},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"hidden": true,
"init_cell": true
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
- "hidden": true,
"init_cell": true
},
"outputs": [],
"source": [
- "# 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": 6,
- "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')"
]
},
{
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"multiplot(taheat, tsheat, cols=2)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Blah, blah, comment, blah."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"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": 10,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
}
],
"source": [
- "ggplotRegression(fit)"
+ "ggplotRegression(fit.ta)"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
}
],
"source": [
- "fit <- lm(heat ~ TS, data = cemheat)\n",
- "summary(fit)\n",
- "anova(fit)"
+ "fit.ts <- lm(heat ~ TS, data = cemheat)\n",
+ "summary(fit.ts)\n",
+ "anova(fit.ts)"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
}
],
"source": [
- "ggplotRegression(fit)"
+ "ggplotRegression(fit.ts)"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
}
],
"source": [
- "fit <- lm(heat ~ TA + 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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now combine the results into one dataframe for easy comparison."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ "<thead><tr><th scope=col>Vars</th><th scope=col>Adj.R.2</th></tr></thead>\n",
+ "<tbody>\n",
+ "\t<tr><td>TA </td><td>0.4915797</td></tr>\n",
+ "\t<tr><td>TS </td><td>0.6359290</td></tr>\n",
+ "\t<tr><td>TA, TS </td><td>0.9744140</td></tr>\n",
+ "</tbody>\n",
+ "</table>\n"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|ll}\n",
+ " Vars & Adj.R.2\\\\\n",
+ "\\hline\n",
+ "\t TA & 0.4915797\\\\\n",
+ "\t TS & 0.6359290\\\\\n",
+ "\t TA, TS & 0.9744140\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "Vars | Adj.R.2 | \n",
+ "|---|---|---|\n",
+ "| TA | 0.4915797 | \n",
+ "| TS | 0.6359290 | \n",
+ "| TA, TS | 0.9744140 | \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ " Vars Adj.R.2 \n",
+ "1 TA 0.4915797\n",
+ "2 TS 0.6359290\n",
+ "3 TA, TS 0.9744140"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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)$adj.r.squared)\n",
+ ")"
]
},
{
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 113,
"metadata": {},
"outputs": [
{
}
],
"source": [
- "predict(fit, data.frame(\"TA\" = 15, \"TS\" = 55))"
+ "predict(fit.tats, data.frame(\"TA\" = 15, \"TS\" = 55))"
]
},
{
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 114,
"metadata": {},
"outputs": [
{
}
],
"source": [
- "autoplot(fit)"
+ "autoplot(fit.tats)"
]
},
{
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
"metadata": {},
"source": [
"### Now form a new variable `oxy2`, say, by squaring oxygen.\n",
- "(Create a new column in the `anearobic` dataframe which is `anaerobic$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)."
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
projects / ou-jupyter-r-demo.git / blobdiff