Modelos lineales mixtos
Brandon Stiven Solano Solano// Daniel Gustavo Yanez Pulido
2023-06-25
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(broom.mixed)## Warning: package 'broom.mixed' was built under R version 4.3.1library(lme4)## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack#creación de datos
CO2## Grouped Data: uptake ~ conc | Plant
## Plant Type Treatment conc uptake
## 1 Qn1 Quebec nonchilled 95 16.0
## 2 Qn1 Quebec nonchilled 175 30.4
## 3 Qn1 Quebec nonchilled 250 34.8
## 4 Qn1 Quebec nonchilled 350 37.2
## 5 Qn1 Quebec nonchilled 500 35.3
## 6 Qn1 Quebec nonchilled 675 39.2
## 7 Qn1 Quebec nonchilled 1000 39.7
## 8 Qn2 Quebec nonchilled 95 13.6
## 9 Qn2 Quebec nonchilled 175 27.3
## 10 Qn2 Quebec nonchilled 250 37.1
## 11 Qn2 Quebec nonchilled 350 41.8
## 12 Qn2 Quebec nonchilled 500 40.6
## 13 Qn2 Quebec nonchilled 675 41.4
## 14 Qn2 Quebec nonchilled 1000 44.3
## 15 Qn3 Quebec nonchilled 95 16.2
## 16 Qn3 Quebec nonchilled 175 32.4
## 17 Qn3 Quebec nonchilled 250 40.3
## 18 Qn3 Quebec nonchilled 350 42.1
## 19 Qn3 Quebec nonchilled 500 42.9
## 20 Qn3 Quebec nonchilled 675 43.9
## 21 Qn3 Quebec nonchilled 1000 45.5
## 22 Qc1 Quebec chilled 95 14.2
## 23 Qc1 Quebec chilled 175 24.1
## 24 Qc1 Quebec chilled 250 30.3
## 25 Qc1 Quebec chilled 350 34.6
## 26 Qc1 Quebec chilled 500 32.5
## 27 Qc1 Quebec chilled 675 35.4
## 28 Qc1 Quebec chilled 1000 38.7
## 29 Qc2 Quebec chilled 95 9.3
## 30 Qc2 Quebec chilled 175 27.3
## 31 Qc2 Quebec chilled 250 35.0
## 32 Qc2 Quebec chilled 350 38.8
## 33 Qc2 Quebec chilled 500 38.6
## 34 Qc2 Quebec chilled 675 37.5
## 35 Qc2 Quebec chilled 1000 42.4
## 36 Qc3 Quebec chilled 95 15.1
## 37 Qc3 Quebec chilled 175 21.0
## 38 Qc3 Quebec chilled 250 38.1
## 39 Qc3 Quebec chilled 350 34.0
## 40 Qc3 Quebec chilled 500 38.9
## 41 Qc3 Quebec chilled 675 39.6
## 42 Qc3 Quebec chilled 1000 41.4
## 43 Mn1 Mississippi nonchilled 95 10.6
## 44 Mn1 Mississippi nonchilled 175 19.2
## 45 Mn1 Mississippi nonchilled 250 26.2
## 46 Mn1 Mississippi nonchilled 350 30.0
## 47 Mn1 Mississippi nonchilled 500 30.9
## 48 Mn1 Mississippi nonchilled 675 32.4
## 49 Mn1 Mississippi nonchilled 1000 35.5
## 50 Mn2 Mississippi nonchilled 95 12.0
## 51 Mn2 Mississippi nonchilled 175 22.0
## 52 Mn2 Mississippi nonchilled 250 30.6
## 53 Mn2 Mississippi nonchilled 350 31.8
## 54 Mn2 Mississippi nonchilled 500 32.4
## 55 Mn2 Mississippi nonchilled 675 31.1
## 56 Mn2 Mississippi nonchilled 1000 31.5
## 57 Mn3 Mississippi nonchilled 95 11.3
## 58 Mn3 Mississippi nonchilled 175 19.4
## 59 Mn3 Mississippi nonchilled 250 25.8
## 60 Mn3 Mississippi nonchilled 350 27.9
## 61 Mn3 Mississippi nonchilled 500 28.5
## 62 Mn3 Mississippi nonchilled 675 28.1
## 63 Mn3 Mississippi nonchilled 1000 27.8
## 64 Mc1 Mississippi chilled 95 10.5
## 65 Mc1 Mississippi chilled 175 14.9
## 66 Mc1 Mississippi chilled 250 18.1
## 67 Mc1 Mississippi chilled 350 18.9
## 68 Mc1 Mississippi chilled 500 19.5
## 69 Mc1 Mississippi chilled 675 22.2
## 70 Mc1 Mississippi chilled 1000 21.9
## 71 Mc2 Mississippi chilled 95 7.7
## 72 Mc2 Mississippi chilled 175 11.4
## 73 Mc2 Mississippi chilled 250 12.3
## 74 Mc2 Mississippi chilled 350 13.0
## 75 Mc2 Mississippi chilled 500 12.5
## 76 Mc2 Mississippi chilled 675 13.7
## 77 Mc2 Mississippi chilled 1000 14.4
## 78 Mc3 Mississippi chilled 95 10.6
## 79 Mc3 Mississippi chilled 175 18.0
## 80 Mc3 Mississippi chilled 250 17.9
## 81 Mc3 Mississippi chilled 350 17.9
## 82 Mc3 Mississippi chilled 500 17.9
## 83 Mc3 Mississippi chilled 675 18.9
## 84 Mc3 Mississippi chilled 1000 19.9datos=CO2
View(datos)
uptake_2= runif(84,0.02,0.04)
up=uptake_2+CO2$uptake
datos_n= data.frame(CO2$Plant, CO2$Type, CO2$Treatment, CO2$conc, up)
datos_n## CO2.Plant CO2.Type CO2.Treatment CO2.conc up
## 1 Qn1 Quebec nonchilled 95 16.026297
## 2 Qn1 Quebec nonchilled 175 30.435972
## 3 Qn1 Quebec nonchilled 250 34.823886
## 4 Qn1 Quebec nonchilled 350 37.232888
## 5 Qn1 Quebec nonchilled 500 35.322103
## 6 Qn1 Quebec nonchilled 675 39.224760
## 7 Qn1 Quebec nonchilled 1000 39.725725
## 8 Qn2 Quebec nonchilled 95 13.627980
## 9 Qn2 Quebec nonchilled 175 27.333127
## 10 Qn2 Quebec nonchilled 250 37.134386
## 11 Qn2 Quebec nonchilled 350 41.832380
## 12 Qn2 Quebec nonchilled 500 40.628455
## 13 Qn2 Quebec nonchilled 675 41.424386
## 14 Qn2 Quebec nonchilled 1000 44.339815
## 15 Qn3 Quebec nonchilled 95 16.221948
## 16 Qn3 Quebec nonchilled 175 32.432990
## 17 Qn3 Quebec nonchilled 250 40.331765
## 18 Qn3 Quebec nonchilled 350 42.139784
## 19 Qn3 Quebec nonchilled 500 42.932919
## 20 Qn3 Quebec nonchilled 675 43.926320
## 21 Qn3 Quebec nonchilled 1000 45.538265
## 22 Qc1 Quebec chilled 95 14.233396
## 23 Qc1 Quebec chilled 175 24.138150
## 24 Qc1 Quebec chilled 250 30.322738
## 25 Qc1 Quebec chilled 350 34.638108
## 26 Qc1 Quebec chilled 500 32.522706
## 27 Qc1 Quebec chilled 675 35.425608
## 28 Qc1 Quebec chilled 1000 38.732399
## 29 Qc2 Quebec chilled 95 9.335057
## 30 Qc2 Quebec chilled 175 27.323376
## 31 Qc2 Quebec chilled 250 35.024087
## 32 Qc2 Quebec chilled 350 38.832181
## 33 Qc2 Quebec chilled 500 38.631348
## 34 Qc2 Quebec chilled 675 37.529962
## 35 Qc2 Quebec chilled 1000 42.438603
## 36 Qc3 Quebec chilled 95 15.136276
## 37 Qc3 Quebec chilled 175 21.037558
## 38 Qc3 Quebec chilled 250 38.137662
## 39 Qc3 Quebec chilled 350 34.022657
## 40 Qc3 Quebec chilled 500 38.936024
## 41 Qc3 Quebec chilled 675 39.622491
## 42 Qc3 Quebec chilled 1000 41.421495
## 43 Mn1 Mississippi nonchilled 95 10.638828
## 44 Mn1 Mississippi nonchilled 175 19.220886
## 45 Mn1 Mississippi nonchilled 250 26.224325
## 46 Mn1 Mississippi nonchilled 350 30.023920
## 47 Mn1 Mississippi nonchilled 500 30.930636
## 48 Mn1 Mississippi nonchilled 675 32.431158
## 49 Mn1 Mississippi nonchilled 1000 35.522488
## 50 Mn2 Mississippi nonchilled 95 12.037434
## 51 Mn2 Mississippi nonchilled 175 22.028804
## 52 Mn2 Mississippi nonchilled 250 30.630772
## 53 Mn2 Mississippi nonchilled 350 31.829970
## 54 Mn2 Mississippi nonchilled 500 32.429665
## 55 Mn2 Mississippi nonchilled 675 31.133149
## 56 Mn2 Mississippi nonchilled 1000 31.528443
## 57 Mn3 Mississippi nonchilled 95 11.339485
## 58 Mn3 Mississippi nonchilled 175 19.433139
## 59 Mn3 Mississippi nonchilled 250 25.823863
## 60 Mn3 Mississippi nonchilled 350 27.938194
## 61 Mn3 Mississippi nonchilled 500 28.532179
## 62 Mn3 Mississippi nonchilled 675 28.125024
## 63 Mn3 Mississippi nonchilled 1000 27.837945
## 64 Mc1 Mississippi chilled 95 10.526255
## 65 Mc1 Mississippi chilled 175 14.927160
## 66 Mc1 Mississippi chilled 250 18.125956
## 67 Mc1 Mississippi chilled 350 18.928346
## 68 Mc1 Mississippi chilled 500 19.525301
## 69 Mc1 Mississippi chilled 675 22.238223
## 70 Mc1 Mississippi chilled 1000 21.933761
## 71 Mc2 Mississippi chilled 95 7.734252
## 72 Mc2 Mississippi chilled 175 11.439835
## 73 Mc2 Mississippi chilled 250 12.337009
## 74 Mc2 Mississippi chilled 350 13.030858
## 75 Mc2 Mississippi chilled 500 12.539608
## 76 Mc2 Mississippi chilled 675 13.730471
## 77 Mc2 Mississippi chilled 1000 14.431417
## 78 Mc3 Mississippi chilled 95 10.637937
## 79 Mc3 Mississippi chilled 175 18.033533
## 80 Mc3 Mississippi chilled 250 17.936751
## 81 Mc3 Mississippi chilled 350 17.930183
## 82 Mc3 Mississippi chilled 500 17.920969
## 83 Mc3 Mississippi chilled 675 18.926589
## 84 Mc3 Mississippi chilled 1000 19.920409#analisis descriptivo
plot(datos_n$up~ datos_n$CO2.conc, pch=16)
library(ggplot2)
datos_n## CO2.Plant CO2.Type CO2.Treatment CO2.conc up
## 1 Qn1 Quebec nonchilled 95 16.026297
## 2 Qn1 Quebec nonchilled 175 30.435972
## 3 Qn1 Quebec nonchilled 250 34.823886
## 4 Qn1 Quebec nonchilled 350 37.232888
## 5 Qn1 Quebec nonchilled 500 35.322103
## 6 Qn1 Quebec nonchilled 675 39.224760
## 7 Qn1 Quebec nonchilled 1000 39.725725
## 8 Qn2 Quebec nonchilled 95 13.627980
## 9 Qn2 Quebec nonchilled 175 27.333127
## 10 Qn2 Quebec nonchilled 250 37.134386
## 11 Qn2 Quebec nonchilled 350 41.832380
## 12 Qn2 Quebec nonchilled 500 40.628455
## 13 Qn2 Quebec nonchilled 675 41.424386
## 14 Qn2 Quebec nonchilled 1000 44.339815
## 15 Qn3 Quebec nonchilled 95 16.221948
## 16 Qn3 Quebec nonchilled 175 32.432990
## 17 Qn3 Quebec nonchilled 250 40.331765
## 18 Qn3 Quebec nonchilled 350 42.139784
## 19 Qn3 Quebec nonchilled 500 42.932919
## 20 Qn3 Quebec nonchilled 675 43.926320
## 21 Qn3 Quebec nonchilled 1000 45.538265
## 22 Qc1 Quebec chilled 95 14.233396
## 23 Qc1 Quebec chilled 175 24.138150
## 24 Qc1 Quebec chilled 250 30.322738
## 25 Qc1 Quebec chilled 350 34.638108
## 26 Qc1 Quebec chilled 500 32.522706
## 27 Qc1 Quebec chilled 675 35.425608
## 28 Qc1 Quebec chilled 1000 38.732399
## 29 Qc2 Quebec chilled 95 9.335057
## 30 Qc2 Quebec chilled 175 27.323376
## 31 Qc2 Quebec chilled 250 35.024087
## 32 Qc2 Quebec chilled 350 38.832181
## 33 Qc2 Quebec chilled 500 38.631348
## 34 Qc2 Quebec chilled 675 37.529962
## 35 Qc2 Quebec chilled 1000 42.438603
## 36 Qc3 Quebec chilled 95 15.136276
## 37 Qc3 Quebec chilled 175 21.037558
## 38 Qc3 Quebec chilled 250 38.137662
## 39 Qc3 Quebec chilled 350 34.022657
## 40 Qc3 Quebec chilled 500 38.936024
## 41 Qc3 Quebec chilled 675 39.622491
## 42 Qc3 Quebec chilled 1000 41.421495
## 43 Mn1 Mississippi nonchilled 95 10.638828
## 44 Mn1 Mississippi nonchilled 175 19.220886
## 45 Mn1 Mississippi nonchilled 250 26.224325
## 46 Mn1 Mississippi nonchilled 350 30.023920
## 47 Mn1 Mississippi nonchilled 500 30.930636
## 48 Mn1 Mississippi nonchilled 675 32.431158
## 49 Mn1 Mississippi nonchilled 1000 35.522488
## 50 Mn2 Mississippi nonchilled 95 12.037434
## 51 Mn2 Mississippi nonchilled 175 22.028804
## 52 Mn2 Mississippi nonchilled 250 30.630772
## 53 Mn2 Mississippi nonchilled 350 31.829970
## 54 Mn2 Mississippi nonchilled 500 32.429665
## 55 Mn2 Mississippi nonchilled 675 31.133149
## 56 Mn2 Mississippi nonchilled 1000 31.528443
## 57 Mn3 Mississippi nonchilled 95 11.339485
## 58 Mn3 Mississippi nonchilled 175 19.433139
## 59 Mn3 Mississippi nonchilled 250 25.823863
## 60 Mn3 Mississippi nonchilled 350 27.938194
## 61 Mn3 Mississippi nonchilled 500 28.532179
## 62 Mn3 Mississippi nonchilled 675 28.125024
## 63 Mn3 Mississippi nonchilled 1000 27.837945
## 64 Mc1 Mississippi chilled 95 10.526255
## 65 Mc1 Mississippi chilled 175 14.927160
## 66 Mc1 Mississippi chilled 250 18.125956
## 67 Mc1 Mississippi chilled 350 18.928346
## 68 Mc1 Mississippi chilled 500 19.525301
## 69 Mc1 Mississippi chilled 675 22.238223
## 70 Mc1 Mississippi chilled 1000 21.933761
## 71 Mc2 Mississippi chilled 95 7.734252
## 72 Mc2 Mississippi chilled 175 11.439835
## 73 Mc2 Mississippi chilled 250 12.337009
## 74 Mc2 Mississippi chilled 350 13.030858
## 75 Mc2 Mississippi chilled 500 12.539608
## 76 Mc2 Mississippi chilled 675 13.730471
## 77 Mc2 Mississippi chilled 1000 14.431417
## 78 Mc3 Mississippi chilled 95 10.637937
## 79 Mc3 Mississippi chilled 175 18.033533
## 80 Mc3 Mississippi chilled 250 17.936751
## 81 Mc3 Mississippi chilled 350 17.930183
## 82 Mc3 Mississippi chilled 500 17.920969
## 83 Mc3 Mississippi chilled 675 18.926589
## 84 Mc3 Mississippi chilled 1000 19.920409library(collapsibleTree)
collapsibleTreeSummary(CO2,
c("Type", "Treatment","Plant", "conc"), collapsed = F)ggplot(datos_n, aes(x = CO2.conc, y = up, color = datos_n$CO2.Type))+
geom_point(aes(shape = CO2.Treatment))+
geom_path(aes(group = CO2.Plant, lty = CO2.Treatment))+
theme_bw()## Warning: Use of `datos_n$CO2.Type` is discouraged.
## ℹ Use `CO2.Type` instead.
## Use of `datos_n$CO2.Type` is discouraged.
## ℹ Use `CO2.Type` instead.
#se puede observar que el mejor tratamiento es en Quebec Nonchilled.
#modelo lineal
#a: modelo lienal no se usa en este caso
f1= lm(up~I(log(datos_n$CO2.conc)) + datos_n$CO2.Type+datos_n$CO2.Treatment,data =datos_n)
#modelo linaal mixto uso correcto
f2=lmer(up~I(log(datos_n$CO2.conc))+datos_n$CO2.Type:datos_n$CO2.Treatment+(1|datos_n$CO2.Plant), data=datos_n)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienta=broom::glance(f1)#error
b=broom::tidy(f1)#error
a;b## # A tibble: 1 × 12
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.799 0.792 4.93 106. 7.94e-28 3 -251. 512. 525.
## # ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>## # A tibble: 4 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -12.4 4.28 -2.89 4.94e- 3
## 2 I(log(datos_n$CO2.conc)) 8.48 0.717 11.8 2.97e-19
## 3 datos_n$CO2.TypeMississippi -12.7 1.08 -11.8 4.03e-19
## 4 datos_n$CO2.Treatmentchilled -6.86 1.08 -6.37 1.11e- 8broom.mixed::glance(f2)## # A tibble: 1 × 7
## nobs sigma logLik AIC BIC REMLcrit df.residual
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 84 4.50 -241. 496. 513. 482. 77broom.mixed::tidy(f2)## # A tibble: 7 × 6
## effect group term estimate std.error statistic
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Intercept) -33.5 4.02 -8.33
## 2 fixed <NA> I(log(datos_n$CO2.con… 8.48 0.654 13.0
## 3 fixed <NA> datos_n$CO2.TypeQuebe… 19.5 1.83 10.6
## 4 fixed <NA> datos_n$CO2.TypeMissi… 10.1 1.83 5.53
## 5 fixed <NA> datos_n$CO2.TypeQuebe… 15.9 1.83 8.69
## 6 ran_pars datos_n$CO2.Plant sd__(Intercept) 1.47 NA NA
## 7 ran_pars Residual sd__Observation 4.50 NA NA#CONCLUSIÓN: El Type Quebec con nonchilled presentó la mayor pendiente con un valor de 19.52 siendo el mejor tratamiento para implementar, confirmando lo visto en el analisis descriptivo #el uso de un modelo (lm) no el adecuado en estos casos, ya que se enfoca en un solo efecto; en el caso de modelo lineal mixto (lmer), se emplean cuando hay efectos fijos (controlados) y efectos aleatorios, sin que, presenten una distribución normal pero los datos estan correlacionados, en este caso poisson
#utilidad del modelo mixto en la agricultura: Mejoramiento genético en plantas,mirar el mejor individual y permite analizar variables con diferentes comportamientos no lineales, Poisson, Gamma, Binomial, entre otras.
data("ChickWeight");data## function (..., list = character(), package = NULL, lib.loc = NULL,
## verbose = getOption("verbose"), envir = .GlobalEnv, overwrite = TRUE)
## {
## fileExt <- function(x) {
## db <- grepl("\\.[^.]+\\.(gz|bz2|xz)$", x)
## ans <- sub(".*\\.", "", x)
## ans[db] <- sub(".*\\.([^.]+\\.)(gz|bz2|xz)$", "\\1\\2",
## x[db])
## ans
## }
## my_read_table <- function(...) {
## lcc <- Sys.getlocale("LC_COLLATE")
## on.exit(Sys.setlocale("LC_COLLATE", lcc))
## Sys.setlocale("LC_COLLATE", "C")
## read.table(...)
## }
## stopifnot(is.character(list))
## names <- c(as.character(substitute(list(...))[-1L]), list)
## if (!is.null(package)) {
## if (!is.character(package))
## stop("'package' must be a character vector or NULL")
## }
## paths <- find.package(package, lib.loc, verbose = verbose)
## if (is.null(lib.loc))
## paths <- c(path.package(package, TRUE), if (!length(package)) getwd(),
## paths)
## paths <- unique(normalizePath(paths[file.exists(paths)]))
## paths <- paths[dir.exists(file.path(paths, "data"))]
## dataExts <- tools:::.make_file_exts("data")
## if (length(names) == 0L) {
## db <- matrix(character(), nrow = 0L, ncol = 4L)
## for (path in paths) {
## entries <- NULL
## packageName <- if (file_test("-f", file.path(path,
## "DESCRIPTION")))
## basename(path)
## else "."
## if (file_test("-f", INDEX <- file.path(path, "Meta",
## "data.rds"))) {
## entries <- readRDS(INDEX)
## }
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## dataDir <- file.path(path, "data")
## entries <- tools::list_files_with_type(dataDir,
## "data")
## if (length(entries)) {
## entries <- unique(tools::file_path_sans_ext(basename(entries)))
## entries <- cbind(entries, "")
## }
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## if (NROW(entries)) {
## if (is.matrix(entries) && ncol(entries) == 2L)
## db <- rbind(db, cbind(packageName, dirname(path),
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## sQuote(packageName)), domain = NA, call. = FALSE)
## }
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## colnames(db) <- c("Package", "LibPath", "Item", "Title")
## footer <- if (missing(package))
## paste0("Use ", sQuote(paste("data(package =", ".packages(all.available = TRUE))")),
## "\n", "to list the data sets in all *available* packages.")
## else NULL
## y <- list(title = "Data sets", header = NULL, results = db,
## footer = footer)
## class(y) <- "packageIQR"
## return(y)
## }
## paths <- file.path(paths, "data")
## for (name in names) {
## found <- FALSE
## for (p in paths) {
## tmp_env <- if (overwrite)
## envir
## else new.env()
## if (file_test("-f", file.path(p, "Rdata.rds"))) {
## rds <- readRDS(file.path(p, "Rdata.rds"))
## if (name %in% names(rds)) {
## found <- TRUE
## if (verbose)
## message(sprintf("name=%s:\t found in Rdata.rds",
## name), domain = NA)
## thispkg <- sub(".*/([^/]*)/data$", "\\1", p)
## thispkg <- sub("_.*$", "", thispkg)
## thispkg <- paste0("package:", thispkg)
## objs <- rds[[name]]
## lazyLoad(file.path(p, "Rdata"), envir = tmp_env,
## filter = function(x) x %in% objs)
## break
## }
## else if (verbose)
## message(sprintf("name=%s:\t NOT found in names() of Rdata.rds, i.e.,\n\t%s\n",
## name, paste(names(rds), collapse = ",")),
## domain = NA)
## }
## if (file_test("-f", file.path(p, "Rdata.zip"))) {
## warning("zipped data found for package ", sQuote(basename(dirname(p))),
## ".\nThat is defunct, so please re-install the package.",
## domain = NA)
## if (file_test("-f", fp <- file.path(p, "filelist")))
## files <- file.path(p, scan(fp, what = "", quiet = TRUE))
## else {
## warning(gettextf("file 'filelist' is missing for directory %s",
## sQuote(p)), domain = NA)
## next
## }
## }
## else {
## files <- list.files(p, full.names = TRUE)
## }
## files <- files[grep(name, files, fixed = TRUE)]
## if (length(files) > 1L) {
## o <- match(fileExt(files), dataExts, nomatch = 100L)
## paths0 <- dirname(files)
## paths0 <- factor(paths0, levels = unique(paths0))
## files <- files[order(paths0, o)]
## }
## if (length(files)) {
## for (file in files) {
## if (verbose)
## message("name=", name, ":\t file= ...", .Platform$file.sep,
## basename(file), "::\t", appendLF = FALSE,
## domain = NA)
## ext <- fileExt(file)
## if (basename(file) != paste0(name, ".", ext))
## found <- FALSE
## else {
## found <- TRUE
## zfile <- file
## zipname <- file.path(dirname(file), "Rdata.zip")
## if (file.exists(zipname)) {
## Rdatadir <- tempfile("Rdata")
## dir.create(Rdatadir, showWarnings = FALSE)
## topic <- basename(file)
## rc <- .External(C_unzip, zipname, topic,
## Rdatadir, FALSE, TRUE, FALSE, FALSE)
## if (rc == 0L)
## zfile <- file.path(Rdatadir, topic)
## }
## if (zfile != file)
## on.exit(unlink(zfile))
## switch(ext, R = , r = {
## library("utils")
## sys.source(zfile, chdir = TRUE, envir = tmp_env)
## }, RData = , rdata = , rda = load(zfile,
## envir = tmp_env), TXT = , txt = , tab = ,
## tab.gz = , tab.bz2 = , tab.xz = , txt.gz = ,
## txt.bz2 = , txt.xz = assign(name, my_read_table(zfile,
## header = TRUE, as.is = FALSE), envir = tmp_env),
## CSV = , csv = , csv.gz = , csv.bz2 = ,
## csv.xz = assign(name, my_read_table(zfile,
## header = TRUE, sep = ";", as.is = FALSE),
## envir = tmp_env), found <- FALSE)
## }
## if (found)
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## }
## if (verbose)
## message(if (!found)
## "*NOT* ", "found", domain = NA)
## }
## if (found)
## break
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## if (!found) {
## warning(gettextf("data set %s not found", sQuote(name)),
## domain = NA)
## }
## else if (!overwrite) {
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## if (exists(o, envir = envir, inherits = FALSE))
## warning(gettextf("an object named %s already exists and will not be overwritten",
## sQuote(o)))
## else assign(o, get(o, envir = tmp_env, inherits = FALSE),
## envir = envir)
## }
## rm(tmp_env)
## }
## }
## invisible(names)
## }
## <bytecode: 0x0000021d0e45a7d0>
## <environment: namespace:utils>view(ChickWeight)
f1_poisson= glm(weight~Diet:Time, data= ChickWeight, family=poisson())
tidy(f1_poisson)## # A tibble: 5 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 3.86 0.00931 415. 0
## 2 Diet1:Time 0.0656 0.000722 90.8 0
## 3 Diet2:Time 0.0754 0.000768 98.1 0
## 4 Diet3:Time 0.0862 0.000729 118. 0
## 5 Diet4:Time 0.0823 0.000756 109. 0ggplot(ChickWeight, aes(x=Time, y=weight))+
geom_point(aes(color=Diet))+
geom_path(aes(color=Diet, group=Chick))