# The significance of the sector on the salary in Sweden, a comparison between different occupational groups

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In my last post, I found that the sector has a significant impact on the salary of engineers. Is the significance of the sector unique to engineers or are there similar correlations in other occupational groups?

Statistics Sweden use NUTS (Nomenclature des Unités Territoriales Statistiques), which is the EU’s hierarchical regional division, to specify the regions.

The F-value from the Anova table is used as the single value to discriminate how much the region and salary correlates. For exploratory analysis, the Anova value seems good enough.

First, define libraries and functions.

library (tidyverse) ## -- Attaching packages ------------------------------------- tidyverse 1.3.0 -- ## v ggplot2 3.2.1 v purrr 0.3.3 ## v tibble 2.1.3 v dplyr 0.8.3 ## v tidyr 1.0.2 v stringr 1.4.0 ## v readr 1.3.1 v forcats 0.4.0 ## -- Conflicts ---------------------------------------- tidyverse_conflicts() -- ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag() library (broom) library (car) ## Loading required package: carData ## ## Attaching package: 'car' ## The following object is masked from 'package:dplyr': ## ## recode ## The following object is masked from 'package:purrr': ## ## some library (swemaps) # devtools::install_github('reinholdsson/swemaps') library(sjPlot) ## Registered S3 methods overwritten by 'lme4': ## method from ## cooks.distance.influence.merMod car ## influence.merMod car ## dfbeta.influence.merMod car ## dfbetas.influence.merMod car ## #refugeeswelcome readfile <- function (file1){ read_csv (file1, col_types = cols(), locale = readr::locale (encoding = "latin1"), na = c("..", "NA")) %>% gather (starts_with("19"), starts_with("20"), key = "year", value = salary) %>% drop_na() %>% mutate (year_n = parse_number (year)) } nuts <-read_csv ("nuts.csv", col_types = cols(), locale = readr::locale (encoding = "latin1"), na = c("..", "NA")) %>% mutate(NUTS2_sh = substr(NUTS2, 1, 4)) ## Warning: Missing column names filled in: 'X1' [1] nuts %>% distinct (NUTS2_en) %>% knitr::kable( booktabs = TRUE, caption = 'Nomenclature des Unités Territoriales Statistiques (NUTS)')

NUTS2_en |
---|

SE11 Stockholm |

SE12 East-Central Sweden |

SE21 Småland and islands |

SE22 South Sweden |

SE23 West Sweden |

SE31 North-Central Sweden |

SE32 Central Norrland |

SE33 Upper Norrland |

map_ln_n <- map_ln %>% mutate(lnkod_n = as.numeric(lnkod))

The data table is downloaded from Statistics Sweden. It is saved as a comma-delimited file without heading, 000000CG.csv, http://www.statistikdatabasen.scb.se/pxweb/en/ssd/.

I have renamed the file to 000000CG_sector.csv because the filename 000000CG.csv was used in a previous post.

The table: Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex. Year 2014 – 2018 Monthly salary 1-3 public sector 4-5 private sector

Only 17 occupational groups have employees in both the public and the private sector in all regions and both genders.

In the plot and tables, you can also find information on how the increase in salaries per year for each occupational group is affected when the interactions are taken into account.

tb <- readfile ("000000CG_sector.csv") %>% left_join(nuts %>% distinct (NUTS2_en, NUTS2_sh), by = c("region" = "NUTS2_en")) tb_map <- readfile ("000000CG_sector.csv") %>% left_join(nuts, by = c("region" = "NUTS2_en")) %>% right_join(map_ln_n, by = c("Länskod" = "lnkod_n")) summary_table = vector() anova_table = vector() for (i in unique(tb$`occuptional (SSYK 2012)`)){ temp <- filter(tb, `occuptional (SSYK 2012)` == i) if (dim(temp)[1] > 150){ model <- lm(log(salary) ~ region + sex + year_n + sector, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "none")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "none")) model <- lm(log(salary) ~ region + sex + year_n * sector, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector and year")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector and year")) model <- lm(log(salary) ~ region + year_n + sex * sector, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sex and sector")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sex and sector")) model <- lm(log(salary) ~ region * sector + year_n + sex, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "region and sector")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "region and sector")) model <- lm(log(salary) ~ region * sector * year_n * sex, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "region, sector, year and sex")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "region, sector, year and sex")) } } ## Note: model has aliased coefficients ## sums of squares computed by model comparison anova_table <- anova_table %>% rowwise() %>% mutate(contcol = str_count(term, ":")) summary_table <- summary_table %>% rowwise() %>% mutate(contcol = str_count(term, ":")) merge(summary_table, anova_table, by = c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (term.y == "sector") %>% filter (interaction == "none") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% ggplot () + geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) + labs( x = "Increase in salaries (% / year)", y = "F-value for sector" )

merge(summary_table, anova_table, by = c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% # only look at the interactions between all four variables in the case with interaction region, sector, year and sex filter (!(contcol.y < 3 & interaction == "region, sector, year and sex")) %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% ggplot () + geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) + labs( x = "Increase in salaries (% / year)", y = "F-value for interaction" )

The tables with all occupational groups sorted by F-value in descending order.

merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (term.y == "sector") %>% filter (interaction == "none") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sector) and the yearly increase in salaries')

ssyk | Increase in salary | F-value | interaction |
---|---|---|---|

962 Newspaper distributors, janitors and other service workers | 2.109153 | 606.2792907 | none |

331 Financial and accounting associate professionals | 1.994107 | 429.7668310 | none |

411 Office assistants and other secretaries | 2.237594 | 350.7420490 | none |

251 ICT architects, systems analysts and test managers | 2.316554 | 337.4390522 | none |

241 Accountants, financial analysts and fund managers | 2.142349 | 325.6103179 | none |

321 Medical and pharmaceutical technicians | 2.609024 | 160.8870308 | none |

242 Organisation analysts, policy administrators and human resource specialists | 2.064925 | 160.8111547 | none |

351 ICT operations and user support technicians | 2.310435 | 121.7693426 | none |

214 Engineering professionals | 2.575584 | 87.3548855 | none |

541 Other surveillance and security workers | 2.391839 | 67.5617978 | none |

432 Stores and transport clerks | 1.541572 | 62.6562426 | none |

534 Attendants, personal assistants and related workers | 1.850523 | 40.6132964 | none |

911 Cleaners and helpers | 2.087753 | 28.9529104 | none |

311 Physical and engineering science technicians | 2.743468 | 23.7179785 | none |

422 Client information clerks | 2.549989 | 9.7604793 | none |

941 Fast-food workers, food preparation assistants | 2.144353 | 4.3115207 | none |

332 Insurance advisers, sales and purchasing agents | 2.228746 | 0.9809486 | none |

merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% filter (interaction == "sector and year") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sector and year) and the yearly increase in salaries')

ssyk | Increase in salary | F-value | interaction |
---|---|---|---|

422 Client information clerks | 3.433516 | 33.8973413 | sector and year |

534 Attendants, personal assistants and related workers | 2.307155 | 29.5467642 | sector and year |

351 ICT operations and user support technicians | 3.105104 | 15.0659029 | sector and year |

214 Engineering professionals | 3.207414 | 13.0526621 | sector and year |

962 Newspaper distributors, janitors and other service workers | 2.516074 | 9.1796329 | sector and year |

311 Physical and engineering science technicians | 3.006941 | 2.5233548 | sector and year |

332 Insurance advisers, sales and purchasing agents | 2.574538 | 2.3362535 | sector and year |

321 Medical and pharmaceutical technicians | 2.274618 | 2.1961501 | sector and year |

411 Office assistants and other secretaries | 2.451836 | 1.8814544 | sector and year |

241 Accountants, financial analysts and fund managers | 2.373536 | 0.7836435 | sector and year |

432 Stores and transport clerks | 1.346673 | 0.7147906 | sector and year |

251 ICT architects, systems analysts and test managers | 2.419651 | 0.4459847 | sector and year |

541 Other surveillance and security workers | 2.331794 | 0.1848832 | sector and year |

242 Organisation analysts, policy administrators and human resource specialists | 2.194200 | 0.1846190 | sector and year |

911 Cleaners and helpers | 2.022334 | 0.1743591 | sector and year |

941 Fast-food workers, food preparation assistants | 2.161504 | 0.0068543 | sector and year |

331 Financial and accounting associate professionals | 1.969149 | 0.0050259 | sector and year |

merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% filter (interaction == "sex and sector") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sex and sector) and the yearly increase in salaries')

ssyk | Increase in salary | F-value | interaction |
---|---|---|---|

241 Accountants, financial analysts and fund managers | 2.133410 | 79.736907 | sex and sector |

332 Insurance advisers, sales and purchasing agents | 2.228746 | 75.904108 | sex and sector |

911 Cleaners and helpers | 2.087753 | 68.455216 | sex and sector |

331 Financial and accounting associate professionals | 1.994107 | 67.051008 | sex and sector |

351 ICT operations and user support technicians | 2.279052 | 38.608554 | sex and sector |

321 Medical and pharmaceutical technicians | 2.592312 | 30.583657 | sex and sector |

941 Fast-food workers, food preparation assistants | 2.144353 | 22.396698 | sex and sector |

541 Other surveillance and security workers | 2.383471 | 16.198924 | sex and sector |

242 Organisation analysts, policy administrators and human resource specialists | 2.083508 | 15.156166 | sex and sector |

432 Stores and transport clerks | 1.541572 | 13.144544 | sex and sector |

534 Attendants, personal assistants and related workers | 1.850523 | 11.478531 | sex and sector |

251 ICT architects, systems analysts and test managers | 2.316554 | 9.349359 | sex and sector |

411 Office assistants and other secretaries | 2.234948 | 3.109736 | sex and sector |

962 Newspaper distributors, janitors and other service workers | 2.109153 | 2.668667 | sex and sector |

311 Physical and engineering science technicians | 2.743468 | 2.370694 | sex and sector |

214 Engineering professionals | 2.575584 | 1.834672 | sex and sector |

422 Client information clerks | 2.549989 | 1.337874 | sex and sector |

merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% filter (interaction == "region and sector") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (region and sector) and the yearly increase in salaries')

ssyk | Increase in salary | F-value | interaction |
---|---|---|---|

432 Stores and transport clerks | 1.541572 | 16.7817739 | region and sector |

251 ICT architects, systems analysts and test managers | 2.316554 | 15.6603633 | region and sector |

242 Organisation analysts, policy administrators and human resource specialists | 1.987167 | 14.5447961 | region and sector |

214 Engineering professionals | 2.575584 | 10.9929801 | region and sector |

321 Medical and pharmaceutical technicians | 2.635986 | 9.9897434 | region and sector |

331 Financial and accounting associate professionals | 1.994107 | 9.9608534 | region and sector |

332 Insurance advisers, sales and purchasing agents | 2.228746 | 8.3539434 | region and sector |

411 Office assistants and other secretaries | 2.238072 | 5.7558819 | region and sector |

241 Accountants, financial analysts and fund managers | 2.143390 | 5.6578803 | region and sector |

541 Other surveillance and security workers | 2.376311 | 4.6423707 | region and sector |

351 ICT operations and user support technicians | 2.307531 | 3.8460603 | region and sector |

311 Physical and engineering science technicians | 2.737061 | 3.5329715 | region and sector |

534 Attendants, personal assistants and related workers | 1.850523 | 3.0881293 | region and sector |

422 Client information clerks | 2.549989 | 2.2991877 | region and sector |

911 Cleaners and helpers | 2.087753 | 1.4424330 | region and sector |

962 Newspaper distributors, janitors and other service workers | 2.109153 | 1.3179558 | region and sector |

941 Fast-food workers, food preparation assistants | 2.155241 | 0.8264589 | region and sector |

merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 1) %>% filter (interaction == "region and sector") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (region, year and sex) and the yearly increase in salaries')

ssyk | Increase in salary | F-value | interaction |
---|

merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 1) %>% filter (interaction == "region, sector, year and sex") %>% filter (!(contcol.y < 3 & interaction == "region, sector, year and sex")) %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (region, year and sex) and the yearly increase in salaries')

ssyk | Increase in salary | F-value | interaction |
---|---|---|---|

351 ICT operations and user support technicians | 5.218326 | 2.7426159 | region, sector, year and sex |

411 Office assistants and other secretaries | 2.276319 | 2.6593769 | region, sector, year and sex |

331 Financial and accounting associate professionals | 1.629354 | 2.4639758 | region, sector, year and sex |

941 Fast-food workers, food preparation assistants | 2.364653 | 2.2289954 | region, sector, year and sex |

251 ICT architects, systems analysts and test managers | 2.629773 | 2.2261226 | region, sector, year and sex |

332 Insurance advisers, sales and purchasing agents | 2.036891 | 2.0868544 | region, sector, year and sex |

242 Organisation analysts, policy administrators and human resource specialists | 1.867532 | 1.8789097 | region, sector, year and sex |

541 Other surveillance and security workers | 2.147019 | 1.7672297 | region, sector, year and sex |

911 Cleaners and helpers | 2.270852 | 1.6781257 | region, sector, year and sex |

321 Medical and pharmaceutical technicians | 2.594389 | 0.9665480 | region, sector, year and sex |

241 Accountants, financial analysts and fund managers | 2.028698 | 0.7221792 | region, sector, year and sex |

432 Stores and transport clerks | 1.067457 | 0.6784005 | region, sector, year and sex |

962 Newspaper distributors, janitors and other service workers | 2.885436 | 0.5793881 | region, sector, year and sex |

214 Engineering professionals | 3.169526 | 0.4994580 | region, sector, year and sex |

311 Physical and engineering science technicians | 2.071111 | 0.4768272 | region, sector, year and sex |

534 Attendants, personal assistants and related workers | 2.349001 | 0.3542025 | region, sector, year and sex |

422 Client information clerks | 2.489542 | 0.2528065 | region, sector, year and sex |

Let’s check what we have found.

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "962 Newspaper distributors, janitors and other service workers") model <-lm (log(salary) ~ year_n + sex + NUTS2_sh + sector, data = temp) plot_model(model, type = "pred", terms = c("sector")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

tb_map %>% filter(`occuptional (SSYK 2012)` == "962 Newspaper distributors, janitors and other service workers") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ sector) + coord_equal()

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "332 Insurance advisers, sales and purchasing agents") model <-lm (log(salary) ~ year_n + sex + NUTS2_sh + sector, data = temp) plot_model(model, type = "pred", terms = c("sector")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "241 Accountants, financial analysts and fund managers") model <-lm (log(salary) ~ year_n + sex * sector + NUTS2_sh, data = temp) plot_model(model, type = "pred", terms = c("sector", "sex")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

tb_map %>% filter(`occuptional (SSYK 2012)` == "241 Accountants, financial analysts and fund managers") %>% filter (sector == "1-3 public sector") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ sex) + coord_equal()

tb_map %>% filter(`occuptional (SSYK 2012)` == "241 Accountants, financial analysts and fund managers") %>% filter (sector == "4-5 private sector") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ sex) + coord_equal()

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "422 Client information clerks") model <-lm (log(salary) ~ year_n + sex * sector + NUTS2_sh, data = temp) plot_model(model, type = "pred", terms = c("sector", "sex")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "422 Client information clerks") model <-lm (log(salary) ~ year_n * sector + NUTS2_sh + sex , data = temp) plot_model(model, type = "pred", terms = c("year_n", "sector")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

tb_map %>% filter(`occuptional (SSYK 2012)` == "422 Client information clerks") %>% filter (sector == "1-3 public sector") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ year) + coord_equal()

tb_map %>% filter(`occuptional (SSYK 2012)` == "422 Client information clerks") %>% filter (sector == "4-5 private sector") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ year) + coord_equal()

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "331 Financial and accounting associate professionals") model <-lm (log(salary) ~ year_n * sector + NUTS2_sh + sex , data = temp) plot_model(model, type = "pred", terms = c("year_n", "sector")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "432 Stores and transport clerks") model <-lm (log(salary) ~ year_n + sector * NUTS2_sh + sex , data = temp) plot_model(model, type = "pred", terms = c("NUTS2_sh", "sector")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

tb_map %>% filter(`occuptional (SSYK 2012)` == "432 Stores and transport clerks") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ sector) + coord_equal()

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "941 Fast-food workers, food preparation assistants") model <-lm (log(salary) ~ year_n + sector * NUTS2_sh + sex , data = temp) plot_model(model, type = "pred", terms = c("NUTS2_sh", "sector")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "351 ICT operations and user support technicians") model <-lm (log(salary) ~ year_n * NUTS2_sh * sex * sector, data = temp) plot_model(model, type = "pred", terms = c("NUTS2_sh", "year_n", "sector", "sex")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale. ## Warning: Package `see` needed to plot multiple panels in one integrated figure. ## Please install it by typing `install.packages("see", dependencies = TRUE)` into ## the console. ## [[1]]

## ## [[2]]

tb_map %>% filter(`occuptional (SSYK 2012)` == "351 ICT operations and user support technicians") %>% filter (sector == "1-3 public sector") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ year) + coord_equal()

tb_map %>% filter(`occuptional (SSYK 2012)` == "351 ICT operations and user support technicians") %>% filter (sector == "4-5 private sector") %>% ggplot() + geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) + facet_grid(. ~ year) + coord_equal()

temp <- tb %>% filter(`occuptional (SSYK 2012)` == "422 Client information clerks") model <-lm (log(salary) ~ year_n * NUTS2_sh * sex * sector, data = temp) plot_model(model, type = "pred", terms = c("NUTS2_sh", "year_n", "sector", "sex")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale. ## Warning: Package `see` needed to plot multiple panels in one integrated figure. ## Please install it by typing `install.packages("see", dependencies = TRUE)` into ## the console. ## [[1]]

## ## [[2]]

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