resid_plots

options(warn = -1)

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(lmerTest)

Loading required package: lme4

Loading required package: Matrix

Attaching package: 'lmerTest'

The following object is masked from 'package:lme4':

    lmer

The following object is masked from 'package:stats':

    step

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ readr     2.1.5
✔ ggplot2   3.5.1     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::expand() masks Matrix::expand()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
✖ tidyr::pack()   masks Matrix::pack()
✖ tidyr::unpack() masks Matrix::unpack()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(modelr)
library(purrr)
library(emmeans)

Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'

library(gridExtra)

Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine

library(writexl)
library(gt)
library(webshot2)
library(broom.mixed)
library(ggplot2)

load("Z:/Isaac/Visual Features/1-5/step2.RData")

aug_res_10_filt <- aug_res_10 %>%
  group_by(sow) %>%
  filter(n() > 2000) %>%
  ungroup()
aug_res_20_filt <- aug_res_20 %>%
  group_by(sow) %>%
  filter(n() > 1100) %>%
  ungroup()
aug_res_30_filt <- aug_res_30 %>%
  group_by(sow) %>%
  filter(n() > 700) %>%
  ungroup()
aug_res_60_filt <- aug_res_60 %>%
  group_by(sow) %>%
  filter(n() > 400) %>%
  ungroup()

plots for the mean value residuals accounting for hour of the day

options(repr.plot.width = 16, repr.plot.height = 250)
library(ggforce)
for (i in 1:5){
  p1<- ggplot(aug_res_10_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("10 min window mean")
  print(p1)
  p2<- ggplot(aug_res_20_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("20 min window mean")
  print(p2)
  p3<- ggplot(aug_res_30_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("30 min window mean")
  print(p3)
  p4<- ggplot(aug_res_60_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("60 min window mean")
  print(p4)
}

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

aug_res_10_var_filt <- aug_res_10_var %>%
  group_by(sow) %>%
  filter(n() > 2000) %>%
  ungroup()
aug_res_20_var_filt <- aug_res_20_var %>%
  group_by(sow) %>%
  filter(n() > 1100) %>%
  ungroup()
aug_res_30_var_filt <- aug_res_30_var %>%
  group_by(sow) %>%
  filter(n() > 700) %>%
  ungroup()
aug_res_60_var_filt <- aug_res_60_var %>%
  group_by(sow) %>%
  filter(n() > 400) %>%
  ungroup()

plots for the var value residuals accounting for hour of the day

for (i in 1:5){
  p1<- ggplot(aug_res_10_var_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("10 min window var")
  print(p1)
  p2<- ggplot(aug_res_20_var_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("20 min window var")
  print(p2)
  p3<- ggplot(aug_res_30_var_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("30 min window var")
  print(p3)
  p4<- ggplot(aug_res_60_var_filt,aes(x=ttf,y=.resid,color=sow))+
        geom_smooth(se=F)+
        geom_smooth(aes(x=ttf,y=.resid),linewidth=1.2,color="black")+
        facet_wrap_paginate(~feature,scales="free",ncol=2,nrow=2,page=i)+
        ggtitle("60 min window var")
  print(p4)
}

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'