Get rid of numeric_leading_zero_linter() lints

easystats/easystats#334

R/compute_variances.R

@@ -953,7 +953,7 @@
           # we here check if random slopes from correlated and uncorrelated
           # are duplicated (i.e. their difference is 0 - including a tolerance)
           # and then remove duplicated elements
-          the_same <- which(abs(outer(out[j], rndslopes, `-`)) < .0001)
+          the_same <- which(abs(outer(out[j], rndslopes, `-`)) < 0.0001)
           if (length(the_same) && grepl(dn[the_same], names(out[j]), fixed = TRUE)) {
             to_remove <- c(to_remove, j)
           }

R/get_loglikelihood.R

@@ -459,7 +459,7 @@ get_loglikelihood.cpglm <- get_loglikelihood.plm
       } else if (trans == "expm1") {
         .weighted_sum((get_response(x, as_proportion = TRUE) - 1), w = model_weights)
       } else if (trans == "sqrt") {
-        .weighted_sum(log(.5 / sqrt(get_response(x, as_proportion = TRUE))), w = model_weights)
+        .weighted_sum(log(0.5 / sqrt(get_response(x, as_proportion = TRUE))), w = model_weights)
       } else if (is.null(model_weights)) {
         .ll_log_adjustment(x)
       } else {

R/get_statistic.R

@@ -2304,7 +2304,7 @@ get_statistic.metaplus <- function(x, ...) {
   ci_low <- as.vector(x$results[, "95% ci.lb"])
   ci_high <- as.vector(x$results[, "95% ci.ub"])
   cis <- apply(cbind(ci_low, ci_high), MARGIN = 1, diff)
-  se <- cis / (2 * stats::qnorm(.975))
+  se <- cis / (2 * stats::qnorm(0.975))
 
   out <- data.frame(
     Parameter = params$Parameter,

tests/testthat/test-BayesFactorBF.R

@@ -66,7 +66,7 @@ test_that("find_parameters", {
   expect_equal(find_parameters(t2d)[[1]], "Difference")
 })
 
-t <- c(-.15, 2.39, 2.42, 2.43)
+t <- c(-0.15, 2.39, 2.42, 2.43)
 N <- c(100, 150, 97, 99)
 x <- meta.ttestBF(t = t, n1 = N, rscale = 1)
 test_that("get_data", {
@@ -303,9 +303,9 @@ ttest_BFk <- ttestBF(sleep$extra[sleep$group == 1], sleep$extra[sleep$group == 2
   nullInterval = c(-3, 0)
 )
 
-prop_BF1 <- proportionBF(y = 15, N = 25, p = .5, progress = FALSE)
+prop_BF1 <- proportionBF(y = 15, N = 25, p = 0.5, progress = FALSE)
 prop_BFk <- proportionBF(
-  y = 15, N = 25, p = .5, progress = FALSE,
+  y = 15, N = 25, p = 0.5, progress = FALSE,
   nullInterval = c(0, 0.3)
 )
 

tests/testthat/test-GLMMadaptive.R

@@ -155,7 +155,7 @@ if (.runThisTest) {
     })
 
     test_that("link_inverse", {
-      expect_identical(link_inverse(m)(.2), exp(.2))
+      expect_identical(link_inverse(m)(0.2), exp(0.2))
     })
 
     test_that("clean_names", {

tests/testthat/test-Gam2.R

@@ -28,7 +28,7 @@ if (.runThisTest &&
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-LORgee.R

@@ -49,7 +49,7 @@ if (requiet("multgee")) {
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-MCMCglmm.R

@@ -47,7 +47,7 @@ if (.Platform$OS.type == "windows" &&
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.5), .5, tolerance = 1e-1)
+    expect_equal(link_inverse(m1)(0.5), 0.5, tolerance = 1e-1)
   })
 
   test_that("get_data", {
@@ -84,7 +84,7 @@ if (.Platform$OS.type == "windows" &&
   })
 
   test_that("linkfun", {
-    expect_equal(link_function(m1)(.5), .5, tolerance = 1e-1)
+    expect_equal(link_function(m1)(0.5), 0.5, tolerance = 1e-1)
   })
 
   test_that("find_parameters", {

tests/testthat/test-aovlist.R

@@ -39,8 +39,8 @@ test_that("get_predictors", {
 })
 
 test_that("link_inverse", {
-  expect_equal(link_inverse(m1)(.2), .2, tolerance = 1e-5)
-  expect_equal(link_inverse(m2)(.2), .2, tolerance = 1e-5)
+  expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
+  expect_equal(link_inverse(m2)(0.2), 0.2, tolerance = 1e-5)
 })
 
 test_that("get_data", {

tests/testthat/test-betabin.R

@@ -49,11 +49,11 @@ if (requiet("aod")) {
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
   })
 
   test_that("link_function", {
-    expect_equal(link_function(m1)(.2), qlogis(.2), tolerance = 1e-5)
+    expect_equal(link_function(m1)(0.2), qlogis(0.2), tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-betareg.R

@@ -35,7 +35,7 @@ if (
   })
 
   test_that("link_inverse", {
-    expect_identical(link_inverse(m1)(.2), plogis(.2))
+    expect_identical(link_inverse(m1)(0.2), plogis(0.2))
   })
 
   test_that("get_data", {

tests/testthat/test-bigglm.R

@@ -54,7 +54,7 @@ if (.runThisTest && requiet("glmmTMB") && requiet("biglm")) {
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), exp(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), exp(0.2), tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-blmer.R

@@ -101,8 +101,8 @@ if (requiet("blme")) {
   })
 
   test_that("link_inverse", {
-    expect_identical(link_inverse(m1)(.2), .2)
-    expect_identical(link_inverse(m2)(.2), .2)
+    expect_identical(link_inverse(m1)(0.2), 0.2)
+    expect_identical(link_inverse(m2)(0.2), 0.2)
   })
 
   test_that("get_data", {

tests/testthat/test-censReg.R

@@ -67,7 +67,7 @@ if (requiet("censReg") && requiet("AER")) {
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), .2, tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-clm.R

@@ -1,6 +1,4 @@
-if (
-
-  requiet("ordinal")) {
+if (requiet("ordinal")) {
   data(wine, package = "ordinal")
   m1 <- clm(rating ~ temp * contact, data = wine)
 
@@ -37,7 +35,7 @@ if (
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-clm2.R

@@ -41,7 +41,7 @@ if (
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
   })
 
   test_that("find_weights", {

tests/testthat/test-clmm.R

@@ -14,11 +14,7 @@ osx <- tryCatch(
 
 .runThisTest <- Sys.getenv("RunAllinsightTests") == "yes"
 
-if (.runThisTest &&
-
-
-  requiet("lme4") &&
-  requiet("ordinal")) {
+if (.runThisTest && requiet("lme4") && requiet("ordinal")) {
   data(wine, package = "ordinal")
   data(soup)
 
@@ -92,8 +88,8 @@ if (.runThisTest &&
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
-    expect_equal(link_inverse(m2)(.2), pnorm(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m2)(0.2), pnorm(0.2), tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-coxme.R

@@ -36,8 +36,8 @@ if (
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
-    expect_equal(link_inverse(m2)(.2), plogis(.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
+    expect_equal(link_inverse(m2)(0.2), plogis(0.2), tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-coxph.R

@@ -25,7 +25,7 @@ test_that("find_response", {
 })
 
 test_that("link_inverse", {
-  expect_equal(link_inverse(m1)(.2), plogis(.2), tolerance = 1e-5)
+  expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
 })
 
 test_that("get_data", {

tests/testthat/test-cpglmm.R

@@ -85,11 +85,11 @@ if (
 
 
   test_that("link_function", {
-    expect_equal(link_function(m1)(.2), log(.2), tolerance = 1e-3)
+    expect_equal(link_function(m1)(0.2), log(0.2), tolerance = 1e-3)
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), exp(.2), tolerance = 1e-3)
+    expect_equal(link_inverse(m1)(0.2), exp(0.2), tolerance = 1e-3)
   })
 
 

tests/testthat/test-crch.R

@@ -39,7 +39,7 @@ if (
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), .2, tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-crq.R

@@ -72,11 +72,11 @@ if (requiet("quantreg")) {
   })
 
   test_that("link_function", {
-    expect_equal(link_function(m1)(.2), .2, tolerance = 1e-5)
+    expect_equal(link_function(m1)(0.2), 0.2, tolerance = 1e-5)
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), .2, tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
   })
 
   test_that("find_parameters", {

tests/testthat/test-feis.R

@@ -67,7 +67,7 @@ if (!osx && requiet("plm") && requiet("feisr")) {
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), .2, tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-felm.R

@@ -65,7 +65,7 @@ if (
   })
 
   test_that("link_inverse", {
-    expect_equal(link_inverse(m1)(.2), .2, tolerance = 1e-5)
+    expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
   })
 
   test_that("get_data", {

tests/testthat/test-find_predictor_nested_re.R

@@ -1,7 +1,7 @@
 if (requiet("lme4")) {
   set.seed(1984)
   dat <- data.frame(
-    y = rnorm(1000 * 5, sd = 1 - .20),
+    y = rnorm(1000 * 5, sd = 1 - 0.20),
     time = rep(1:10, 100 * 5),
     g1 = sort(rep(1:100, 10 * 5)),
     g2 = sort(rep(1:10, 100 * 5))

tests/testthat/test-find_terms.R

@@ -28,7 +28,7 @@ if (requiet("lme4")) {
 
   set.seed(1984)
   dat <- data.frame(
-    y = rnorm(100 * 5, sd = 1 - .20),
+    y = rnorm(100 * 5, sd = 1 - 0.20),
     time = rep(1:10, 10 * 5),
     g1 = sort(rep(1:100, 5)),
     g2 = sort(rep(1:10, 10 * 5))

tests/testthat/test-find_weights.R

@@ -1,13 +1,13 @@
 if (requiet("lme4")) {
   test_that("find_weights", {
     data(mtcars)
-    mtcars$weight <- rnorm(nrow(mtcars), 1, .3)
+    mtcars$weight <- rnorm(nrow(mtcars), 1, 0.3)
     m <- lm(mpg ~ wt + cyl + vs, data = mtcars, weights = weight)
     expect_equal(find_weights(m), "weight")
   })
   test_that("find_weights", {
     data(iris)
-    iris$wgt <- rnorm(nrow(iris), 1, .3)
+    iris$wgt <- rnorm(nrow(iris), 1, 0.3)
     m <- lmer(Sepal.Width ~ Sepal.Length + (1 | Species), data = iris, weights = wgt)
     expect_equal(find_weights(m), "wgt")
   })

tests/testthat/test-fixest.R

@@ -107,15 +107,15 @@ test_that("get_predictors", {
 })
 
 test_that("link_inverse", {
-  expect_equal(link_inverse(m1)(.2), exp(.2), tolerance = 1e-4)
-  expect_equal(link_inverse(m2)(.2), .2, tolerance = 1e-4)
-  expect_equal(link_inverse(m3)(.2), exp(.2), tolerance = 1e-4)
+  expect_equal(link_inverse(m1)(0.2), exp(0.2), tolerance = 1e-4)
+  expect_equal(link_inverse(m2)(0.2), 0.2, tolerance = 1e-4)
+  expect_equal(link_inverse(m3)(0.2), exp(0.2), tolerance = 1e-4)
 })
 
 test_that("link_function", {
-  expect_equal(link_function(m1)(.2), log(.2), tolerance = 1e-4)
-  expect_equal(link_function(m2)(.2), .2, tolerance = 1e-4)
-  expect_equal(link_function(m3)(.2), log(.2), tolerance = 1e-4)
+  expect_equal(link_function(m1)(0.2), log(0.2), tolerance = 1e-4)
+  expect_equal(link_function(m2)(0.2), 0.2, tolerance = 1e-4)
+  expect_equal(link_function(m3)(0.2), log(0.2), tolerance = 1e-4)
 })
 
 test_that("get_data", {
@@ -264,14 +264,14 @@ test_that("get_predicted", {
   b <- get_predicted(m1, type = "link", predict = NULL)
   expect_equal(a, b)
   # these used to raise warnings
-  expect_warning(get_predicted(m1, ci = .4), NA)
+  expect_warning(get_predicted(m1, ci = 0.4), NA)
   expect_warning(get_predicted(m1, predict = NULL, type = "link"), NA)
 })
 
 test_that("get_data works when model data has name of  reserved words", {
   ## NOTE check back every now and then and see if tests still work
   skip("works interactively")
-  rep <- data.frame(Y = runif(100) > .5, X = rnorm(100))
+  rep <- data.frame(Y = runif(100) > 0.5, X = rnorm(100))
   m <- feglm(Y ~ X, data = rep, family = binomial)
   out <- get_data(m)
   expect_s3_class(out, "data.frame")