idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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42,101 | Is this a problem for Survival analysis? | A few thoughts:
As @PeterFlom mentioned, if you just want to find out whether or not they "make it", that's a 0/1 outcome that is amenable to logistic or binomial regression, and can be addressed outside the context of survival analysis if you don't much care about the time they took to fail.
However…you said you do ... | Is this a problem for Survival analysis? | A few thoughts:
As @PeterFlom mentioned, if you just want to find out whether or not they "make it", that's a 0/1 outcome that is amenable to logistic or binomial regression, and can be addressed out | Is this a problem for Survival analysis?
A few thoughts:
As @PeterFlom mentioned, if you just want to find out whether or not they "make it", that's a 0/1 outcome that is amenable to logistic or binomial regression, and can be addressed outside the context of survival analysis if you don't much care about the time the... | Is this a problem for Survival analysis?
A few thoughts:
As @PeterFlom mentioned, if you just want to find out whether or not they "make it", that's a 0/1 outcome that is amenable to logistic or binomial regression, and can be addressed out |
42,102 | Multi-variate normal distribution distance from vector sub-space | Let us generally assume $U$ is an affine subspace. Letting $\nu$ be a unit normal to $U$ and $\delta$ the distance from $U$ to the origin (in the $\nu$ direction),
$$U = \{x\in \mathbb{R}^n \ |\ \nu \cdot x = \delta\}.$$
The vector $\nu$ can be completed to a basis $\{\nu, e_2, e_3, \ldots, e_n\}$ of $\mathbb{R}^n$ ... | Multi-variate normal distribution distance from vector sub-space | Let us generally assume $U$ is an affine subspace. Letting $\nu$ be a unit normal to $U$ and $\delta$ the distance from $U$ to the origin (in the $\nu$ direction),
$$U = \{x\in \mathbb{R}^n \ |\ \n | Multi-variate normal distribution distance from vector sub-space
Let us generally assume $U$ is an affine subspace. Letting $\nu$ be a unit normal to $U$ and $\delta$ the distance from $U$ to the origin (in the $\nu$ direction),
$$U = \{x\in \mathbb{R}^n \ |\ \nu \cdot x = \delta\}.$$
The vector $\nu$ can be complet... | Multi-variate normal distribution distance from vector sub-space
Let us generally assume $U$ is an affine subspace. Letting $\nu$ be a unit normal to $U$ and $\delta$ the distance from $U$ to the origin (in the $\nu$ direction),
$$U = \{x\in \mathbb{R}^n \ |\ \n |
42,103 | Threshold in precision/recall curve | Short answer: Torgo describes the usual method of generating such curves.
You can choose your threshold (= cut-off limit in the cited text) at any value. The cited text refers to one such choice as a working point.
That is, for a given working point, you'll observe exactly one (precision; recall) pair, i.e. one point ... | Threshold in precision/recall curve | Short answer: Torgo describes the usual method of generating such curves.
You can choose your threshold (= cut-off limit in the cited text) at any value. The cited text refers to one such choice as a | Threshold in precision/recall curve
Short answer: Torgo describes the usual method of generating such curves.
You can choose your threshold (= cut-off limit in the cited text) at any value. The cited text refers to one such choice as a working point.
That is, for a given working point, you'll observe exactly one (prec... | Threshold in precision/recall curve
Short answer: Torgo describes the usual method of generating such curves.
You can choose your threshold (= cut-off limit in the cited text) at any value. The cited text refers to one such choice as a |
42,104 | Threshold in precision/recall curve | For your aim, precision/recall curves are not that relevant. Yours is a probability estimation problem, something that logistic regression and its many variations can do quite nicely. There is nothing magic about a probability threshold of 0.5 nor is it needed. You can choose $k$ based on cost and inspect the units ... | Threshold in precision/recall curve | For your aim, precision/recall curves are not that relevant. Yours is a probability estimation problem, something that logistic regression and its many variations can do quite nicely. There is nothi | Threshold in precision/recall curve
For your aim, precision/recall curves are not that relevant. Yours is a probability estimation problem, something that logistic regression and its many variations can do quite nicely. There is nothing magic about a probability threshold of 0.5 nor is it needed. You can choose $k$ ... | Threshold in precision/recall curve
For your aim, precision/recall curves are not that relevant. Yours is a probability estimation problem, something that logistic regression and its many variations can do quite nicely. There is nothi |
42,105 | Comparison of Kaplan-Meier curves across ordered groups | Function comp in survMisc may be close to what you're after. From the docs:
Tests for trend are designed to detect ordered differences in survival curves.
That is, for at least one group: $$S_1(t) \geq S_2(t) \geq ... \geq S_K(t) \quad t \leq \tau$$ where $\tau$ is the largest $t$ where all groups have at least one su... | Comparison of Kaplan-Meier curves across ordered groups | Function comp in survMisc may be close to what you're after. From the docs:
Tests for trend are designed to detect ordered differences in survival curves.
That is, for at least one group: $$S_1(t) \g | Comparison of Kaplan-Meier curves across ordered groups
Function comp in survMisc may be close to what you're after. From the docs:
Tests for trend are designed to detect ordered differences in survival curves.
That is, for at least one group: $$S_1(t) \geq S_2(t) \geq ... \geq S_K(t) \quad t \leq \tau$$ where $\tau$ ... | Comparison of Kaplan-Meier curves across ordered groups
Function comp in survMisc may be close to what you're after. From the docs:
Tests for trend are designed to detect ordered differences in survival curves.
That is, for at least one group: $$S_1(t) \g |
42,106 | Use a clustering as a segmentation | Have you looked at stream clustering algorithms?
There has been some research on changing data sets, and related challenges such as concept drift.
Also, get rid of thinking in k-means terms; more modern clustering algorithms do not have spherical clusters that can be summarized with just a centroid.
Thinking of cluster... | Use a clustering as a segmentation | Have you looked at stream clustering algorithms?
There has been some research on changing data sets, and related challenges such as concept drift.
Also, get rid of thinking in k-means terms; more mode | Use a clustering as a segmentation
Have you looked at stream clustering algorithms?
There has been some research on changing data sets, and related challenges such as concept drift.
Also, get rid of thinking in k-means terms; more modern clustering algorithms do not have spherical clusters that can be summarized with j... | Use a clustering as a segmentation
Have you looked at stream clustering algorithms?
There has been some research on changing data sets, and related challenges such as concept drift.
Also, get rid of thinking in k-means terms; more mode |
42,107 | Zero-inflated negative binomial | You remember it wrongly: a zero-inflated negative binomial (ZINB) is a mixture of a point mass at zero and a negative binomial(NB) distribution.
$$
f_{ZINB}(Y;\pi, \mu, \theta) = \pi Z(Y) + (1-\pi)f_{NB}(Y;\mu, \theta)
$$
with Z(Y) a point mass at zero. Zero observation can come from either distribution, non-zero ones... | Zero-inflated negative binomial | You remember it wrongly: a zero-inflated negative binomial (ZINB) is a mixture of a point mass at zero and a negative binomial(NB) distribution.
$$
f_{ZINB}(Y;\pi, \mu, \theta) = \pi Z(Y) + (1-\pi)f_ | Zero-inflated negative binomial
You remember it wrongly: a zero-inflated negative binomial (ZINB) is a mixture of a point mass at zero and a negative binomial(NB) distribution.
$$
f_{ZINB}(Y;\pi, \mu, \theta) = \pi Z(Y) + (1-\pi)f_{NB}(Y;\mu, \theta)
$$
with Z(Y) a point mass at zero. Zero observation can come from ei... | Zero-inflated negative binomial
You remember it wrongly: a zero-inflated negative binomial (ZINB) is a mixture of a point mass at zero and a negative binomial(NB) distribution.
$$
f_{ZINB}(Y;\pi, \mu, \theta) = \pi Z(Y) + (1-\pi)f_ |
42,108 | What are the main differences between classical and Gibbs sampling Latent Dirichlet Allocations? | LDA is a probabilistic graphical model for a document generating process as explained in Blei et. al in JMLR 2003(For more intuition on generative process see http://videolectures.net/mlss09uk_blei_tm/). Now the main and obvious idea here is that we can generate documents if we knew the parameters, in a sense in which ... | What are the main differences between classical and Gibbs sampling Latent Dirichlet Allocations? | LDA is a probabilistic graphical model for a document generating process as explained in Blei et. al in JMLR 2003(For more intuition on generative process see http://videolectures.net/mlss09uk_blei_tm | What are the main differences between classical and Gibbs sampling Latent Dirichlet Allocations?
LDA is a probabilistic graphical model for a document generating process as explained in Blei et. al in JMLR 2003(For more intuition on generative process see http://videolectures.net/mlss09uk_blei_tm/). Now the main and ob... | What are the main differences between classical and Gibbs sampling Latent Dirichlet Allocations?
LDA is a probabilistic graphical model for a document generating process as explained in Blei et. al in JMLR 2003(For more intuition on generative process see http://videolectures.net/mlss09uk_blei_tm |
42,109 | Does zero correlation between 2 differenced series implies no cointegration between original series? | The existence or not of a linear relationship does not necessarily go hand-in-hand with co-integration. Variables co-integrated in levels won't necessarily exhibit correlation in first-differences.
Assume that the following relation holds:
$$y_t = a + b x_t + \varepsilon_t, \; \varepsilon_t=\text {i.i.d} $$
i.e. the va... | Does zero correlation between 2 differenced series implies no cointegration between original series? | The existence or not of a linear relationship does not necessarily go hand-in-hand with co-integration. Variables co-integrated in levels won't necessarily exhibit correlation in first-differences.
As | Does zero correlation between 2 differenced series implies no cointegration between original series?
The existence or not of a linear relationship does not necessarily go hand-in-hand with co-integration. Variables co-integrated in levels won't necessarily exhibit correlation in first-differences.
Assume that the follo... | Does zero correlation between 2 differenced series implies no cointegration between original series?
The existence or not of a linear relationship does not necessarily go hand-in-hand with co-integration. Variables co-integrated in levels won't necessarily exhibit correlation in first-differences.
As |
42,110 | Does zero correlation between 2 differenced series implies no cointegration between original series? | Since correlation is a measure of the degree of linear dependence, first differences should tease this out. Now, I am assuming you check cointegration across multiple lags, not just contemporaneous values, since there could be something like $y_t = a + b x_{t-1} + \varepsilon_t$ going on, which may complicate matters. ... | Does zero correlation between 2 differenced series implies no cointegration between original series? | Since correlation is a measure of the degree of linear dependence, first differences should tease this out. Now, I am assuming you check cointegration across multiple lags, not just contemporaneous va | Does zero correlation between 2 differenced series implies no cointegration between original series?
Since correlation is a measure of the degree of linear dependence, first differences should tease this out. Now, I am assuming you check cointegration across multiple lags, not just contemporaneous values, since there c... | Does zero correlation between 2 differenced series implies no cointegration between original series?
Since correlation is a measure of the degree of linear dependence, first differences should tease this out. Now, I am assuming you check cointegration across multiple lags, not just contemporaneous va |
42,111 | Does zero correlation between 2 differenced series implies no cointegration between original series? | So if my understanding of @Alecos very insightful analysis is correct, he has 2 points: even if returns are linearly related $\Delta y_t = b\cdot\Delta x_t + \Delta\varepsilon_t$ then
true correlation between $\Delta y_t$ and $\Delta x_t$ can be anything between 0 and 1 depending on noise/signal ratio $var(\Delta \var... | Does zero correlation between 2 differenced series implies no cointegration between original series? | So if my understanding of @Alecos very insightful analysis is correct, he has 2 points: even if returns are linearly related $\Delta y_t = b\cdot\Delta x_t + \Delta\varepsilon_t$ then
true correlatio | Does zero correlation between 2 differenced series implies no cointegration between original series?
So if my understanding of @Alecos very insightful analysis is correct, he has 2 points: even if returns are linearly related $\Delta y_t = b\cdot\Delta x_t + \Delta\varepsilon_t$ then
true correlation between $\Delta y... | Does zero correlation between 2 differenced series implies no cointegration between original series?
So if my understanding of @Alecos very insightful analysis is correct, he has 2 points: even if returns are linearly related $\Delta y_t = b\cdot\Delta x_t + \Delta\varepsilon_t$ then
true correlatio |
42,112 | Outlier treatment in Vector Autoregression (VAR) Model | Why would you do VAR on product demand and rainfall? VAR assumes that the impact goes both ways, and I find it unusual to assume that demand for the product causes rainfall. It's not entire impossible, of course. After all our farming impacts weather according to global warming alarmists. However, me thinks in your cas... | Outlier treatment in Vector Autoregression (VAR) Model | Why would you do VAR on product demand and rainfall? VAR assumes that the impact goes both ways, and I find it unusual to assume that demand for the product causes rainfall. It's not entire impossible | Outlier treatment in Vector Autoregression (VAR) Model
Why would you do VAR on product demand and rainfall? VAR assumes that the impact goes both ways, and I find it unusual to assume that demand for the product causes rainfall. It's not entire impossible, of course. After all our farming impacts weather according to g... | Outlier treatment in Vector Autoregression (VAR) Model
Why would you do VAR on product demand and rainfall? VAR assumes that the impact goes both ways, and I find it unusual to assume that demand for the product causes rainfall. It's not entire impossible |
42,113 | Outlier treatment in Vector Autoregression (VAR) Model | You could always try an estimator that is robust to outlying values, such as this one:
Muler & Yohai (2013): Robust estimation for vector autoregressive models
From a cursory Google search, it does seem that you might have to code this up yourself. Regardless, the literature discussion in the first two sections might ... | Outlier treatment in Vector Autoregression (VAR) Model | You could always try an estimator that is robust to outlying values, such as this one:
Muler & Yohai (2013): Robust estimation for vector autoregressive models
From a cursory Google search, it does s | Outlier treatment in Vector Autoregression (VAR) Model
You could always try an estimator that is robust to outlying values, such as this one:
Muler & Yohai (2013): Robust estimation for vector autoregressive models
From a cursory Google search, it does seem that you might have to code this up yourself. Regardless, the... | Outlier treatment in Vector Autoregression (VAR) Model
You could always try an estimator that is robust to outlying values, such as this one:
Muler & Yohai (2013): Robust estimation for vector autoregressive models
From a cursory Google search, it does s |
42,114 | Outlier treatment in Vector Autoregression (VAR) Model | You can include dummy variables for the outliers, if they are caused by special events in the demand for the product. The dummy take the value of 1 on the outliers, and zero otherwise. | Outlier treatment in Vector Autoregression (VAR) Model | You can include dummy variables for the outliers, if they are caused by special events in the demand for the product. The dummy take the value of 1 on the outliers, and zero otherwise. | Outlier treatment in Vector Autoregression (VAR) Model
You can include dummy variables for the outliers, if they are caused by special events in the demand for the product. The dummy take the value of 1 on the outliers, and zero otherwise. | Outlier treatment in Vector Autoregression (VAR) Model
You can include dummy variables for the outliers, if they are caused by special events in the demand for the product. The dummy take the value of 1 on the outliers, and zero otherwise. |
42,115 | Relative variances of higher-order vs. lower-order random terms in mixed models | Blockquote
In general, I agree with the original hypotheses that higher-order terms are often associated with smaller variances. But, this also depends on the type of data.
In plant breeding, a rule of thumb (Gauch, 1996, page 90) for multi-environment trials is that the variation in the data is:
70% location,
20% lo... | Relative variances of higher-order vs. lower-order random terms in mixed models | Blockquote
In general, I agree with the original hypotheses that higher-order terms are often associated with smaller variances. But, this also depends on the type of data.
In plant breeding, a rule | Relative variances of higher-order vs. lower-order random terms in mixed models
Blockquote
In general, I agree with the original hypotheses that higher-order terms are often associated with smaller variances. But, this also depends on the type of data.
In plant breeding, a rule of thumb (Gauch, 1996, page 90) for mul... | Relative variances of higher-order vs. lower-order random terms in mixed models
Blockquote
In general, I agree with the original hypotheses that higher-order terms are often associated with smaller variances. But, this also depends on the type of data.
In plant breeding, a rule |
42,116 | Relative variances of higher-order vs. lower-order random terms in mixed models | I have discovered that the regularity I described in my question has in fact been written about by several authors in the literature on Design of Experiments (DoE). It has been called the "hierarchical ordering principle" and also sometimes the "sparsity-of-effects principle."
In the chapter on fractional factorial des... | Relative variances of higher-order vs. lower-order random terms in mixed models | I have discovered that the regularity I described in my question has in fact been written about by several authors in the literature on Design of Experiments (DoE). It has been called the "hierarchica | Relative variances of higher-order vs. lower-order random terms in mixed models
I have discovered that the regularity I described in my question has in fact been written about by several authors in the literature on Design of Experiments (DoE). It has been called the "hierarchical ordering principle" and also sometimes... | Relative variances of higher-order vs. lower-order random terms in mixed models
I have discovered that the regularity I described in my question has in fact been written about by several authors in the literature on Design of Experiments (DoE). It has been called the "hierarchica |
42,117 | Measures of multidimensional spread or variance | It depends what you want to measure, exactly, but here are two suggestions.
Let $\Sigma$ denote the covariance matrix, and note that the trace of $\Sigma$ is equal to the sum of the eigenvalues, while the determinant is equal to their product. This means that
$$\text{Tr}(\Sigma)=\sum \lambda_i$$
will give you the "to... | Measures of multidimensional spread or variance | It depends what you want to measure, exactly, but here are two suggestions.
Let $\Sigma$ denote the covariance matrix, and note that the trace of $\Sigma$ is equal to the sum of the eigenvalues, while | Measures of multidimensional spread or variance
It depends what you want to measure, exactly, but here are two suggestions.
Let $\Sigma$ denote the covariance matrix, and note that the trace of $\Sigma$ is equal to the sum of the eigenvalues, while the determinant is equal to their product. This means that
$$\text{Tr... | Measures of multidimensional spread or variance
It depends what you want to measure, exactly, but here are two suggestions.
Let $\Sigma$ denote the covariance matrix, and note that the trace of $\Sigma$ is equal to the sum of the eigenvalues, while |
42,118 | Can you use the paired t-test in what seems like an unpaired t-test situation? | The use of matching on other characteristics than that being tested, followed by a paired t-test is fine; there's a point to that (to control for the effect of other variables thought to be relevant to the outcome).
The matching makes it paired, because - if the characteristics on which the matching was carried out* a... | Can you use the paired t-test in what seems like an unpaired t-test situation? | The use of matching on other characteristics than that being tested, followed by a paired t-test is fine; there's a point to that (to control for the effect of other variables thought to be relevant t | Can you use the paired t-test in what seems like an unpaired t-test situation?
The use of matching on other characteristics than that being tested, followed by a paired t-test is fine; there's a point to that (to control for the effect of other variables thought to be relevant to the outcome).
The matching makes it pa... | Can you use the paired t-test in what seems like an unpaired t-test situation?
The use of matching on other characteristics than that being tested, followed by a paired t-test is fine; there's a point to that (to control for the effect of other variables thought to be relevant t |
42,119 | Testing for differences between a sample and a subsample | Your last thought is the right one. Test between two sub-samples, not between a sample and a sub-sample. In this, a t-test might be appropriate, if it's other assumptions are met.
The only way I can think of to test between a sample and a subsample would be simulation, but that would wind up equivalent to testing bet... | Testing for differences between a sample and a subsample | Your last thought is the right one. Test between two sub-samples, not between a sample and a sub-sample. In this, a t-test might be appropriate, if it's other assumptions are met.
The only way I can | Testing for differences between a sample and a subsample
Your last thought is the right one. Test between two sub-samples, not between a sample and a sub-sample. In this, a t-test might be appropriate, if it's other assumptions are met.
The only way I can think of to test between a sample and a subsample would be sim... | Testing for differences between a sample and a subsample
Your last thought is the right one. Test between two sub-samples, not between a sample and a sub-sample. In this, a t-test might be appropriate, if it's other assumptions are met.
The only way I can |
42,120 | When does a distribution not have a mean or a variance? | The canonical example of a distribution with no mean (and hence no variance) is the Cauchy distribution.
True, you could compute the sample mean for a sample of data from a Cauchy distribution, but you would have to interpret such a sample mean carefully; increasing the sample size would not correspond to getting a bet... | When does a distribution not have a mean or a variance? | The canonical example of a distribution with no mean (and hence no variance) is the Cauchy distribution.
True, you could compute the sample mean for a sample of data from a Cauchy distribution, but yo | When does a distribution not have a mean or a variance?
The canonical example of a distribution with no mean (and hence no variance) is the Cauchy distribution.
True, you could compute the sample mean for a sample of data from a Cauchy distribution, but you would have to interpret such a sample mean carefully; increasi... | When does a distribution not have a mean or a variance?
The canonical example of a distribution with no mean (and hence no variance) is the Cauchy distribution.
True, you could compute the sample mean for a sample of data from a Cauchy distribution, but yo |
42,121 | auto.arima and prediction | The reason is that ARIMA is auto-projective which uses the most recent data to compute essentially a weighted average of past values. When forecasting, the 1 step ahead is used to predict the second step etc. .This then leads to long term forecasts that approach an asymptote. When fitting , the actual history is used t... | auto.arima and prediction | The reason is that ARIMA is auto-projective which uses the most recent data to compute essentially a weighted average of past values. When forecasting, the 1 step ahead is used to predict the second s | auto.arima and prediction
The reason is that ARIMA is auto-projective which uses the most recent data to compute essentially a weighted average of past values. When forecasting, the 1 step ahead is used to predict the second step etc. .This then leads to long term forecasts that approach an asymptote. When fitting , th... | auto.arima and prediction
The reason is that ARIMA is auto-projective which uses the most recent data to compute essentially a weighted average of past values. When forecasting, the 1 step ahead is used to predict the second s |
42,122 | Equivalence between Elastic Net formulations | Starting from
$$\hat{\beta} = \arg \min_\beta \|X\beta - y\|_2^2 \text{ s.t. } (1-\alpha)\|\beta\|_1 + \alpha\|\beta\|_2^2 \leq t,$$
we can write the dual Lagragian formulation of this optimization problem as
$$ \begin{array}{rcl}
L(\beta,\alpha,\lambda) & = & \|X\beta - y\|_2^2 + \lambda \left( (1-\alpha)\|\beta\|_1 ... | Equivalence between Elastic Net formulations | Starting from
$$\hat{\beta} = \arg \min_\beta \|X\beta - y\|_2^2 \text{ s.t. } (1-\alpha)\|\beta\|_1 + \alpha\|\beta\|_2^2 \leq t,$$
we can write the dual Lagragian formulation of this optimization pr | Equivalence between Elastic Net formulations
Starting from
$$\hat{\beta} = \arg \min_\beta \|X\beta - y\|_2^2 \text{ s.t. } (1-\alpha)\|\beta\|_1 + \alpha\|\beta\|_2^2 \leq t,$$
we can write the dual Lagragian formulation of this optimization problem as
$$ \begin{array}{rcl}
L(\beta,\alpha,\lambda) & = & \|X\beta - y\... | Equivalence between Elastic Net formulations
Starting from
$$\hat{\beta} = \arg \min_\beta \|X\beta - y\|_2^2 \text{ s.t. } (1-\alpha)\|\beta\|_1 + \alpha\|\beta\|_2^2 \leq t,$$
we can write the dual Lagragian formulation of this optimization pr |
42,123 | Two slopes between 0 and 1, neither different from 0, could they be different from each other? | It depends on how you do the testing. For instance, consider this model for the three variables $x$, $y_0$, and $y_1$:
$$\cases{
\mathbb{E}[y_0] = \beta_{0} + \beta_{1}x \\
y_1 = y_0 + \gamma x
}$$
where $\beta_0$, $\beta_1$, and $\gamma$ are parameters and $\gamma$ is the difference in slopes. Evidently
$$ \mat... | Two slopes between 0 and 1, neither different from 0, could they be different from each other? | It depends on how you do the testing. For instance, consider this model for the three variables $x$, $y_0$, and $y_1$:
$$\cases{
\mathbb{E}[y_0] = \beta_{0} + \beta_{1}x \\
y_1 = y_0 + \gamma x
} | Two slopes between 0 and 1, neither different from 0, could they be different from each other?
It depends on how you do the testing. For instance, consider this model for the three variables $x$, $y_0$, and $y_1$:
$$\cases{
\mathbb{E}[y_0] = \beta_{0} + \beta_{1}x \\
y_1 = y_0 + \gamma x
}$$
where $\beta_0$, $\bet... | Two slopes between 0 and 1, neither different from 0, could they be different from each other?
It depends on how you do the testing. For instance, consider this model for the three variables $x$, $y_0$, and $y_1$:
$$\cases{
\mathbb{E}[y_0] = \beta_{0} + \beta_{1}x \\
y_1 = y_0 + \gamma x
} |
42,124 | What is the sampling distribution of a sum of scores? | You suggest you're happy with assuming approximate normality. Then it's straightforward - a linear combination of multivariate normal variables is itself normal. The mean and variance follow from elementary properties of mean and variance.
If $X\sim N(\mathbf{\mu},\Sigma)$ then $a'X \sim N(a'\mathbf{\mu},a'\Sigma a)$.
... | What is the sampling distribution of a sum of scores? | You suggest you're happy with assuming approximate normality. Then it's straightforward - a linear combination of multivariate normal variables is itself normal. The mean and variance follow from elem | What is the sampling distribution of a sum of scores?
You suggest you're happy with assuming approximate normality. Then it's straightforward - a linear combination of multivariate normal variables is itself normal. The mean and variance follow from elementary properties of mean and variance.
If $X\sim N(\mathbf{\mu},\... | What is the sampling distribution of a sum of scores?
You suggest you're happy with assuming approximate normality. Then it's straightforward - a linear combination of multivariate normal variables is itself normal. The mean and variance follow from elem |
42,125 | Fisher-type unit-root test for panel data. Results interpretation in Stata | The null hypothesis of this test is that all panels contain a unit root. Given your results we reject this hypothesis. If you look at your tests P, Z, L* and Pm, you get a value for these test statistics (77.8047, -7.2246, and so on) and in the next column you see the p-value. Since they are all smaller than 0.01, you ... | Fisher-type unit-root test for panel data. Results interpretation in Stata | The null hypothesis of this test is that all panels contain a unit root. Given your results we reject this hypothesis. If you look at your tests P, Z, L* and Pm, you get a value for these test statist | Fisher-type unit-root test for panel data. Results interpretation in Stata
The null hypothesis of this test is that all panels contain a unit root. Given your results we reject this hypothesis. If you look at your tests P, Z, L* and Pm, you get a value for these test statistics (77.8047, -7.2246, and so on) and in the ... | Fisher-type unit-root test for panel data. Results interpretation in Stata
The null hypothesis of this test is that all panels contain a unit root. Given your results we reject this hypothesis. If you look at your tests P, Z, L* and Pm, you get a value for these test statist |
42,126 | Generalizing results - what is the population? | I asked myself the very same question some time ago. In particular I was reading a paper of fellow researchers. They did a study with students and in their paper they said the population was people aged 0-99.
So...
Thinking about the problem, the population to which you can generalize depends largely on the experiment.... | Generalizing results - what is the population? | I asked myself the very same question some time ago. In particular I was reading a paper of fellow researchers. They did a study with students and in their paper they said the population was people ag | Generalizing results - what is the population?
I asked myself the very same question some time ago. In particular I was reading a paper of fellow researchers. They did a study with students and in their paper they said the population was people aged 0-99.
So...
Thinking about the problem, the population to which you ca... | Generalizing results - what is the population?
I asked myself the very same question some time ago. In particular I was reading a paper of fellow researchers. They did a study with students and in their paper they said the population was people ag |
42,127 | Get a desired percentage of censored observations in a simulation of Cox PH Model | I am not sure if this answers your question but please find below some R code which follows Bender et al. (2005). They describe an approach to simulate a Cox PH regression model with given properties like the proportion of censored events (see line "dat <- data.frame(T = T, X, event = rbinom(n, 1, 0.30))", i.e. 70% of ... | Get a desired percentage of censored observations in a simulation of Cox PH Model | I am not sure if this answers your question but please find below some R code which follows Bender et al. (2005). They describe an approach to simulate a Cox PH regression model with given properties | Get a desired percentage of censored observations in a simulation of Cox PH Model
I am not sure if this answers your question but please find below some R code which follows Bender et al. (2005). They describe an approach to simulate a Cox PH regression model with given properties like the proportion of censored events... | Get a desired percentage of censored observations in a simulation of Cox PH Model
I am not sure if this answers your question but please find below some R code which follows Bender et al. (2005). They describe an approach to simulate a Cox PH regression model with given properties |
42,128 | Two-sample location test when data is heavily skewed towards zero? | You might try a Heckman two-step model. If you don't have an exclusion restriction--a variable that changes whether a customer buys or not, but does not affect how much he spends directly--the identification will be fragile and you can get wacky results. But in some marketing examples, a two-step approach can make sens... | Two-sample location test when data is heavily skewed towards zero? | You might try a Heckman two-step model. If you don't have an exclusion restriction--a variable that changes whether a customer buys or not, but does not affect how much he spends directly--the identif | Two-sample location test when data is heavily skewed towards zero?
You might try a Heckman two-step model. If you don't have an exclusion restriction--a variable that changes whether a customer buys or not, but does not affect how much he spends directly--the identification will be fragile and you can get wacky results... | Two-sample location test when data is heavily skewed towards zero?
You might try a Heckman two-step model. If you don't have an exclusion restriction--a variable that changes whether a customer buys or not, but does not affect how much he spends directly--the identif |
42,129 | Two-sample location test when data is heavily skewed towards zero? | The Wilcoxon-Mann-Whitney 2-sample test may still work for this problem if you handle the excessive ties correctly when computing the $P$-value. Or use the fact that the Wilcoxon test is a special case of the proportional odds ordinal logistic model, and that model's likelihood ratio $\chi^2$ test accounts for excessi... | Two-sample location test when data is heavily skewed towards zero? | The Wilcoxon-Mann-Whitney 2-sample test may still work for this problem if you handle the excessive ties correctly when computing the $P$-value. Or use the fact that the Wilcoxon test is a special ca | Two-sample location test when data is heavily skewed towards zero?
The Wilcoxon-Mann-Whitney 2-sample test may still work for this problem if you handle the excessive ties correctly when computing the $P$-value. Or use the fact that the Wilcoxon test is a special case of the proportional odds ordinal logistic model, a... | Two-sample location test when data is heavily skewed towards zero?
The Wilcoxon-Mann-Whitney 2-sample test may still work for this problem if you handle the excessive ties correctly when computing the $P$-value. Or use the fact that the Wilcoxon test is a special ca |
42,130 | Is bootstrapping appropriate for estimating a multivariate normal covariance matrix using a small sample size? | Is the Bootstrap a good option to estimate $\Sigma$?
No, the boostrap will help you infer about the uncertainty of your sample estimate. Specifically, it might be used to get confidence intervals on the elements of $\widehat{\Sigma}$.
And how does the bootstrap work?
The approach is to create $R$ replicate datasets... | Is bootstrapping appropriate for estimating a multivariate normal covariance matrix using a small sa | Is the Bootstrap a good option to estimate $\Sigma$?
No, the boostrap will help you infer about the uncertainty of your sample estimate. Specifically, it might be used to get confidence intervals on | Is bootstrapping appropriate for estimating a multivariate normal covariance matrix using a small sample size?
Is the Bootstrap a good option to estimate $\Sigma$?
No, the boostrap will help you infer about the uncertainty of your sample estimate. Specifically, it might be used to get confidence intervals on the elem... | Is bootstrapping appropriate for estimating a multivariate normal covariance matrix using a small sa
Is the Bootstrap a good option to estimate $\Sigma$?
No, the boostrap will help you infer about the uncertainty of your sample estimate. Specifically, it might be used to get confidence intervals on |
42,131 | Implementing kernel logistic regression using IRWLS | I think this might help, although I'm still unsure myself on the relationship between kernel logistic regression and good old generalised additive models with locally weighted regression smooths. The NIPS paper suggests they're at least strongly related, if not the same.
You can think of kernel logistic regression as f... | Implementing kernel logistic regression using IRWLS | I think this might help, although I'm still unsure myself on the relationship between kernel logistic regression and good old generalised additive models with locally weighted regression smooths. The | Implementing kernel logistic regression using IRWLS
I think this might help, although I'm still unsure myself on the relationship between kernel logistic regression and good old generalised additive models with locally weighted regression smooths. The NIPS paper suggests they're at least strongly related, if not the sa... | Implementing kernel logistic regression using IRWLS
I think this might help, although I'm still unsure myself on the relationship between kernel logistic regression and good old generalised additive models with locally weighted regression smooths. The |
42,132 | Implementing kernel logistic regression using IRWLS | I did some calculations by hand -- assuming that the bias term $b$ is the last value of the $\alpha$ vector, we get the following update equation:
$$\alpha^{(k + 1)} \leftarrow (\phi^{T} W \phi + \lambda R)^{-1} ((\phi^{T} W \phi + \lambda R) \alpha^{(k)} - (\phi^{T}(\mu - y) + \lambda \phi \alpha^{(k)}_{0})),$$
with $... | Implementing kernel logistic regression using IRWLS | I did some calculations by hand -- assuming that the bias term $b$ is the last value of the $\alpha$ vector, we get the following update equation:
$$\alpha^{(k + 1)} \leftarrow (\phi^{T} W \phi + \lam | Implementing kernel logistic regression using IRWLS
I did some calculations by hand -- assuming that the bias term $b$ is the last value of the $\alpha$ vector, we get the following update equation:
$$\alpha^{(k + 1)} \leftarrow (\phi^{T} W \phi + \lambda R)^{-1} ((\phi^{T} W \phi + \lambda R) \alpha^{(k)} - (\phi^{T}(... | Implementing kernel logistic regression using IRWLS
I did some calculations by hand -- assuming that the bias term $b$ is the last value of the $\alpha$ vector, we get the following update equation:
$$\alpha^{(k + 1)} \leftarrow (\phi^{T} W \phi + \lam |
42,133 | Implementing kernel logistic regression using IRWLS | There is a slightly better way of implementing IRWLS for kernel logistic regression that doesn't use the (weighted) normal equations and tends to have slightly better numerical properties. It basically fits the KLR model as a sequence of weighted Least-Squares Support Vector Machines (but with the logistic link functi... | Implementing kernel logistic regression using IRWLS | There is a slightly better way of implementing IRWLS for kernel logistic regression that doesn't use the (weighted) normal equations and tends to have slightly better numerical properties. It basical | Implementing kernel logistic regression using IRWLS
There is a slightly better way of implementing IRWLS for kernel logistic regression that doesn't use the (weighted) normal equations and tends to have slightly better numerical properties. It basically fits the KLR model as a sequence of weighted Least-Squares Suppor... | Implementing kernel logistic regression using IRWLS
There is a slightly better way of implementing IRWLS for kernel logistic regression that doesn't use the (weighted) normal equations and tends to have slightly better numerical properties. It basical |
42,134 | Why does regularization of coefficient magnitude improve the generalization of linear regression? [duplicate] | You can also think of regularization by norm penalization as conceptually similar to random effects (see for example, the beginning of section 2 of Koenker 2004 in particular the first proposition).
Depending on your background, you may be more receptive/familiar to arguments supporting the use of random effects than ... | Why does regularization of coefficient magnitude improve the generalization of linear regression? [d | You can also think of regularization by norm penalization as conceptually similar to random effects (see for example, the beginning of section 2 of Koenker 2004 in particular the first proposition).
| Why does regularization of coefficient magnitude improve the generalization of linear regression? [duplicate]
You can also think of regularization by norm penalization as conceptually similar to random effects (see for example, the beginning of section 2 of Koenker 2004 in particular the first proposition).
Depending ... | Why does regularization of coefficient magnitude improve the generalization of linear regression? [d
You can also think of regularization by norm penalization as conceptually similar to random effects (see for example, the beginning of section 2 of Koenker 2004 in particular the first proposition).
|
42,135 | Why does regularization of coefficient magnitude improve the generalization of linear regression? [duplicate] | Generally, people use error on a holdout set as a proxy for generalization error. I think a fair response is to say if using an l1 or l2 penalty reduced
error on the holdout test, then whatever you were doing was probably overfitting.
Now, as to why it works: for regression, you can consider a l2 penalty as a normal p... | Why does regularization of coefficient magnitude improve the generalization of linear regression? [d | Generally, people use error on a holdout set as a proxy for generalization error. I think a fair response is to say if using an l1 or l2 penalty reduced
error on the holdout test, then whatever you w | Why does regularization of coefficient magnitude improve the generalization of linear regression? [duplicate]
Generally, people use error on a holdout set as a proxy for generalization error. I think a fair response is to say if using an l1 or l2 penalty reduced
error on the holdout test, then whatever you were doing ... | Why does regularization of coefficient magnitude improve the generalization of linear regression? [d
Generally, people use error on a holdout set as a proxy for generalization error. I think a fair response is to say if using an l1 or l2 penalty reduced
error on the holdout test, then whatever you w |
42,136 | Why does regularization of coefficient magnitude improve the generalization of linear regression? [duplicate] | The norm is a smooth way to get some coefficients to zero. If more coefficients are zero, then the model is more parsimonious, hopefully allowing better generalization. | Why does regularization of coefficient magnitude improve the generalization of linear regression? [d | The norm is a smooth way to get some coefficients to zero. If more coefficients are zero, then the model is more parsimonious, hopefully allowing better generalization. | Why does regularization of coefficient magnitude improve the generalization of linear regression? [duplicate]
The norm is a smooth way to get some coefficients to zero. If more coefficients are zero, then the model is more parsimonious, hopefully allowing better generalization. | Why does regularization of coefficient magnitude improve the generalization of linear regression? [d
The norm is a smooth way to get some coefficients to zero. If more coefficients are zero, then the model is more parsimonious, hopefully allowing better generalization. |
42,137 | How to find the level curves of a multivariate normal? | OK, scratching my own itch. We want the $1-\alpha$ level curve of $y \sim N_2(\mu,\Sigma)$
The $1-\alpha$ level curve of the $\mathcal N_2(0,I)$ distribution function is a circle of radius $\sigma = \sqrt{ \chi^2_{2,1-\alpha} }$ centred at the origin. This holds because if we consider some $x$ drawn from that distribut... | How to find the level curves of a multivariate normal? | OK, scratching my own itch. We want the $1-\alpha$ level curve of $y \sim N_2(\mu,\Sigma)$
The $1-\alpha$ level curve of the $\mathcal N_2(0,I)$ distribution function is a circle of radius $\sigma = \ | How to find the level curves of a multivariate normal?
OK, scratching my own itch. We want the $1-\alpha$ level curve of $y \sim N_2(\mu,\Sigma)$
The $1-\alpha$ level curve of the $\mathcal N_2(0,I)$ distribution function is a circle of radius $\sigma = \sqrt{ \chi^2_{2,1-\alpha} }$ centred at the origin. This holds be... | How to find the level curves of a multivariate normal?
OK, scratching my own itch. We want the $1-\alpha$ level curve of $y \sim N_2(\mu,\Sigma)$
The $1-\alpha$ level curve of the $\mathcal N_2(0,I)$ distribution function is a circle of radius $\sigma = \ |
42,138 | Fitting mixture distributions and computing goodness-of-fit? | Mixture modelling in my experience can be tricky. Getting a good fit from a mixture model can be much more difficult than realising that a mixture may be a good approach.
I have to say that I don't find the fit convincing here. Your graphics alone do not give much support to your summary of "decent".
Kernel density e... | Fitting mixture distributions and computing goodness-of-fit? | Mixture modelling in my experience can be tricky. Getting a good fit from a mixture model can be much more difficult than realising that a mixture may be a good approach.
I have to say that I don't f | Fitting mixture distributions and computing goodness-of-fit?
Mixture modelling in my experience can be tricky. Getting a good fit from a mixture model can be much more difficult than realising that a mixture may be a good approach.
I have to say that I don't find the fit convincing here. Your graphics alone do not giv... | Fitting mixture distributions and computing goodness-of-fit?
Mixture modelling in my experience can be tricky. Getting a good fit from a mixture model can be much more difficult than realising that a mixture may be a good approach.
I have to say that I don't f |
42,139 | Fitting mixture distributions and computing goodness-of-fit? | Assessing the goodness of a fit of a model requires specifying the cost of the model choice and the reward of the model fit. Usually, these decisions cannot be made from the data alone, and require more from the context of how the model is to be used. To see why cost the model choice is important, you could put a delta... | Fitting mixture distributions and computing goodness-of-fit? | Assessing the goodness of a fit of a model requires specifying the cost of the model choice and the reward of the model fit. Usually, these decisions cannot be made from the data alone, and require mo | Fitting mixture distributions and computing goodness-of-fit?
Assessing the goodness of a fit of a model requires specifying the cost of the model choice and the reward of the model fit. Usually, these decisions cannot be made from the data alone, and require more from the context of how the model is to be used. To see ... | Fitting mixture distributions and computing goodness-of-fit?
Assessing the goodness of a fit of a model requires specifying the cost of the model choice and the reward of the model fit. Usually, these decisions cannot be made from the data alone, and require mo |
42,140 | Fitting mixture distributions and computing goodness-of-fit? | I'm not certain how the package you are using creates the fit, but if you have a closed form for the mixture (5 parameters, the mixing percentage $p$ and the meanlog and sdlog of the two lognormals) what you can do is generate $\lfloor{(\textrm{a large number like } 10^7)\cdot p}\rfloor$ samples from lognormal A and th... | Fitting mixture distributions and computing goodness-of-fit? | I'm not certain how the package you are using creates the fit, but if you have a closed form for the mixture (5 parameters, the mixing percentage $p$ and the meanlog and sdlog of the two lognormals) w | Fitting mixture distributions and computing goodness-of-fit?
I'm not certain how the package you are using creates the fit, but if you have a closed form for the mixture (5 parameters, the mixing percentage $p$ and the meanlog and sdlog of the two lognormals) what you can do is generate $\lfloor{(\textrm{a large number... | Fitting mixture distributions and computing goodness-of-fit?
I'm not certain how the package you are using creates the fit, but if you have a closed form for the mixture (5 parameters, the mixing percentage $p$ and the meanlog and sdlog of the two lognormals) w |
42,141 | How to handle samples that belong to multiple classes in supervised learning? | This looks like a classic Multi Label Classification. There are dozens of possible approaches, in particular sklearn python library implements such methods.
In the most simple scenario, you can train classifiers on the "labels" basis. There won't be any problems with feature matrix, as you can simply divide your $m$ la... | How to handle samples that belong to multiple classes in supervised learning? | This looks like a classic Multi Label Classification. There are dozens of possible approaches, in particular sklearn python library implements such methods.
In the most simple scenario, you can train | How to handle samples that belong to multiple classes in supervised learning?
This looks like a classic Multi Label Classification. There are dozens of possible approaches, in particular sklearn python library implements such methods.
In the most simple scenario, you can train classifiers on the "labels" basis. There w... | How to handle samples that belong to multiple classes in supervised learning?
This looks like a classic Multi Label Classification. There are dozens of possible approaches, in particular sklearn python library implements such methods.
In the most simple scenario, you can train |
42,142 | How to handle samples that belong to multiple classes in supervised learning? | would a hierarchy of classifiers be a solution for you?
on a first level you find the classifiers corresponding to clusters, and then have classifiers for the subclusters | How to handle samples that belong to multiple classes in supervised learning? | would a hierarchy of classifiers be a solution for you?
on a first level you find the classifiers corresponding to clusters, and then have classifiers for the subclusters | How to handle samples that belong to multiple classes in supervised learning?
would a hierarchy of classifiers be a solution for you?
on a first level you find the classifiers corresponding to clusters, and then have classifiers for the subclusters | How to handle samples that belong to multiple classes in supervised learning?
would a hierarchy of classifiers be a solution for you?
on a first level you find the classifiers corresponding to clusters, and then have classifiers for the subclusters |
42,143 | A distribution similar to the Poisson one? | (got too long for a comment; hopefully it will become a more complete answer if you respond to some of these issues)
Most important:
when you say 'number of days' are you looking at intervals of time?
Intervals of time, such as a count of days between some start event and some end event won't be Poisson.
Poisson is for... | A distribution similar to the Poisson one? | (got too long for a comment; hopefully it will become a more complete answer if you respond to some of these issues)
Most important:
when you say 'number of days' are you looking at intervals of time? | A distribution similar to the Poisson one?
(got too long for a comment; hopefully it will become a more complete answer if you respond to some of these issues)
Most important:
when you say 'number of days' are you looking at intervals of time?
Intervals of time, such as a count of days between some start event and some... | A distribution similar to the Poisson one?
(got too long for a comment; hopefully it will become a more complete answer if you respond to some of these issues)
Most important:
when you say 'number of days' are you looking at intervals of time? |
42,144 | Random Forests and data transformations | Random Forests are Decision Tree - based. So all they do is comparisons of your individual variables with some thresholds. Squaring your parameters simply will shift these thresholds, introducing no change to the actual output. So, in theory, RF's are not sensitive to rescaling. | Random Forests and data transformations | Random Forests are Decision Tree - based. So all they do is comparisons of your individual variables with some thresholds. Squaring your parameters simply will shift these thresholds, introducing no c | Random Forests and data transformations
Random Forests are Decision Tree - based. So all they do is comparisons of your individual variables with some thresholds. Squaring your parameters simply will shift these thresholds, introducing no change to the actual output. So, in theory, RF's are not sensitive to rescaling. | Random Forests and data transformations
Random Forests are Decision Tree - based. So all they do is comparisons of your individual variables with some thresholds. Squaring your parameters simply will shift these thresholds, introducing no c |
42,145 | For bootstrapping, why does a higher subsample size lead to lower variance? | First off, you should not resample a bootstrapped sample of size bigger than that of your original sample. So regardless of your population size, if your sample size is 200 you should not resample those values more than 200 times. In fact you should resample them precisely 200 times. So it's your friend who's got the c... | For bootstrapping, why does a higher subsample size lead to lower variance? | First off, you should not resample a bootstrapped sample of size bigger than that of your original sample. So regardless of your population size, if your sample size is 200 you should not resample tho | For bootstrapping, why does a higher subsample size lead to lower variance?
First off, you should not resample a bootstrapped sample of size bigger than that of your original sample. So regardless of your population size, if your sample size is 200 you should not resample those values more than 200 times. In fact you s... | For bootstrapping, why does a higher subsample size lead to lower variance?
First off, you should not resample a bootstrapped sample of size bigger than that of your original sample. So regardless of your population size, if your sample size is 200 you should not resample tho |
42,146 | Overdispersion parameter | You are right, the NB2 model should not have a worse fit than the poisson model, because the latter is a special case of the former, where $\alpha=0$. There is definitely a problem. It sounds to me like your numerical maximum likelihood maximizer had problems with your specification and therefore didn't really hit the ... | Overdispersion parameter | You are right, the NB2 model should not have a worse fit than the poisson model, because the latter is a special case of the former, where $\alpha=0$. There is definitely a problem. It sounds to me li | Overdispersion parameter
You are right, the NB2 model should not have a worse fit than the poisson model, because the latter is a special case of the former, where $\alpha=0$. There is definitely a problem. It sounds to me like your numerical maximum likelihood maximizer had problems with your specification and therefo... | Overdispersion parameter
You are right, the NB2 model should not have a worse fit than the poisson model, because the latter is a special case of the former, where $\alpha=0$. There is definitely a problem. It sounds to me li |
42,147 | Overlapping sets (3) - significance test | Let there be $n$ elements total with $a$, $b$, and $c$ in the subsets. Consider $N$ already partitioned into $A$ and $N-A$.
The number of ways in which a $b$-element set can be drawn which has $l$ elements in common with $A$ is equal to the number of $l$-element subsets of $A$ times the number of $b-l$ element subsets... | Overlapping sets (3) - significance test | Let there be $n$ elements total with $a$, $b$, and $c$ in the subsets. Consider $N$ already partitioned into $A$ and $N-A$.
The number of ways in which a $b$-element set can be drawn which has $l$ el | Overlapping sets (3) - significance test
Let there be $n$ elements total with $a$, $b$, and $c$ in the subsets. Consider $N$ already partitioned into $A$ and $N-A$.
The number of ways in which a $b$-element set can be drawn which has $l$ elements in common with $A$ is equal to the number of $l$-element subsets of $A$ ... | Overlapping sets (3) - significance test
Let there be $n$ elements total with $a$, $b$, and $c$ in the subsets. Consider $N$ already partitioned into $A$ and $N-A$.
The number of ways in which a $b$-element set can be drawn which has $l$ el |
42,148 | Choosing the number of breakpoints for segmented regression (in R) | You're missing a really important caveat to the question: even if I pick 3 breakpoints, the location of those breakpoints will affect the model performance. So not only the number, but the location is important. I will assume for this problem that you have a prespecified list of possible breakpoints and the question is... | Choosing the number of breakpoints for segmented regression (in R) | You're missing a really important caveat to the question: even if I pick 3 breakpoints, the location of those breakpoints will affect the model performance. So not only the number, but the location is | Choosing the number of breakpoints for segmented regression (in R)
You're missing a really important caveat to the question: even if I pick 3 breakpoints, the location of those breakpoints will affect the model performance. So not only the number, but the location is important. I will assume for this problem that you h... | Choosing the number of breakpoints for segmented regression (in R)
You're missing a really important caveat to the question: even if I pick 3 breakpoints, the location of those breakpoints will affect the model performance. So not only the number, but the location is |
42,149 | Choosing the number of breakpoints for segmented regression (in R) | I recommend the efp(), Fstats(), and breakpoints() functions in the R strucchange package. The bfast package is nice too and provides wrappers for strucchange and segmented. You can read more about these functions here and in the works cited therein. Please note that these functions are slow on very large datasets. Whi... | Choosing the number of breakpoints for segmented regression (in R) | I recommend the efp(), Fstats(), and breakpoints() functions in the R strucchange package. The bfast package is nice too and provides wrappers for strucchange and segmented. You can read more about th | Choosing the number of breakpoints for segmented regression (in R)
I recommend the efp(), Fstats(), and breakpoints() functions in the R strucchange package. The bfast package is nice too and provides wrappers for strucchange and segmented. You can read more about these functions here and in the works cited therein. Pl... | Choosing the number of breakpoints for segmented regression (in R)
I recommend the efp(), Fstats(), and breakpoints() functions in the R strucchange package. The bfast package is nice too and provides wrappers for strucchange and segmented. You can read more about th |
42,150 | Metropolis Sampling and invalid states | From your question:
It is possible that the transition function can suggest a move to an invalid part of the state space, and the jump should be obviously rejected.
The jump should not be rejected
This is a great article on the problem with immediately rejecting an errant jump. As a consequence, your problem vanishe... | Metropolis Sampling and invalid states | From your question:
It is possible that the transition function can suggest a move to an invalid part of the state space, and the jump should be obviously rejected.
The jump should not be rejected
| Metropolis Sampling and invalid states
From your question:
It is possible that the transition function can suggest a move to an invalid part of the state space, and the jump should be obviously rejected.
The jump should not be rejected
This is a great article on the problem with immediately rejecting an errant jump.... | Metropolis Sampling and invalid states
From your question:
It is possible that the transition function can suggest a move to an invalid part of the state space, and the jump should be obviously rejected.
The jump should not be rejected
|
42,151 | Metropolis Sampling and invalid states | [Reproducing a blog entry I wrote a while ago:]
It is indeed a popular belief that something needs to be done to counteract restricted supports. However, there is no mathematical reason for doing so! If we look at the Metropolis-Hastings acceptance probability
$$\rho(x_t,y_{t+1})= \text{min} (1, \pi(y_{t+1})q(x_t|y_{t+... | Metropolis Sampling and invalid states | [Reproducing a blog entry I wrote a while ago:]
It is indeed a popular belief that something needs to be done to counteract restricted supports. However, there is no mathematical reason for doing so! | Metropolis Sampling and invalid states
[Reproducing a blog entry I wrote a while ago:]
It is indeed a popular belief that something needs to be done to counteract restricted supports. However, there is no mathematical reason for doing so! If we look at the Metropolis-Hastings acceptance probability
$$\rho(x_t,y_{t+1})=... | Metropolis Sampling and invalid states
[Reproducing a blog entry I wrote a while ago:]
It is indeed a popular belief that something needs to be done to counteract restricted supports. However, there is no mathematical reason for doing so! |
42,152 | On combining SVMs | You might find the following article helpful. Various techniques are outlined to get probability estimates for the outputs of SVM in Milgram.
In combining the probability estimates a weighted or unweighted sum of probabilities, naive Bayes or various other techniques can be used. See Chapter 5 for a comprehensive study... | On combining SVMs | You might find the following article helpful. Various techniques are outlined to get probability estimates for the outputs of SVM in Milgram.
In combining the probability estimates a weighted or unwei | On combining SVMs
You might find the following article helpful. Various techniques are outlined to get probability estimates for the outputs of SVM in Milgram.
In combining the probability estimates a weighted or unweighted sum of probabilities, naive Bayes or various other techniques can be used. See Chapter 5 for a c... | On combining SVMs
You might find the following article helpful. Various techniques are outlined to get probability estimates for the outputs of SVM in Milgram.
In combining the probability estimates a weighted or unwei |
42,153 | On combining SVMs | Try
A] Majority Voting
B] Weighted Voting (Considering the distance to hyperplane as the weight or confidence of each hyperplane in their classification)
C] AdaBoost [1] Algorithm.
[1] http://en.wikipedia.org/wiki/AdaBoost | On combining SVMs | Try
A] Majority Voting
B] Weighted Voting (Considering the distance to hyperplane as the weight or confidence of each hyperplane in their classification)
C] AdaBoost [1] Algorithm.
[1] http://en.wi | On combining SVMs
Try
A] Majority Voting
B] Weighted Voting (Considering the distance to hyperplane as the weight or confidence of each hyperplane in their classification)
C] AdaBoost [1] Algorithm.
[1] http://en.wikipedia.org/wiki/AdaBoost | On combining SVMs
Try
A] Majority Voting
B] Weighted Voting (Considering the distance to hyperplane as the weight or confidence of each hyperplane in their classification)
C] AdaBoost [1] Algorithm.
[1] http://en.wi |
42,154 | Variables that do not converge in winbugs | 5000? That's nothing :-) In papers it is usual to use like 200,000. Variance parameters are always the worse for estimation. You have a huge autocorrelation in the chains - you should increase your thin parameter at least 16 times. What is your thin parameter now?
Anyway, I think the variables would converge quite well... | Variables that do not converge in winbugs | 5000? That's nothing :-) In papers it is usual to use like 200,000. Variance parameters are always the worse for estimation. You have a huge autocorrelation in the chains - you should increase your th | Variables that do not converge in winbugs
5000? That's nothing :-) In papers it is usual to use like 200,000. Variance parameters are always the worse for estimation. You have a huge autocorrelation in the chains - you should increase your thin parameter at least 16 times. What is your thin parameter now?
Anyway, I thi... | Variables that do not converge in winbugs
5000? That's nothing :-) In papers it is usual to use like 200,000. Variance parameters are always the worse for estimation. You have a huge autocorrelation in the chains - you should increase your th |
42,155 | Variables that do not converge in winbugs | The second and third picture don't seem to converge. You can look at Rhat value. If it is 1.0, then it is converged. If it is far away from that, say 1.5, probably it doesn't converge. | Variables that do not converge in winbugs | The second and third picture don't seem to converge. You can look at Rhat value. If it is 1.0, then it is converged. If it is far away from that, say 1.5, probably it doesn't converge. | Variables that do not converge in winbugs
The second and third picture don't seem to converge. You can look at Rhat value. If it is 1.0, then it is converged. If it is far away from that, say 1.5, probably it doesn't converge. | Variables that do not converge in winbugs
The second and third picture don't seem to converge. You can look at Rhat value. If it is 1.0, then it is converged. If it is far away from that, say 1.5, probably it doesn't converge. |
42,156 | Biased estimates when intercept is included in a linear regression | There is a mistake in my question: OLS doesn't give an unbiased estimator for the autoregressive coefficients of an $\mathrm{AR}(p)$ process! In fact the OLS estimator is biased toward zero as explained in this article:
A. Maeshiro (2000), An Illustration of the Bias of OLS for $Y_t = \lambda Y_{t-1} + U_t$,
J. Econ... | Biased estimates when intercept is included in a linear regression | There is a mistake in my question: OLS doesn't give an unbiased estimator for the autoregressive coefficients of an $\mathrm{AR}(p)$ process! In fact the OLS estimator is biased toward zero as explain | Biased estimates when intercept is included in a linear regression
There is a mistake in my question: OLS doesn't give an unbiased estimator for the autoregressive coefficients of an $\mathrm{AR}(p)$ process! In fact the OLS estimator is biased toward zero as explained in this article:
A. Maeshiro (2000), An Illustrat... | Biased estimates when intercept is included in a linear regression
There is a mistake in my question: OLS doesn't give an unbiased estimator for the autoregressive coefficients of an $\mathrm{AR}(p)$ process! In fact the OLS estimator is biased toward zero as explain |
42,157 | Random walk or not? | Your data looks random-walk-y to me. Intuition is notoriously unreliable when it comes to random walks; witness technical chart analysis for stock markets.
Let's put your data into context. Estimate the standard deviation of the differences, assuming a mean increment of zero:
foo <- c(59, 334, 333, 402, 450, 461, 452, ... | Random walk or not? | Your data looks random-walk-y to me. Intuition is notoriously unreliable when it comes to random walks; witness technical chart analysis for stock markets.
Let's put your data into context. Estimate t | Random walk or not?
Your data looks random-walk-y to me. Intuition is notoriously unreliable when it comes to random walks; witness technical chart analysis for stock markets.
Let's put your data into context. Estimate the standard deviation of the differences, assuming a mean increment of zero:
foo <- c(59, 334, 333, ... | Random walk or not?
Your data looks random-walk-y to me. Intuition is notoriously unreliable when it comes to random walks; witness technical chart analysis for stock markets.
Let's put your data into context. Estimate t |
42,158 | Understanding the exponential distribution | Suppose you have some mildly radioactive substance so that you expect to wait $20$ seconds between decays. That is a rate of $1/20$ per second. The average number of decays in one second is $1/20$.
One way to get an average count of $1/20$ would be if the count were $1$ with probability $1/20$ and $0$ with probability... | Understanding the exponential distribution | Suppose you have some mildly radioactive substance so that you expect to wait $20$ seconds between decays. That is a rate of $1/20$ per second. The average number of decays in one second is $1/20$.
O | Understanding the exponential distribution
Suppose you have some mildly radioactive substance so that you expect to wait $20$ seconds between decays. That is a rate of $1/20$ per second. The average number of decays in one second is $1/20$.
One way to get an average count of $1/20$ would be if the count were $1$ with ... | Understanding the exponential distribution
Suppose you have some mildly radioactive substance so that you expect to wait $20$ seconds between decays. That is a rate of $1/20$ per second. The average number of decays in one second is $1/20$.
O |
42,159 | Understanding the exponential distribution | There is a well known approximation for $e^x$ when $x$ is very close to zero:
$$ e^x \approx 1 + x $$
If we take $ x = -0.05 $, we get $ e^{-0.05} \approx 1 - 0.05 = 0.95 $.
So we can say:
$$ 1 - e^{-0.05} \approx 1 - 0.95 = 0.05 $$
You make a mistake when you think that a good approximation is the same as the exact va... | Understanding the exponential distribution | There is a well known approximation for $e^x$ when $x$ is very close to zero:
$$ e^x \approx 1 + x $$
If we take $ x = -0.05 $, we get $ e^{-0.05} \approx 1 - 0.05 = 0.95 $.
So we can say:
$$ 1 - e^{- | Understanding the exponential distribution
There is a well known approximation for $e^x$ when $x$ is very close to zero:
$$ e^x \approx 1 + x $$
If we take $ x = -0.05 $, we get $ e^{-0.05} \approx 1 - 0.05 = 0.95 $.
So we can say:
$$ 1 - e^{-0.05} \approx 1 - 0.95 = 0.05 $$
You make a mistake when you think that a goo... | Understanding the exponential distribution
There is a well known approximation for $e^x$ when $x$ is very close to zero:
$$ e^x \approx 1 + x $$
If we take $ x = -0.05 $, we get $ e^{-0.05} \approx 1 - 0.05 = 0.95 $.
So we can say:
$$ 1 - e^{- |
42,160 | Predicting time series with NNs: should the data set be shuffled? | Say you have a single time series you want to learn on. Then you can use the first half for the development of your model and the second half for testing.
You now cut your two halves into windows individually and can shuffle those of the training set.
From what I know, the shuffling is actually not done because of gene... | Predicting time series with NNs: should the data set be shuffled? | Say you have a single time series you want to learn on. Then you can use the first half for the development of your model and the second half for testing.
You now cut your two halves into windows indi | Predicting time series with NNs: should the data set be shuffled?
Say you have a single time series you want to learn on. Then you can use the first half for the development of your model and the second half for testing.
You now cut your two halves into windows individually and can shuffle those of the training set.
Fr... | Predicting time series with NNs: should the data set be shuffled?
Say you have a single time series you want to learn on. Then you can use the first half for the development of your model and the second half for testing.
You now cut your two halves into windows indi |
42,161 | Predicting time series with NNs: should the data set be shuffled? | If you are using a not-recurrent NN like a traditional MLP you don't NEED to shuffle dataset especially if you're using a batch learning algorithm. Anyway, in my experience it's a good idea to shuffle datasets for a faster training and more clear results. I can suggest you "Efficient BackProp" by LeCun et al., where yo... | Predicting time series with NNs: should the data set be shuffled? | If you are using a not-recurrent NN like a traditional MLP you don't NEED to shuffle dataset especially if you're using a batch learning algorithm. Anyway, in my experience it's a good idea to shuffle | Predicting time series with NNs: should the data set be shuffled?
If you are using a not-recurrent NN like a traditional MLP you don't NEED to shuffle dataset especially if you're using a batch learning algorithm. Anyway, in my experience it's a good idea to shuffle datasets for a faster training and more clear results... | Predicting time series with NNs: should the data set be shuffled?
If you are using a not-recurrent NN like a traditional MLP you don't NEED to shuffle dataset especially if you're using a batch learning algorithm. Anyway, in my experience it's a good idea to shuffle |
42,162 | How can I debug and check the consistency of a Kalman filter? | Generate your own measurements, in a known scenario, that fulfills the assumptions
Test it in the known scenario. If you do it in 1 or 2 dimensions, you can even do it by hand.
Look at the properties. The autocorrelation of the innovation must be white if I am not mistaken (in a linear scenario, not 100% sure, but I kn... | How can I debug and check the consistency of a Kalman filter? | Generate your own measurements, in a known scenario, that fulfills the assumptions
Test it in the known scenario. If you do it in 1 or 2 dimensions, you can even do it by hand.
Look at the properties. | How can I debug and check the consistency of a Kalman filter?
Generate your own measurements, in a known scenario, that fulfills the assumptions
Test it in the known scenario. If you do it in 1 or 2 dimensions, you can even do it by hand.
Look at the properties. The autocorrelation of the innovation must be white if I ... | How can I debug and check the consistency of a Kalman filter?
Generate your own measurements, in a known scenario, that fulfills the assumptions
Test it in the known scenario. If you do it in 1 or 2 dimensions, you can even do it by hand.
Look at the properties. |
42,163 | How can I debug and check the consistency of a Kalman filter? | As a follow up to @Marcel's answer, here is a more detailed explanation of how to debug and check the consistency of a Kalman filter. This explanation is an expansion of the one from section 2.2.3, page 18, of the lecture notes titled Estimation II written by Ian Reid at Oxford in 2001, which is the same set of lecture... | How can I debug and check the consistency of a Kalman filter? | As a follow up to @Marcel's answer, here is a more detailed explanation of how to debug and check the consistency of a Kalman filter. This explanation is an expansion of the one from section 2.2.3, pa | How can I debug and check the consistency of a Kalman filter?
As a follow up to @Marcel's answer, here is a more detailed explanation of how to debug and check the consistency of a Kalman filter. This explanation is an expansion of the one from section 2.2.3, page 18, of the lecture notes titled Estimation II written b... | How can I debug and check the consistency of a Kalman filter?
As a follow up to @Marcel's answer, here is a more detailed explanation of how to debug and check the consistency of a Kalman filter. This explanation is an expansion of the one from section 2.2.3, pa |
42,164 | How to assess overdispersion in Poisson GLMM, lmer( ) | My general preference, when comparing a more complex model (here, NB) to a less complex one (here Poisson) is not to rely on any statistical test, but to run both and see if the predicted values are substantially different. (And what 'substantially' means is dependent on the field you are working in). If they are, then... | How to assess overdispersion in Poisson GLMM, lmer( ) | My general preference, when comparing a more complex model (here, NB) to a less complex one (here Poisson) is not to rely on any statistical test, but to run both and see if the predicted values are s | How to assess overdispersion in Poisson GLMM, lmer( )
My general preference, when comparing a more complex model (here, NB) to a less complex one (here Poisson) is not to rely on any statistical test, but to run both and see if the predicted values are substantially different. (And what 'substantially' means is depende... | How to assess overdispersion in Poisson GLMM, lmer( )
My general preference, when comparing a more complex model (here, NB) to a less complex one (here Poisson) is not to rely on any statistical test, but to run both and see if the predicted values are s |
42,165 | Simulation of forecasted values in ARIMA (0,1,1) | There are a couple of problems with your code:
The default prediction intervals are 80% and 95%, but you simulate 90% intervals.
The forecasts should use the last residual in the first forecast.
Your ma coefficient is applied to $e_t$ rather than $e_{t-1}$.
Here is some corrected code:
library(forecast)
f4 <- Arima(W... | Simulation of forecasted values in ARIMA (0,1,1) | There are a couple of problems with your code:
The default prediction intervals are 80% and 95%, but you simulate 90% intervals.
The forecasts should use the last residual in the first forecast.
Your | Simulation of forecasted values in ARIMA (0,1,1)
There are a couple of problems with your code:
The default prediction intervals are 80% and 95%, but you simulate 90% intervals.
The forecasts should use the last residual in the first forecast.
Your ma coefficient is applied to $e_t$ rather than $e_{t-1}$.
Here is som... | Simulation of forecasted values in ARIMA (0,1,1)
There are a couple of problems with your code:
The default prediction intervals are 80% and 95%, but you simulate 90% intervals.
The forecasts should use the last residual in the first forecast.
Your |
42,166 | Why do we take the absolute value in a hypothesis test? | The absolute value is taken merely to give a concise way to define extremes in both directions. So |T|>=|t$_0$| simply means T>=|t$_0$| or T<=-|t$_0$|. | Why do we take the absolute value in a hypothesis test? | The absolute value is taken merely to give a concise way to define extremes in both directions. So |T|>=|t$_0$| simply means T>=|t$_0$| or T<=-|t$_0$|. | Why do we take the absolute value in a hypothesis test?
The absolute value is taken merely to give a concise way to define extremes in both directions. So |T|>=|t$_0$| simply means T>=|t$_0$| or T<=-|t$_0$|. | Why do we take the absolute value in a hypothesis test?
The absolute value is taken merely to give a concise way to define extremes in both directions. So |T|>=|t$_0$| simply means T>=|t$_0$| or T<=-|t$_0$|. |
42,167 | Ranking randomly distributed variables based on decreasingness | Instead of approximating a really small probability, then rescaling it by something huge (like $44! \approx 2.7 \times 10^54$) you would do better to compute integer-valued statistics such as the count of values $i$ so that $O_i \gt O_{i+1}$. This will be a more efficient use of any MonteCarlo runs you perform.
One ide... | Ranking randomly distributed variables based on decreasingness | Instead of approximating a really small probability, then rescaling it by something huge (like $44! \approx 2.7 \times 10^54$) you would do better to compute integer-valued statistics such as the coun | Ranking randomly distributed variables based on decreasingness
Instead of approximating a really small probability, then rescaling it by something huge (like $44! \approx 2.7 \times 10^54$) you would do better to compute integer-valued statistics such as the count of values $i$ so that $O_i \gt O_{i+1}$. This will be a... | Ranking randomly distributed variables based on decreasingness
Instead of approximating a really small probability, then rescaling it by something huge (like $44! \approx 2.7 \times 10^54$) you would do better to compute integer-valued statistics such as the coun |
42,168 | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | I've seen the two terms are sometimes used synonymously, and they're both an attempt to control for a particular type of confounding, namely that some patients have a set of covariate risk factors the predispose them to the outcome.
Generally speaking, I've found "case-mix" most often used in studies where the unit of ... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | I've seen the two terms are sometimes used synonymously, and they're both an attempt to control for a particular type of confounding, namely that some patients have a set of covariate risk factors the | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
I've seen the two terms are sometimes used synonymously, and they're both an attempt to control for a particular type of confounding, namely that some patients have a set of covariate risk factors the predispose them to th... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
I've seen the two terms are sometimes used synonymously, and they're both an attempt to control for a particular type of confounding, namely that some patients have a set of covariate risk factors the |
42,169 | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | I do not think there is any real difference between "case-mix adjustment" and "risk adjustment" in this context. I would say the terms are used interchangeably.
They refer to adjusting for confounding due to patient ("case") mix, or the patients' risks of the outcome being examined.
Funders often want to compare hospit... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | I do not think there is any real difference between "case-mix adjustment" and "risk adjustment" in this context. I would say the terms are used interchangeably.
They refer to adjusting for confounding | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
I do not think there is any real difference between "case-mix adjustment" and "risk adjustment" in this context. I would say the terms are used interchangeably.
They refer to adjusting for confounding due to patient ("case... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
I do not think there is any real difference between "case-mix adjustment" and "risk adjustment" in this context. I would say the terms are used interchangeably.
They refer to adjusting for confounding |
42,170 | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | http://www.rti.org/files/HHS-HCC_Risk_Adjustment_Model.pdf
AdamO has asked how the terms case mix and risk adjustment are defined and used in research results posted in the medical literature, " their exact usage and motivation from a modeling perspective." My experience with case mix modeling is exclusively within hea... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | http://www.rti.org/files/HHS-HCC_Risk_Adjustment_Model.pdf
AdamO has asked how the terms case mix and risk adjustment are defined and used in research results posted in the medical literature, " their | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
http://www.rti.org/files/HHS-HCC_Risk_Adjustment_Model.pdf
AdamO has asked how the terms case mix and risk adjustment are defined and used in research results posted in the medical literature, " their exact usage and motiv... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
http://www.rti.org/files/HHS-HCC_Risk_Adjustment_Model.pdf
AdamO has asked how the terms case mix and risk adjustment are defined and used in research results posted in the medical literature, " their |
42,171 | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | In the specific context of hospital financial reporting, such as
* average cost per admission, or
* average revenue per admission
"case-mix adjusted" is a term of art that means "DRG-adjusted".
In other words, sum[of whatever] / sum[of DRG weights] for the group of patients being reported.
And 99% of the time, unless... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | In the specific context of hospital financial reporting, such as
* average cost per admission, or
* average revenue per admission
"case-mix adjusted" is a term of art that means "DRG-adjusted".
In o | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
In the specific context of hospital financial reporting, such as
* average cost per admission, or
* average revenue per admission
"case-mix adjusted" is a term of art that means "DRG-adjusted".
In other words, sum[of wha... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
In the specific context of hospital financial reporting, such as
* average cost per admission, or
* average revenue per admission
"case-mix adjusted" is a term of art that means "DRG-adjusted".
In o |
42,172 | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | http://www.nber.org/MedicareAdvtgSpecRateStats/risk-adjustment/2011/Evaluation2011/Evaluation_Risk_Adj_Model_2011.pdf
This link explains risk adjustment scores and risk adjustment transfers in the CMS-HCC (hierarchical condition categories) methodology. HHS, the parent to CMS, has its own methodology, which is applied ... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective? | http://www.nber.org/MedicareAdvtgSpecRateStats/risk-adjustment/2011/Evaluation2011/Evaluation_Risk_Adj_Model_2011.pdf
This link explains risk adjustment scores and risk adjustment transfers in the CMS | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
http://www.nber.org/MedicareAdvtgSpecRateStats/risk-adjustment/2011/Evaluation2011/Evaluation_Risk_Adj_Model_2011.pdf
This link explains risk adjustment scores and risk adjustment transfers in the CMS-HCC (hierarchical con... | Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?
http://www.nber.org/MedicareAdvtgSpecRateStats/risk-adjustment/2011/Evaluation2011/Evaluation_Risk_Adj_Model_2011.pdf
This link explains risk adjustment scores and risk adjustment transfers in the CMS |
42,173 | Whether to apply structural equation modelling separately to each of a set of heterogeneous correlation matrices in a meta-analysis context? | Cheung and Cheung (2010) gave a presentation discussing two appraoches to meta-analytic Structural Equation Modelling (SEM). They label the two approaches:
Correlation matrices Meta-Analysis: An average correlation matrix is extracted from the primary studies and SEM is applied to this single correlation matrix.
Model... | Whether to apply structural equation modelling separately to each of a set of heterogeneous correlat | Cheung and Cheung (2010) gave a presentation discussing two appraoches to meta-analytic Structural Equation Modelling (SEM). They label the two approaches:
Correlation matrices Meta-Analysis: An aver | Whether to apply structural equation modelling separately to each of a set of heterogeneous correlation matrices in a meta-analysis context?
Cheung and Cheung (2010) gave a presentation discussing two appraoches to meta-analytic Structural Equation Modelling (SEM). They label the two approaches:
Correlation matrices M... | Whether to apply structural equation modelling separately to each of a set of heterogeneous correlat
Cheung and Cheung (2010) gave a presentation discussing two appraoches to meta-analytic Structural Equation Modelling (SEM). They label the two approaches:
Correlation matrices Meta-Analysis: An aver |
42,174 | Optimizing regression coefficients to predict the largest outcomes | It seems that you either need to relax linearity assumptions for covariates (e.g., use restricted cubic splines, i.e., natural splines) or to define a new optimality criterion (other than maximum likelihood) and then to optimize the fit to that. I'm glad you clarified that you don't want to discard observations. Th... | Optimizing regression coefficients to predict the largest outcomes | It seems that you either need to relax linearity assumptions for covariates (e.g., use restricted cubic splines, i.e., natural splines) or to define a new optimality criterion (other than maximum like | Optimizing regression coefficients to predict the largest outcomes
It seems that you either need to relax linearity assumptions for covariates (e.g., use restricted cubic splines, i.e., natural splines) or to define a new optimality criterion (other than maximum likelihood) and then to optimize the fit to that. I'm ... | Optimizing regression coefficients to predict the largest outcomes
It seems that you either need to relax linearity assumptions for covariates (e.g., use restricted cubic splines, i.e., natural splines) or to define a new optimality criterion (other than maximum like |
42,175 | Optimizing regression coefficients to predict the largest outcomes | What you have written suggests that logistic regression, with attention to diagnostics and residuals, will be your best bet. It can help you take into account nonlinearity in the relationship between predictor and outcome You'll want to test multiplicative effects (interactions), as the thresholds you talk about may ... | Optimizing regression coefficients to predict the largest outcomes | What you have written suggests that logistic regression, with attention to diagnostics and residuals, will be your best bet. It can help you take into account nonlinearity in the relationship between | Optimizing regression coefficients to predict the largest outcomes
What you have written suggests that logistic regression, with attention to diagnostics and residuals, will be your best bet. It can help you take into account nonlinearity in the relationship between predictor and outcome You'll want to test multiplic... | Optimizing regression coefficients to predict the largest outcomes
What you have written suggests that logistic regression, with attention to diagnostics and residuals, will be your best bet. It can help you take into account nonlinearity in the relationship between |
42,176 | Model selection in a paper: what to say about the dropped variables? | It depends on your aim. Since A "really should" have an effect on Y but appears not to, I would definitely include the full model. As Procrastinator suggests, model selection might be useful to propose a parsimonious new model - if that's your objective. But if your objective is to estimate the relationships between... | Model selection in a paper: what to say about the dropped variables? | It depends on your aim. Since A "really should" have an effect on Y but appears not to, I would definitely include the full model. As Procrastinator suggests, model selection might be useful to prop | Model selection in a paper: what to say about the dropped variables?
It depends on your aim. Since A "really should" have an effect on Y but appears not to, I would definitely include the full model. As Procrastinator suggests, model selection might be useful to propose a parsimonious new model - if that's your objec... | Model selection in a paper: what to say about the dropped variables?
It depends on your aim. Since A "really should" have an effect on Y but appears not to, I would definitely include the full model. As Procrastinator suggests, model selection might be useful to prop |
42,177 | What does $\beta$ tell us in linear regression analysis? | I think your understanding of linear regression is fine. One thing that may interest you to know is that if both of your variables (e.g., A1 and B) are standardized, the $\beta$ from a simple regression will equal the r-score (i.e., the correlation coefficient, which when squared gives you the model's $R^2$), but this... | What does $\beta$ tell us in linear regression analysis? | I think your understanding of linear regression is fine. One thing that may interest you to know is that if both of your variables (e.g., A1 and B) are standardized, the $\beta$ from a simple regress | What does $\beta$ tell us in linear regression analysis?
I think your understanding of linear regression is fine. One thing that may interest you to know is that if both of your variables (e.g., A1 and B) are standardized, the $\beta$ from a simple regression will equal the r-score (i.e., the correlation coefficient, ... | What does $\beta$ tell us in linear regression analysis?
I think your understanding of linear regression is fine. One thing that may interest you to know is that if both of your variables (e.g., A1 and B) are standardized, the $\beta$ from a simple regress |
42,178 | Expected value for discrete (nominal) variable? | There are several different measures of "location" or "central tendency". Expected value is the most popular one, but there are others -- median, mode, geometric mean, etc.
While all measures of central tendency are, in some ways, similar, it is important to remember that they actually measure different things. Here ar... | Expected value for discrete (nominal) variable? | There are several different measures of "location" or "central tendency". Expected value is the most popular one, but there are others -- median, mode, geometric mean, etc.
While all measures of centr | Expected value for discrete (nominal) variable?
There are several different measures of "location" or "central tendency". Expected value is the most popular one, but there are others -- median, mode, geometric mean, etc.
While all measures of central tendency are, in some ways, similar, it is important to remember that... | Expected value for discrete (nominal) variable?
There are several different measures of "location" or "central tendency". Expected value is the most popular one, but there are others -- median, mode, geometric mean, etc.
While all measures of centr |
42,179 | Pre-processing time series data for data mining / predictive modeling input | I agree with what user765195 said: there's no magic bullet here that will work for all your problems. You've got to come up with potentially-useful features based on your domain knowledge. I've never worked in a bank, so take these suggestions with a grain of salt, but how about
"Volatility" When I've changed banks, I... | Pre-processing time series data for data mining / predictive modeling input | I agree with what user765195 said: there's no magic bullet here that will work for all your problems. You've got to come up with potentially-useful features based on your domain knowledge. I've never | Pre-processing time series data for data mining / predictive modeling input
I agree with what user765195 said: there's no magic bullet here that will work for all your problems. You've got to come up with potentially-useful features based on your domain knowledge. I've never worked in a bank, so take these suggestions ... | Pre-processing time series data for data mining / predictive modeling input
I agree with what user765195 said: there's no magic bullet here that will work for all your problems. You've got to come up with potentially-useful features based on your domain knowledge. I've never |
42,180 | Pre-processing time series data for data mining / predictive modeling input | It all depends on how to plan to make predictions after you've finished pre-processing there data. For example, if you expect consecutive observations of a time series $y_t$ to be linearly related to previous measurements:
$$ y_{t} = \beta_0 + \beta_1 y_{t-1} + \cdots $$
then you'd better choose a linear transformat... | Pre-processing time series data for data mining / predictive modeling input | It all depends on how to plan to make predictions after you've finished pre-processing there data. For example, if you expect consecutive observations of a time series $y_t$ to be linearly related to | Pre-processing time series data for data mining / predictive modeling input
It all depends on how to plan to make predictions after you've finished pre-processing there data. For example, if you expect consecutive observations of a time series $y_t$ to be linearly related to previous measurements:
$$ y_{t} = \beta_0 +... | Pre-processing time series data for data mining / predictive modeling input
It all depends on how to plan to make predictions after you've finished pre-processing there data. For example, if you expect consecutive observations of a time series $y_t$ to be linearly related to |
42,181 | How to verify that the model is real? | The stability of your results depends on the number of data points you use to estimate your model's parameters, not so much on whether your model captures reality.
Take, for example, a simple univariate Gaussian distribution with a fixed variance and varying mean (a statistical model). The variance of the estimated mea... | How to verify that the model is real? | The stability of your results depends on the number of data points you use to estimate your model's parameters, not so much on whether your model captures reality.
Take, for example, a simple univaria | How to verify that the model is real?
The stability of your results depends on the number of data points you use to estimate your model's parameters, not so much on whether your model captures reality.
Take, for example, a simple univariate Gaussian distribution with a fixed variance and varying mean (a statistical mod... | How to verify that the model is real?
The stability of your results depends on the number of data points you use to estimate your model's parameters, not so much on whether your model captures reality.
Take, for example, a simple univaria |
42,182 | Cohen's d for 2x2 interaction | You can translate pretty much any of the effect sizes to Cohen's d, so start with omega and work your way over. Equations are available for them all online... the wikipedia page for effect size is good for this.
In your particular case though you can get a numerator for cohen's d as (a1 - b1) - (a2 - b2). That's the ... | Cohen's d for 2x2 interaction | You can translate pretty much any of the effect sizes to Cohen's d, so start with omega and work your way over. Equations are available for them all online... the wikipedia page for effect size is go | Cohen's d for 2x2 interaction
You can translate pretty much any of the effect sizes to Cohen's d, so start with omega and work your way over. Equations are available for them all online... the wikipedia page for effect size is good for this.
In your particular case though you can get a numerator for cohen's d as (a1 -... | Cohen's d for 2x2 interaction
You can translate pretty much any of the effect sizes to Cohen's d, so start with omega and work your way over. Equations are available for them all online... the wikipedia page for effect size is go |
42,183 | How to compute importance sampling? | In case your difficulty is with the simulation per se, here is my R code to compare simulations from $f$ (plain), $g$ equal to
$$
\frac{1}{2} \frac{1}{\pi} \frac{1}{1+x^2} + \frac{1}{2}\frac{1}{4}\frac{\mathbb{I}_{[0,2]}(x)}{\sqrt{|1-x|}}
$$
(mixture of Cauchy and power distributions) and $m$ equal to
$$
\frac{1}{2}\fr... | How to compute importance sampling? | In case your difficulty is with the simulation per se, here is my R code to compare simulations from $f$ (plain), $g$ equal to
$$
\frac{1}{2} \frac{1}{\pi} \frac{1}{1+x^2} + \frac{1}{2}\frac{1}{4}\fra | How to compute importance sampling?
In case your difficulty is with the simulation per se, here is my R code to compare simulations from $f$ (plain), $g$ equal to
$$
\frac{1}{2} \frac{1}{\pi} \frac{1}{1+x^2} + \frac{1}{2}\frac{1}{4}\frac{\mathbb{I}_{[0,2]}(x)}{\sqrt{|1-x|}}
$$
(mixture of Cauchy and power distributions... | How to compute importance sampling?
In case your difficulty is with the simulation per se, here is my R code to compare simulations from $f$ (plain), $g$ equal to
$$
\frac{1}{2} \frac{1}{\pi} \frac{1}{1+x^2} + \frac{1}{2}\frac{1}{4}\fra |
42,184 | Robust logistic regression vs logistic regression | First I would ask what do you mean by robust logistic regression (it could mean a couple of different things ...). Nevertheless, assuming that you are using "robust" in the sense that you want to control for heteroscedasticity in binary outcome models what I know is the following:
1) You should read in detail the 15th ... | Robust logistic regression vs logistic regression | First I would ask what do you mean by robust logistic regression (it could mean a couple of different things ...). Nevertheless, assuming that you are using "robust" in the sense that you want to cont | Robust logistic regression vs logistic regression
First I would ask what do you mean by robust logistic regression (it could mean a couple of different things ...). Nevertheless, assuming that you are using "robust" in the sense that you want to control for heteroscedasticity in binary outcome models what I know is the... | Robust logistic regression vs logistic regression
First I would ask what do you mean by robust logistic regression (it could mean a couple of different things ...). Nevertheless, assuming that you are using "robust" in the sense that you want to cont |
42,185 | Truncated normal distribution with WinBUGS | In openbugs (so I would assume the same in winbugs, but could be wrong) you can do a truncated distribution using the T function, something like:
y[i] ~ dnorm(x[i],tau)T(0,10) | Truncated normal distribution with WinBUGS | In openbugs (so I would assume the same in winbugs, but could be wrong) you can do a truncated distribution using the T function, something like:
y[i] ~ dnorm(x[i],tau)T(0,10) | Truncated normal distribution with WinBUGS
In openbugs (so I would assume the same in winbugs, but could be wrong) you can do a truncated distribution using the T function, something like:
y[i] ~ dnorm(x[i],tau)T(0,10) | Truncated normal distribution with WinBUGS
In openbugs (so I would assume the same in winbugs, but could be wrong) you can do a truncated distribution using the T function, something like:
y[i] ~ dnorm(x[i],tau)T(0,10) |
42,186 | Truncated normal distribution with WinBUGS | I found the solution by consulting the WinBUGS User Manual (Page 47). I had to modify the initial parameters but I end up using the I() function with dnorm.
undefined real result indicates numerical overflow. Possible reasons
include:
initial values generated from a 'vague' prior distribution may be numerically ext... | Truncated normal distribution with WinBUGS | I found the solution by consulting the WinBUGS User Manual (Page 47). I had to modify the initial parameters but I end up using the I() function with dnorm.
undefined real result indicates numerical | Truncated normal distribution with WinBUGS
I found the solution by consulting the WinBUGS User Manual (Page 47). I had to modify the initial parameters but I end up using the I() function with dnorm.
undefined real result indicates numerical overflow. Possible reasons
include:
initial values generated from a 'vague... | Truncated normal distribution with WinBUGS
I found the solution by consulting the WinBUGS User Manual (Page 47). I had to modify the initial parameters but I end up using the I() function with dnorm.
undefined real result indicates numerical |
42,187 | Truncated normal distribution with WinBUGS | According to http://www.unc.edu/courses/2010fall/ecol/563/001/docs/solutions/assign10.htm, two gotchas that may apply are that
the upper truncation point may need to be adjusted (e.g., from 10 to 10.0001)
initial values that seed the chains need to be specified explicitly, because WinBUGS may pick ones that lie outsi... | Truncated normal distribution with WinBUGS | According to http://www.unc.edu/courses/2010fall/ecol/563/001/docs/solutions/assign10.htm, two gotchas that may apply are that
the upper truncation point may need to be adjusted (e.g., from 10 to 10 | Truncated normal distribution with WinBUGS
According to http://www.unc.edu/courses/2010fall/ecol/563/001/docs/solutions/assign10.htm, two gotchas that may apply are that
the upper truncation point may need to be adjusted (e.g., from 10 to 10.0001)
initial values that seed the chains need to be specified explicitly, b... | Truncated normal distribution with WinBUGS
According to http://www.unc.edu/courses/2010fall/ecol/563/001/docs/solutions/assign10.htm, two gotchas that may apply are that
the upper truncation point may need to be adjusted (e.g., from 10 to 10 |
42,188 | Automated parameter selection for a GARCH model, in a similar manner to the forecast package | My experience with equities suggested that if you are confined to garch(p,q), then garch(1,1) is what you will want. Using a components model (Lee and Engle) is better -- it is sort of like a garch(2,2) but not quite the same.
When modeling multivariate garch (where there was a lot of choice in parameterization), it s... | Automated parameter selection for a GARCH model, in a similar manner to the forecast package | My experience with equities suggested that if you are confined to garch(p,q), then garch(1,1) is what you will want. Using a components model (Lee and Engle) is better -- it is sort of like a garch(2 | Automated parameter selection for a GARCH model, in a similar manner to the forecast package
My experience with equities suggested that if you are confined to garch(p,q), then garch(1,1) is what you will want. Using a components model (Lee and Engle) is better -- it is sort of like a garch(2,2) but not quite the same.... | Automated parameter selection for a GARCH model, in a similar manner to the forecast package
My experience with equities suggested that if you are confined to garch(p,q), then garch(1,1) is what you will want. Using a components model (Lee and Engle) is better -- it is sort of like a garch(2 |
42,189 | On error probability bounds in Bayesian hypothesis testing | It is known that the best strategy, that is the one that minimizes the probability of uncorrect guess, is to choose the hypothesis $H \in \{A,B\}$ that maximizes the $p(H|x)$, where $x$ is the observation.
As a personal peeve, I dislike this particular statement because in my
mind it hides what is really going on, an... | On error probability bounds in Bayesian hypothesis testing | It is known that the best strategy, that is the one that minimizes the probability of uncorrect guess, is to choose the hypothesis $H \in \{A,B\}$ that maximizes the $p(H|x)$, where $x$ is the observa | On error probability bounds in Bayesian hypothesis testing
It is known that the best strategy, that is the one that minimizes the probability of uncorrect guess, is to choose the hypothesis $H \in \{A,B\}$ that maximizes the $p(H|x)$, where $x$ is the observation.
As a personal peeve, I dislike this particular statem... | On error probability bounds in Bayesian hypothesis testing
It is known that the best strategy, that is the one that minimizes the probability of uncorrect guess, is to choose the hypothesis $H \in \{A,B\}$ that maximizes the $p(H|x)$, where $x$ is the observa |
42,190 | On error probability bounds in Bayesian hypothesis testing | When $p(A) = p(B) = 1/2$, equation $(3)$ is basically computing the total variation distance (also known as the variation distance, for short). The variation distance is an ${\cal L}_1$ distance between two probability measures, which is why the ${\cal L}_1$ norm appears in your equation $(3)$.
It is well-known that t... | On error probability bounds in Bayesian hypothesis testing | When $p(A) = p(B) = 1/2$, equation $(3)$ is basically computing the total variation distance (also known as the variation distance, for short). The variation distance is an ${\cal L}_1$ distance betw | On error probability bounds in Bayesian hypothesis testing
When $p(A) = p(B) = 1/2$, equation $(3)$ is basically computing the total variation distance (also known as the variation distance, for short). The variation distance is an ${\cal L}_1$ distance between two probability measures, which is why the ${\cal L}_1$ n... | On error probability bounds in Bayesian hypothesis testing
When $p(A) = p(B) = 1/2$, equation $(3)$ is basically computing the total variation distance (also known as the variation distance, for short). The variation distance is an ${\cal L}_1$ distance betw |
42,191 | Are both of these generalized additive models? | With the lrm command using rcs() you are constructing a cubic spline which is then used in the logistic regression. However, no penalty is applied to the coefficients of the cubic spline, unlike in gam, where a penalty for "roughness" is applied and the appropriate magnitude of the penalty is estimated using generaliz... | Are both of these generalized additive models? | With the lrm command using rcs() you are constructing a cubic spline which is then used in the logistic regression. However, no penalty is applied to the coefficients of the cubic spline, unlike in g | Are both of these generalized additive models?
With the lrm command using rcs() you are constructing a cubic spline which is then used in the logistic regression. However, no penalty is applied to the coefficients of the cubic spline, unlike in gam, where a penalty for "roughness" is applied and the appropriate magnit... | Are both of these generalized additive models?
With the lrm command using rcs() you are constructing a cubic spline which is then used in the logistic regression. However, no penalty is applied to the coefficients of the cubic spline, unlike in g |
42,192 | Estimating joint distributions using copula package in R | Check out A Short, Comprehensive, Practical Guide to Copulas by Atillio Meucci. The paper provides further references in case you'd like to learn more. Steps #3 and #5 are addressed by this paper.
Step #4 is specific to this particular function which I am not too familiar. Since you have so many questions you'd probab... | Estimating joint distributions using copula package in R | Check out A Short, Comprehensive, Practical Guide to Copulas by Atillio Meucci. The paper provides further references in case you'd like to learn more. Steps #3 and #5 are addressed by this paper.
St | Estimating joint distributions using copula package in R
Check out A Short, Comprehensive, Practical Guide to Copulas by Atillio Meucci. The paper provides further references in case you'd like to learn more. Steps #3 and #5 are addressed by this paper.
Step #4 is specific to this particular function which I am not to... | Estimating joint distributions using copula package in R
Check out A Short, Comprehensive, Practical Guide to Copulas by Atillio Meucci. The paper provides further references in case you'd like to learn more. Steps #3 and #5 are addressed by this paper.
St |
42,193 | Transformation to fit gamma distribution for glm | A gamma distribution definitely doesn't make sense for you data. Gamma takes support on the entire real line and is always skewed right. The example data you provide in your code would be horrible data to try to fit a gamma to.
It would definitely be nicer to know more about the data generation process. But one thin... | Transformation to fit gamma distribution for glm | A gamma distribution definitely doesn't make sense for you data. Gamma takes support on the entire real line and is always skewed right. The example data you provide in your code would be horrible d | Transformation to fit gamma distribution for glm
A gamma distribution definitely doesn't make sense for you data. Gamma takes support on the entire real line and is always skewed right. The example data you provide in your code would be horrible data to try to fit a gamma to.
It would definitely be nicer to know more... | Transformation to fit gamma distribution for glm
A gamma distribution definitely doesn't make sense for you data. Gamma takes support on the entire real line and is always skewed right. The example data you provide in your code would be horrible d |
42,194 | Transformation to fit gamma distribution for glm | I was going to suggest using binomial regression with a probit link on the original hit/false alarm counts, but before writing out the details I googled for this idea. Apparently, someone else thought of it already (there goes a publication :)
Here is the reference (in case the link is restricted), there is also an R p... | Transformation to fit gamma distribution for glm | I was going to suggest using binomial regression with a probit link on the original hit/false alarm counts, but before writing out the details I googled for this idea. Apparently, someone else thought | Transformation to fit gamma distribution for glm
I was going to suggest using binomial regression with a probit link on the original hit/false alarm counts, but before writing out the details I googled for this idea. Apparently, someone else thought of it already (there goes a publication :)
Here is the reference (in c... | Transformation to fit gamma distribution for glm
I was going to suggest using binomial regression with a probit link on the original hit/false alarm counts, but before writing out the details I googled for this idea. Apparently, someone else thought |
42,195 | How many data points to fit an ex-Gaussian distribution? | If I recall correctly, as sample size decreases, mu will manifest positive bias while tau will manifest negative bias. Sigma will manifest either positive or negative bias depending on true values of the other parameters. This paper compares two methods of fitting ex-Gaussians, and from the figures I'd probably avoid g... | How many data points to fit an ex-Gaussian distribution? | If I recall correctly, as sample size decreases, mu will manifest positive bias while tau will manifest negative bias. Sigma will manifest either positive or negative bias depending on true values of | How many data points to fit an ex-Gaussian distribution?
If I recall correctly, as sample size decreases, mu will manifest positive bias while tau will manifest negative bias. Sigma will manifest either positive or negative bias depending on true values of the other parameters. This paper compares two methods of fittin... | How many data points to fit an ex-Gaussian distribution?
If I recall correctly, as sample size decreases, mu will manifest positive bias while tau will manifest negative bias. Sigma will manifest either positive or negative bias depending on true values of |
42,196 | How many data points to fit an ex-Gaussian distribution? | Since the parameters are estimated using maximum-likelihood method you have that
$$\sqrt{n}(\hat\theta -\theta)\to N(0,I_{\theta}^{-1}),$$
where $I_{\theta}$ is the Fisher information matrix, and $\theta$ is the vector of parameters in question. Hence
$$Var(\hat\theta-\theta)\approx 1/nI_{\theta}^{-1}$$
If you have cal... | How many data points to fit an ex-Gaussian distribution? | Since the parameters are estimated using maximum-likelihood method you have that
$$\sqrt{n}(\hat\theta -\theta)\to N(0,I_{\theta}^{-1}),$$
where $I_{\theta}$ is the Fisher information matrix, and $\th | How many data points to fit an ex-Gaussian distribution?
Since the parameters are estimated using maximum-likelihood method you have that
$$\sqrt{n}(\hat\theta -\theta)\to N(0,I_{\theta}^{-1}),$$
where $I_{\theta}$ is the Fisher information matrix, and $\theta$ is the vector of parameters in question. Hence
$$Var(\hat\... | How many data points to fit an ex-Gaussian distribution?
Since the parameters are estimated using maximum-likelihood method you have that
$$\sqrt{n}(\hat\theta -\theta)\to N(0,I_{\theta}^{-1}),$$
where $I_{\theta}$ is the Fisher information matrix, and $\th |
42,197 | Bicubic/bilinear interpolation in R | Check out the akima package's interp.
These functions implement bivariate interpolation onto a grid for irregularly spaced input data. Bilinear or bicubic spline interpolation is applied using different versions of algorithms from Akima.
Usage
interp(x, y, z, xo=seq(min(x), max(x), length = 40),
yo=seq(min(y)... | Bicubic/bilinear interpolation in R | Check out the akima package's interp.
These functions implement bivariate interpolation onto a grid for irregularly spaced input data. Bilinear or bicubic spline interpolation is applied using differ | Bicubic/bilinear interpolation in R
Check out the akima package's interp.
These functions implement bivariate interpolation onto a grid for irregularly spaced input data. Bilinear or bicubic spline interpolation is applied using different versions of algorithms from Akima.
Usage
interp(x, y, z, xo=seq(min(x), max(x), ... | Bicubic/bilinear interpolation in R
Check out the akima package's interp.
These functions implement bivariate interpolation onto a grid for irregularly spaced input data. Bilinear or bicubic spline interpolation is applied using differ |
42,198 | Bicubic/bilinear interpolation in R | You could use image.smooth in the fields package. | Bicubic/bilinear interpolation in R | You could use image.smooth in the fields package. | Bicubic/bilinear interpolation in R
You could use image.smooth in the fields package. | Bicubic/bilinear interpolation in R
You could use image.smooth in the fields package. |
42,199 | Bicubic/bilinear interpolation in R | To export the data you have interpolated , you could use the follow way:
model <- interp(x, y, z, xo=seq(min(x), max(x), length = 40), yo=seq(min(y),
max(y), length = 40), linear = TRUE, extrap=FALSE, duplicate = "error",
dupfun = NULL, ncp = NULL)
interpData <- model$z
Load the follow libraries
library(rJava... | Bicubic/bilinear interpolation in R | To export the data you have interpolated , you could use the follow way:
model <- interp(x, y, z, xo=seq(min(x), max(x), length = 40), yo=seq(min(y),
max(y), length = 40), linear = TRUE, extrap=FA | Bicubic/bilinear interpolation in R
To export the data you have interpolated , you could use the follow way:
model <- interp(x, y, z, xo=seq(min(x), max(x), length = 40), yo=seq(min(y),
max(y), length = 40), linear = TRUE, extrap=FALSE, duplicate = "error",
dupfun = NULL, ncp = NULL)
interpData <- model$z
Loa... | Bicubic/bilinear interpolation in R
To export the data you have interpolated , you could use the follow way:
model <- interp(x, y, z, xo=seq(min(x), max(x), length = 40), yo=seq(min(y),
max(y), length = 40), linear = TRUE, extrap=FA |
42,200 | Why the statistical model of sampling with replacement is incomplete? | I'm not sure what your model for sampling with replacement is, but I suspect (and hope) it will include the following simple example, a standard model of a Bernoulli experiment (such as a coin flip). The idea behind it is to make a random variable depend on the order in which outcomes are observed in the sample. We c... | Why the statistical model of sampling with replacement is incomplete? | I'm not sure what your model for sampling with replacement is, but I suspect (and hope) it will include the following simple example, a standard model of a Bernoulli experiment (such as a coin flip). | Why the statistical model of sampling with replacement is incomplete?
I'm not sure what your model for sampling with replacement is, but I suspect (and hope) it will include the following simple example, a standard model of a Bernoulli experiment (such as a coin flip). The idea behind it is to make a random variable d... | Why the statistical model of sampling with replacement is incomplete?
I'm not sure what your model for sampling with replacement is, but I suspect (and hope) it will include the following simple example, a standard model of a Bernoulli experiment (such as a coin flip). |
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