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 1.594013e75
plot(Y~X,data2)
curve(exp(coef(fit.incorrect)[1]+x*coef(fit.incorrect)[2]),
add=T,col="red")
curve(predict(fit.correct, type="response",newdata=data.frame(X=x)),
add=T,col="blue")
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)),
add=TRUE,col="blue")
# b0 fixed so that Y0 = 300
fit.fixed <glm(Y~X1+offset(b0), data2,family=poisson)
curve(predict(fit.fixed,type="response",newdata=data.frame(X=x,b0=log(300))),
add=TRUE,col="green")
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Find xintercept and yintercept of a loop in R
By : edpd1b
Date : March 29 2020, 07:55 AM
To fix this issue How can I find xintercept and the yintercept (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 yaxis 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 righthand 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 lefthand 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 9.748302e01
X1 0.6559541 0.6559541 0.7974851 0.6559541 0.1415309 2.220446e16
X2 0.7986957 0.7986957 0.9344306 0.7986957 0.1357348 4.440892e16

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:

