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v2.1 notebooks excluding 10,13

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Jonathan Taylor
2023-08-20 19:31:42 -07:00
parent 5c29f1c9e4
commit fc0c9152cb
20 changed files with 3663 additions and 3808 deletions
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21
Ch02-statlearn-lab.Rmd
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@@ -1,16 +1,3 @@
---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 2 # Chapter 2
@@ -78,7 +65,7 @@ print('fit a model with', 11, 'variables')
The following command will provide information about the `print()` function. The following command will provide information about the `print()` function.
```{python} ```{python}
# print? print?
``` ```
@@ -228,7 +215,7 @@ documentation associated with the function `fun`, if it exists.
We can try this for `np.array()`. We can try this for `np.array()`.
```{python} ```{python}
# np.array? np.array?
``` ```
This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`. This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`.
@@ -1303,8 +1290,8 @@ The plot methods of a data frame return a familiar object:
an axes. We can use it to update the plot as we did previously: an axes. We can use it to update the plot as we did previously:
```{python} ```{python}
ax = Auto.plot.scatter('horsepower', 'mpg'); ax = Auto.plot.scatter('horsepower', 'mpg')
ax.set_title('Horsepower vs. MPG') ax.set_title('Horsepower vs. MPG');
``` ```
If we want to save If we want to save
the figure that contains a given axes, we can find the relevant figure the figure that contains a given axes, we can find the relevant figure

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Ch02-statlearn-lab.ipynb
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19
Ch03-linreg-lab.Rmd
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---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
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text_representation:
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jupytext_version: 1.14.7
---
# Chapter 3 # Chapter 3
# Lab: Linear Regression # Lab: Linear Regression
## Importing packages ## Importing packages
@@ -277,7 +262,7 @@ value of 25.05 for `medv` when `lstat` equals
10), but the latter are substantially wider. 10), but the latter are substantially wider.
Next we will plot `medv` and `lstat` Next we will plot `medv` and `lstat`
using `DataFrame.plot.scatter()`, using `DataFrame.plot.scatter()`, \definelongblankMR{plot.scatter()}{plot.slashslashscatter()}
and wish to and wish to
add the regression line to the resulting plot. add the regression line to the resulting plot.
@@ -399,7 +384,7 @@ Notice how we have compacted the first line into a succinct expression describin
The `Boston` data set contains 12 variables, and so it would be cumbersome The `Boston` data set contains 12 variables, and so it would be cumbersome
to have to type all of these in order to perform a regression using all of the predictors. to have to type all of these in order to perform a regression using all of the predictors.
Instead, we can use the following short-hand: Instead, we can use the following short-hand:\definelongblankMR{columns.drop()}{columns.slashslashdrop()}
```{python} ```{python}
terms = Boston.columns.drop('medv') terms = Boston.columns.drop('medv')

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Ch03-linreg-lab.ipynb
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15
Ch04-classification-lab.Rmd
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---
jupyter:
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main_language: python
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jupytext_version: 1.14.7
---
# Chapter 4 # Chapter 4
@@ -807,7 +794,7 @@ feature_std.std()
``` ```
Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 103. Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 200.
In this case it does not matter, as long as the variables are all on the same scale. In this case it does not matter, as long as the variables are all on the same scale.
Using the function `train_test_split()` we now split the observations into a test set, Using the function `train_test_split()` we now split the observations into a test set,

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Ch04-classification-lab.ipynb
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17
Ch05-resample-lab.Rmd
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---
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---
# Chapter 5 # Chapter 5
@@ -518,13 +505,13 @@ slope. Interestingly, these are somewhat different from the estimates
obtained using the bootstrap. Does this indicate a problem with the obtained using the bootstrap. Does this indicate a problem with the
bootstrap? In fact, it suggests the opposite. Recall that the bootstrap? In fact, it suggests the opposite. Recall that the
standard formulas given in standard formulas given in
{Equation 3.8 on page 80} {Equation 3.8 on page 82}
rely on certain assumptions. For example, rely on certain assumptions. For example,
they depend on the unknown parameter $\sigma^2$, the noise they depend on the unknown parameter $\sigma^2$, the noise
variance. We then estimate $\sigma^2$ using the RSS. Now although the variance. We then estimate $\sigma^2$ using the RSS. Now although the
formula for the standard errors do not rely on the linear model being formula for the standard errors do not rely on the linear model being
correct, the estimate for $\sigma^2$ does. We see correct, the estimate for $\sigma^2$ does. We see
{in Figure 3.8 on page 106} that there is {in Figure 3.8 on page 108} that there is
a non-linear relationship in the data, and so the residuals from a a non-linear relationship in the data, and so the residuals from a
linear fit will be inflated, and so will $\hat{\sigma}^2$. Secondly, linear fit will be inflated, and so will $\hat{\sigma}^2$. Secondly,
the standard formulas assume (somewhat unrealistically) that the $x_i$ the standard formulas assume (somewhat unrealistically) that the $x_i$

330
Ch05-resample-lab.ipynb
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@@ -2,7 +2,7 @@
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"\n", "\n",
@@ -12,7 +12,7 @@
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"# Lab: Cross-Validation and the Bootstrap\n", "# Lab: Cross-Validation and the Bootstrap\n",
@@ -26,13 +26,13 @@
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"There are several new imports needed for this lab." "There are several new imports needed for this lab."
@@ -58,13 +58,13 @@
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@@ -81,7 +81,7 @@
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"cell_type": "markdown", "cell_type": "markdown",
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"## The Validation Set Approach\n", "## The Validation Set Approach\n",
@@ -102,13 +102,13 @@
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@@ -121,7 +121,7 @@
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"cell_type": "markdown", "cell_type": "markdown",
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"metadata": {}, "metadata": {},
"source": [ "source": [
"Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`." "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`."
@@ -130,13 +130,13 @@
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@@ -150,7 +150,7 @@
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"We now use the `predict()` method of `results` evaluated on the model matrix for this model\n", "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n",
@@ -160,13 +160,13 @@
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@@ -190,7 +190,7 @@
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"id": "6dba5d55", "id": "f4369ee6",
"metadata": {}, "metadata": {},
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"Hence our estimate for the validation MSE of the linear regression\n", "Hence our estimate for the validation MSE of the linear regression\n",
@@ -204,13 +204,13 @@
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@@ -235,7 +235,7 @@
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"cell_type": "markdown", "cell_type": "markdown",
"id": "39ab59b1", "id": "0271dc50",
"metadata": {}, "metadata": {},
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"Let’s use this function to estimate the validation MSE\n", "Let’s use this function to estimate the validation MSE\n",
@@ -247,13 +247,13 @@
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@@ -280,7 +280,7 @@
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"These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n", "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n",
@@ -291,13 +291,13 @@
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@@ -327,7 +327,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "f5ceb357", "id": "00785402",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Using this split of the observations into a training set and a validation set,\n", "Using this split of the observations into a training set and a validation set,\n",
@@ -341,7 +341,7 @@
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"cell_type": "markdown", "cell_type": "markdown",
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"## Cross-Validation\n", "## Cross-Validation\n",
@@ -374,13 +374,13 @@
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@@ -410,7 +410,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "5b0f6f30", "id": "c17e2bc8",
"metadata": {}, "metadata": {},
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"The arguments to `cross_validate()` are as follows: an\n", "The arguments to `cross_validate()` are as follows: an\n",
@@ -426,7 +426,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "b527f67f", "id": "5c7901f2",
"metadata": {}, "metadata": {},
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"We can repeat this procedure for increasingly complex polynomial fits.\n", "We can repeat this procedure for increasingly complex polynomial fits.\n",
@@ -442,13 +442,13 @@
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@@ -480,7 +480,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "792f1304", "id": "03706248",
"metadata": {}, "metadata": {},
"source": [ "source": [
"As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and\n", "As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and\n",
@@ -499,13 +499,13 @@
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@@ -530,7 +530,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "983625b2", "id": "f5092f1b",
"metadata": {}, "metadata": {},
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"In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n", "In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n",
@@ -541,13 +541,13 @@
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@@ -580,7 +580,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "32bf6662", "id": "c4ec6afb",
"metadata": {}, "metadata": {},
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"Notice that the computation time is much shorter than that of LOOCV.\n", "Notice that the computation time is much shorter than that of LOOCV.\n",
@@ -595,7 +595,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "1e89127b", "id": "5edf407f",
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"source": [ "source": [
"The `cross_validate()` function is flexible and can take\n", "The `cross_validate()` function is flexible and can take\n",
@@ -606,13 +606,13 @@
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"execution_count": 13, "execution_count": 13,
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@@ -641,7 +641,7 @@
}, },
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"cell_type": "markdown", "cell_type": "markdown",
"id": "2c0fb0d5", "id": "a081be63",
"metadata": {}, "metadata": {},
"source": [ "source": [
"One can estimate the variability in the test error by running the following:" "One can estimate the variability in the test error by running the following:"
@@ -650,13 +650,13 @@
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"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 14,
"id": "746aeccd", "id": "0407ad56",
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@@ -684,7 +684,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "3310fe80", "id": "b66db3cb",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Note that this standard deviation is not a valid estimate of the\n", "Note that this standard deviation is not a valid estimate of the\n",
@@ -724,13 +724,13 @@
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@@ -745,7 +745,7 @@
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"metadata": {}, "metadata": {},
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"This function returns an estimate for $\\alpha$\n", "This function returns an estimate for $\\alpha$\n",
@@ -758,13 +758,13 @@
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"execution_count": 16, "execution_count": 16,
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"outputs": [ "outputs": [
@@ -785,7 +785,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "cc18244c", "id": "58a78f00",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Next we randomly select\n", "Next we randomly select\n",
@@ -797,13 +797,13 @@
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"execution_count": 17, "execution_count": 17,
"id": "5754d6d5", "id": "bcd40175",
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@@ -829,7 +829,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "0e97e132", "id": "e6058be4",
"metadata": {}, "metadata": {},
"source": [ "source": [
"This process can be generalized to create a simple function `boot_SE()` for\n", "This process can be generalized to create a simple function `boot_SE()` for\n",
@@ -840,13 +840,13 @@
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@@ -872,7 +872,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "a1d25cfe", "id": "d94d383e",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Notice the use of `_` as a loop variable in `for _ in range(B)`. This is often used if the value of the counter is\n", "Notice the use of `_` as a loop variable in `for _ in range(B)`. This is often used if the value of the counter is\n",
@@ -885,13 +885,13 @@
{ {
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"execution_count": 19, "execution_count": 19,
"id": "e656aa1f", "id": "4a323513",
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"execution": { "execution": {
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@@ -916,7 +916,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "258ccf67", "id": "22343f53",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The final output shows that the bootstrap estimate for ${\\rm SE}(\\hat{\\alpha})$ is $0.0912$.\n", "The final output shows that the bootstrap estimate for ${\\rm SE}(\\hat{\\alpha})$ is $0.0912$.\n",
@@ -951,13 +951,13 @@
{ {
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"id": "c5d14195", "id": "0220f3af",
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@@ -972,7 +972,7 @@
}, },
{ {
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"id": "89a6fb3e", "id": "df0c7f05",
"metadata": {}, "metadata": {},
"source": [ "source": [
"This is not quite what is needed as the first argument to\n", "This is not quite what is needed as the first argument to\n",
@@ -986,13 +986,13 @@
{ {
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"execution_count": 21, "execution_count": 21,
"id": "7e0523f0", "id": "62037dcb",
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@@ -1003,7 +1003,7 @@
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{ {
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"metadata": {}, "metadata": {},
"source": [ "source": [
"Typing `hp_func?` will show that it has two arguments `D`\n", "Typing `hp_func?` will show that it has two arguments `D`\n",
@@ -1019,13 +1019,13 @@
{ {
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@@ -1060,7 +1060,7 @@
}, },
{ {
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"metadata": {}, "metadata": {},
"source": [ "source": [
"Next, we use the `boot_SE()` {} function to compute the standard\n", "Next, we use the `boot_SE()` {} function to compute the standard\n",
@@ -1070,13 +1070,13 @@
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@@ -1104,7 +1104,7 @@
}, },
{ {
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"id": "1d0db4c6", "id": "38c65fbf",
"metadata": {}, "metadata": {},
"source": [ "source": [
"This indicates that the bootstrap estimate for ${\\rm SE}(\\hat{\\beta}_0)$ is\n", "This indicates that the bootstrap estimate for ${\\rm SE}(\\hat{\\beta}_0)$ is\n",
@@ -1120,13 +1120,13 @@
{ {
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"id": "6b1213ac", "id": "c9aea297",
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@@ -1152,7 +1152,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "2b158ef6", "id": "d870ad6b",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n", "The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n",
@@ -1164,13 +1164,13 @@
"obtained using the bootstrap. Does this indicate a problem with the\n", "obtained using the bootstrap. Does this indicate a problem with the\n",
"bootstrap? In fact, it suggests the opposite. Recall that the\n", "bootstrap? In fact, it suggests the opposite. Recall that the\n",
"standard formulas given in\n", "standard formulas given in\n",
" {Equation 3.8 on page 80}\n", " {Equation 3.8 on page 82}\n",
"rely on certain assumptions. For example,\n", "rely on certain assumptions. For example,\n",
"they depend on the unknown parameter $\\sigma^2$, the noise\n", "they depend on the unknown parameter $\\sigma^2$, the noise\n",
"variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n", "variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
"formula for the standard errors do not rely on the linear model being\n", "formula for the standard errors do not rely on the linear model being\n",
"correct, the estimate for $\\sigma^2$ does. We see\n", "correct, the estimate for $\\sigma^2$ does. We see\n",
" {in Figure 3.8 on page 106} that there is\n", " {in Figure 3.8 on page 108} that there is\n",
"a non-linear relationship in the data, and so the residuals from a\n", "a non-linear relationship in the data, and so the residuals from a\n",
"linear fit will be inflated, and so will $\\hat{\\sigma}^2$. Secondly,\n", "linear fit will be inflated, and so will $\\hat{\\sigma}^2$. Secondly,\n",
"the standard formulas assume (somewhat unrealistically) that the $x_i$\n", "the standard formulas assume (somewhat unrealistically) that the $x_i$\n",
@@ -1192,13 +1192,13 @@
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@@ -1226,7 +1226,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "1badcfd1", "id": "9fccbbbd",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We compare the results to the standard errors computed using `sm.OLS()`." "We compare the results to the standard errors computed using `sm.OLS()`."
@@ -1235,13 +1235,13 @@
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"\n", "\n",
@@ -1279,8 +1279,8 @@
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Ch06-varselect-lab.Rmd
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---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 6 # Chapter 6
@@ -45,7 +32,7 @@ from ISLP.models import \
(Stepwise, (Stepwise,
sklearn_selected, sklearn_selected,
sklearn_selection_path) sklearn_selection_path)
# !pip install l0bnb !pip install l0bnb
from l0bnb import fit_path from l0bnb import fit_path
``` ```
@@ -540,7 +527,7 @@ grid.best_params_['ridge__alpha']
grid.best_estimator_ grid.best_estimator_
``` ```
Recall we set up the `kfold` object for 5-fold cross-validation on page 296. We now plot the cross-validated MSE as a function of $-\log(\lambda)$, which has shrinkage decreasing from left Recall we set up the `kfold` object for 5-fold cross-validation on page 298. We now plot the cross-validated MSE as a function of $-\log(\lambda)$, which has shrinkage decreasing from left
to right. to right.
```{python} ```{python}
@@ -728,7 +715,7 @@ ax.set_ylabel('Standardized coefficiients', fontsize=20);
``` ```
The smallest cross-validated error is lower than the test set MSE of the null model The smallest cross-validated error is lower than the test set MSE of the null model
and of least squares, and very similar to the test MSE of 115526.71 of ridge and of least squares, and very similar to the test MSE of 115526.71 of ridge
regression (page 303) with $\lambda$ chosen by cross-validation. regression (page 305) with $\lambda$ chosen by cross-validation.
```{python} ```{python}
np.min(tuned_lasso.mse_path_.mean(1)) np.min(tuned_lasso.mse_path_.mean(1))

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Ch07-nonlin-lab.Rmd
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---
jupyter:
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# Chapter 7 # Chapter 7
@@ -83,7 +70,7 @@ for feature transformations such as `PCA()` seen in Section 6.5.3) which
allows for easy evaluation of the polynomial at new data points. Here `poly()` is referred to as a *helper* function, and sets up the transformation; `Poly()` is the actual workhorse that computes the transformation. See also allows for easy evaluation of the polynomial at new data points. Here `poly()` is referred to as a *helper* function, and sets up the transformation; `Poly()` is the actual workhorse that computes the transformation. See also
the the
discussion of transformations on discussion of transformations on
page 127. page 129.
In the code above, the first line executes the `fit()` method In the code above, the first line executes the `fit()` method
using the dataframe using the dataframe
@@ -748,7 +735,6 @@ ANOVA, $\mathcal{M}_2$ is preferred.
We can repeat the same process for `age` as well. We see there is very clear evidence that We can repeat the same process for `age` as well. We see there is very clear evidence that
a non-linear term is required for `age`. a non-linear term is required for `age`.
\newpage
```{python} ```{python}
gam_0 = LinearGAM(year_term + gam_0 = LinearGAM(year_term +

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---
jupyter:
jupytext:
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formats: ipynb,Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 8 # Chapter 8

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# Chapter 9 # Chapter 9

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"\n", "\n",
@@ -12,7 +12,7 @@
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"# Lab: Support Vector Machines\n", "# Lab: Support Vector Machines\n",
@@ -25,13 +25,13 @@
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"We also collect the new imports\n", "We also collect the new imports\n",
@@ -55,13 +55,13 @@
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@@ -73,7 +73,7 @@
}, },
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"We will use the function `RocCurveDisplay.from_estimator()` to\n", "We will use the function `RocCurveDisplay.from_estimator()` to\n",
@@ -83,13 +83,13 @@
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@@ -99,7 +99,7 @@
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"id": "f666c212", "id": "38115984",
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"## Support Vector Classifier\n", "## Support Vector Classifier\n",
@@ -123,13 +123,13 @@
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"They are not. We now fit the classifier." "They are not. We now fit the classifier."
@@ -168,13 +168,13 @@
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@@ -200,7 +200,7 @@
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"The support vector classifier with two features can\n", "The support vector classifier with two features can\n",
@@ -212,13 +212,13 @@
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@@ -243,7 +243,7 @@
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"The decision\n", "The decision\n",
@@ -257,13 +257,13 @@
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}, },
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"source": [ "source": [
"With a smaller value of the cost parameter, we\n", "With a smaller value of the cost parameter, we\n",
@@ -303,13 +303,13 @@
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"execution_count": 8, "execution_count": 8,
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@@ -331,7 +331,7 @@
}, },
{ {
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"id": "90a0ee53", "id": "0693b3eb",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Since the support vector machine is an estimator in `sklearn`, we\n", "Since the support vector machine is an estimator in `sklearn`, we\n",
@@ -341,13 +341,13 @@
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"execution_count": 9, "execution_count": 9,
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@@ -378,7 +378,7 @@
}, },
{ {
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"We can easily access the cross-validation errors for each of these models\n", "We can easily access the cross-validation errors for each of these models\n",
@@ -389,13 +389,13 @@
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"We see that `C=1` results in the highest cross-validation\n", "We see that `C=1` results in the highest cross-validation\n",
@@ -430,13 +430,13 @@
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@@ -448,7 +448,7 @@
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"Now we predict the class labels of these test observations. Here we\n", "Now we predict the class labels of these test observations. Here we\n",
@@ -459,13 +459,13 @@
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@@ -534,7 +534,7 @@
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"Thus, with this value of `C`,\n", "Thus, with this value of `C`,\n",
@@ -546,13 +546,13 @@
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@@ -622,7 +622,7 @@
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"In this case 60% of test observations are correctly classified.\n", "In this case 60% of test observations are correctly classified.\n",
@@ -637,13 +637,13 @@
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@@ -675,13 +675,13 @@
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"We fit the\n", "We fit the\n",
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"Indeed no training errors were made and only three support vectors were used.\n", "Indeed no training errors were made and only three support vectors were used.\n",
@@ -807,13 +807,13 @@
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@@ -882,7 +882,7 @@
}, },
{ {
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"id": "39a432d1", "id": "d0684844",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Using `C=0.1`, we again do not misclassify any training observations, but we\n", "Using `C=0.1`, we again do not misclassify any training observations, but we\n",
@@ -894,13 +894,13 @@
{ {
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"execution_count": 18, "execution_count": 18,
"id": "c67591a1", "id": "63a9d752",
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@@ -926,7 +926,7 @@
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{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "25e61f65", "id": "a70d84f4",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Support Vector Machine\n", "## Support Vector Machine\n",
@@ -947,13 +947,13 @@
{ {
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"execution_count": 19, "execution_count": 19,
"id": "322be574", "id": "2fee8df5",
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"shell.execute_reply": "2023-08-07T00:19:29.993635Z" "shell.execute_reply": "2023-08-21T02:30:00.344774Z"
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@@ -966,7 +966,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "22fe2182", "id": "d5c7545e",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Plotting the data makes it clear that the class boundary is indeed non-linear." "Plotting the data makes it clear that the class boundary is indeed non-linear."
@@ -975,13 +975,13 @@
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@@ -989,7 +989,7 @@
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"<matplotlib.collections.PathCollection at 0x17f2b35d0>" "<matplotlib.collections.PathCollection at 0x28b7c65d0>"
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"execution_count": 20, "execution_count": 20,
@@ -1017,7 +1017,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "64913fe3", "id": "7c0bc32b",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The data is randomly split into training and testing groups. We then\n", "The data is randomly split into training and testing groups. We then\n",
@@ -1028,13 +1028,13 @@
{ {
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"execution_count": 21, "execution_count": 21,
"id": "0c2690d1", "id": "4acc3246",
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"execution": { "execution": {
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} }
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"outputs": [ "outputs": [
@@ -1066,7 +1066,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "5da9efdb", "id": "b2c7e95e",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The plot shows that the resulting SVM has a decidedly non-linear\n", "The plot shows that the resulting SVM has a decidedly non-linear\n",
@@ -1076,13 +1076,13 @@
{ {
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"execution_count": 22, "execution_count": 22,
"id": "3eb171e8", "id": "e9852a28",
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"execution": { "execution": {
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@@ -1107,7 +1107,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "ab5b1446", "id": "acfa4bed",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We can see from the figure that there are a fair number of training\n", "We can see from the figure that there are a fair number of training\n",
@@ -1120,13 +1120,13 @@
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"execution_count": 23, "execution_count": 23,
"id": "9a6b905b", "id": "01232fc9",
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"execution": { "execution": {
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} }
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"outputs": [ "outputs": [
@@ -1153,7 +1153,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
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"source": [ "source": [
"We can perform cross-validation using `skm.GridSearchCV()` to select the\n", "We can perform cross-validation using `skm.GridSearchCV()` to select the\n",
@@ -1164,13 +1164,13 @@
{ {
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"execution_count": 24, "execution_count": 24,
"id": "5ab01d6c", "id": "bcbd15a4",
"metadata": { "metadata": {
"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:30.611940Z" "shell.execute_reply": "2023-08-21T02:30:01.243203Z"
} }
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"outputs": [ "outputs": [
@@ -1201,7 +1201,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "1bb987ae", "id": "997bbfbd",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The best choice of parameters under five-fold CV is achieved at `C=1`\n", "The best choice of parameters under five-fold CV is achieved at `C=1`\n",
@@ -1212,13 +1212,13 @@
{ {
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"execution_count": 25, "execution_count": 25,
"id": "166a6acb", "id": "28ca551e",
"metadata": { "metadata": {
"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:30.850653Z" "shell.execute_reply": "2023-08-21T02:30:01.600574Z"
} }
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"outputs": [ "outputs": [
@@ -1303,7 +1303,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "39ee6f32", "id": "48e671f4",
"metadata": {}, "metadata": {},
"source": [ "source": [
"With these parameters, 12% of test\n", "With these parameters, 12% of test\n",
@@ -1312,7 +1312,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "f0ea699d", "id": "eaed0a87",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## ROC Curves\n", "## ROC Curves\n",
@@ -1346,13 +1346,13 @@
{ {
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"execution_count": 26, "execution_count": 26,
"id": "0607fc41", "id": "68ac9421",
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@@ -1380,7 +1380,7 @@
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{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "54446e71", "id": "0c35d32a",
"metadata": {}, "metadata": {},
"source": [ "source": [
" In this example, the SVM appears to provide accurate predictions. By increasing\n", " In this example, the SVM appears to provide accurate predictions. By increasing\n",
@@ -1391,13 +1391,13 @@
{ {
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"execution_count": 27, "execution_count": 27,
"id": "5211a882", "id": "f79a9e0a",
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"execution": { "execution": {
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} }
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"outputs": [ "outputs": [
@@ -1428,7 +1428,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "de7e4be8", "id": "7bd1a22b",
"metadata": {}, "metadata": {},
"source": [ "source": [
"However, these ROC curves are all on the training data. We are really\n", "However, these ROC curves are all on the training data. We are really\n",
@@ -1440,13 +1440,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 28,
"id": "12acc4ff", "id": "bdb9e503",
"metadata": { "metadata": {
"execution": { "execution": {
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} }
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@@ -1462,7 +1462,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "eb5c8aeb", "id": "8a42e924",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Let’s look at our tuned SVM." "Let’s look at our tuned SVM."
@@ -1471,13 +1471,13 @@
{ {
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"execution_count": 29, "execution_count": 29,
"id": "21c81913", "id": "329f5d2c",
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"execution": { "execution": {
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} }
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@@ -1510,7 +1510,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "b9fefe9f", "id": "bac19279",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## SVM with Multiple Classes\n", "## SVM with Multiple Classes\n",
@@ -1526,13 +1526,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 30,
"id": "2fff4fa8", "id": "267e113d",
"metadata": { "metadata": {
"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:31.294593Z" "shell.execute_reply": "2023-08-21T02:30:02.034069Z"
} }
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"outputs": [ "outputs": [
@@ -1558,7 +1558,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "b7adc87d", "id": "a9f4297c",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We now fit an SVM to the data:" "We now fit an SVM to the data:"
@@ -1567,13 +1567,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 31,
"id": "5396f2df", "id": "64cbebd0",
"metadata": { "metadata": {
"execution": { "execution": {
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@@ -1605,7 +1605,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "837644f5", "id": "62c5d16e",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The `sklearn.svm` library can also be used to perform support vector\n", "The `sklearn.svm` library can also be used to perform support vector\n",
@@ -1614,7 +1614,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "a6bc0cbc", "id": "5c0824b6",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Application to Gene Expression Data\n", "## Application to Gene Expression Data\n",
@@ -1631,13 +1631,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 32,
"id": "f63c575e", "id": "b6e6f12b",
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"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:31.958769Z" "shell.execute_reply": "2023-08-21T02:30:03.098760Z"
} }
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@@ -1659,7 +1659,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "bfd6492c", "id": "e3fbaa58",
"metadata": {}, "metadata": {},
"source": [ "source": [
"This data set consists of expression measurements for 2,308\n", "This data set consists of expression measurements for 2,308\n",
@@ -1677,13 +1677,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 33,
"id": "32091338", "id": "273a10b2",
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"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:31.989868Z" "shell.execute_reply": "2023-08-21T02:30:03.129845Z"
} }
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"outputs": [ "outputs": [
@@ -1777,7 +1777,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "23043ab0", "id": "31cad43a",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We see that there are *no* training\n", "We see that there are *no* training\n",
@@ -1791,13 +1791,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 34,
"id": "d9058023", "id": "bc3079a7",
"metadata": { "metadata": {
"execution": { "execution": {
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@@ -1889,7 +1889,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "d0d5aba4", "id": "0d059312",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We see that using `C=10` yields two test set errors on these data.\n", "We see that using `C=10` yields two test set errors on these data.\n",
@@ -1900,8 +1900,8 @@
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13
Ch11-surv-lab.Rmd
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@@ -1,16 +1,3 @@
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# Chapter 11 # Chapter 11

424
Ch11-surv-lab.ipynb
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@@ -2,7 +2,7 @@
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"\n", "\n",
@@ -12,7 +12,7 @@
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"# Lab: Survival Analysis\n", "# Lab: Survival Analysis\n",
@@ -31,13 +31,13 @@
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@@ -51,7 +51,7 @@
}, },
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"id": "a3dbcbbf", "id": "70fe80b5",
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"We also collect the new imports\n", "We also collect the new imports\n",
@@ -61,13 +61,13 @@
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@@ -83,7 +83,7 @@
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"## Brain Cancer Data\n", "## Brain Cancer Data\n",
@@ -94,13 +94,13 @@
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@@ -122,7 +122,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "e798f172", "id": "4ac65a33",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The rows index the 88 patients, while the 8 columns contain the predictors and outcome variables.\n", "The rows index the 88 patients, while the 8 columns contain the predictors and outcome variables.\n",
@@ -132,13 +132,13 @@
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@@ -196,13 +196,13 @@
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"id": "572f0b9e", "id": "33bc4d3c",
"metadata": { "metadata": {
"execution": { "execution": {
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@@ -226,7 +226,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "fbd132de", "id": "eb9c6d4f",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Before beginning an analysis, it is important to know how the\n", "Before beginning an analysis, it is important to know how the\n",
@@ -252,13 +252,13 @@
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@@ -292,7 +292,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "f037665b", "id": "2cc511cd",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Next we create Kaplan-Meier survival curves that are stratified by\n", "Next we create Kaplan-Meier survival curves that are stratified by\n",
@@ -318,13 +318,13 @@
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"execution_count": 8, "execution_count": 8,
"id": "3fc7848c", "id": "9e6f2e70",
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"execution": { "execution": {
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} }
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@@ -350,7 +350,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "c0c1a16a", "id": "4d7efefb",
"metadata": {}, "metadata": {},
"source": [ "source": [
"As discussed in Section 11.4, we can perform a\n", "As discussed in Section 11.4, we can perform a\n",
@@ -363,13 +363,13 @@
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"execution_count": 9, "execution_count": 9,
"id": "bf30d26f", "id": "c135f7aa",
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"execution": { "execution": {
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@@ -462,7 +462,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "e270649c", "id": "bd14317d",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The resulting $p$-value is $0.23$, indicating no evidence of a\n", "The resulting $p$-value is $0.23$, indicating no evidence of a\n",
@@ -476,13 +476,13 @@
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"execution_count": 10, "execution_count": 10,
"id": "2ab78e07", "id": "5f9303dd",
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"execution": { "execution": {
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} }
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@@ -553,7 +553,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "b58b93ae", "id": "7e56e83e",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The first argument to `fit` should be a data frame containing\n", "The first argument to `fit` should be a data frame containing\n",
@@ -569,13 +569,13 @@
{ {
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"execution_count": 11, "execution_count": 11,
"id": "4716b7b0", "id": "bcc8470c",
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} }
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@@ -659,7 +659,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "2820f486", "id": "2e6163ca",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Regardless of which test we use, we see that there is no clear\n", "Regardless of which test we use, we see that there is no clear\n",
@@ -675,13 +675,13 @@
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"id": "c2767d88", "id": "c26a3499",
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} }
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@@ -800,7 +800,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "eee4ab1f", "id": "a96e311c",
"metadata": {}, "metadata": {},
"source": [ "source": [
" The `diagnosis` variable has been coded so that the baseline\n", " The `diagnosis` variable has been coded so that the baseline\n",
@@ -823,13 +823,13 @@
{ {
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"execution_count": 13, "execution_count": 13,
"id": "ede1d219", "id": "8d999f26",
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@@ -845,7 +845,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "e1c307ae", "id": "bf628fd8",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We make four\n", "We make four\n",
@@ -856,13 +856,13 @@
{ {
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"execution_count": 14, "execution_count": 14,
"id": "dc032a71", "id": "a1f6b355",
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@@ -974,7 +974,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "84da2586", "id": "3f6334e4",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We then construct the model matrix based on the model specification `all_MS` used to fit\n", "We then construct the model matrix based on the model specification `all_MS` used to fit\n",
@@ -984,13 +984,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 15,
"id": "e7c1fe43", "id": "a1a9d5b3",
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@@ -1117,7 +1117,7 @@
}, },
{ {
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"id": "3cfe1ec4", "id": "3eaec7e6",
"metadata": {}, "metadata": {},
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"We can use the `predict_survival_function()` method to obtain the estimated survival function." "We can use the `predict_survival_function()` method to obtain the estimated survival function."
@@ -1126,13 +1126,13 @@
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@@ -1276,7 +1276,7 @@
}, },
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"id": "29afd641", "id": "7d533f90",
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"This returns a data frame,\n", "This returns a data frame,\n",
@@ -1287,13 +1287,13 @@
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"id": "8f0329b4", "id": "ff3de29c",
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@@ -1316,7 +1316,7 @@
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{ {
"cell_type": "markdown", "cell_type": "markdown",
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"source": [ "source": [
"## Publication Data\n", "## Publication Data\n",
@@ -1330,13 +1330,13 @@
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"shell.execute_reply": "2023-08-07T00:19:35.286859Z" "shell.execute_reply": "2023-08-21T02:30:06.243300Z"
} }
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"outputs": [ "outputs": [
@@ -1363,7 +1363,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "6fcb22f7", "id": "d8f0f687",
"metadata": {}, "metadata": {},
"source": [ "source": [
"As discussed previously, the $p$-values from fitting Cox’s\n", "As discussed previously, the $p$-values from fitting Cox’s\n",
@@ -1375,13 +1375,13 @@
{ {
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"execution_count": 19, "execution_count": 19,
"id": "d070f716", "id": "6af7106e",
"metadata": { "metadata": {
"execution": { "execution": {
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@@ -1453,7 +1453,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "513a55b1", "id": "8ef28b8b",
"metadata": {}, "metadata": {},
"source": [ "source": [
"However, the results change dramatically when we include other\n", "However, the results change dramatically when we include other\n",
@@ -1464,13 +1464,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 20,
"id": "2bbcdd0c", "id": "b6ebefa7",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.323119Z", "iopub.execute_input": "2023-08-21T02:30:06.283282Z",
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"shell.execute_reply": "2023-08-07T00:19:35.362438Z" "shell.execute_reply": "2023-08-21T02:30:06.326646Z"
} }
}, },
"outputs": [ "outputs": [
@@ -1573,7 +1573,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "75bb8aa6", "id": "d463e623",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We see that there are a number of statistically significant variables,\n", "We see that there are a number of statistically significant variables,\n",
@@ -1583,7 +1583,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "bfe236e5", "id": "a23c38e0",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Call Center Data\n", "## Call Center Data\n",
@@ -1608,13 +1608,13 @@
{ {
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"execution_count": 21, "execution_count": 21,
"id": "b8ece43a", "id": "098f42ea",
"metadata": { "metadata": {
"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:35.368291Z" "shell.execute_reply": "2023-08-21T02:30:06.332425Z"
} }
}, },
"outputs": [], "outputs": [],
@@ -1637,7 +1637,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "c93e44f3", "id": "2f54ed03",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We then build a model matrix (omitting the intercept)" "We then build a model matrix (omitting the intercept)"
@@ -1646,13 +1646,13 @@
{ {
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"execution_count": 22, "execution_count": 22,
"id": "3e4f766f", "id": "26d5d0d0",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.370485Z", "iopub.execute_input": "2023-08-21T02:30:06.334692Z",
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"shell.execute_reply": "2023-08-07T00:19:35.377469Z" "shell.execute_reply": "2023-08-21T02:30:06.343708Z"
} }
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"outputs": [], "outputs": [],
@@ -1666,7 +1666,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "cad1ed19", "id": "a1a8f65d",
"metadata": {}, "metadata": {},
"source": [ "source": [
"It is worthwhile to take a peek at the model matrix `X`, so\n", "It is worthwhile to take a peek at the model matrix `X`, so\n",
@@ -1678,13 +1678,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 23,
"id": "72f42d14", "id": "77500663",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.380244Z", "iopub.execute_input": "2023-08-21T02:30:06.345660Z",
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"shell.execute_reply": "2023-08-07T00:19:35.384259Z" "shell.execute_reply": "2023-08-21T02:30:06.349797Z"
} }
}, },
"outputs": [ "outputs": [
@@ -1781,7 +1781,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "38c40ae1", "id": "fd7bd61c",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Next, we specify the coefficients and the hazard function." "Next, we specify the coefficients and the hazard function."
@@ -1790,13 +1790,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 24,
"id": "8b921536", "id": "74324a56",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.386034Z", "iopub.execute_input": "2023-08-21T02:30:06.351738Z",
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"shell.execute_reply": "2023-08-07T00:19:35.405608Z" "shell.execute_reply": "2023-08-21T02:30:06.441484Z"
} }
}, },
"outputs": [], "outputs": [],
@@ -1808,7 +1808,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "a0698ffd", "id": "cfe879e6",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Here, we have set the coefficient associated with `Operators` to\n", "Here, we have set the coefficient associated with `Operators` to\n",
@@ -1837,13 +1837,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 25,
"id": "96ce0f99", "id": "d4be10c2",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.484657Z", "iopub.execute_input": "2023-08-21T02:30:06.449822Z",
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@@ -1854,7 +1854,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "1956e4c2", "id": "6095cfc1",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We are now ready to generate data under the Cox proportional hazards\n", "We are now ready to generate data under the Cox proportional hazards\n",
@@ -1868,13 +1868,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 26,
"id": "63d78ff9", "id": "c98d396f",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.522637Z", "iopub.execute_input": "2023-08-21T02:30:06.461931Z",
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"shell.execute_reply": "2023-08-07T00:19:35.627960Z" "shell.execute_reply": "2023-08-21T02:30:06.624026Z"
} }
}, },
"outputs": [], "outputs": [],
@@ -1886,7 +1886,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "035e4ecf", "id": "ed2e23ea",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We now simulate our censoring variable, for which we assume\n", "We now simulate our censoring variable, for which we assume\n",
@@ -1897,13 +1897,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 27,
"id": "fe008dbf", "id": "caf627bc",
"metadata": { "metadata": {
"execution": { "execution": {
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@@ -2005,13 +2005,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 28,
"id": "c3a2bec7", "id": "e63242f9",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.653001Z", "iopub.execute_input": "2023-08-21T02:30:06.632357Z",
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"shell.execute_reply": "2023-08-07T00:19:35.656500Z" "shell.execute_reply": "2023-08-21T02:30:06.634305Z"
} }
}, },
"outputs": [ "outputs": [
@@ -2032,7 +2032,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "207937e5", "id": "5f345011",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We now plot Kaplan-Meier survival curves. First, we stratify by `Center`." "We now plot Kaplan-Meier survival curves. First, we stratify by `Center`."
@@ -2041,13 +2041,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 29,
"id": "2b27af56", "id": "338db71d",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2023-08-07T00:19:35.658421Z", "iopub.execute_input": "2023-08-21T02:30:06.636188Z",
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"shell.execute_reply": "2023-08-07T00:19:35.811449Z" "shell.execute_reply": "2023-08-21T02:30:06.791521Z"
} }
}, },
"outputs": [ "outputs": [
@@ -2084,7 +2084,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "be6d37f7", "id": "1b5a1230",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Next, we stratify by `Time`." "Next, we stratify by `Time`."
@@ -2093,13 +2093,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 30,
"id": "9625598d", "id": "c1db6e15",
"metadata": { "metadata": {
"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:36.040708Z" "shell.execute_reply": "2023-08-21T02:30:06.991803Z"
} }
}, },
"outputs": [ "outputs": [
@@ -2136,7 +2136,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "1408ebc0", "id": "deb73d38",
"metadata": {}, "metadata": {},
"source": [ "source": [
"It seems that calls at Call Center B take longer to be answered than\n", "It seems that calls at Call Center B take longer to be answered than\n",
@@ -2149,13 +2149,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 31,
"id": "75a744ef", "id": "02ea4212",
"metadata": { "metadata": {
"execution": { "execution": {
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}, },
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}, },
@@ -2247,7 +2247,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "be5055e4", "id": "db9cc6ee",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Next, we consider the effect of `Time`." "Next, we consider the effect of `Time`."
@@ -2256,13 +2256,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 32,
"id": "9badb3e3", "id": "0ac610d5",
"metadata": { "metadata": {
"execution": { "execution": {
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}, },
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@@ -2354,7 +2354,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "64b2bc33", "id": "0946d3ef",
"metadata": {}, "metadata": {},
"source": [ "source": [
"As in the case of a categorical variable with 2 levels, these\n", "As in the case of a categorical variable with 2 levels, these\n",
@@ -2366,13 +2366,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 33,
"id": "026e9ff8", "id": "107cedad",
"metadata": { "metadata": {
"execution": { "execution": {
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"shell.execute_reply": "2023-08-07T00:19:36.208076Z" "shell.execute_reply": "2023-08-21T02:30:07.160639Z"
}, },
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@@ -2462,7 +2462,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "4ed54fe0", "id": "10f2a0c1",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Next, we look at the results for `Time`." "Next, we look at the results for `Time`."
@@ -2471,13 +2471,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 34,
"id": "7cab3789", "id": "334eb331",
"metadata": { "metadata": {
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@@ -2567,7 +2567,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "2d250dc9", "id": "774963d4",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We find that differences between centers are highly significant, as\n", "We find that differences between centers are highly significant, as\n",
@@ -2579,13 +2579,13 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 35,
"id": "5cc4b898", "id": "421811c5",
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"execution": { "execution": {
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@@ -2684,7 +2684,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "bec9d61d", "id": "3c65063f",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The $p$-values for Center B and evening time\n", "The $p$-values for Center B and evening time\n",
@@ -2703,8 +2703,8 @@
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@@ -1,16 +1,3 @@
---
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jupytext:
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# Chapter 12 # Chapter 12
@@ -220,7 +207,7 @@ We can plot the PVE explained by each component, as well as the cumulative PVE.
plot the proportion of variance explained. plot the proportion of variance explained.
```{python} ```{python}
# %%capture %%capture
fig, axes = plt.subplots(1, 2, figsize=(15, 6)) fig, axes = plt.subplots(1, 2, figsize=(15, 6))
ticks = np.arange(pcaUS.n_components_)+1 ticks = np.arange(pcaUS.n_components_)+1
ax = axes[0] ax = axes[0]

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