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Update README
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@haziqj
haziqj committed May 29, 2024
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7 changes: 4 additions & 3 deletions README.Rmd
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Expand Up @@ -13,6 +13,7 @@ knitr::opts_chunk$set(
fig.path = "man/figures/README-",
out.width = "100%"
)
library(INLAvaan)
library(lavaan)
library(blavaan)
Expand Down Expand Up @@ -217,8 +218,8 @@ The `i` in `isem` stands for `INLA` (following the convention of `bsem` for `{bl
library(INLAvaan)
fit <- isem(model = mod, data = dat)
fit_lav <- sem(mod, dat)
fit_blav <- bsem(mod, dat, n.chains = 1, sample = 2000, burnin = 1000)
fit_blavvb <- bsem(mod, dat, target = "vb", sample = 2000, burnin = 1000)
fit_blav <- bsem(mod, dat, sample = 2000, burnin = 1000)
fit_blavvb <- bsem(mod, dat, target = "vb", n.chains = 1, sample = 2000, burnin = 1000)
```

```{r}
Expand Down Expand Up @@ -325,7 +326,7 @@ res |>
theme_bw() +
labs(x = "Sample size", y = "Run time (s)",
title = "Total run time to fit two factor SEM with varying sample sizes",
caption = "For MCMC sampling, 1000 burnin and 2000 samples were obtained.\nINLA ran on 8 parallel threads.")
caption = "For MCMC sampling, 1000 burnin and 2000 samples were obtained.\nINLA ran on 6 parallel threads.")
```

## Outro
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50 changes: 25 additions & 25 deletions README.md
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Expand Up @@ -116,12 +116,12 @@ mod <- "
dplyr::glimpse(dat)
#> Rows: 10,000
#> Columns: 6
#> $ y1 <dbl> -1.27571798, -0.09407202, -2.05151029, -1.15385961, 2.22560881, -0.…
#> $ y2 <dbl> -2.09632166, -0.19867560, -1.96302810, -0.48787050, 1.93058032, -0.…
#> $ y3 <dbl> -2.44225102, -0.45471235, -3.19202260, -1.36024551, 2.61948039, -0.…
#> $ y4 <dbl> -1.06902978, -1.15104358, -1.11122765, -2.31535838, 1.52493390, 0.5…
#> $ y5 <dbl> -1.23227671, -1.37170113, -1.19170701, -2.00573380, 1.13141524, 0.2…
#> $ y6 <dbl> -1.9777087, -2.0222614, -1.0814154, -3.2868422, 1.0235407, 0.580349…
#> $ y1 <dbl> 0.709801263, 0.842340759, -1.213499961, -0.200593044, 0.798170874, …
#> $ y2 <dbl> 1.15008029, 1.20330819, -0.47440788, -0.33209759, 0.73791700, 0.019…
#> $ y3 <dbl> 1.2848640572, 1.7572442266, -0.6549680413, -0.3067896758, 1.2151127…
#> $ y4 <dbl> 1.24142876, 0.56811857, -2.12670803, -0.50739519, 0.03233622, -0.92…
#> $ y5 <dbl> 1.15048010, 0.46779008, -2.74135310, -0.91711369, -0.04315028, -1.1…
#> $ y6 <dbl> 1.34254059, 0.54066565, -2.90934210, -0.88349219, -0.08309935, -1.4…
```

To fit this model using `{INLAvaan}`, use the familiar `{lavaan}`
Expand All @@ -134,7 +134,7 @@ fit <- isem(model = mod, data = dat)
summary(fit)
```

#> INLAvaan 0.1.0.9011 ended normally after 35 seconds
#> INLAvaan 0.1.0.9011 ended normally after 36 seconds
#>
#> Estimator BAYES
#> Optimization method INLA
Expand All @@ -143,7 +143,7 @@ summary(fit)
#> Number of observations 10000
#>
#> Statistic MargLogLik PPP
#> Value -51647.090 NA
#> Value -51887.404 NA
#>
#> Parameter Estimates:
#>
Expand All @@ -152,37 +152,37 @@ summary(fit)
#> Estimate Post.SD pi.lower pi.upper Prior
#> eta1 =~
#> y1 1.000
#> y2 1.205 0.004 1.196 1.214 normal(0,10)
#> y3 1.512 0.005 1.502 1.522 normal(0,10)
#> y2 1.193 0.004 1.185 1.202 normal(0,10)
#> y3 1.494 0.005 1.483 1.503 normal(0,10)
#> eta2 =~
#> y4 1.000
#> y5 1.193 0.004 1.185 1.201 normal(0,10)
#> y6 1.493 0.005 1.484 1.503 normal(0,10)
#> y5 1.202 0.004 1.194 1.210 normal(0,10)
#> y6 1.502 0.005 1.493 1.512 normal(0,10)
#>
#> Regressions:
#> Estimate Post.SD pi.lower pi.upper Prior
#> eta2 ~
#> eta1 0.296 0.010 0.276 0.316 normal(0,10)
#> eta1 0.303 0.010 0.283 0.323 normal(0,10)
#>
#> Covariances:
#> Estimate Post.SD pi.lower pi.upper Prior
#> .y1 ~~
#> .y4 0.049 0.001 0.047 0.052 beta(1,1)
#> .y4 0.050 0.001 0.048 0.053 beta(1,1)
#> .y2 ~~
#> .y5 0.048 0.001 0.046 0.051 beta(1,1)
#> .y5 0.053 0.001 0.050 0.056 beta(1,1)
#> .y3 ~~
#> .y6 0.050 0.002 0.046 0.053 beta(1,1)
#> .y6 0.046 0.002 0.042 0.050 beta(1,1)
#>
#> Variances:
#> Estimate Post.SD pi.lower pi.upper Prior
#> .y1 0.102 0.002 0.099 0.106 gamma(1,.5)[sd]
#> .y2 0.098 0.002 0.094 0.102 gamma(1,.5)[sd]
#> .y3 0.098 0.003 0.092 0.105 gamma(1,.5)[sd]
#> .y4 0.096 0.002 0.092 0.099 gamma(1,.5)[sd]
#> .y5 0.100 0.002 0.095 0.105 gamma(1,.5)[sd]
#> .y6 0.098 0.003 0.091 0.103 gamma(1,.5)[sd]
#> eta1 0.995 0.015 0.966 1.025 gamma(1,.5)[sd]
#> .eta2 0.997 0.015 0.969 1.027 gamma(1,.5)[sd]
#> .y1 0.100 0.002 0.096 0.104 gamma(1,.5)[sd]
#> .y2 0.102 0.002 0.098 0.106 gamma(1,.5)[sd]
#> .y3 0.100 0.003 0.094 0.106 gamma(1,.5)[sd]
#> .y4 0.099 0.002 0.096 0.103 gamma(1,.5)[sd]
#> .y5 0.103 0.002 0.099 0.108 gamma(1,.5)[sd]
#> .y6 0.093 0.003 0.088 0.100 gamma(1,.5)[sd]
#> eta1 1.021 0.015 0.991 1.052 gamma(1,.5)[sd]
#> .eta2 0.999 0.015 0.970 1.029 gamma(1,.5)[sd]

Compare model fit to `{lavaan}` and `{blavaan}` (MCMC sampling using
Stan on a single thread obtaining 1000 burnin and 2000 samples, as well
Expand All @@ -194,7 +194,7 @@ as variational Bayes):
#> ── Compare timing (seconds) ──
#>
#> INLAvaan lavaan blavaan blavaan_vb
#> 35.687 0.034 47.335 92.147
#> 36.734 0.030 131.507 89.568

A little experiment to see how sample size affects run time:

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