Visualise expanded model

Intro

Packages

library(tidyverse)

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## ✔ tibble  1.4.2     ✔ dplyr   0.7.6
## ✔ tidyr   0.8.1     ✔ stringr 1.3.1
## ✔ readr   1.1.1     ✔ forcats 0.3.0

## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(magrittr)

## 
## Attaching package: 'magrittr'

## The following object is masked from 'package:purrr':
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##     set_names

## The following object is masked from 'package:tidyr':
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##     extract

library(feather)
library(effects)

## Loading required package: carData

## lattice theme set by effectsTheme()
## See ?effectsTheme for details.

theme_set(theme_minimal(base_size = 16))

Data

Expanded model

orplot(expglm_df, slice_range=2:15, ref_level="19-24", prefix="age_grp", ylab="Age groups")

orplot(expglm_df, slice_range=16, ref_level="Female", prefix="sex", ylab="Sex")

orplot(expglm_df, slice_range=33:41, ref_level="Decile 10 - highest", prefix="loinc_decile", ylab="Income deciles")

orplot(expglm_df, slice_range=43:54, ref_level="Ontario", sorted=TRUE, prefix="province", ylab="Province")

orplot(expglm_df, slice_range=42, ref_level="Urban", prefix="rur_urb", ylab="Rural/urban")

orplot(expglm_df, slice_range=19:20, ref_level="Born in Canada, both parents born in Canada", prefix="generation", ylab="Migrant generation")

orplot(expglm_df, slice_range=21:32, ref_level="High school", sorted=TRUE, prefix="education", ylab="Education")

orplot(expglm_df, slice_range=55:58, ref_level="Married", sorted=TRUE, prefix="mar_stat", ylab="Marital status")

orplot(expglm_df, slice_range=83:84, ref_level="No kids", prefix="no_kids", ylab="No. of children")

orplot(expglm_df, slice_range=17, ref_level="No aboriginal ID", prefix="ab_id_dichot", ylab="Aboriginal ID")

orplot(expglm_df, slice_range=27:37, ref_level="Not a visible minority (except indigenous)", prefix="vis_minority", ylab="Visible minority (except indigenous)", sorted=TRUE)

orplot(expglm_df, slice_range=17:18, ref_level="Full-time work", prefix="employ_status", ylab="Employment status")

orplot(expglm_df, slice_range=38:40, ref_level="English", prefix="first_lang", ylab="First language")

orplot(expglm_df, slice_range=18:20, ref_level="No ADL difficulties", prefix="adl_difficulty", ylab="Difficulty in activities of daily living")

Model with interactions

#orplot(intglm_df, slice_range=2:15, ref_level="19-24", prefix="age_grp", ylab="Age groups")

orplot(intglm_df, slice_range=16, ref_level="Female", prefix="sex", ylab="Sex")

orplot(intglm_df, slice_range=17, ref_level="No aboriginal ID", prefix="ab_id_dichot", ylab="Aboriginal ID")

orplot(intglm_df, slice_range=57:65, ref_level="Decile 10 - highest", prefix="loinc_decile", ylab="Income deciles")

orplot(intglm_df, slice_range=67:78, ref_level="Ontario", sorted=TRUE, prefix="province", ylab="Province")

orplot(intglm_df, slice_range=43:44, ref_level="Born in Canada, both parents born in Canada", prefix="generation", ylab="Migrant generation")

orplot(intglm_df, slice_range=45:56, ref_level="No formal education", sorted=TRUE, prefix="education", ylab="Education")

orplot(intglm_df, slice_range=79:80, ref_level="Married", sorted=TRUE, prefix="mar_stat", ylab="Marital status")

orplot(intglm_df, slice_range=83:84, ref_level="No kids", prefix="no_kids", ylab="No. of children")

orplot(intglm_df, slice_range=27:37, ref_level="Not a visible minority (except indigenous)", prefix="vis_minority", ylab="Visible minority (except indigenous)", sorted=TRUE)

orplot(intglm_df, slice_range=41:42, ref_level="Full-time work", prefix="employ_status", ylab="Employment status")

orplot(intglm_df, slice_range=38:40, ref_level="English", prefix="first_lang", ylab="First language")

orplot(intglm_df, slice_range=18:20, ref_level="None", prefix="adl_difficulty", ylab="Difficulty in activities of daily living")

Effect plots