adding stuff

NAMESPACE

@@ -1,5 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
+export(Influence)
+export(coeffs)
 export(geo_mean)
 export(get_bayes_R2)
 export(get_coefs)
@@ -25,11 +27,13 @@ export(plot_influ)
 export(plot_predicted_residuals)
 export(plot_qq)
 export(plot_step)
+export(ses)
 export(table_criterion)
 import(brms)
 import(dplyr)
 import(ggplot2)
 import(patchwork)
+import(proto)
 importFrom(brms,add_criterion)
 importFrom(brms,posterior_samples)
 importFrom(gtable,gtable_filter)

R/data.R

@@ -0,0 +1,7 @@
+#' Simulated catch of lobsters per pot
+#'
+#' Simulated catch of lobsters per pot.
+#'
+#' @format A data.frame with four fields:
+#'
+"lobsters_per_pot"

R/get-coefs.R

@@ -60,6 +60,7 @@ get_coefs_raw <- function(fit, var = "area") {
   if (!is.brmsfit(fit)) stop("fit is not an object of class brmsfit.")
 
   ps <- as_draws_df(x = fit, variable = var, regex = TRUE) %>%
+    as.data.frame() %>%
     mutate(iteration = .data$.draw) %>%
     select(-.data$.iteration, -.data$.chain, -.data$.draw) %>%
     melt(id.vars = "iteration") %>%
@@ -148,6 +149,7 @@ get_coefs <- function(fit, var = "area",
     #   data.frame() %>%
     #   mutate(variable = rownames(.))
     ps <- as_draws_df(x = fit, variable = paste0("r_", var), regex = TRUE) %>%
+      as.data.frame() %>%
       mutate(iteration = .data$.draw) %>%
       select(-.data$.iteration, -.data$.chain, -.data$.draw) %>%
       melt(id.vars = "iteration") %>%
@@ -169,6 +171,7 @@ get_coefs <- function(fit, var = "area",
     # names(e2) <- names(eff)
     # eff <- rbind(e2, eff)
     ps <- as_draws_df(x = fit, variable = var, regex = TRUE) %>%
+      as.data.frame() %>%
       mutate(iteration = .data$.draw) %>%
       select(-.data$.iteration, -.data$.chain, -.data$.draw) %>%
       melt(id.vars = "iteration") %>%

tests/testthat/influ.R → R/influ.R

@@ -36,11 +36,12 @@
 #' @export Influence
 NULL
 
-library(proto)
-
 #' The influence prototype object.
 #'
 #' @seealso Influence$new
+#' @import proto
+#' @export
+#' 
 Influence = proto()
 
 #' Create a new Influence object.
@@ -57,6 +58,7 @@ Influence = proto()
 #' @param response The response term in the model
 #' @param focus The focus term for which influence is to be calculated.
 #' @return A new Influence object
+#' 
 Influence$new <- function(.,model,data=NULL,response=NULL,focus=NULL){
   instance = .$proto(
     model = model,
@@ -74,6 +76,7 @@ Influence$new <- function(.,model,data=NULL,response=NULL,focus=NULL){
 #'
 #' @name Influence$init
 #' @return None
+#' 
 Influence$init <- function(.){
 
   # If no data was supplied....
@@ -113,7 +116,9 @@ Influence$init <- function(.){
 #' @name coeffs
 #' @param model The model to extract coefficients from
 #' @param term The term in the model for which coefficients are extracted
-#' @return A vector of coefficients                                                                     
+#' @return A vector of coefficients
+#' @export
+#'                                                                    
 coeffs = function(.,model=.$model,term=.$focus){
   coeffs = coefficients(model)
   rows = substr(names(coeffs),1,nchar(term))==term
@@ -127,6 +132,8 @@ coeffs = function(.,model=.$model,term=.$focus){
 #' @name ses
 #' @param model The model to extract standard errors for a coefficient from
 #' @param term The term in the model for which coefficients SEs are extracted
+#' @export
+#' 
 ses = function(.,model=.$model,term=.$focus){
   type = class(model)[1]
   if(type=='glm'|type=='negbin'){
@@ -156,6 +163,7 @@ ses = function(.,model=.$model,term=.$focus){
 #' @param model The model to extract effects from
 #' @param term The term in the model for which effects are extracted
 #' @return A numeric vector of effects for the model term
+#' 
 Influence$effects = function(.,model=.$model,term=.$focus){
   coeffs = .$coeffs(model,term)
   exp(coeffs-mean(coeffs))
@@ -167,6 +175,7 @@ Influence$effects = function(.,model=.$model,term=.$focus){
 #'
 #' @name Influence$calc
 #' @return None
+#' 
 Influence$calc <- function(.){
   #Get observed values
   observed = .$model$model[,.$response]
@@ -293,6 +302,7 @@ Influence$calc <- function(.){
 #'
 #' @name Influence$stanPlot
 #' @return None
+#' 
 Influence$stanPlot <- function(.){
   with(.$indices,{
     plot(NA,ylim=c(0,max(unstan,stanUpper)),xlim=c(1,length(level)),las=1,ylab='Index',xlab=.$labels[[.$focus]],xaxt='n')
@@ -313,6 +323,7 @@ Influence$stanPlot <- function(.){
 #'  panel with the previous steps shown as a dashed line and other steps shown as dashed grey lines. If FALSE then a single panel
 #'  plot is produced with symbols for each step
 #' @return None
+#' 
 Influence$stepPlot <- function(.,panels=T,setpar=T){
   startCol = 6
   cols = startCol:ncol(.$indices)
@@ -350,6 +361,7 @@ Influence$stepPlot <- function(.,panels=T,setpar=T){
 #' @name Influence$influPlot
 #' @param panels Whether or not to produce a plot with separate panels for each variable
 #' @return None
+#' 
 Influence$influPlot <- function(.,panels=T,setpar=T){
   cols = 2:ncol(.$influences)
   ylim=exp(range(.$influences[,cols]))
@@ -381,6 +393,7 @@ Influence$influPlot <- function(.,panels=T,setpar=T){
 #'
 #' @name Influence$stepAndInfluPlot
 #' @return None
+#' 
 Influence$stepAndInfluPlot <- function(.){
   par(mfcol=c(ncol(.$indices)-5,2),mar=c(0,5,0,0),oma=c(4,1,1,1))
   .$stepPlot(setpar=F)
@@ -397,6 +410,7 @@ Influence$stepAndInfluPlot <- function(.){
 #' In most cases this can be deduced from the term, but for complexes terms this argumey may also need to be supplied (an error message will tell you if it does).
 #' @return None
 #' @seealso cdiPlotAll
+#' 
 Influence$cdiPlot <- function(.,term,variable=NULL){
   par(oma=c(1,1,1,1),cex.lab=1.25,cex.axis=1.25)
   layout(matrix(c(1,1,0,2,2,3,2,2,3),3,3,byrow=TRUE))
@@ -496,6 +510,7 @@ Influence$cdiPlot <- function(.,term,variable=NULL){
 #' @name Influence$cdiPlotAll
 #' @param done A function that is called (with the term string as an argument) after each CDI plot is generated. Optional.
 #' @return None
+#' 
 Influence$cdiPlotAll <- function(.,done=function(term){
   cat("cdiPlot for",term,". Press enter for next")
   scan()

README.md

@@ -1,8 +1,12 @@
 # influ2 <img src="man/figures/logo.png" align="right" height=140/>
 
-[![Build Status](https://travis-ci.org/quantifish/influ2.svg?branch=master)](https://travis-ci.com/quantifish/influ2)
-
-Nokome Bentley's R package `influ` was developed for use with frequentist models fitted in R using the `glm` function. The `infu2` package is the Bayesian couterpart and has been developed for use with `brms`. It works with population-level or group-level effects, the Bayesian equivalents of fixed-effects and random-effects. It contains functions for extracting coefficients, calculating the influence of terms, generating CDI plots, step plots, and other diagnostic plots.
+Nokome Bentley's R package `influ` was developed for use with frequentist models 
+fitted in R using the `glm` function. The `infu2` package is the Bayesian 
+couterpart and has been developed for use with `brms`. It works with 
+population-level or group-level effects, the Bayesian equivalents of 
+fixed-effects and random-effects. It contains functions for extracting 
+coefficients, calculating the influence of terms, generating CDI plots, step 
+plots, and other diagnostic plots.
 
 ## Installation