  20   1
#>  876 +1.998208372e+00 +5.379e-06  36 1.00e+00 5.13e-12  20   1
#>  877 +1.998208372e+00 +5.379e-06  36 1.00e+00 8.21e-13  20   1
#>  878 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.67e-12  20   1
#>  879 +1.998208372e+00 +5.379e-06  36 1.00e+00 9.25e-14  20   1
#>  880 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.61e-12  20   1
#>  881 +1.998208372e+00 +5.379e-06  36 1.00e+00 2.57e-12  20   1
#>  882 +1.998208372e+00 +5.379e-06  36 1.00e+00 5.29e-12  20   1
#>  883 +1.998208372e+00 +5.379e-06  36 1.00e+00 5.05e-12  20   1
#>  884 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.33e-12  20   1
#>  885 +1.998208372e+00 +5.379e-06  36 1.00e+00 2.90e-12  20   1
#>  886 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.51e-12  20   1
#>  887 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.91e-12  20   1
#>  888 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.52e-12  20   1
#>  889 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.86e-12  20   1
#>  890 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.33e-12  20   1
#>  891 +1.998208372e+00 +5.379e-06  36 1.00e+00 5.63e-12  20   1
#>  892 +1.998208372e+00 +5.379e-06  36 1.00e+00 3.69e-12  20   1
#>  893 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.42e-12  20   1
#>  894 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.33e-12  20   1
#>  895 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.38e-12  20   1
#>  896 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.26e-13  20   1
#>  897 +1.998208372e+00 +5.379e-06  36 1.00e+00 2.75e-12  20   1
#>  898 +1.998208372e+00 +5.379e-06  36 1.00e+00 7.58e-12  20   1
#>  899 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.53e-12  20   1
#>  900 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.39e-12  20   1
#>  901 +1.998208372e+00 +5.379e-06  36 1.00e+00 7.34e-12  20   1
#>  902 +1.998208372e+00 +5.379e-06  36 1.00e+00 3.02e-12  20   1
#>  903 +1.998208372e+00 +5.379e-06  36 1.00e+00 9.76e-12  20   1
#>  904 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.42e-12  20   1
#>  905 +1.998208372e+00 +5.379e-06  36 1.00e+00 9.40e-12  20   1
#>  906 +1.998208372e+00 +5.379e-06  36 1.00e+00 4.85e-12  20   1
#>  907 +1.998208372e+00 +5.379e-06  36 1.00e+00 7.80e-12  20   1
#>  908 +1.998208372e+00 +5.379e-06  36 1.00e+00 7.90e-12  20   1
#>  909 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.35e-12  20   1
#>  910 +1.998208372e+00 +5.379e-06  36 1.00e+00 7.30e-12  20   1
#>  911 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.22e-11  20   1
#>  912 +1.998208372e+00 +5.379e-06  36 1.00e+00 2.12e-12  20   1
#>  913 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.84e-12  20   1
#>  914 +1.998208372e+00 +5.379e-06  36 1.00e+00 8.76e-12  20   1
#>  915 +1.998208372e+00 +5.379e-06  36 1.00e+00 2.91e-12  20   1
#>  916 +1.998208372e+00 +5.379e-06  36 1.00e+00 3.58e-12  20   1
#>  917 +1.998208372e+00 +5.379e-06  36 1.00e+00 8.62e-12  20   1
#>  918 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.10e-11  20   1
#>  919 +1.998208372e+00 +5.379e-06  36 1.00e+00 6.90e-12  20   1
#>  920 +1.998208372e+00 +5.379e-06  36 1.00e+00 2.88e-12  20   1
#>  921 +1.998208372e+00 +5.379e-06  36 1.00e+00 7.55e-12  20   1
#>  922 +1.998208372e+00 +5.379e-06  36 1.00e+00 5.01e-12  20   1
#>  923 +1.998208372e+00 +5.379e-06  36 1.00e+00 1.01e-12  20   1
#>  924 +1.998208359e+00 +5.396e-06  36 1.00e+00 9.19e-04  20   1
#>  925 +1.998208359e+00 +5.396e-06  36 1.00e+00 6.26e-07  20   1
#>  926 +1.998208359e+00 +5.396e-06  36 1.00e+00 6.12e-09  20   1
#>  927 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.49e-10  20   1
#>  928 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.57e-11  20   1
#>  929 +1.998208359e+00 +5.396e-06  36 1.00e+00 6.82e-12  20   1
#>  930 +1.998208359e+00 +5.396e-06  36 1.00e+00 8.10e-12  20   1
#>  931 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.13e-12  20   1
#>  932 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.99e-12  20   1
#>  933 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.96e-12  20   1
#>  934 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.29e-12  20   1
#>  935 +1.998208359e+00 +5.396e-06  36 1.00e+00 7.89e-12  20   1
#>  936 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.53e-12  20   1
#>  937 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.07e-12  20   1
#>  938 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.46e-12  20   1
#>  939 +1.998208359e+00 +5.396e-06  36 1.00e+00 6.44e-12  20   1
#>  940 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.70e-12  20   1
#>  941 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.37e-12  20   1
#>  942 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.77e-12  20   1
#>  943 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.02e-11  20   1
#>  944 +1.998208359e+00 +5.396e-06  36 1.00e+00 9.45e-12  20   1
#>  945 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.53e-12  20   1
#>  946 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.92e-12  20   1
#>  947 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.15e-12  20   1
#>  948 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.56e-12  20   1
#>  949 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.29e-12  20   1
#>  950 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.30e-12  20   1
#>  951 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.13e-12  20   1
#>  952 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.85e-12  20   1
#>  953 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.35e-13  20   1
#>  954 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.12e-12  20   1
#>  955 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.12e-12  20   1
#>  956 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.04e-11  20   1
#>  957 +1.998208359e+00 +5.396e-06  36 1.00e+00 8.38e-12  20   1
#>  958 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.15e-12  20   1
#>  959 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.86e-12  20   1
#>  960 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.18e-11  20   1
#>  961 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.32e-11  20   1
#>  962 +1.998208359e+00 +5.396e-06  36 1.00e+00 8.12e-12  20   1
#>  963 +1.998208359e+00 +5.396e-06  36 1.00e+00 7.02e-12  20   1
#>  964 +1.998208359e+00 +5.396e-06  36 1.00e+00 6.09e-12  20   1
#>  965 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.21e-12  20   1
#>  966 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.55e-12  20   1
#>  967 +1.998208359e+00 +5.396e-06  36 1.00e+00 7.31e-12  20   1
#>  968 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.26e-11  20   1
#>  969 +1.998208359e+00 +5.396e-06  36 1.00e+00 7.81e-12  20   1
#>  970 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.99e-12  20   1
#>  971 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.02e-11  20   1
#>  972 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.50e-12  20   1
#>  973 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.74e-12  20   1
#>  974 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.16e-12  20   1
#>  975 +1.998208359e+00 +5.396e-06  36 1.00e+00 6.40e-13  20   1
#>  976 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.87e-13  20   1
#>  977 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.54e-12  20   1
#>  978 +1.998208359e+00 +5.396e-06  36 1.00e+00 8.25e-13  20   1
#>  979 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.42e-12  20   1
#>  980 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.50e-12  20   1
#>  981 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.58e-12  20   1
#>  982 +1.998208359e+00 +5.396e-06  36 1.00e+00 6.35e-12  20   1
#>  983 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.40e-12  20   1
#>  984 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.98e-12  20   1
#>  985 +1.998208359e+00 +5.396e-06  36 1.00e+00 8.82e-12  20   1
#>  986 +1.998208359e+00 +5.396e-06  36 1.00e+00 9.41e-12  20   1
#>  987 +1.998208359e+00 +5.396e-06  36 1.00e+00 4.87e-12  20   1
#>  988 +1.998208359e+00 +5.396e-06  36 1.00e+00 9.22e-12  20   1
#>  989 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.63e-13  20   1
#>  990 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.96e-12  20   1
#>  991 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.13e-12  20   1
#>  992 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.08e-12  20   1
#>  993 +1.998208359e+00 +5.396e-06  36 1.00e+00 3.35e-12  20   1
#>  994 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.19e-12  20   1
#>  995 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.56e-13  20   1
#>  996 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.41e-13  20   1
#>  997 +1.998208359e+00 +5.396e-06  36 1.00e+00 2.86e-12  20   1
#>  998 +1.998208359e+00 +5.396e-06  36 1.00e+00 5.48e-13  20   1
#>  999 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.75e-12  20   1
#> 1000 +1.998208359e+00 +5.396e-06  36 1.00e+00 1.85e-12  20   1
#> Optimization took 668.76 seconds.
#> Warning in mixsqp::mixsqp(A, w, pi_init, control = control): Failed to converge within iterations limit. If "maxiter.sqp" is
#> small, consider increasing it. Otherwise, convergence failure is
#> typically a numerical issue remedied by increasing "eps" slightly,
#> at the cost of slightly less accurate solution; see help(mixsqp).
#> An issue report may also be submitted to
#> https://github.com/stephenslab/mixsqp/issues, accompanied by an
#> .rds or .RData file containing the mixsqp inputs. If these inputs
#> are not accessible, an .RData file containing the inputs can be
#> generated by setting options(mixsqp.debug.mode = TRUE) before
#> running mixsqp.
#> Warning in estimate_mixprop(data, g, prior, optmethod = optmethod, control
#> = control, : Optimization failed to converge. Results may be unreliable.
#> Try increasing maxiter and rerunning.


sessionInfo()
#> R version 3.5.3 (2019-03-11)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS Mojave 10.14.6
#> 
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] forcats_0.4.0   stringr_1.4.0   dplyr_0.8.0.1   purrr_0.3.2    
#> [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.2.0  
#> [9] tidyverse_1.2.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_0.2.5  xfun_0.6          ashr_2.2-50      
#>  [4] haven_2.1.1       lattice_0.20-38   colorspace_1.4-1 
#>  [7] generics_0.0.2    htmltools_0.3.6   yaml_2.2.0       
#> [10] rlang_0.4.2       mixsqp_0.3-31     pillar_1.3.1     
#> [13] glue_1.3.1        withr_2.1.2       modelr_0.1.5     
#> [16] readxl_1.3.1      munsell_0.5.0     gtable_0.3.0     
#> [19] workflowr_1.2.0   cellranger_1.1.0  rvest_0.3.4      
#> [22] evaluate_0.13     labeling_0.3      knitr_1.22       
#> [25] invgamma_1.1      irlba_2.3.3       broom_0.5.1      
#> [28] Rcpp_1.0.1        scales_1.0.0      backports_1.1.3  
#> [31] jsonlite_1.6      truncnorm_1.0-8   fs_1.2.7         
#> [34] hms_0.4.2         digest_0.6.18     stringi_1.4.3    
#> [37] ebnm_0.1-21       grid_3.5.3        rprojroot_1.3-2  
#> [40] cli_1.1.0         tools_3.5.3       magrittr_1.5     
#> [43] lazyeval_0.2.2    crayon_1.3.4      whisker_0.3-2    
#> [46] pkgconfig_2.0.2   Matrix_1.2-15     SQUAREM_2017.10-1
#> [49] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.1 
#> [52] rmarkdown_1.12    httr_1.4.0        rstudioapi_0.10  
#> [55] R6_2.4.0          nlme_3.1-137      git2r_0.25.2     
#> [58] compiler_3.5.3