typos correction

NEWS.md

@@ -24,13 +24,13 @@
 - bug Fix related to storing `vector` in `model_frame` when predicting `y_hat` in mass imputation `glm` model when X is based in one auxiliary variable only - fix provided converting it to `data.frame` object.
 
 ### Features
-- add information to `summary` about quality of estimation basing on difference between estimated and known total values of auxiliary variables
-- add estimation of exact standard error for k-nearest neighbor estimator.
-- add breaking change to `controlOut` function by switching values for `predictive_match` argument. From now on, the `predictive_match = 1` means $\hat{y}-\hat{y}$ in predictive mean matching imputation and `predictive_match = 2` corresponds to $\hat{y}-y$ matching.
-- implement `div` option when variable selection (more in documentation) for doubly robust estimation.
-- add more insights to `nonprob` output such as gradient, hessian and jacobian derived from IPW estimation for `mle` and `gee` methods when `IPW` or `DR` model executed.
-- add estimated inclusion probabilities and its derivatives for probability and non-probability samples to `nonprob` output when `IPW` or `DR` model executed.
-- add `model_frame` matrix data from probability sample used for mass imputation to `nonprob` when `MI` or `DR` model executed.
+- added information to `summary` about quality of estimation basing on difference between estimated and known total values of auxiliary variables
+- added estimation of exact standard error for k-nearest neighbor estimator.
+- added breaking change to `controlOut` function by switching values for `predictive_match` argument. From now on, the `predictive_match = 1` means $\hat{y}-\hat{y}$ in predictive mean matching imputation and `predictive_match = 2` corresponds to $\hat{y}-y$ matching.
+- implemented `div` option when variable selection (more in documentation) for doubly robust estimation.
+- added more insights to `nonprob` output such as gradient, hessian and jacobian derived from IPW estimation for `mle` and `gee` methods when `IPW` or `DR` model executed.
+- added estimated inclusion probabilities and its derivatives for probability and non-probability samples to `nonprob` output when `IPW` or `DR` model executed.
+- added `model_frame` matrix data from probability sample used for mass imputation to `nonprob` when `MI` or `DR` model executed.
 
 ### Unit tests
 - added unit tests for variable selection models and mi estimation with vector of population totals available

R/bias_correction_ipw.R

@@ -57,10 +57,10 @@ mm <- function(X,
       "2" = warning("Relatively convergent algorithm when fitting selection model by nleqslv, but user must check if function values are acceptably small."),
       "3" = warning("Algorithm did not find suitable point - has stalled cannot find an acceptable new point when fitting selection model by nleqslv."),
       "4" = warning("Iteration limit exceeded when fitting selection model by nleqslv."),
-      "5" = warning("ill-conditioned Jacobian when fitting selection model by nleqslv."),
+      "5" = warning("Ill-conditioned Jacobian when fitting selection model by nleqslv."),
       "6" = warning("Jacobian is singular when fitting selection model by nleqslv."),
       "7" = warning("Jacobian is unusable when fitting selection model by nleqslv."),
-      "-10" = warning("user specified Jacobian is incorrect when fitting selection model by nleqslv.")
+      "-10" = warning("User specified Jacobian is incorrect when fitting selection model by nleqslv.")
     )
   }
 

R/cloglogModel.R

@@ -154,9 +154,9 @@ 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.")
+          "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.")
         )
       }
       theta <- maxLik_an$par

R/control_selection.R

@@ -20,7 +20,7 @@
 #'   \frac{\pi(\mathbf{x}, \boldsymbol{\theta})}{\mathbf{x}}}
 #'   \item if \code{2} then \mjseqn{ \mathbf{h}\left(\mathbf{x}, \boldsymbol{\theta}\right) = \mathbf{x}}
 #'   }
-#' @param penalty The penanlization function used during variables selection.
+#' @param penalty The penalization function used during variables selection.
 #' @param a_SCAD The tuning parameter of the SCAD penalty for selection model. Default is 3.7.
 #' @param a_MCP The tuning parameter of the MCP penalty for selection model. Default is 3.
 #' @param lambda A user-specified \mjseqn{\lambda} value during variable selection model fitting.

R/data.R

@@ -1,7 +1,7 @@
 #' Job Vacancy Survey
 #'
 #' @description
-#' This is a subset of the subset of the Job Vacancy Survey from Poland (for one quarter).
+#' This is a subset of the Job Vacancy Survey from Poland (for one quarter).
 #' The data has been subject to slight manipulation, but the relationships in the data have been preserved.
 #' For further details on the JVS, please refer to the following link:
 #' \url{https://stat.gov.pl/obszary-tematyczne/rynek-pracy/popyt-na-prace/zeszyt-metodologiczny-popyt-na-prace,3,1.html}.

R/logitModel.R

@@ -134,8 +134,8 @@ logit_model_nonprobsvy <- function(...) {
         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.")
+          "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.")
         )
       }
 

R/main_function_documentation.R

@@ -104,7 +104,7 @@ NULL
 #'    Using the imputed values for the probability sample and the (known) design weights,
 #'    we can build a population mean estimator of the form:
 #'    \mjsdeqn{\hat{\mu}_{MI} = \frac{1}{N^B}\sum_{i \in S_{B}} d_{i}^{B} \hat{y}_i.}
-#'    It opens the the door to a very flexible method for imputation models. The package uses generalized linear models from [stats::glm()],
+#'    It opens the door to a very flexible method for imputation models. The package uses generalized linear models from [stats::glm()],
 #'    the nearest neighbour algorithm using [RANN::nn2()] and predictive mean matching.
 #'
 #' 3. Doubly robust estimation -- The IPW and MI estimators are sensitive to misspecified models for the propensity score and outcome variable, respectively.

R/nonprob.R

@@ -51,7 +51,7 @@ nonprob <- function(data,
   if (!(family_outcome %in% c("gaussian", "binomial", "poisson"))) stop("Invalid family for outcome formula.")
   if (!is.null(control_selection$key)) {
     if (!(control_selection$key %in% colnames(data)) || !(control_selection$key %in% colnames(svydesign$variables))) {
-      stop("key variable for overlapping units must be defined with this same name in prob and nonprob sample.")
+      stop("Key variable for overlapping units must be defined with this same name in prob and nonprob sample.")
     }
   }
 

R/probitModel.R

@@ -116,7 +116,7 @@ probit_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."),
+          "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"),
@@ -144,10 +144,10 @@ probit_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 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.")
         )
       }
 

R/theta_funcs.R

@@ -138,10 +138,10 @@ theta_h_estimation <- function(R,
       "2" = warning("Relatively convergent algorithm when fitting selection model by nleqslv, but user must check if function values are acceptably small."),
       "3" = warning("Algorithm did not find suitable point - has stalled cannot find an acceptable new point when fitting selection model by nleqslv."),
       "4" = warning("Iteration limit exceeded when fitting selection model by nleqslv."),
-      "5" = warning("ill-conditioned Jacobian when fitting selection model by nleqslv."),
+      "5" = warning("Ill-conditioned Jacobian when fitting selection model by nleqslv."),
       "6" = warning("Jacobian is singular when fitting selection model by nleqslv."),
       "7" = warning("Jacobian is unusable when fitting selection model by nleqslv."),
-      "-10" = warning("user specified Jacobian is incorrect when fitting selection model by nleqslv.")
+      "-10" = warning("User specified Jacobian is incorrect when fitting selection model by nleqslv.")
     )
   }
   theta_h <- as.vector(theta_root)

README.Rmd

@@ -44,7 +44,7 @@ population or probability sample is available:
 
 -   inverse probability weighting estimators with possible calibration
     constraints [@chen2020],
--   mass imputation estimators based in nearest neighbours [@yang2021],
+-   mass imputation estimators based on nearest neighbours [@yang2021],
     predictive mean matching and regression imputation [@kim2021],
 -   doubly robust estimators with bias minimization [@chen2020,
     @yang2020].
@@ -61,12 +61,12 @@ The package allows for:
 -   different links for selection (`logit`, `probit` and `cloglog`) and
     outcome (`gaussian`, `binomial` and `poisson`) variables.
 
-Details on use of the package be found:
+Details on the use of the package can be found:
 
--   on the draft (and not proofread) version the book [Modern inference
+-   in the draft (and not proofread) version of the book [Modern inference
     methods for non-probability samples with
     R](https://ncn-foreigners.github.io/nonprobsvy-book/),
--   example codes that reproduce papers are available at github in the
+-   in example codes that reproduce papers available on github in the
     repository [software
     tutorials](https://github.com/ncn-foreigners/software-tutorials).
 

README.md

@@ -28,7 +28,7 @@ population or probability sample is available:
 
 - inverse probability weighting estimators with possible calibration
   constraints ([Chen, Li, and Wu 2020](#ref-chen2020)),
-- mass imputation estimators based in nearest neighbours ([Yang, Kim,
+- mass imputation estimators based on nearest neighbours ([Yang, Kim,
   and Hwang 2021](#ref-yang2021)), predictive mean matching and
   regression imputation ([Kim et al. 2021](#ref-kim2021)),
 - doubly robust estimators with bias minimization Yang, Kim, and Song
@@ -46,12 +46,12 @@ The package allows for:
 - different links for selection (`logit`, `probit` and `cloglog`) and
   outcome (`gaussian`, `binomial` and `poisson`) variables.
 
-Details on use of the package be found:
+Details on the use of the package can be found:
 
-- on the draft (and not proofread) version the book [Modern inference
+- in the draft (and not proofread) version of the book [Modern inference
   methods for non-probability samples with
   R](https://ncn-foreigners.github.io/nonprobsvy-book/),
-- example codes that reproduce papers are available at github in the
+- in example codes that reproduce papers available on github in the
   repository [software
   tutorials](https://github.com/ncn-foreigners/software-tutorials).
 
@@ -579,13 +579,13 @@ summary(result_ipw)
 #>     target = ~y1, svydesign = sample_prob)
 #> 
 #> -------------------------
-#> Estimated population mean: 2.925 with overall std.err of: 0.05
+#> Estimated population mean: 2.925 with overall std.err of: 0.04999
 #> And std.err for nonprobability and probability samples being respectively:
-#> 0.001586 and 0.04997
+#> 0.001325 and 0.04997
 #> 
 #> 95% Confidence inverval for popualtion mean:
 #>    lower_bound upper_bound
-#> y1     2.82679    3.022776
+#> y1    2.826805    3.022761
 #> 
 #> 
 #> Based on: Inverse probability weighted method