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feat(coef.estimate): tests for s3 method + setting options.lava(messages = 1) by default #23

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a4727b9
set messages=1 in options + bump testthat
Apr 24, 2026
003a86b
readme add information about messages
Apr 24, 2026
d92efd4
set seed in readme
Apr 24, 2026
eeb1be0
description
Apr 24, 2026
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,7 @@ Suggests:
rgl,
svglite,
targeted (>= 0.4),
testthat (>= 0.11),
testthat (>= 3.1.0),
vdiffr,
visNetwork
VignetteBuilder: knitr,rmarkdown
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2 changes: 1 addition & 1 deletion R/onload.R
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Expand Up @@ -6,7 +6,7 @@
addhook("ordinal_sim_hook","sim.hooks")
addhook("color_ordinal","color.hooks")
addhook("ordinal_remove_hook","remove.hooks")
lava.options(cluster.index = packagecheck("mets"))
lava.options(cluster.index = packagecheck("mets"), messages = 1)
}

'.onAttach' <- function(libname, pkgname="lava") {
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11 changes: 10 additions & 1 deletion README.Rmd
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Expand Up @@ -120,6 +120,15 @@ To cite that `lava` package please use one of the following references
#| results: 'hide'
#| echo: FALSE
library(lava)
set.seed(42)
```

The package uses the `message` function to provide additional information to
users. All messages can be disabled session-wide with

```{r lava_options}
#| eval: FALSE
lava.options(messages = 0) # the default is 1
```

### Influence functions
Expand Down Expand Up @@ -195,7 +204,7 @@ regression(m) <- z1 + z2 + z3 ~ u2
latent(m) <- ~ u1 + u2
regression(m) <- u2 ~ u1 + x
regression(m) <- u1 ~ x

plot(m)
```

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68 changes: 36 additions & 32 deletions README.md
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Expand Up @@ -96,6 +96,13 @@ To cite that `lava` package please use one of the following references

## Examples

The package uses the `message` function to provide additional
information to users. All messages can be disabled session-wide with

``` r
lava.options(messages = 0) # the default is 1
```

### Influence functions

Construct `estimate` objects from parameter coefficients and estimated
Expand All @@ -119,22 +126,22 @@ distribution via their estimated influence functions
``` r
e <- c(a, b)
vcov(e) # joint distribution
#> a b
#> a 0.1023667491 0.0001747415
#> b 0.0001747415 0.0625808426
#> a b
#> a 0.07280218 0.03491347
#> b 0.03491347 0.24018309
summary(e, null=c(0, 0))
#> Call: estimate.default(contrast = as.list(seq_along(p)), null = ..1,
#> Call: estimate.default(f = FALSE, contrast = contrast, null = ..1,
#> vcov = vcov(object, messages = 0), coef = p)
#> ────────────────────────────────────────────────────────────
#> Estimate Std.Err 2.5% 97.5% P-value
#> a 0.5 0.3199 -0.1271 1.127 0.118111
#> b 0.8 0.2502 0.3097 1.290 0.001384
#> Estimate Std.Err 2.5% 97.5% P-value
#> a 0.5 0.2698 -0.02884 1.029 0.06387
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@benesom benesom Apr 24, 2026

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@kkholst , I assume that we forgot to render the readme file during previous changes, because I don't see how the current changes would cause these estimates to change.

#> b 0.8 0.4901 -0.16055 1.761 0.10260
#> ────────────────────────────────────────────────────────────
#> Null Hypothesis:
#> [a] = 0
#> [b] = 0
#>
#> chisq = 12.6472, df = 2, p-value = 0.001793
#> chisq = 4.8386, df = 2, p-value = 0.08899
```

Parameter transformations can be calculated directly as in the following
Expand All @@ -144,64 +151,61 @@ Products

``` r
a * b
#> Estimate Std.Err 2.5% 97.5% P-value
#> a 0.4 0.2851 -0.1588 0.9588 0.1607
#> Estimate Std.Err 2.5% 97.5% P-value
#> a 0.4 0.3668 -0.319 1.119 0.2755
```

General transformations

``` r
(3 * cos(a) / sqrt(b) + 1) / a^2
#> Estimate Std.Err 2.5% 97.5% P-value
#> a 15.77 22.33 -27.98 59.53 0.4798
#> a 15.77 20.02 -23.46 55.01 0.4307
```

Inner product, sums, and products

``` r
c(iprod=e %*% c(a, b^2), sum=sum(e), prod=prod(e))
#> Estimate Std.Err 2.5% 97.5% P-value
#> iprod 0.762 0.5777 -0.3703 1.8943 0.187160
#> ─────
#> sum 1.300 0.4066 0.5031 2.0969 0.001386
#> ─────
#> prod 0.400 0.2851 -0.1588 0.9588 0.160658
#> Estimate Std.Err 2.5% 97.5% P-value
#> iprod 0.762 1.0451 -1.28640 2.810 0.46594
#> sum 1.300 0.6187 0.08733 2.513 0.03563
#> prod 0.400 0.3668 -0.31899 1.119 0.27554
```

Exponentiation and renaming of parameter

``` r
c(pow = a^b)
#> Estimate Std.Err 2.5% 97.5% P-value
#> pow 0.5743 0.3102 -0.03368 1.182 0.06411
#> Estimate Std.Err 2.5% 97.5% P-value
#> pow 0.5743 0.272 0.04118 1.108 0.03474
```

Transformation and subsetting

``` r
c(e["a"] * e["b"] / a, e["b"])
#> Estimate Std.Err 2.5% 97.5% P-value
#> a 0.8 0.2502 0.3097 1.29 0.001384
#> ─
#> b 0.8 0.2502 0.3097 1.29 0.001384
#> Estimate Std.Err 2.5% 97.5% P-value
#> a 0.8 0.4901 -0.1605 1.761 0.1026
#> b 0.8 0.4901 -0.1605 1.761 0.1026
```

For the `%*%*` operator we can also use a general contrast matrix

``` r
B <- rbind(c(1,-1), c(1,0), c(0,1))
B %*% e
#> Estimate Std.Err 2.5% 97.5% P-value
#> [a] - [b] -0.3 0.4057 -1.0952 0.4952 0.459634
#> [a] 0.5 0.3199 -0.1271 1.1271 0.118111
#> [b] 0.8 0.2502 0.3097 1.2903 0.001384
#>
#> Null Hypothesis:
#> Estimate Std.Err 2.5% 97.5% P-value
#> [a] - [b] -0.3 0.4931 -1.26648 0.6665 0.54293
#> a 0.5 0.2698 -0.02884 1.0288 0.06387
#> b 0.8 0.4901 -0.16055 1.7605 0.10260
#> ────────────────────────────────────────────────────────────
#> Null Hypothesis:
#> [a] - [b] = 0
#> [a] = 0
#> [b] = 0
#>
#> chisq = 12.6472, df = 2, p-value = 0.001793
#> chisq = 4.8386, df = 2, p-value = 0.08899
plot(B %*% e)
```

Expand All @@ -218,7 +222,7 @@ regression(m) <- z1 + z2 + z3 ~ u2
latent(m) <- ~ u1 + u2
regression(m) <- u2 ~ u1 + x
regression(m) <- u1 ~ x

plot(m)
```

Expand Down Expand Up @@ -358,7 +362,7 @@ onerun <- function(...) {
}
val <- sim(onerun, 100)
summary(val, estimate=1:4, se=5:8, short=TRUE)
#> 100 replications Time: 2.457s
#> 100 replications Time: 2.641s
#>
#> Total.Estimate Direct.Estimate Indirect.Estimate S~x~z.Estimate
#> Mean 1.99533 1.00468 0.99066 0.99066
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