knitr::opts_chunk$set(echo = FALSE)Consumption_model_reworked
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library(tidymodels)## Registered S3 method overwritten by 'tune':
## method from
## required_pkgs.model_spec parsnip## -- Attaching packages -------------------------------------- tidymodels 0.1.3 --## v broom 0.7.8 v recipes 0.1.16
## v dials 0.0.9 v rsample 0.0.9
## v dplyr 1.0.7 v tibble 3.1.2
## v ggplot2 3.3.3 v tidyr 1.1.3
## v infer 0.5.4 v tune 0.1.5
## v modeldata 0.1.0 v workflows 0.2.2
## v parsnip 0.1.6 v workflowsets 0.0.2
## v purrr 0.3.4 v yardstick 0.0.8## -- Conflicts ----------------------------------------- tidymodels_conflicts() --
## x purrr::discard() masks scales::discard()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x recipes::step() masks stats::step()
## * Use tidymodels_prefer() to resolve common conflicts.library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v readr 1.4.0 v forcats 0.5.1
## v stringr 1.4.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x readr::col_factor() masks scales::col_factor()
## x purrr::discard() masks scales::discard()
## x dplyr::filter() masks stats::filter()
## x stringr::fixed() masks recipes::fixed()
## x dplyr::lag() masks stats::lag()
## x readr::spec() masks yardstick::spec()library(readr)
consumptionfctn_household_ABS_DATA<-read_csv("~/1.Excel/_research_resources_folder/consumptionfctn_household_ABS_DATA.csv",col_types = cols(`Series ID` = col_character()))
cons_data <- as_tibble(consumptionfctn_household_ABS_DATA)
colnames(cons_data) <- c("d","y","c")
glimpse(cons_data)## Rows: 248
## Columns: 3
## $ d <chr> "Sep-1959", "Dec-1959", "Mar-1960", "Jun-1960", "Sep-1960", "Dec-196~
## $ y <dbl> 3239, 3592, 2932, 2927, 3381, 3846, 3199, 2959, 3453, 3900, 3182, 31~
## $ c <dbl> 2267, 2512, 2325, 2470, 2503, 2713, 2443, 2523, 2528, 2739, 2543, 26~#Initialize a linear regression object, linear_model
linear_model <- linear_reg() %>%
# Set the model engine
set_engine('lm') %>%
# Set the model mode
set_mode('regression')
lm_fit <- linear_model %>%
fit(c ~ y, data = cons_data)
tidy(lm_fit)## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 824. 584. 1.41 1.60e- 1
## 2 y 0.841 0.00408 206. 1.09e-277## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
mod<-lm(c ~ y, data = cons_data)