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tests/TestsPseudoR2.R
In DescTools: Tools for Descriptive Statistics
# library(DescTools)
# library(haven) #for read_dta
# library(MASS) #for polr
# library(nnet) #for multinom
# library(VGAM)
# library(plyr) #for testing recode of factor, using revalue
# # source("R/LinMod.R")
#
#
# # ==== notes ===
#
# #checks needed:
# # A) Ensure data parameter should work it is 1) explicitly defined, 2 found in environment, 3) not found
# # B) Check "special" parameters (substitute, weight, and na.action parameters)
# # C) check non-literal variables
#
# hsb2 <- as.data.frame(read_dta("https://stats.idre.ucla.edu/stat/stata/notes/hsb2.dta"))
# hsb2$honcomp <- hsb2$write >= 60
#
# hsb2$write_cat <- cut(hsb2$write, breaks = c(30,40,50,60,70))
# hsb2$race_cat <- factor(hsb2$race)
#
#
# # ==== GLM ====
#
# #"Data" and "model" object components are both usable (we give priority to model)
# base.logit <- glm(honcomp ~ female + read + science + ses, hsb2, family="binomial")
# PseudoR2(base.logit)
#
# #"Data" object is reference to global environment (but we have a model object)
# base2.logit <- glm(hsb2$honcomp ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial")
# PseudoR2(base2.logit)
#
# #POSSIBLE ISSUE: no model object (only data), and a non-literal DV (eg, read > 60)
# #A1 tests are covered above
#
# #A2a - variables in global environment
# y <- hsb2$honcomp
# test_a2.logit <- glm(y ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial", model = FALSE)
# PseudoR2(test_a2.logit)
# #NB: doesn't give useful name of object that needs new evaluation
#
# #A3a - "data" object is reference to global environment
# z <- hsb2$honcomp
# test_a3a.logit <- glm(z ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial", model = FALSE, x = TRUE, y = TRUE)
# rm(z)
# PseudoR2(test_a3a.logit)
#
# #A3b - "data" object is contained in data frame
# tempdf <- hsb2
# test_a3b.logit <- glm(honcomp ~ female + read + science + ses, tempdf, family="binomial", model = FALSE)
# PseudoR2(test_a3b.logit)
# tempdf <- tempdf[1:100,]
# PseudoR2(test_a3b.logit)
# rm(tempdf)
# PseudoR2(test_a3b.logit)
#
#
# # ---- B ----
# #WEIGHTS
# #Weights are created on-the-fly via runif
# test_b1.logit <- glm(honcomp ~ female + read + science + ses, hsb2, family="binomial", weights = runif(nrow(hsb2)), model = FALSE)
# PseudoR2(test_b1.logit)
# PseudoR2(test_b1.logit)
#
# #Weights are saved
# test_weights <- runif(nrow(hsb2))
# test_b2.logit <- glm(honcomp ~ female + read + science + ses, data = hsb2, family="binomial", weights = test_weights, model = FALSE)
# rm(test_weights)
# PseudoR2(test_b2.logit)
#
# withna.df <- rbind(hsb2[1:100,], NA, NA, hsb2[101:200,])
# test_b3.logit <- glm(honcomp ~ female + read + science + ses, data = withna.df, family="binomial", weights = runif(nrow(withna.df)), model = FALSE)
# PseudoR2(test_b3.logit)
#
# #NA.ACTION
# #Could try using the na.omit attribute of glms here to handle, but it's a lot of work for little return
# test_b4.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = FALSE, na.action = na.omit)
# PseudoR2(test_b4.logit)
#
# test_naAction <- na.omit
# test_b5.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = TRUE, na.action = test_naAction)
# rm(test_naAction)
# PseudoR2(test_b5.logit)
#
# test_naAction <- na.omit
# test_b6.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = FALSE, na.action = test_naAction)
# rm(test_naAction)
# PseudoR2(test_b6.logit)
#
#
# # ---- C ----
#
# #DV, With model
# test_c1.logit <- glm((hsb2$write_cat == "(30,40]" | hsb2$write_cat == "(40,50]") ~ female + read + science + ses, hsb2, family="binomial")
# PseudoR2(test_c1.logit)
#
# #DV, without model, out of data frame
# test_c2.logit <- glm((hsb2$write_cat == "(30,40]" | hsb2$write_cat == "(40,50]") ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial", model = FALSE)
# PseudoR2(test_c2.logit)
#
# #DV, without model, in data frmae
# test_c3.logit <- glm((write_cat == "(30,40]" | hsb2$write_cat == "(40,50]") ~ female + read + science + ses, hsb2, family="binomial", model = FALSE)
# PseudoR2(test_c3.logit)
#
# #IV, without model, no data frame
# test_c4.logit <- glm(hsb2$honcomp ~ hsb2$female + (hsb2$read > 50) + hsb2$science + hsb2$ses, family="binomial", model = FALSE)
# PseudoR2(test_c4.logit)
#
# #IV, without model, with data frame
# test_c5.logit <- glm(honcomp ~ female + (read > 50) + science + ses, family="binomial", data = hsb2, model = FALSE)
# PseudoR2(test_c5.logit)
#
#
# # ==== POLR ====
#
# #"Data" and "model" object components are both usable (we give priority to model)
# base.polr <- polr(write_cat ~ female + read + science + ses, hsb2)
# PseudoR2(base.polr)
#
# # ---- A ----
#
# #A1: polr does not return "data" component, so explicit reference is impossible
#
# #A2: polr object "call" component references valid data frame
# #Unlike in glm, polr doesn't save a data object
# test_a2.polr <- polr(write_cat ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_a2.polr)
#
# #POSSIBLE ISSUE: no model object (only data), and a non-literal DV (eg, read > 60)
#
# #A3
# #"call" references objects in global environment
# y <- cut(hsb2$write, breaks = c(30,40,50,60,70))
# test_a3a.polr <- polr(y ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_a3a.polr)
#
# #A3b - "call" references invalid data frame
# tempdf <- hsb2
# test_a3b.polr <- polr(write_cat ~ female + read + science + ses, data = tempdf, model = FALSE)
# rm(tempdf)
# PseudoR2(test_a3b.polr)
#
#
#
# # ---- B ----
# #WEIGHTS
# #Weights are created on-the-fly via runif
# test_b1.polr <- polr(write_cat ~ female + read + science + ses, hsb2, weights = runif(nrow(hsb2)), model = FALSE)
# PseudoR2(test_b1.polr)
#
# #Weights are saved
# test_weights <- runif(nrow(hsb2))
# test_b2.polr <- polr(write_cat ~ female + read + science + ses, weights = test_weights, data = hsb2, model = FALSE)
# PseudoR2(test_b2.polr)
# rm(test_weights)
# PseudoR2(test_b2.polr)
#
# withna.df <- rbind(hsb2[1:100,], NA, NA, hsb2[101:200,])
# test_b3.polr <- polr(write_cat ~ female + read + science + ses, data = withna.df, weights = runif(nrow(withna.df)), model = FALSE)
# PseudoR2(test_b3.polr)
#
# #NA.ACTION
# test_b4.polr <- polr(write_cat ~ female + read + science + ses, data = rbind(hsb2, NA), model = FALSE, na.action = na.omit)
# PseudoR2(test_b4.polr)
#
# test_naAction <- na.omit
# test_b5.polr <- polr(write_cat~ female + read + science + ses, data = rbind(hsb2, NA), model = TRUE, na.action = test_naAction)
# PseudoR2(test_b5.polr)
# test_naAction <- na.fail
# PseudoR2(test_b5.polr)
#
# test_naAction <- na.omit
# test_b6.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = FALSE, na.action = test_naAction)
# PseudoR2(test_b6.logit)
# test_naAction <- na.fail
# PseudoR2(test_b6.logit)
#
# # ---- C ----
#
#
# #DV, without model, out of data frame
# test_c1.polr <- polr(revalue(write_cat, c("(30,40]" = "(30,50]", "(40,50]" = "(30,50]")) ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_c1.polr)
#
# #DV, without model, in data frmae
# test_c2.polr <- polr(revalue(hsb2$write_cat, c("(30,40]" = "(30,50]", "(40,50]" = "(30,50]")) ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c2.polr)
#
# #IV, without model, no data frame
# test_c3.polr <- polr(hsb2$write_cat ~ hsb2$female + (hsb2$read > 50) + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c3.polr)
#
# #IV, without model, with data frame
# test_c4.polr <- polr(write_cat ~ female + (read > 50) + science + ses, hsb2, model = FALSE)
# PseudoR2(test_c4.polr)
#
#
# # ==== Multinom ====
#
# #"Data" and "model" object components are both usable (we give priority to model)
# base.multinom <- multinom(race_cat ~ female + read + science + ses, hsb2, model = TRUE)
# PseudoR2(base.multinom)
#
# # ---- A ----
#
# #A1: multinom does not return "data" component, so expciCit reference is impossible
#
# #A2: multinom object "call" component references valid data frame
# test_a2.multinom <- multinom(race_cat ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_a2.multinom)
#
# #A3
# #Unlike in glm, multinom won't save an enviornment labelled as "data"
# #"call" references objects in global environment
# #NOTE: could theoretically get the variables from call$formula, althiugh this would be risky
# y_nominal <- hsb2$race_cat
# test_a3a.multinom <- multinom(y_nominal ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_a3a.multinom)
#
# #A3b - "call" references invalid data frame
# tempdf <- hsb2
# test_a3b.multinom <- multinom(race_cat ~ female + read + science + ses, data = tempdf, model = FALSE)
# PseudoR2(test_a3b.multinom)
# rm(tempdf)
# PseudoR2(test_a3b.multinom)
#
#
#
# # ---- B ----
# #WEIGHTS
# #Weights are created on-the-fly via runif
# #Multinom saves a "weights" element, which is equivalent to the glm "prior.weights" element
# test_b1.multinom <- multinom(race_cat ~ female + read + science + ses, hsb2, weights = runif(nrow(hsb2)), model = TRUE)
# PseudoR2(test_b1.multinom)
#
# #Weights are saved
# test_weights <- runif(nrow(hsb2))
# test_b2.multinom <- multinom(race_cat ~ female + read + science + ses, weights = test_weights, data = hsb2, model = FALSE)
# PseudoR2(test_b2.multinom)
# rm(test_weights)
# PseudoR2(test_b2.multinom)
#
# withna.df <- rbind(hsb2[1:100,], NA, NA, hsb2[101:200,])
# test_b3.multinom <- multinom(race_cat ~ female + read + science + ses, data = withna.df, weights = runif(nrow(withna.df)), model = FALSE)
# PseudoR2(test_b3.multinom)
#
# #NA.ACTION
# test_b4.multinom <- multinom(race_cat ~ female + read + science + ses, data = rbind(hsb2, NA), model = FALSE, na.action = na.omit)
# PseudoR2(test_b4.multinom)
#
# test_naAction <- na.omit
# test_b5.multinom <- multinom(race_cat ~ female + read + science + ses, data = rbind(hsb2, NA), model = TRUE, na.action = test_naAction)
# PseudoR2(test_b5.multinom)
# test_naAction <- na.fail
# PseudoR2(test_b5.multinom)
#
# #QUIETLY RE-FIT MULTINOM
#
# # ---- C ----
#
# #DV, With model
# test_c1.multinom <- multinom(revalue(race_cat, c("1" = "1", "2" = "1")) ~ female + read + science + ses, hsb2, model = TRUE)
# PseudoR2(test_c1.multinom)
#
# #DV, without model, out of data frame
# test_c2.multinom <- multinom(revalue(hsb2$race_cat, c("1" = "1", "2" = "1")) ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c2.multinom)
#
# #DV, without model, in data frame
# test_c2.multinom <- multinom(revalue(race_cat, c("1" = "1", "2" = "1")) ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_c2.multinom)
#
# #IV, without model, no data frame
# test_c4.multinom <- multinom(hsb2$race_cat ~ hsb2$female + (hsb2$read > 50) + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c4.multinom)
#
# #IV, without model, with data frame
# test_c5.multinom <- multinom(race_cat ~ female + (read > 50) + science + ses, data = hsb2, model = FALSE)
# PseudoR2(test_c5.multinom)
#
#
#
# # === VGLM ====
#
# #Because of the very wide variety of possible VGLM models and related parameters + functional forms, we can't easily take the same testing approach as above
# #We'll instead starty by testing the models listed in the VGAM help file
#
# # Example 1. See help(glm)
# print(d.AD <- data.frame(treatment = gl(3, 3),
# outcome = gl(3, 1, 9),
# counts = c(18,17,15,20,10,20,25,13,12)))
# vglm.D93 <- vglm(counts ~ outcome + treatment, family = poissonff,
# data = d.AD, trace = TRUE, model = TRUE)
# summary(vglm.D93)
# PseudoR2(vglm.D93)
#
#
# # Example 2. Multinomial logit model
# pneumo <- transform(pneumo, let = log(exposure.time))
# vglm.pneumo <- vglm(cbind(normal, mild, severe) ~ let, multinomial, data = pneumo, model = TRUE)
# PseudoR2(vglm.pneumo)
#
# # Example 3. Proportional odds model
# fit3 <- vglm(cbind(normal, mild, severe) ~ let, propodds, data = pneumo, model = TRUE)
# PseudoR2(fit3)
#
# # Example 4. Bivariate logistic model
# fit4 <- vglm(cbind(nBnW, nBW, BnW, BW) ~ age, binom2.or, coalminers, model = TRUE)
# PseudoR2(fit4)
#
#
# # Example 5. The use of the xij argument (simple case).
# # The constraint matrix for 'op' has one column.
# nn <- 1000
# eyesdat <- round(data.frame(lop = runif(nn),
# rop = runif(nn),
# op = runif(nn)), digits = 2)
# eyesdat <- transform(eyesdat, eta1 = -1 + 2 * lop,
# eta2 = -1 + 2 * lop)
# eyesdat <- transform(eyesdat,
# leye = rbinom(nn, size = 1, prob = logit(eta1, inverse = TRUE)),
# reye = rbinom(nn, size = 1, prob = logit(eta2, inverse = TRUE)))
# fit5 <- vglm(cbind(leye, reye) ~ op,
# binom2.or(exchangeable = TRUE, zero = 3),
# data = eyesdat, trace = TRUE,
# xij = list(op ~ lop + rop + fill(lop)),
# form2 = ~ op + lop + rop + fill(lop),
# model = TRUE)
# PseudoR2(fit5)
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# library(DescTools)
# library(haven) #for read_dta
# library(MASS) #for polr
# library(nnet) #for multinom
# library(VGAM)
# library(plyr) #for testing recode of factor, using revalue
# # source("R/LinMod.R")
#
#
# # ==== notes ===
#
# #checks needed:
# # A) Ensure data parameter should work it is 1) explicitly defined, 2 found in environment, 3) not found
# # B) Check "special" parameters (substitute, weight, and na.action parameters)
# # C) check non-literal variables
#
# hsb2 <- as.data.frame(read_dta("https://stats.idre.ucla.edu/stat/stata/notes/hsb2.dta"))
# hsb2$honcomp <- hsb2$write >= 60
#
# hsb2$write_cat <- cut(hsb2$write, breaks = c(30,40,50,60,70))
# hsb2$race_cat <- factor(hsb2$race)
#
#
# # ==== GLM ====
#
# #"Data" and "model" object components are both usable (we give priority to model)
# base.logit <- glm(honcomp ~ female + read + science + ses, hsb2, family="binomial")
# PseudoR2(base.logit)
#
# #"Data" object is reference to global environment (but we have a model object)
# base2.logit <- glm(hsb2$honcomp ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial")
# PseudoR2(base2.logit)
#
# #POSSIBLE ISSUE: no model object (only data), and a non-literal DV (eg, read > 60)
# #A1 tests are covered above
#
# #A2a - variables in global environment
# y <- hsb2$honcomp
# test_a2.logit <- glm(y ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial", model = FALSE)
# PseudoR2(test_a2.logit)
# #NB: doesn't give useful name of object that needs new evaluation
#
# #A3a - "data" object is reference to global environment
# z <- hsb2$honcomp
# test_a3a.logit <- glm(z ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial", model = FALSE, x = TRUE, y = TRUE)
# rm(z)
# PseudoR2(test_a3a.logit)
#
# #A3b - "data" object is contained in data frame
# tempdf <- hsb2
# test_a3b.logit <- glm(honcomp ~ female + read + science + ses, tempdf, family="binomial", model = FALSE)
# PseudoR2(test_a3b.logit)
# tempdf <- tempdf[1:100,]
# PseudoR2(test_a3b.logit)
# rm(tempdf)
# PseudoR2(test_a3b.logit)
#
#
# # ---- B ----
# #WEIGHTS
# #Weights are created on-the-fly via runif
# test_b1.logit <- glm(honcomp ~ female + read + science + ses, hsb2, family="binomial", weights = runif(nrow(hsb2)), model = FALSE)
# PseudoR2(test_b1.logit)
# PseudoR2(test_b1.logit)
#
# #Weights are saved
# test_weights <- runif(nrow(hsb2))
# test_b2.logit <- glm(honcomp ~ female + read + science + ses, data = hsb2, family="binomial", weights = test_weights, model = FALSE)
# rm(test_weights)
# PseudoR2(test_b2.logit)
#
# withna.df <- rbind(hsb2[1:100,], NA, NA, hsb2[101:200,])
# test_b3.logit <- glm(honcomp ~ female + read + science + ses, data = withna.df, family="binomial", weights = runif(nrow(withna.df)), model = FALSE)
# PseudoR2(test_b3.logit)
#
# #NA.ACTION
# #Could try using the na.omit attribute of glms here to handle, but it's a lot of work for little return
# test_b4.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = FALSE, na.action = na.omit)
# PseudoR2(test_b4.logit)
#
# test_naAction <- na.omit
# test_b5.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = TRUE, na.action = test_naAction)
# rm(test_naAction)
# PseudoR2(test_b5.logit)
#
# test_naAction <- na.omit
# test_b6.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = FALSE, na.action = test_naAction)
# rm(test_naAction)
# PseudoR2(test_b6.logit)
#
#
# # ---- C ----
#
# #DV, With model
# test_c1.logit <- glm((hsb2$write_cat == "(30,40]" | hsb2$write_cat == "(40,50]") ~ female + read + science + ses, hsb2, family="binomial")
# PseudoR2(test_c1.logit)
#
# #DV, without model, out of data frame
# test_c2.logit <- glm((hsb2$write_cat == "(30,40]" | hsb2$write_cat == "(40,50]") ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, family="binomial", model = FALSE)
# PseudoR2(test_c2.logit)
#
# #DV, without model, in data frmae
# test_c3.logit <- glm((write_cat == "(30,40]" | hsb2$write_cat == "(40,50]") ~ female + read + science + ses, hsb2, family="binomial", model = FALSE)
# PseudoR2(test_c3.logit)
#
# #IV, without model, no data frame
# test_c4.logit <- glm(hsb2$honcomp ~ hsb2$female + (hsb2$read > 50) + hsb2$science + hsb2$ses, family="binomial", model = FALSE)
# PseudoR2(test_c4.logit)
#
# #IV, without model, with data frame
# test_c5.logit <- glm(honcomp ~ female + (read > 50) + science + ses, family="binomial", data = hsb2, model = FALSE)
# PseudoR2(test_c5.logit)
#
#
# # ==== POLR ====
#
# #"Data" and "model" object components are both usable (we give priority to model)
# base.polr <- polr(write_cat ~ female + read + science + ses, hsb2)
# PseudoR2(base.polr)
#
# # ---- A ----
#
# #A1: polr does not return "data" component, so explicit reference is impossible
#
# #A2: polr object "call" component references valid data frame
# #Unlike in glm, polr doesn't save a data object
# test_a2.polr <- polr(write_cat ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_a2.polr)
#
# #POSSIBLE ISSUE: no model object (only data), and a non-literal DV (eg, read > 60)
#
# #A3
# #"call" references objects in global environment
# y <- cut(hsb2$write, breaks = c(30,40,50,60,70))
# test_a3a.polr <- polr(y ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_a3a.polr)
#
# #A3b - "call" references invalid data frame
# tempdf <- hsb2
# test_a3b.polr <- polr(write_cat ~ female + read + science + ses, data = tempdf, model = FALSE)
# rm(tempdf)
# PseudoR2(test_a3b.polr)
#
#
#
# # ---- B ----
# #WEIGHTS
# #Weights are created on-the-fly via runif
# test_b1.polr <- polr(write_cat ~ female + read + science + ses, hsb2, weights = runif(nrow(hsb2)), model = FALSE)
# PseudoR2(test_b1.polr)
#
# #Weights are saved
# test_weights <- runif(nrow(hsb2))
# test_b2.polr <- polr(write_cat ~ female + read + science + ses, weights = test_weights, data = hsb2, model = FALSE)
# PseudoR2(test_b2.polr)
# rm(test_weights)
# PseudoR2(test_b2.polr)
#
# withna.df <- rbind(hsb2[1:100,], NA, NA, hsb2[101:200,])
# test_b3.polr <- polr(write_cat ~ female + read + science + ses, data = withna.df, weights = runif(nrow(withna.df)), model = FALSE)
# PseudoR2(test_b3.polr)
#
# #NA.ACTION
# test_b4.polr <- polr(write_cat ~ female + read + science + ses, data = rbind(hsb2, NA), model = FALSE, na.action = na.omit)
# PseudoR2(test_b4.polr)
#
# test_naAction <- na.omit
# test_b5.polr <- polr(write_cat~ female + read + science + ses, data = rbind(hsb2, NA), model = TRUE, na.action = test_naAction)
# PseudoR2(test_b5.polr)
# test_naAction <- na.fail
# PseudoR2(test_b5.polr)
#
# test_naAction <- na.omit
# test_b6.logit <- glm(honcomp ~ female + read + science + ses, data = rbind(hsb2, NA), family="binomial", model = FALSE, na.action = test_naAction)
# PseudoR2(test_b6.logit)
# test_naAction <- na.fail
# PseudoR2(test_b6.logit)
#
# # ---- C ----
#
#
# #DV, without model, out of data frame
# test_c1.polr <- polr(revalue(write_cat, c("(30,40]" = "(30,50]", "(40,50]" = "(30,50]")) ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_c1.polr)
#
# #DV, without model, in data frmae
# test_c2.polr <- polr(revalue(hsb2$write_cat, c("(30,40]" = "(30,50]", "(40,50]" = "(30,50]")) ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c2.polr)
#
# #IV, without model, no data frame
# test_c3.polr <- polr(hsb2$write_cat ~ hsb2$female + (hsb2$read > 50) + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c3.polr)
#
# #IV, without model, with data frame
# test_c4.polr <- polr(write_cat ~ female + (read > 50) + science + ses, hsb2, model = FALSE)
# PseudoR2(test_c4.polr)
#
#
# # ==== Multinom ====
#
# #"Data" and "model" object components are both usable (we give priority to model)
# base.multinom <- multinom(race_cat ~ female + read + science + ses, hsb2, model = TRUE)
# PseudoR2(base.multinom)
#
# # ---- A ----
#
# #A1: multinom does not return "data" component, so expciCit reference is impossible
#
# #A2: multinom object "call" component references valid data frame
# test_a2.multinom <- multinom(race_cat ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_a2.multinom)
#
# #A3
# #Unlike in glm, multinom won't save an enviornment labelled as "data"
# #"call" references objects in global environment
# #NOTE: could theoretically get the variables from call$formula, althiugh this would be risky
# y_nominal <- hsb2$race_cat
# test_a3a.multinom <- multinom(y_nominal ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_a3a.multinom)
#
# #A3b - "call" references invalid data frame
# tempdf <- hsb2
# test_a3b.multinom <- multinom(race_cat ~ female + read + science + ses, data = tempdf, model = FALSE)
# PseudoR2(test_a3b.multinom)
# rm(tempdf)
# PseudoR2(test_a3b.multinom)
#
#
#
# # ---- B ----
# #WEIGHTS
# #Weights are created on-the-fly via runif
# #Multinom saves a "weights" element, which is equivalent to the glm "prior.weights" element
# test_b1.multinom <- multinom(race_cat ~ female + read + science + ses, hsb2, weights = runif(nrow(hsb2)), model = TRUE)
# PseudoR2(test_b1.multinom)
#
# #Weights are saved
# test_weights <- runif(nrow(hsb2))
# test_b2.multinom <- multinom(race_cat ~ female + read + science + ses, weights = test_weights, data = hsb2, model = FALSE)
# PseudoR2(test_b2.multinom)
# rm(test_weights)
# PseudoR2(test_b2.multinom)
#
# withna.df <- rbind(hsb2[1:100,], NA, NA, hsb2[101:200,])
# test_b3.multinom <- multinom(race_cat ~ female + read + science + ses, data = withna.df, weights = runif(nrow(withna.df)), model = FALSE)
# PseudoR2(test_b3.multinom)
#
# #NA.ACTION
# test_b4.multinom <- multinom(race_cat ~ female + read + science + ses, data = rbind(hsb2, NA), model = FALSE, na.action = na.omit)
# PseudoR2(test_b4.multinom)
#
# test_naAction <- na.omit
# test_b5.multinom <- multinom(race_cat ~ female + read + science + ses, data = rbind(hsb2, NA), model = TRUE, na.action = test_naAction)
# PseudoR2(test_b5.multinom)
# test_naAction <- na.fail
# PseudoR2(test_b5.multinom)
#
# #QUIETLY RE-FIT MULTINOM
#
# # ---- C ----
#
# #DV, With model
# test_c1.multinom <- multinom(revalue(race_cat, c("1" = "1", "2" = "1")) ~ female + read + science + ses, hsb2, model = TRUE)
# PseudoR2(test_c1.multinom)
#
# #DV, without model, out of data frame
# test_c2.multinom <- multinom(revalue(hsb2$race_cat, c("1" = "1", "2" = "1")) ~ hsb2$female + hsb2$read + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c2.multinom)
#
# #DV, without model, in data frame
# test_c2.multinom <- multinom(revalue(race_cat, c("1" = "1", "2" = "1")) ~ female + read + science + ses, hsb2, model = FALSE)
# PseudoR2(test_c2.multinom)
#
# #IV, without model, no data frame
# test_c4.multinom <- multinom(hsb2$race_cat ~ hsb2$female + (hsb2$read > 50) + hsb2$science + hsb2$ses, model = FALSE)
# PseudoR2(test_c4.multinom)
#
# #IV, without model, with data frame
# test_c5.multinom <- multinom(race_cat ~ female + (read > 50) + science + ses, data = hsb2, model = FALSE)
# PseudoR2(test_c5.multinom)
#
#
#
# # === VGLM ====
#
# #Because of the very wide variety of possible VGLM models and related parameters + functional forms, we can't easily take the same testing approach as above
# #We'll instead starty by testing the models listed in the VGAM help file
#
# # Example 1. See help(glm)
# print(d.AD <- data.frame(treatment = gl(3, 3),
# outcome = gl(3, 1, 9),
# counts = c(18,17,15,20,10,20,25,13,12)))
# vglm.D93 <- vglm(counts ~ outcome + treatment, family = poissonff,
# data = d.AD, trace = TRUE, model = TRUE)
# summary(vglm.D93)
# PseudoR2(vglm.D93)
#
#
# # Example 2. Multinomial logit model
# pneumo <- transform(pneumo, let = log(exposure.time))
# vglm.pneumo <- vglm(cbind(normal, mild, severe) ~ let, multinomial, data = pneumo, model = TRUE)
# PseudoR2(vglm.pneumo)
#
# # Example 3. Proportional odds model
# fit3 <- vglm(cbind(normal, mild, severe) ~ let, propodds, data = pneumo, model = TRUE)
# PseudoR2(fit3)
#
# # Example 4. Bivariate logistic model
# fit4 <- vglm(cbind(nBnW, nBW, BnW, BW) ~ age, binom2.or, coalminers, model = TRUE)
# PseudoR2(fit4)
#
#
# # Example 5. The use of the xij argument (simple case).
# # The constraint matrix for 'op' has one column.
# nn <- 1000
# eyesdat <- round(data.frame(lop = runif(nn),
# rop = runif(nn),
# op = runif(nn)), digits = 2)
# eyesdat <- transform(eyesdat, eta1 = -1 + 2 * lop,
# eta2 = -1 + 2 * lop)
# eyesdat <- transform(eyesdat,
# leye = rbinom(nn, size = 1, prob = logit(eta1, inverse = TRUE)),
# reye = rbinom(nn, size = 1, prob = logit(eta2, inverse = TRUE)))
# fit5 <- vglm(cbind(leye, reye) ~ op,
# binom2.or(exchangeable = TRUE, zero = 3),
# data = eyesdat, trace = TRUE,
# xij = list(op ~ lop + rop + fill(lop)),
# form2 = ~ op + lop + rop + fill(lop),
# model = TRUE)
# PseudoR2(fit5)
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