cleaning up imports for enviromtx

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: enviromtx
 Title: Predicting Species-Species Interactions from Environmental Metatranscriptomics Data
-Version: 1.1.0
+Version: 1.1.1
 Authors@R: 
     c(person("Amy D", "Willis", email = "adwillis@uw.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2802-4317")),
       person("Sarah", "Teichman", role = "aut"),
@@ -15,9 +15,9 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 Imports: 
     dplyr,
-    geepack,
-    geeasy,
     lazyeval, 
     raoBust (>= 1.1.1),
-    tibble
+    tibble,
+    geeasy,
+    geepack
 Remotes: statdivlab/raoBust

NAMESPACE

@@ -3,9 +3,5 @@
 export(fit_mgx_model)
 import(geeasy)
 import(geepack)
-import(stats)
-importFrom(dplyr,bind_cols)
-importFrom(lazyeval,as_call)
-importFrom(lazyeval,f_new)
+importFrom(raoBust,gee_test)
 importFrom(raoBust,glm_test)
-importFrom(tibble,tibble)

R/fit_mgx_model.R

@@ -26,13 +26,9 @@
 #'    Users can override some or all of these.
 #'    If \code{center = TRUE}, then covariates will be centered before being included in the model.
 #'
-#' @importFrom tibble tibble
-#' @importFrom dplyr bind_cols
-#' @importFrom lazyeval f_new as_call
-#' @importFrom raoBust glm_test
-#' @import stats
-#' @import geepack
+#' @importFrom raoBust glm_test gee_test
 #' @import geeasy
+#' @import geepack
 #'
 #' @examples
 #' my_df <- data.frame(xx = rpois(20, lambda = 400),
@@ -163,7 +159,7 @@ fit_mgx_model <- function(
       stop("At least one replicate provided is infinite. Please fix this and then rerun.")
     }
   }
-  covs <- all.vars(update(formula, . ~ .))[-1]
+  covs <- all.vars(stats::update(formula, . ~ .))[-1]
   for (i in 1:length(covs)) {
     if (any(is.infinite(my_df[[covs[i]]]))) {
       stop(paste0("At least one value of covariate ", covs[i], " is infinite. Please fix this and rerun."))
@@ -178,7 +174,7 @@ fit_mgx_model <- function(
   # center covariates if desired
   if (control$center) {
     # get variable names from the formula
-    vars <- setdiff(all.vars(update(formula, . ~ .))[-1], "predictor")
+    vars <- setdiff(all.vars(stats::update(formula, . ~ .))[-1], "predictor")
 
     # copy data so we don't overwrite
     data_centered <- my_df
@@ -222,11 +218,11 @@ fit_mgx_model <- function(
   ## E[Y]/X = gamma0 * (X^*/X)^beta1 * e^(beta2*salinity + beta3*iron)
 
   if (is.null(replicates)) {
-    raoBust_out <- raoBust::glm_test(formula = formula,
-                                     offset = offset,
-                                     family = stats::poisson(link = "log"),
-                                     data = my_df,
-                                     weights = wts)$coef_tab
+    raoBust_out <- glm_test(formula = formula,
+                            offset = offset,
+                            family = stats::poisson(link = "log"),
+                            data = my_df,
+                            weights = wts)$coef_tab
   } else {
     if (!all(my_df[[wts]] == 1L)) {
       warning("Run this by Amy; not sure what this is doing off-the-cuff")
@@ -239,13 +235,13 @@ fit_mgx_model <- function(
       id <- "id"
     }
 
-    raoBust_out <- raoBust::gee_test(formula = formula,
-                                     offset = offset,
-                                     family = stats::poisson(link="log"),
-                                     id = id,
-                                     data = my_df,
-                                     use_jack_se = use_jack_se,
-                                     cluster_corr_coef = cluster_corr_coef)$coef_tab
+    raoBust_out <- gee_test(formula = formula,
+                            offset = offset,
+                            family = stats::poisson(link="log"),
+                            id = id,
+                            data = my_df,
+                            use_jack_se = use_jack_se,
+                            cluster_corr_coef = cluster_corr_coef)$coef_tab
   }
 
 

tests/testthat/test-accuracy-correlated.R

@@ -20,7 +20,7 @@ test_that("reasonably accurate estimates with covariates and correlation", {
   # yy1 <- rpois(n, lambda=eta)
   yy1 <- rnbinom(n, mu=eta, size=eta^2)
 
-  my_df <- tibble(yy1, xx1, xstar1, xx_covariates1, xx_covariates2, id)
+  my_df <- tibble::tibble(yy1, xx1, xstar1, xx_covariates1, xx_covariates2, id)
 
   output10 <- fit_mgx_model(enviro_df = my_df,
                             yy = "yy1",

tests/testthat/test-accuracy-uncorrelated.R

@@ -87,7 +87,7 @@ test_that("reasonably accurate estimates with covariates and correlation", {
   # yy1 <- rpois(n, lambda=eta)
   yy1 <- rnbinom(n, mu=eta, size=eta^2)
 
-  my_df <- tibble(yy1, xx1, predictor = log((xstar1 + 1)/(xx1 + 1)), xx_covariates1, xx_covariates2, id)
+  my_df <- tibble::tibble(yy1, xx1, predictor = log((xstar1 + 1)/(xx1 + 1)), xx_covariates1, xx_covariates2, id)
 
   output9 <- fit_mgx_model(data.frame(yy = yy1,
                 xstar = xstar1,

tests/testthat/test-fit_mgx_model_centering.R

@@ -52,7 +52,7 @@ test_that("with small parameter values, centering does not affect results", {
   eta <- xx1 * beta0 * (xstar1/xx1)^beta1 * exp(beta2 * xx_covariates1 + beta3 * xx_covariates2 + b[id])
   yy1 <- rnbinom(n, mu=eta, size=eta^2)
 
-  my_df <- tibble(yy1, xx1, predictor = log((xstar1 + 1)/(xx1 + 1)), xx_covariates1, xx_covariates2, id)
+  my_df <- tibble::tibble(yy1, xx1, predictor = log((xstar1 + 1)/(xx1 + 1)), xx_covariates1, xx_covariates2, id)
 
   output3 <- fit_mgx_model(data.frame(yy = yy1,
                                       xstar = xstar1,
@@ -245,7 +245,7 @@ test_that("reasonably accurate estimates with covariates and correlation", {
   # yy1 <- rpois(n, lambda=eta)
   yy1 <- rnbinom(n, mu=eta, size=eta^2)
 
-  my_df <- tibble(yy1, xx1, xstar1, xx_covariates1, xx_covariates2, id)
+  my_df <- tibble::tibble(yy1, xx1, xstar1, xx_covariates1, xx_covariates2, id)
 
   output9 <- fit_mgx_model(yy = "yy1",
                            xstar = "xstar1",