more informative information

NAMESPACE

@@ -21,7 +21,6 @@ export(cloglog_model_nonprobsvy)
 export(control_inf)
 export(control_out)
 export(control_sel)
-export(genSimData)
 export(logit_model_nonprobsvy)
 export(nonprob)
 export(pop_size)
@@ -53,15 +52,13 @@ importFrom(stats,coef)
 importFrom(stats,confint)
 importFrom(stats,contrasts)
 importFrom(stats,cooks.distance)
-importFrom(stats,cor)
 importFrom(stats,cov)
 importFrom(stats,delete.response)
 importFrom(stats,deviance)
 importFrom(stats,dnorm)
 importFrom(stats,get_all_vars)
 importFrom(stats,glm.fit)
 importFrom(stats,hatvalues)
-importFrom(stats,lm.fit)
 importFrom(stats,loess)
 importFrom(stats,loess.control)
 importFrom(stats,logLik)
@@ -77,18 +74,12 @@ importFrom(stats,printCoefmat)
 importFrom(stats,pt)
 importFrom(stats,qlogis)
 importFrom(stats,qnorm)
-importFrom(stats,rbinom)
-importFrom(stats,rchisq)
 importFrom(stats,reformulate)
 importFrom(stats,residuals)
-importFrom(stats,rexp)
-importFrom(stats,rnorm)
-importFrom(stats,runif)
 importFrom(stats,sd)
 importFrom(stats,setNames)
 importFrom(stats,summary.glm)
 importFrom(stats,terms)
-importFrom(stats,uniroot)
 importFrom(stats,update)
 importFrom(stats,var)
 importFrom(stats,vcov)

NEWS.md

@@ -5,6 +5,8 @@
 ### Breaking changes
 
 - functions `pop.size`, `controlSel`, `controlOut` and `controlInf` were renamed to `pop_size`, `control_sel`, `control_out` and `control_inf` respectively.
+- function `genSimData` removed completely as it is not used anywhere in the package.
+- argument `maxLik_method` renamed to `maxlik_method` in the `control_sel` function.
 
 ### Features
 
@@ -15,6 +17,9 @@
 ### Bugfixes
 - basic methods and functions related to variance estimation, weights and probability linking methods have been rewritten in a more optimal and readable way.
 
+### Other
+- more informative error messages added.
+
 ### Documentation
 
 - annotation has been added that arguments such as `strata`, `subset` and `na_action` are not supported for the time being.

R/boot_mi.R

@@ -57,7 +57,7 @@ bootMI <- function(X_rand,
     if (class(svydesign)[1] != "pps") {
       rep_weights <- survey::as.svrepdesign(svydesign, type = rep_type, replicates = num_boot)$repweights$weights
     } else {
-      stop("pps bootstrap variance in development")
+      stop("The pps bootstrap variance estimator is under development")
     }
     if (method == "glm") {
       while (k <= num_boot) {
@@ -444,7 +444,7 @@ bootMI_multicore <- function(X_rand,
     family_nonprobsvy <- family_nonprobsvy()
   }
 
-  if (verbose) message("Multicores bootstrap in progress..")
+  if (verbose) message("Multicores bootstrap in progress...")
 
   cl <- parallel::makeCluster(cores)
   doParallel::registerDoParallel(cl)
@@ -460,7 +460,7 @@ bootMI_multicore <- function(X_rand,
     if (class(svydesign)[1] != "pps") {
       rep_weights <- survey::as.svrepdesign(svydesign, type = rep_type, replicates = num_boot)$repweights$weights
     } else {
-      stop("pps bootstrap variance in development")
+      stop("The pps bootstrap variance estimator is under development.")
     }
     if (method == "glm") {
       k <- 1:num_boot

R/cloglogModel.R

@@ -125,12 +125,12 @@ cloglog_model_nonprobsvy <- function(...) {
 
       if (maxLik_an$code %in% c(3:7, 100)) {
         switch(as.character(maxLik_an$code),
-          "3" = warning("Warning in fitting selection model with maxLik: probably not converged."),
-          "4" = warning("Maxiteration limit reached in fitting selection model by maxLik."),
-          "5" = stop("Inifinite value of log_like in fitting selection model by maxLik, error code 5"),
-          "6" = stop("Inifinite value of gradient in fitting selection model by maxLik, error code 6"),
-          "7" = stop("Inifinite value of hessian in fitting selection model by maxLik, error code 7"),
-          "100" = stop("Error in fitting selection model with maxLik, error code 100:: Bad start."),
+          "3" = warning("Warning in fitting selection model with the `maxLik` package: probably not converged."),
+          "4" = warning("Maxiteration limit reached in fitting selection model by the `maxLik` package."),
+          "5" = stop("Infinite value of log_like in fitting selection model by the `maxLik` package, error code 5."),
+          "6" = stop("Infinite value of gradient in fitting selection model by the `maxLik` package, error code 6."),
+          "7" = stop("Infinite value of hessian in fitting selection model by the `maxLik` package, error code 7."),
+          "100" = stop("Error in fitting selection model with the `maxLik` package, error code 100: Bad start."),
         )
       }
 
@@ -153,18 +153,18 @@ cloglog_model_nonprobsvy <- function(...) {
       )
       if (maxLik_an$convergence %in% c(1, 10, 51, 52)) {
         switch(as.character(maxLik_an$convergence),
-          "1" = warning("Warning in fitting selection model with optim: the iteration limit maxit had been reached."),
-          "10" = warning("Degeneracy of the Nelder Mead simplex in fitting selection model by optim."), # TODO -
-          "51" = warning("Warning from the L BFGS B when fitting by optim."), # TODO -
-          "52" = stop("Indicates an error from the L-BFGS-B method when fitting by optim.")
+          "1" = warning("Warning in fitting selection model with the `optim` function: the iteration limit maxit had been reached."),
+          "10" = warning("Degeneracy of the Nelder Mead simplex in fitting selection model by the `optim` function."), # TODO -
+          "51" = warning("Warning from the L-BFGS-B when fitting by the `optim` function."), # TODO -
+          "52" = stop("Indicates an error from the L-BFGS-B method when fitting by the `optim` function.")
         )
       }
       theta <- maxLik_an$par
       log_likelihood <- log_like(theta)
       grad <- gradient(theta)
       hess <- hessian(theta)
     } else {
-      stop("Provided invalid optimizer.")
+      stop("Provide valid optimizer (`optim` or `maxLik`).")
     }
 
     list(

R/control_inference.R

@@ -68,13 +68,13 @@ control_inf <- function(vars_selection = FALSE,
       TRUE
     } else {
       if (!is.logical(keep_boot)) {
-        stop("keep_boot argument for controlInf must be logical")
+        stop("The `keep_boot` argument for `control_inf` function must be logical.")
       } else {
         keep_boot
       }
     },
     nn_exact_se = if (!is.logical(nn_exact_se) & length(nn_exact_se) == 1) {
-      stop("Argument nn_exact_se must be a logical scalar")
+      stop("The `nn_exact_se` argument must be a logical scalar.")
     } else {
       nn_exact_se
     },

R/control_selection.R

@@ -10,7 +10,7 @@
 #' @param trace logical value. If `TRUE` trace steps of the fitting algorithms. Default is `FALSE`
 #' @param optimizer - optimization function for maximum likelihood estimation.
 #' @param optim_method maximisation method that will be passed to [stats::optim()] function. Default is `BFGS`.
-#' @param maxLik_method maximisation method that will be passed to [maxLik::maxLik()] function. Default is `NR`.
+#' @param maxlik_method maximisation method that will be passed to [maxLik::maxLik()] function. Default is `NR`.
 #' @param dependence logical value - `TRUE` if samples are dependent.
 #' @param key binary key variable
 #' @param est_method_sel Method of estimation for propensity score model.
@@ -51,7 +51,7 @@ control_sel <- function(method = "glm.fit", # perhaps another control function f
                        maxit = 500,
                        trace = FALSE,
                        optimizer = c("maxLik", "optim"),
-                       maxLik_method = "NR",
+                       maxlik_method = "NR",
                        optim_method = "BFGS",
                        dependence = FALSE,
                        key = NULL,
@@ -77,7 +77,7 @@ control_sel <- function(method = "glm.fit", # perhaps another control function f
     maxit = maxit,
     trace = trace,
     optimizer = if (missing(optimizer)) "optim" else optimizer,
-    maxLik_method = maxLik_method,
+    maxlik_method = maxlik_method,
     optim_method = optim_method,
     dependence = dependence,
     key = key,

R/data_manip.R

@@ -52,7 +52,7 @@ model_frame <- function(formula, data, weights = NULL, svydesign = NULL, pop_tot
       #  X_rand <- model.matrix(formula, svydesign$variables[, nons_names])# matrix of probability sample with intercept
       #  }
     } else {
-      stop("Variable names in data and svydesign do not match")
+      stop("The names of the variables in the data and in svydesign do not match.")
     }
 
     list(

R/internals.R

@@ -155,7 +155,7 @@ ff <- function(formula) {
   formula_string <- paste(deparse(formula), collapse = " ")
   formula_parts <- strsplit(formula_string, "~")[[1]]
   if (length(formula_parts) != 2) {
-    stop("The formula must contain exactly one '~' operator.")
+    stop("The `formula` must contain exactly one '~' operator.")
   }
 
   lhs <- trimws(formula_parts[1])
@@ -165,7 +165,7 @@ ff <- function(formula) {
   independent_vars <- strsplit(rhs, "\\s*\\+\\s*")[[1]]
 
   if (any(duplicated(dependent_vars))) {
-    warning("Duplicate dependent variable names detected. They have been made unique.")
+    warning("Duplicate dependent variable names have been detected. They have been made unique.")
     dependent_vars <- unique(dependent_vars)
   }
   outcome_formulas <- vector("list", length(dependent_vars))
@@ -261,7 +261,7 @@ process_family <- function(family_spec) {
   } else if (inherits(family_spec, "family")) {
     family <- family_spec
   } else {
-    stop("Invalid family specification")
+    stop("Invalid family specification.")
   }
   return(family)
 }

R/logitModel.R

@@ -97,7 +97,7 @@ logit_model_nonprobsvy <- function(...) {
         logLik = log_like,
         grad = gradient,
         hess = hessian,
-        method = control$maxLik_method,
+        method = control$maxlik_method,
         start = start,
         printLevel = control$print_level
       )
@@ -108,12 +108,12 @@ logit_model_nonprobsvy <- function(...) {
       log_likelihood <- log_like(theta)
       if (maxLik_an$code %in% c(3:7, 100)) {
         switch(as.character(maxLik_an$code),
-          "3" = warning("Warning in fitting selection model with maxLik: probably not converged."),
-          "4" = warning("Max iteration limit reached in fitting selection model by maxLik."),
-          "5" = stop("Inifinite value of log_like in fitting selection model by maxLik, error code 5"),
-          "6" = stop("Inifinite value of gradient in fitting selection model by maxLik, error code 6"),
-          "7" = stop("Inifinite value of hessian in fitting selection model by maxLik, error code 7"),
-          "100" = stop("Error in fitting selection model with maxLik, error code 100:: Bad start."),
+          "3" = warning("Warning in fitting selection model with the `maxLik` package: probably not converged."),
+          "4" = warning("Max iteration limit reached in fitting selection model by the `maxLik` package."),
+          "5" = stop("Infinite value of log_like in fitting selection model by the `maxLik` package, error code 5."),
+          "6" = stop("Infinite value of gradient in fitting selection model by the `maxLik` package, error code 6."),
+          "7" = stop("Infinite value of hessian in fitting selection model by the `maxLik` package, error code 7."),
+          "100" = stop("Error in fitting selection model with the `maxLik` package, error code 100: Bad start."),
         )
       }
     } else if (control$optimizer == "optim") { # TODO add optimParallel for high-dimensional data
@@ -132,10 +132,10 @@ logit_model_nonprobsvy <- function(...) {
       )
       if (maxLik_an$convergence %in% c(1, 10, 51, 52)) {
         switch(as.character(maxLik_an$convergence),
-          "1" = warning("Warning in fitting selection model with optim: the iteration limit maxit had been reached."),
-          "10" = warning("degeneracy of the Nelder Mead simplex in fitting selection model by optim."), # TODO -
-          "51" = warning("Warning from the L BFGS B when fitting by optim."), # TODO -
-          "52" = stop("Indicates an error from the L BFGS B method when fitting by optim.")
+          "1" = warning("Warning in fitting selection model with the `optim` function: the iteration limit maxit had been reached."),
+          "10" = warning("degeneracy of the Nelder Mead simplex in fitting selection model by the `optim` function."), # TODO -
+          "51" = warning("Warning from the L-BFGS-B when fitting by the `optim` function."), # TODO -
+          "52" = stop("Indicates an error from the L-BFGS-B method when fitting by the `optim` function.")
         )
       }
 
@@ -144,7 +144,7 @@ logit_model_nonprobsvy <- function(...) {
       grad <- gradient(theta)
       hess <- hessian(theta)
     } else {
-      stop("Provided invalid optimizer.")
+      stop("Provide valid optimizer (`optim` or `maxLik`).")
     }
 
     list(