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# How to fix intercept value of glm

By : Max sheh
Date : November 14 2020, 04:48 PM
help you fix your problem You are using glm(...) incorrectly, which IMO is a much bigger problem than offsets.
The main underlying assumption in least squares regression is that the error in the response is normally distributed with constant variance. If the error in Y is normally distributed, then log(Y) most certainly is not. So, while you can "run the numbers" on a fit of log(Y)~X, the results will not be meaningful. The theory of generalized linear modelling was developed to deal with this problem. So using glm, rather than fit log(Y) ~X you should fit Y~X with family=poisson. The former fits
code :
``````fit.incorrect <- glm(log(Y)~X,data=data2)
fit.correct   <- glm(Y~X,data=data2,family=poisson)
coef(summary(fit.incorrect))
#               Estimate Std. Error  t value     Pr(>|t|)
# (Intercept)  6.0968294 0.44450740 13.71592 0.0001636875
# X           -0.2984013 0.07340798 -4.06497 0.0152860490
coef(summary(fit.correct))
#               Estimate Std. Error   z value     Pr(>|z|)
# (Intercept)  5.8170223 0.04577816 127.06982 0.000000e+00
# X           -0.2063744 0.01122240 -18.38951 1.594013e-75
``````
``````plot(Y~X,data2)
curve(exp(coef(fit.incorrect)[1]+x*coef(fit.incorrect)[2]),
curve(predict(fit.correct,  type="response",newdata=data.frame(X=x)),
``````
``````data2\$b0 <- log(300)   # add the offset as a separate column
# b0 not fixed
fit <- glm(Y~X,data2,family=poisson)
plot(Y~X,data2)
curve(predict(fit,type="response",newdata=data.frame(X=x)),
# b0 fixed so that Y0 = 300
fit.fixed <-glm(Y~X-1+offset(b0), data2,family=poisson)
curve(predict(fit.fixed,type="response",newdata=data.frame(X=x,b0=log(300))),
``````

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## Find x-intercept and y-intercept of a loop in R

By : edpd1b
Date : March 29 2020, 07:55 AM
To fix this issue How can I find x-intercept and the y-intercept (all four) of a loop with R? , Here is an approxfun solution:
code :
``````intercepts <- function(x,y) {
x.s <- approxfun(x[y<=0], y[y<=0])(0)
x.n <- approxfun(x[y>=0], y[y>=0])(0)
y.w <- approxfun(y[x<=0], x[x<=0])(0)
y.e <- approxfun(y[x>=0], x[x>=0])(0)

list(x.s, x.n, y.w, y.e)
}
``````

## Getting the y-axis intercept and slope from a linear regression of multiple data and passing the intercept and slope val

By : Paul Valdivia
Date : March 29 2020, 07:55 AM
wish of those help I suppose you're looking for geom_smooth. If you call this function with the argument method = "lm", it will calculate a linear fit for all groups:
code :
``````ggplot(xm, aes(x = x, y = value, color = cols)) +
geom_point(size = 3) +
labs(x = "x", y = "y") +
geom_smooth(method = "lm", se = FALSE)
``````
``````ggplot(xm, aes(x = x, y = value, color=cols)) +
geom_point(size = 3) +
labs(x = "x", y = "y") +
geom_smooth(method = "lm", se = FALSE, formula = y ~ poly(x, 2))
``````
``````# create a list of coefficients
fits <- by(xm[-2], xm\$cols, function(i) coef(lm(value ~ x, i)))

# create a data frame
data.frame(cols = names(fits), do.call(rbind, fits))

#   cols X.Intercept.         x
# y    y   -277.20000 105.40000
# s    s    -99.06667  35.13333
# t    t   -594.40000 210.80000
``````

## How to intercept Cmd+Q

By : William Febus
Date : March 29 2020, 07:55 AM
This might help you Implement the delegate method applicationShouldTerminate of NSApplication and show a custom modal alert. Depending on the answer return NSTerminateNow, NSTerminateCancel or NSTerminateLater.
In case of NSTerminateLater you can later call [NSApp replyToApplicationShouldTerminate:YES]; to finally quit the app.

## Glmnet is different with intercept=TRUE compared to intercept=FALSE and with penalty.factor=0 for an intercept in x

By : Bilal Eren
Date : March 29 2020, 07:55 AM
like below fixes the issue I contacted the author, who confirmed that this is a bug and added that it is on his list of bug fixes. In the meantime, a workaround is to center the regressors, e.g. with
code :
``````fit_centered <- glmnet(y = Y,
x = scale(X1, T, F),
intercept = F,
lambda = 0)
``````
``````library("glmnet")

set.seed(7)

# Simulate data with 2 features
num_regressors <- 2
num_observations <- 100
X <- matrix(rnorm(num_regressors * num_observations),
ncol = num_regressors,
nrow = num_observations)

# Add an intercept in the right-hand side matrix: X1 = (intercept + X)
X1 <- cbind(matrix(1, ncol = 1, nrow = num_observations), X)

# Set random parameters for the features
beta <- runif(1 + num_regressors)

# Generate observations for the left-hand side
Y <- X1 %*% beta + rnorm(num_observations) / 10

# run OLS
ols <- lm(Y ~ X)
coef_ols <- coef(ols)

# Run glmnet with an intercept in the command, not in the matrix
fit <- glmnet(y = Y,
x = X,
intercept = T,
penalty.factor = rep(1, num_regressors),
lambda = 0)
coef_intercept <- coef(fit)

# run glmnet with an intercept in the matrix with a penalty
# factor of 0 for the intercept and 1 for the rest
fit_no_intercept <- glmnet(y = Y,
x = X1,
intercept = F,
lambda = 0)
coef_no_intercept <- coef(fit_no_intercept)

# run glmnet with an intercept in the matrix with a penalty
# factor of 0 for the intercept and 1 for the rest
# If x is centered, it works (even though y is not centered). Center it with:
#   X1 - matrix(colMeans(X1), nrow = num_observations, ncol = 1 + num_regressors, byrow = T)
# or with
# X1_centered = scale(X1, T, F)

fit_centered <- glmnet(y = Y,
x = scale(X1, T, F),
intercept = F,
lambda = 0)
coef_centered <- coef(fit_centered)

# Compare all three methods in a data frame
# For lasso_intercept and the others, the index starts at 2 because
# position 1 is reserved for intercepts, which is missing in this case
comparison <- data.frame(ols = coef_ols,
lasso_intercept = coef_intercept[1:length(coef_intercept)],
lasso_no_intercept = coef_no_intercept[2:length(coef_no_intercept)],
lasso_centered = coef_centered[2:length(coef_centered)]
)

comparison\$diff_intercept <- comparison\$lasso_intercept - comparison\$lasso_no_intercept
comparison\$diff_centered <- comparison\$lasso_centered - comparison\$lasso_intercept
comparison
``````
``````                  ols lasso_intercept lasso_no_intercept lasso_centered diff_intercept diff_centered
(Intercept) 0.9748302       0.9748302          0.0000000      0.0000000      0.9748302 -9.748302e-01
X1          0.6559541       0.6559541          0.7974851      0.6559541     -0.1415309  2.220446e-16
X2          0.7986957       0.7986957          0.9344306      0.7986957     -0.1357348  4.440892e-16
``````

## Intercept an instance using Unity? Globally intercept a specific type

By : SoulTransfer
Date : March 29 2020, 07:55 AM
Hope that helps Ok, so here goes...
First suppose we have this domain class definition: