},
"outputs": [],
"source": [
- "# From http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/\n",
- "# Multiple plot function\n",
- "#\n",
- "# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n",
- "# - cols: Number of columns in layout\n",
- "# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n",
- "#\n",
- "# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n",
- "# then plot 1 will go in the upper left, 2 will go in the upper right, and\n",
- "# 3 will go all the way across the bottom.\n",
- "#\n",
- "multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n",
- " library(grid)\n",
- "\n",
- " # Make a list from the ... arguments and plotlist\n",
- " plots <- c(list(...), plotlist)\n",
- "\n",
- " numPlots = length(plots)\n",
- "\n",
- " # If layout is NULL, then use 'cols' to determine layout\n",
- " if (is.null(layout)) {\n",
- " # Make the panel\n",
- " # ncol: Number of columns of plots\n",
- " # nrow: Number of rows needed, calculated from # of cols\n",
- " layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n",
- " ncol = cols, nrow = ceiling(numPlots/cols))\n",
- " }\n",
- "\n",
- " if (numPlots==1) {\n",
- " print(plots[[1]])\n",
- "\n",
- " } else {\n",
- " # Set up the page\n",
- " grid.newpage()\n",
- " pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n",
- "\n",
- " # Make each plot, in the correct location\n",
- " for (i in 1:numPlots) {\n",
- " # Get the i,j matrix positions of the regions that contain this subplot\n",
- " matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n",
- "\n",
- " print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n",
- " layout.pos.col = matchidx$col))\n",
- " }\n",
- " }\n",
- "}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "hidden": true,
- "init_cell": true
- },
- "outputs": [],
- "source": [
- "# From https://sejohnston.com/2012/08/09/a-quick-and-easy-function-to-plot-lm-results-in-r/\n",
- "ggplotRegression <- function (fit) {\n",
- "\n",
- "require(ggplot2)\n",
- "\n",
- "ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) + \n",
- " geom_point() +\n",
- " stat_smooth(method = \"lm\", col = \"red\") +\n",
- " labs(title = paste(\"Adj R2 = \",signif(summary(fit)$adj.r.squared, 5),\n",
- " \"Intercept =\",signif(fit$coef[[1]],5 ),\n",
- " \" Slope =\",signif(fit$coef[[2]], 5),\n",
- " \" P =\",signif(summary(fit)$coef[2,4], 5))) + \n",
- " theme(plot.title = element_text(size=12))\n",
- "}"
+ "source('plot_extensions.R')"
]
},
{
"multiplot(taheat, tsheat, cols=2)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Blah, blah, comment, blah."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"metadata": {},
"outputs": [],
"source": [
- "fit <- lm(heat ~ TA, data = cemheat)\n",
- "summary(fit)\n",
- "anova(fit)"
+ "fit.ta <- lm(heat ~ TA, data = cemheat)\n",
+ "summary(fit.ta)\n",
+ "anova(fit.ta)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ggplotRegression(fit.ta)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fit.ts <- lm(heat ~ TS, data = cemheat)\n",
+ "summary(fit.ts)\n",
+ "anova(fit.ts)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "ggplotRegression(fit)"
+ "ggplotRegression(fit.ts)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "fit <- lm(heat ~ TS, data = cemheat)\n",
- "summary(fit)\n",
- "anova(fit)"
+ "fit.tats <- lm(heat ~ TA + TS, data = cemheat)\n",
+ "summary(fit.tats)\n",
+ "anova(fit.tats)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "ggplotRegression(fit)"
+ "ggplotRegression(fit.tats)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now combine the results into one dataframe for easy comparison."
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "fit <- lm(heat ~ TA + TS, data = cemheat)\n",
- "summary(fit)\n",
- "anova(fit)"
+ "fits <- list(fit.ta, fit.ts, fit.tats)\n",
+ "data.frame(\n",
+ " \"Vars\" = sapply(fits, function(x) toString(attr(summary(x)$terms, \"variables\")[-(1:2)]) ),\n",
+ " \"Adj R^2\" = sapply(fits, function(x) summary(x)$adj.r.squared)\n",
+ ")"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "predict(fit, data.frame(\"TA\" = 15, \"TS\" = 55))"
+ "predict(fit.tats, data.frame(\"TA\" = 15, \"TS\" = 55))"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "autoplot(fit)"
+ "autoplot(fit.tats)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "fit <- lm(ventil ~ oxygen, data = anaerobic)\n",
- "summary(fit)\n",
- "anova(fit)"
+ "fit.o <- lm(ventil ~ oxygen, data = anaerobic)\n",
+ "summary(fit.o)\n",
+ "anova(fit.o)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "ggplotRegression(fit)"
+ "ggplotRegression(fit.o)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "autoplot(fit)"
+ "autoplot(fit.o)"
]
},
{
"metadata": {},
"source": [
"### Now form a new variable `oxy2`, say, by squaring oxygen.\n",
- "(Create a new column in the `anearobic` dataframe which is `anaerobic$oxygen ^ 2`.) Perform the regression of ventil on `oxygen` and `oxy2`. Comment on the fit of this model according to the printed output (and with recourse to Figure 3.2 in Example 3.1)."
+ "(Create a new column in the `anearobic` dataframe which is `anaerobic$oxygen ^ 2`.) Perform the regression of `ventil` on `oxygen` and `oxy2`. Comment on the fit of this model according to the printed output (and with recourse to Figure 3.2 in Example 3.1)."
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "fit <- lm(ventil ~ oxygen + oxy2, data = anaerobic)\n",
- "summary(fit)\n",
- "anova(fit)"
+ "fit.o2 <- lm(ventil ~ oxygen + oxy2, data = anaerobic)\n",
+ "summary(fit.o2)\n",
+ "anova(fit.o2)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "ggplotRegression(fit)"
+ "ggplotRegression(fit.o2)"
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "autoplot(fit)"
+ "autoplot(fit.o2)"
]
},
{
projects / ou-jupyter-r-demo.git / blobdiff